Performance

MCP Fusion was designed from the ground up with performance as a first-class concern. This page documents every optimization technique actually implemented in the framework — no theoretical claims, only what exists in the codebase.

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Philosophy: Zero-Cost Abstractions

MCP Fusion follows a core principle: features that are not enabled have zero runtime cost.

Debug observers, State Sync, middleware, Presenters — when not configured, they produce no conditionals, no object allocations, and no function calls in the hot path. The framework pays only for what you use.

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1. Build-Time Pre-Compilation

Middleware Chain Compilation

Traditional middleware frameworks resolve and compose middleware chains on every request — N function lookups per call. MCP Fusion eliminates this entirely.

When buildToolDefinition() is called (or lazily on first execute()), the MiddlewareCompiler wraps middlewares right-to-left around each handler once, producing a single ready-to-call function per action:

// From: src/framework/execution/MiddlewareCompiler.ts
export function compileMiddlewareChains<TContext>(
    actions: readonly InternalAction<TContext>[],
    middlewares: readonly MiddlewareFn<TContext>[],
): CompiledChain<TContext> {
    const compiled: CompiledChain<TContext> = new Map();

    for (const action of actions) {
        let chain = action.handler;

        // Per-action middleware (innermost)
        for (let i = actionMws.length - 1; i >= 0; i--) {
            const nextFn = chain;
            chain = (ctx, args) => mw(ctx, args, () => nextFn(ctx, args));
        }

        // Global middleware (outermost)
        for (let i = middlewares.length - