MCP Tool Integration as Systems Thinking
Most conversations about MCP tool integration focus on mechanics: how to register tools, how to call them, how to handle errors. Those details matter—but they’re not where systems succeed or fail.
The real challenge is systems thinking: understanding how tools behave over time, under load, during failure, and in the hands of people who didn’t build them. MCP tools aren’t just capabilities you add to an agent. They are dependencies that reshape architecture, operations, and trust in subtle but compounding ways.
This blog argues that MCP integration should be treated as platform design, not implementation detail.
This article is for you if:
- You’re architecting multi-tool agent systems expected to run in production
- You’ve experienced cascading failures or unpredictable behavior in tool integrations
- You’re responsible for reliability, security, or operational excellence in AI systems
- You want to understand systems thinking principles applied to MCP
This article is NOT for you if:
- You’re building a simple proof-of-concept with 1-2 tools
- You’re looking for a quick “getting started” tutorial
- You need basic MCP protocol documentation (see official docs instead)
- You prefer framework-specific tutorials over architectural principles
Note on Examples: All patterns are presented as language-agnostic algorithms, flowcharts, and diagrams. The architectural principles apply equally to any language—Python, Go, Rust, Java, C#, or JavaScript
Architecture Overview
Before diving into specifics, here’s how a well-designed MCP tool system is structured:
graph TB
Agent["🤖 Agent Logic<br/>(Intent & Reasoning)"]
Abstraction["🔌 Tool Abstraction Layer<br/>(Registry & Discovery)"]
Execution["⚙️ Execution Layer<br/>(Retry, Timeout, Fallback)"]
Policy["📋 Policy Layer<br/>(Error Handling & Security)"]
Observability["📊 Observability<br/>(Metrics, Logs, Health)"]
Tools["🛠️ MCP Tools<br/>(External Services)"]
Agent -->|"needs capability"| Abstraction
Abstraction -->|"selects tool"| Execution
Execution -->|"applies policies"| Policy
Policy -->|"invokes"| Tools
Tools -->|"emits metrics"| Observability
Observability -->|"informs"| Execution
Observability -->|"alerts"| Policy
style Agent fill:#e1f5ff
style Abstraction fill:#fff4e1
style Execution fill:#ffe1f5
style Policy fill:#f5e1ff
style Observability fill:#e1ffe1
style Tools fill:#ffe1e1
Each layer has a distinct responsibility. When these boundaries blur, complexity compounds. Let’s explore why each layer matters.
Why Tool Integration Breaks Down at Scale
Early-stage MCP systems often feel deceptively simple. A tool call succeeds, the agent responds, and everything appears to work. But as more tools are added, systems cross an invisible threshold where problems stop being local and start being systemic.
At that point, failures are no longer obvious. Latency spikes without a clear cause. Tool errors propagate in unexpected ways. Agents behave inconsistently depending on which tools respond first—or at all.
This breakdown usually comes from three root causes:
- Tools are treated as synchronous function calls rather than distributed dependencies
- Failure is assumed to be rare instead of routine
- Operational concerns are deferred in favor of speed
Once those assumptions are baked into the system, they’re difficult to unwind. Thoughtful integration starts by rejecting them early.
Separation of Concerns Is a Strategic Choice
Keeping MCP tooling separate from agent logic is not just a cleanliness preference—it’s a long-term strategy.
Agents should reason about intent and outcomes. Tooling layers should handle connectivity, protocols, retries, and fallbacks. When those responsibilities blur, every new tool increases cognitive load across the entire codebase.
Well-designed systems introduce a clear boundary:
- A tool registry that knows what tools exist and what they can do
- An execution layer responsible for invocation and error handling
- Protocol abstractions that shield agents from MCP specifics
This separation creates leverage. Teams can evolve tools independently, test them in isolation, and reason about failures without dragging agent behavior into every discussion.
Tool Registry Pattern:
flowchart TD
A[Agent requests tool execution] --> B{Tool exists?}
B -->|No| C[Return error: Tool not found]
B -->|Yes| D[Retrieve tool executor + metadata]
D --> E[Execute with retry policy]
E --> F{Attempt < Max?}
F -->|Yes| G[Execute tool]
G --> H{Success?}
H -->|Yes| I[Return result]
H -->|No| J{Retryable error?}
J -->|Yes| K[Exponential backoff delay]
K --> F
J -->|No| L[Return failure]
F -->|No| L
style A fill:#e1f5ff
style I fill:#e1ffe1
style C fill:#ffe1e1
style L fill:#ffe1e1
Algorithm:
FUNCTION executeTool(toolId, input, context):
executor = registry.lookup(toolId)
IF executor is NULL:
RETURN {success: false, error: "Tool not found"}
RETURN executeWithRetry(executor, input, context)
FUNCTION executeWithRetry(executor, input, context, maxAttempts=3):
FOR attempt FROM 1 TO maxAttempts:
TRY:
result = executor.execute(input, context)
RETURN {success: true, data: result}
CATCH error:
IF attempt == maxAttempts OR NOT isRetryable(error):
RETURN {success: false, error: error.message}
delay = 2^(attempt-1) * 1000 // Exponential backoff
WAIT(delay milliseconds)
FUNCTION isRetryable(error):
RETURN error.type IN [TIMEOUT, RATE_LIMIT] OR
error.statusCode >= 500
Failure Is Normal—Design for It
One of the most dangerous beliefs in tool integration is that failure is exceptional. In reality, tool failure is the default state of distributed systems—it just happens at different frequencies.
The question is not whether a tool will fail, but how much damage that failure causes.
Resilient MCP systems are built around the assumption that something is always degraded:
- A tool may be slow rather than down
- Credentials may expire mid-session
- Rate limits may apply unevenly
- Partial responses may be better than none
Designing for graceful degradation means explicitly deciding which failures are tolerable, which are recoverable, and which must surface to users. This clarity prevents silent corruption and builds trust in the system’s behavior.
Graceful Degradation Flow:
flowchart TD
A[Execute with fallback] --> B[Try primary tool]
B --> C{Success?}
C -->|Yes| D[Return result]
C -->|No| E{Credentials expired?}
E -->|Yes| F[Refresh credentials]
E -->|No| G[Log error]
F --> G
G --> H{Fallback tools available?}
H -->|Yes| I[Try next fallback tool]
I --> J{Success?}
J -->|Yes| K[Log fallback used + Return result]
J -->|No| L{More fallbacks?}
L -->|Yes| I
L -->|No| M[Retrieve cached data]
H -->|No| M
M --> N[Return degraded response]
style D fill:#e1ffe1
style K fill:#fff4e1
style N fill:#ffe1f5
Algorithm:
FUNCTION executeWithFallback(primaryTool, fallbackTools[], input):
tools = [primaryTool] + fallbackTools
errors = []
FOR EACH tool IN tools:
TRY:
result = executeWithTimeout(tool, input, timeout=5000ms)
IF tool != primaryTool:
LOG_WARNING("Used fallback", {primary, fallback, errors})
RETURN {success: true, data: result}
CATCH error:
errors.APPEND({tool: tool.name, error: error})
IF error.type == CREDENTIALS_EXPIRED:
refreshCredentials(tool)
CONTINUE // Try next tool
// All tools failed
cachedData = getCachedResponse(input)
RETURN {
success: false,
degraded: true,
data: cachedData,
errors: errors
}
FUNCTION executeWithTimeout(tool, input, timeoutMs):
RACE [
tool.execute(input),
timeout(timeoutMs)
]
// Returns first to complete or throws if timeout wins
Lazy Loading Is About Control, Not Optimization
Lazy loading tools is often framed as a performance trick. In practice, it’s about control.
Loading every tool at startup assumes all tools are equally important and equally reliable. That assumption rarely holds. Some tools are rarely used. Others are experimental. Some are critical paths.
On-demand initialization creates a more honest system:
- Tools are only paid for when they’re actually used
- Failures surface in context, not during boot
- Resource usage reflects real demand
The trade-off is complexity. First-use latency must be managed, and readiness must be observable. But those costs are usually worth the clarity gained.
Lazy Loading State Machine:
stateDiagram-v2
[*] --> NotLoaded: Tool registered
NotLoaded --> Initializing: First request
Initializing --> Ready: Success
Initializing --> Failed: Error
Ready --> [*]: Tool available
Failed --> Initializing: Retry
Failed --> [*]: Max retries
Initializing: Running factory()<br/>Health check<br/>Recording metrics
Ready: Cached in registry<br/>Requests served<br/>Monitoring active
Algorithm:
FUNCTION getTool(toolId):
// Check if already initialized
IF tools.contains(toolId):
RETURN tools.get(toolId)
// Check if initialization in progress
IF initializationPromises.contains(toolId):
AWAIT initializationPromises.get(toolId)
RETURN tools.get(toolId)
// Start initialization
initPromise = initializeTool(toolId)
initializationPromises.set(toolId, initPromise)
TRY:
tool = AWAIT initPromise
tools.set(toolId, tool)
RETURN tool
FINALLY:
initializationPromises.remove(toolId)
FUNCTION initializeTool(toolId):
factory, config = toolFactories.get(toolId)
startTime = NOW()
LOG("Initializing tool: " + toolId)
TRY:
tool = factory.create(config)
tool.healthCheck() // Verify readiness
duration = NOW() - startTime
LOG("Tool initialized", {toolId, duration})
RETURN tool
CATCH error:
LOG_ERROR("Initialization failed", {toolId, error})
THROW error
FUNCTION getToolStatus(toolId):
IF tools.contains(toolId):
RETURN {status: "ready"}
IF initializationPromises.contains(toolId):
RETURN {status: "initializing"}
RETURN {status: "not-loaded"}
Statelessness Is What Makes Systems Predictable
Stateless tool calls are not glamorous, but they are foundational.
When tool behavior depends on hidden state—session history, implicit configuration, call ordering—the system becomes fragile. Retries become risky. Debugging becomes guesswork.
Stateless, idempotent tools enable:
- Safe retries with confidence
- Meaningful logs and metrics
- Composable workflows
- Predictable orchestration
This is one of those principles that feels restrictive early on and liberating later.
Stateful vs. Stateless Tool Comparison:
❌ STATEFUL TOOL (Fragile):
┌─────────────────────────────────────┐
│ Tool Instance (mutable state) │
│ • filters = [] │
│ • sortBy = 'date' │
└─────────────────────────────────────┘
↓
Call 1: addFilter('recent')
Call 2: setSortOrder('relevance')
Call 3: search('AI tools')
↓
Result depends on call sequence!
Retry of Call 3 → different result
✅ STATELESS TOOL (Robust):
┌─────────────────────────────────────┐
│ Pure Function (no internal state) │
└─────────────────────────────────────┘
↓
Single Call: search({
query: 'AI tools',
filters: ['recent'],
sortBy: 'relevance'
})
↓
Same input → always same output
Safe to retry, cache, parallelize
Algorithm:
// Stateless tool design
FUNCTION search(params):
// All context explicitly passed
query = params.query
filters = params.filters OR []
sortBy = params.sortBy OR 'date'
// No hidden state, idempotent
RETURN api.search(query, filters, sortBy)
// Properties:
// • Idempotent: search(X) == search(X) always
// • Cacheable: Same input → cache key
// • Retryable: Safe to retry on failure
// • Testable: No setup/teardown needed
// • Composable: Output→Input chains work
Observability Is the Difference Between Control and Hope
Without observability, multi-tool MCP systems operate on hope.
Teams hope tools are healthy. Hope retries are working. Hope latency spikes resolve themselves. That hope doesn’t scale.
Thoughtful integration treats observability as a product feature:
- Tool calls are logged with correlation IDs
- Latency and error rates are tracked per tool
- Health checks are continuous, not reactive
This doesn’t just help operators—it shapes better architectural decisions over time.
Observability Architecture:
flowchart LR
A[Tool Execution] --> B[Wrapper Layer]
B --> C[Log: Start<br/>+Correlation ID]
B --> D[Execute Tool]
D --> E{Result}
E -->|Success| F[Record Success Metrics]
E -->|Failure| G[Record Failure Metrics]
F --> H[Log: Complete]
G --> I[Log: Error]
C & H & I --> J[Structured Logs]
F & G --> K[Metrics Store]
K --> L[Health Check]
L --> M{Status}
M -->|Success<90%| N[Alert]
M -->|Latency>5s| N
Algorithm:
FUNCTION executeWithObservability(tool, input, correlationId):
startTime = NOW()
LOG_INFO("Tool execution started", {
correlationId, tool: tool.name,
input: sanitize(input), // Remove: password, apiKey, token, secret
timestamp: NOW()
})
TRY:
result = tool.execute(input)
duration = NOW() - startTime
metrics.record(tool.name, {
status: "success", duration, resultSize: sizeof(result)
})
LOG_INFO("Completed", {correlationId, tool: tool.name, duration})
RETURN result
CATCH error:
duration = NOW() - startTime
metrics.record(tool.name, {
status: "error", duration, errorType: error.type
})
LOG_ERROR("Failed", {correlationId, tool: tool.name, duration, error})
THROW error
FUNCTION getToolHealth(toolName):
recentCalls = metrics.getRecent(toolName, last=5minutes)
IF recentCalls.isEmpty():
RETURN {status: "unknown"}
successRate = count(where status=="success") / count(recentCalls)
avgLatency = average(recentCalls.duration)
p95Latency = percentile(recentCalls.duration, 95)
status = IF successRate > 0.95 THEN "healthy"
ELSE IF successRate > 0.80 THEN "degraded"
ELSE "unhealthy"
IF successRate < 0.90:
ALERT("Tool success rate dropped", {toolName, successRate})
IF avgLatency > 5000:
ALERT("Tool latency high", {toolName, avgLatency})
RETURN {status, successRate, avgLatency, p95Latency, callCount}
Tool Discovery Is a Governance Problem
As systems grow, the question shifts from how do we call tools to which tools should exist at all.
Dynamic discovery and registration enable flexibility, but they also require governance. A tool registry becomes a source of truth, not just a convenience.
Effective registries capture intent:
- What the tool does
- What guarantees it provides
- How expensive or slow it is
- What permissions it requires
This metadata later enables smarter routing, better fallbacks, and informed deprecation decisions.
Tool Metadata Structure:
Tool Metadata Schema:
┌────────────────────────────────────────────────────┐
│ IDENTIFICATION │
│ • toolId: unique identifier │
│ • name: human-readable name │
│ • version: semantic version │
│ • description: purpose and capabilities │
├────────────────────────────────────────────────────┤
│ CAPABILITIES │
│ • capabilities: ['search', 'realtime-data'] │
│ • tags: ['production-ready', 'external'] │
├────────────────────────────────────────────────────┤
│ PERFORMANCE CHARACTERISTICS │
│ • estimatedLatency: fast|medium|slow │
│ (fast<100ms, medium<1s, slow>1s) │
│ • rateLimit: {requests: 100, period: '1m'} │
│ • costPerCall: 0.001 USD │
├────────────────────────────────────────────────────┤
│ RELIABILITY GUARANTEES │
│ • sla: '99.5%' │
│ • retryable: true │
│ • idempotent: true │
├────────────────────────────────────────────────────┤
│ SECURITY REQUIREMENTS │
│ • requiredPermissions: ['network.external'] │
│ • dataClassification: public|internal|sensitive │
│ • piiHandling: none|an|anonymize|encrypt │
├────────────────────────────────────────────────────┤
│ INPUT/OUTPUT SCHEMA │
│ • input: type definitions + validation rules │
│ • output: expected structure │
├────────────────────────────────────────────────────┤
│ OPERATIONAL │
│ • fallbacks: ['cached_search', 'wiki_search'] │
│ • healthCheckEndpoint: URL │
└────────────────────────────────────────────────────┘
Tool Discovery Algorithm:
FUNCTION discoverTools(sourcePath):
toolDefinitions = scanDirectory(sourcePath)
FOR EACH definition IN toolDefinitions:
TRY:
validateToolMetadata(definition)
registry.register(definition)
LOG_INFO("Tool discovered", {
toolId: definition.toolId,
version: definition.version,
capabilities: definition.capabilities
})
CATCH error:
LOG_ERROR("Tool registration failed", {
toolId: definition.toolId,
error: error
})
FUNCTION findToolsByCapability(capability):
RETURN registry.query({
where: {
capabilities CONTAINS capability,
tags CONTAINS 'production-ready'
},
orderBy: 'reliability.sla' DESC
})
FUNCTION routeToOptimalTool(intent, constraints):
candidates = findToolsByCapability(intent.capability)
// Filter by constraints
IF constraints.maxLatency:
candidates = filter(candidates, latency < constraints.maxLatency)
IF constraints.maxCost:
candidates = filter(candidates, cost < constraints.maxCost)
IF constraints.requiredSLA:
candidates = filter(candidates, sla >= constraints.requiredSLA)
// Score and rank
scored = scoreTools(candidates, intent.priority)
RETURN scored[0] // Best match
Error Handling Is a Policy Decision
Error handling should not be improvised at call sites. It should be a policy applied consistently across the system.
That policy answers questions like:
- Which errors trigger retries, and how often
- Which errors alert humans
- Which errors are safe to surface to agents
- When a tool should be disabled automatically
When these rules are centralized, the system behaves coherently under stress. When they aren’t, behavior becomes unpredictable and hard to trust.
Error Classification Decision Tree:
graph TD
A[Error Occurred] --> B{Error Type?}
B -->|Timeout/Network| C[TRANSIENT]
B -->|HTTP 429| D[RATE_LIMIT]
B -->|HTTP 401/403| E[AUTHENTICATION]
B -->|HTTP 4xx| F[VALIDATION]
B -->|HTTP 5xx| C
B -->|Unknown| G[UNKNOWN]
C --> H{Circuit open?}
H -->|Yes| I[FAIL_FAST +<br/>Use Fallback]
H -->|No| J{Retries<br/>exhausted?}
J -->|Yes| K[FAIL +<br/>Use Fallback]
J -->|No| L[RETRY +<br/>Exponential Backoff]
D --> M[RETRY +<br/>Linear Backoff<br/>Honor retry-after]
E --> N{Credentials<br/>refreshed?}
N -->|No| O[Refresh +<br/>RETRY once]
N -->|Yes| P[FAIL +<br/>Alert Operator<br/>Disable Tool]
F --> Q[FAIL +<br/>Surface to Agent<br/>Validation Error]
G --> R[FAIL +<br/>Alert Operator]
style I fill:#ffe1e1
style K fill:#ffe1e1
style L fill:#fff4e1
style M fill:#fff4e1
style O fill:#fff4e1
style P fill:#ffe1e1
style Q fill:#ffe1f5
style R fill:#ffe1e1
Error Handling Algorithm:
FUNCTION handleError(error, context):
classification = classifyError(error)
SWITCH classification.category:
CASE TRANSIENT:
RETURN handleTransient(error, context, classification)
CASE AUTHENTICATION:
RETURN handleAuth(error, context)
CASE RATE_LIMIT:
RETURN handleRateLimit(error, context, classification)
CASE VALIDATION:
RETURN {action: FAIL, surfaceToAgent: true, guidance: error.message}
DEFAULT:
RETURN {action: FAIL, alertOperator: true}
FUNCTION classifyError(error):
IF error.type IN [TIMEOUT, CONNECTION_REFUSED]:
RETURN {category: TRANSIENT, retryable: true, maxRetries: 3, backoff: EXPONENTIAL}
IF error.statusCode == 429:
RETURN {category: RATE_LIMIT, retryable: true, delayMs: error.retryAfter OR 60000}
IF error.statusCode IN [401, 403]:
RETURN {category: AUTHENTICATION, retryable: false, alertOperator: true}
IF error.statusCode >= 500:
RETURN {category: TRANSIENT, retryable: true, maxRetries: 3}
IF error.statusCode >= 400:
RETURN {category: VALIDATION, retryable: false, surfaceToAgent: true}
RETURN {category: UNKNOWN, retryable: false, alertOperator: true}
FUNCTION handleTransient(error, context, classification):
IF isCircuitOpen(context.toolId):
RETURN {action: FAIL_FAST, useFallback: true}
IF context.retryCount >= classification.maxRetries:
recordFailure(context.toolId)
RETURN {action: FAIL, useFallback: true}
delayMs = 2^(context.retryCount) * 1000 // Exponential backoff
RETURN {action: RETRY, delayMs: delayMs}
FUNCTION handleAuth(error, context):
IF NOT context.credentialsRefreshed:
refreshCredentials(context.toolId)
RETURN {action: RETRY, delayMs: 0}
alertOperator(severity=HIGH, toolId=context.toolId)
RETURN {action: FAIL, disableTool: true}
// Circuit Breaker Pattern
FUNCTION recordFailure(toolId):
breaker = circuitBreakers.get(toolId)
breaker.failures += 1
breaker.lastFailure = NOW()
IF breaker.failures >= THRESHOLD:
breaker.state = OPEN
LOG_ERROR("Circuit breaker opened", {toolId})
// Auto-reset after timeout
scheduleTask(after=CIRCUIT_TIMEOUT, action=() => {
breaker.state = HALF_OPEN
breaker.failures = 0
})
Performance Emerges From Architecture
In multi-tool environments, performance is not the result of fast tools alone. It emerges from how tools are composed, cached, and orchestrated.
Small inefficiencies multiply when:
- Tools are called redundantly
- Connections are not reused
- Results are not cached
- Orchestration is overly sequential
Good performance engineering focuses less on micro-optimizations and more on flow: minimizing unnecessary work and making latency predictable.
Performance Optimization Patterns:
flowchart LR
A[Tool Request] --> B{In-flight<br/>request?}
B -->|Yes| C[Wait for<br/>existing]
B -->|No| D{Cached?}
D -->|Yes & Fresh| E[Return cached]
D -->|No/Expired| F[Get pooled<br/>connection]
F --> G[Execute]
G --> H[Cache result]
H --> I[Release connection]
I --> J[Return result]
C --> J
E --> J
style E fill:#e1ffe1
style J fill:#e1ffe1
Key Patterns:
1. REQUEST DEDUPLICATION
Problem: Same request called multiple times simultaneously
Solution: Track in-flight requests, share result
cacheKey = hash(toolId + normalizedInput)
IF inflightRequests.contains(cacheKey):
RETURN AWAIT inflightRequests.get(cacheKey)
promise = executeActual(toolId, input)
inflightRequests.set(cacheKey, promise)
TRY:
result = AWAIT promise
RETURN result
FINALLY:
inflightRequests.remove(cacheKey)
2. CONNECTION POOLING
Problem: Creating connections is expensive
Solution: Reuse idle connections
pool = {connections: [], maxSize: 10, activeCount: 0}
IF pool.hasIdleConnection():
RETURN pool.pop()
ELSE IF pool.activeCount < pool.maxSize:
pool.activeCount++
RETURN createNewConnection()
ELSE:
WAIT_FOR availableConnection()
3. SMART CACHING (TTL by tool characteristics)
Problem: One-size-fits-all caching is inefficient
Solution: Adaptive TTL based on metadata
FUNCTION getCacheTTL(toolId):
metadata = registry.getMetadata(toolId)
IF metadata.latency == 'slow':
RETURN 5_minutes // Expensive, cache longer
ELSE IF 'realtime-data' IN metadata.capabilities:
RETURN 30_seconds // Fresh data needed
ELSE:
RETURN 1_minute // Default
4. PARALLEL EXECUTION WITH CONCURRENCY LIMITS
Problem: Unlimited parallelism overwhelms system
Solution: Sliding window concurrency control
FUNCTION executeParallel(tasks[], maxConcurrency=5):
results = []
executing = []
FOR EACH task IN tasks:
promise = execute(task)
results.APPEND(promise)
executing.APPEND(promise)
IF length(executing) >= maxConcurrency:
AWAIT any(executing) // Wait for one to complete
executing.remove(completed)
RETURN AWAIT all(results)
5. CACHE KEY NORMALIZATION
Problem: Same input, different key (order, formatting)
Solution: Normalize before hashing
FUNCTION getCacheKey(toolId, input):
// Sort keys for consistent ordering
normalized = stringify(input, sortKeys=true)
hash = hashFunction(normalized)
RETURN toolId + ":" + hash
Performance Metrics to Track:
• Cache Hit Rate: hits / (hits + misses)
Target: >80% for cacheable operations
• Connection Pool Utilization: active / maxSize
Target: 60-80% (headroom for spikes)
• Deduplication Rate: deduplicated / totalRequests
Indicates redundant call patterns
• P50/P95/P99 Latency: Response time percentiles
Watch for bimodal distributions
• Throughput: Requests/second sustained
Should scale with concurrency
Tool Selection Is an Exercise in Restraint
One of the most mature signals in an MCP system is not how many tools it uses, but how many it chooses not to.
Tool selection is where strategy shows up:
- Community tools are excellent defaults for standard capabilities
- Custom tools make sense when differentiation matters
- Redundancy should exist for resilience, not indecision
Every tool added increases operational surface area. Thoughtful systems earn complexity deliberately.
Composition, Routing, and Orchestration Are Where Architecture Shows
As agents become more capable, tools stop being called in isolation. They become building blocks.
Higher-level patterns emerge:
- Composition turns simple tools into reusable workflows
- Routing chooses tools dynamically based on context
- Orchestration coordinates multi-step operations
These patterns should be explicit and observable. Hidden orchestration inside prompts or ad-hoc logic tends to collapse under scale.
Tool Composition Pattern:
flowchart LR
A[Input] --> B[Step 1: Search]
B --> C[Step 2: Extract]
C --> D[Step 3: Synthesize]
D --> E[Output]
B -.->|results.urls| C
C -.->|results.documents| D
style A fill:#e1f5ff
style E fill:#e1ffe1
Algorithm:
FUNCTION composeWorkflow(steps[], input):
context = {input: input, results: {}}
FOR EACH step IN steps:
// Map previous results to inputs using $-references
stepInput = resolveInputMapping(step.inputMapping, context)
// Execute step
result = step.tool.execute(stepInput)
// Store result for next steps
context.results[step.name] = result
RETURN context.results[finalStep]
FUNCTION resolveInputMapping(mapping, context):
resolved = {}
FOR EACH (key, value) IN mapping:
IF value.startsWith('$'):
// Reference: $input.topic or $results.search.urls
resolved[key] = getFromContext(value, context)
ELSE:
// Literal value
resolved[key] = value
RETURN resolved
// Example workflow definition:
workflow = {
steps: [
{name: 'search', tool: searchTool,
inputMapping: {query: '$input.topic'}},
{name: 'extract', tool: extractTool,
inputMapping: {urls: '$results.search.urls'}},
{name: 'synthesize', tool: llmTool,
inputMapping: {documents: '$results.extract.documents'}}
]
}
Tool Routing Algorithm:
// Dynamic tool selection based on health, performance, cost
FUNCTION routeTool(intent, context):
candidates = findCandidatesByCapability(intent.capability)
scored = []
FOR EACH tool IN candidates:
score = 100
health = metrics.getToolHealth(tool.id)
// Health scoring
IF health.status == 'unhealthy': score = 0
IF health.status == 'degraded': score -= 20
// Performance scoring
IF context.prioritizeSpeed:
IF health.avgLatency > 1000ms: score -= 30
IF health.avgLatency < 200ms: score += 20
// Cost scoring
IF context.minimizeCost:
score -= tool.metadata.costPerCall * 1000
// Capability match
featureMatch = countMatches(tool.capabilities, context.requirements)
score += featureMatch * 30
// Permission check
IF NOT hasPermissions(tool, context.user): score = 0
scored.APPEND({tool, score})
// Return highest scoring tool
RETURN max(scored, by=score).tool
Orchestration Pattern:
// Multi-step workflow with dependencies and error handling
FUNCTION orchestrateWorkflow(workflow, input):
results = {}
executionLog = []
FOR EACH step IN workflow.steps:
startTime = NOW()
TRY:
// Resolve dependencies
dependencies = {}
FOR EACH dep IN step.dependencies:
IF NOT results.contains(dep):
THROW "Dependency unavailable: " + dep
dependencies[dep] = results[dep]
// Execute step (simple, parallel, or conditional)
IF step.type == 'parallel':
result = executeParallel(step.tools, input)
ELSE IF step.type == 'conditional':
tool = IF step.condition(input) THEN step.trueBranch ELSE step.falseBranch
result = tool.execute(input)
ELSE:
result = step.tool.execute(input)
results[step.id] = result
executionLog.APPEND({step: step.id, status: 'success', duration: NOW() - startTime})
CATCH error:
executionLog.APPEND({step: step.id, status: 'error', error, duration: NOW() - startTime})
// Handle based on error policy
IF step.optional:
CONTINUE // Skip optional steps
IF step.fallback EXISTS:
TRY result = step.fallback.execute(input)
results[step.id] = result
CONTINUE
IF workflow.errorHandling == 'continue-on-error':
CONTINUE
ELSE:
RETURN {status: 'failed', results, executionLog, failedAt: step.id}
RETURN {status: 'completed', results, executionLog}
Security Is Structural, Not Additive
Tool integration expands the blast radius of mistakes.
Security cannot be bolted on after the fact. It must be structural:
- Credentials are scoped and rotated
- Inputs are validated consistently
- Data sharing is minimized by default
- Network boundaries are enforced
The most costly security failures in tool systems are rarely novel—they’re architectural.
Security Lifecycle:
flowchart TB
A[Credential Request] --> B{Permission Check}
B -->|Denied| C[Error: Unauthorized]
B -->|Granted| D[Retrieve Encrypted Credentials]
D --> E{Expired?}
E -->|Yes| F[Rotate Credentials]
F --> G[Store New Encrypted]
E -->|No| H[Decrypt]
G --> H
H --> I[Return to Tool]
I --> J[Execute with Sandbox]
J --> K[Audit Log]
style C fill:#ffe1e1
style I fill:#e1ffe1
Algorithm:
// Credential Management
FUNCTION storeCredentials(toolId, credentials):
encrypted = encrypt(stringify(credentials))
database.save({
toolId: toolId,
encrypted: encrypted,
createdAt: NOW(),
expiresAt: NOW() + 90_days
})
scheduleRotation(toolId, after=90_days)
FUNCTION getCredentials(toolId, userId):
// Permission check
IF NOT hasPermission(userId, toolId):
THROW "Unauthorized access"
record = database.find({toolId: toolId})
IF NOT record.exists:
THROW "Credentials not found"
// Auto-rotate if expired
IF record.expiresAt < NOW():
rotateCredentials(toolId)
RETURN getCredentials(toolId, userId) // Recursive call
// Decrypt only when needed
decrypted = decrypt(record.encrypted)
RETURN parse(decrypted)
FUNCTION rotateCredentials(toolId):
auditLog.record({event: 'credential_rotation', toolId, timestamp: NOW()})
tool = registry.getTool(toolId)
newCredentials = tool.refreshCredentials()
storeCredentials(toolId, newCredentials)
// Input Validation
FUNCTION executeWithValidation(toolId, input, userId):
tool = registry.getTool(toolId)
// Schema validation
errors = validateAgainstSchema(input, tool.schema)
IF errors.isNotEmpty():
THROW "Invalid input: " + errors.join(', ')
// Sanitize input
sanitized = sanitizeInput(input, tool.schema)
// PII detection
IF detectPII(sanitized) AND NOT userConsentedToPII(userId, toolId):
THROW "PII detected but user has not consented"
// Audit log
auditLog.record({
event: 'tool_execution',
userId, toolId,
timestamp: NOW(),
inputHash: hash(sanitized)
})
// Execute with permissions
RETURN executeWithSandbox(toolId, sanitized, userId)
FUNCTION sanitizeInput(input, schema):
sanitized = {}
FOR EACH (key, value) IN input:
fieldSchema = schema.properties[key]
IF NOT fieldSchema.exists:
CONTINUE // Drop unknown fields
IF fieldSchema.type == 'string':
// Remove dangerous characters, enforce max length
sanitized[key] = removeDangerousChars(value)
.substring(0, fieldSchema.maxLength OR 10000)
ELSE IF fieldSchema.type == 'number':
num = parseNumber(value)
IF num.isValid():
// Enforce min/max bounds
sanitized[key] = clamp(num, fieldSchema.minimum, fieldSchema.maximum)
ELSE:
sanitized[key] = value
RETURN sanitized
FUNCTION detectPII(data):
patterns = {
email: /[a-z0-9._%+-]+@[a-z0-9.-]+\.[a-z]{2,}/i,
phone: /\b\d{3}[-.]?\d{3}[-.]?\d{4}\b/,
ssn: /\b\d{3}-\d{2}-\d{4}\b/,
creditCard: /\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b/
}
dataStr = stringify(data)
FOR EACH (type, pattern) IN patterns:
IF pattern.matches(dataStr):
RETURN {detected: true, type: type}
RETURN {detected: false}
// Sandbox Execution
FUNCTION executeWithSandbox(toolId, input, userId):
tool = registry.getTool(toolId)
permissions = tool.metadata.security.requiredPermissions
// Network boundary check
IF 'network.external' IN permissions:
IF NOT canAccessExternal(userId):
THROW "User not authorized for external network access"
// Create restricted context
context = {
userId: userId,
permissions: permissions,
canAccessFileSystem: 'filesystem' IN permissions,
canAccessNetwork: 'network.external' IN permissions,
rateLimiter: getRateLimiter(userId),
logAccess: (resource) => auditLog.record({
event: 'resource_access',
userId, resource,
timestamp: NOW()
})
}
RETURN tool.execute(input, context)
Testing for Failure Is a Form of Respect
Testing only the happy path assumes the system will be treated gently by reality. It won’t.
Serious MCP systems test for:
- Tool outages
- Partial responses
- Network degradation
- Expired credentials
Chaos testing is not pessimism. It’s respect for complexity.
Testing Patterns:
CRITICAL TEST SCENARIOS:
1. TRANSIENT FAILURES
Test: Tool fails twice, succeeds third time
Expected: System retries with exponential backoff, eventually succeeds
Verifies: Retry logic, backoff calculation
2. CIRCUIT BREAKER
Test: Tool fails repeatedly past threshold
Expected: Circuit opens, subsequent calls fail fast
Verifies: Circuit breaker state transitions, fast failure
3. CREDENTIAL EXPIRATION
Test: Tool returns 401, credentials refresh, retry succeeds
Expected: Single refresh attempt, successful retry
Verifies: Credential rotation, retry after refresh
4. GRACEFUL DEGRADATION
Test: Primary fails, fallback succeeds
Expected: System tries fallback, logs usage, returns result
Verifies: Fallback chain, logging
5. CACHE BEHAVIOR
Test: Identical requests within TTL window
Expected: Second request returns cached result
Verifies: Cache key generation, TTL enforcement
// Chaos Testing Algorithm
FUNCTION injectChaos(config):
active = true
failureRate = config.failureRate OR 0.1 // 10%
latencyInjection = config.latencyInjection OR false
maxLatency = config.maxLatency OR 5000ms
FOR EACH tool IN registry.getAllTools():
originalExecute = tool.execute
tool.execute = (input) => {
IF NOT active:
RETURN originalExecute(input)
// Inject latency
IF latencyInjection AND random() < 0.3:
delay = random() * maxLatency
WAIT(delay milliseconds)
// Inject failure
IF random() < failureRate:
errorType = chooseRandom([TIMEOUT, NETWORK_ERROR, AUTH_ERROR, RATE_LIMIT, SERVER_ERROR])
THROW createError(errorType)
RETURN originalExecute(input)
}
FUNCTION chaosTest():
system = createMCPSystem()
chaos = injectChaos({failureRate: 0.2, latencyInjection: true})
results = []
FOR i FROM 1 TO 50:
TRY:
result = system.executeTask({task: 'test'})
results.APPEND({success: true, result})
CATCH error:
results.APPEND({success: false, error})
stopChaos()
// Verify graceful degradation
successCount = count(results where success == true)
failureCount = count(results where success == false)
ASSERT successCount > 30 // At least 60% success despite 20% injected failure
ASSERT failureCount < 20 // Less than 40% failures
// System should degrade gracefully, not catastrophically
Key Testing Principles:
1. Test failure modes explicitly
• Don't just test happy paths
• Inject realistic failures
• Verify recovery mechanisms
2. Test under load
• Concurrent requests
• Rate limit violations
• Connection pool exhaustion
3. Test state transitions
• Circuit breaker: CLOSED → OPEN → HALF_OPEN
• Lazy loading: NOT_LOADED → INITIALIZING → READY
• Credentials: VALID → EXPIRED → ROTATED
4. Test observability
• Verify metrics are recorded
• Verify logs contain correlation IDs
• Verify alerts fire correctly
5. Test security boundaries
• Permission checks
• Input sanitization
• PII detection
• Credential encryption
Final Reflection
MCP tool integration is not about adding capabilities to agents. It’s about building infrastructure that earns trust over time.
The systems that last are not the ones with the most tools, but the ones with:
- Clear boundaries
- Honest assumptions
- Visible behavior
- Disciplined evolution
If you design MCP integration as a system—rather than a shortcut—you give your agents something rare: a foundation that doesn’t crack as they grow.
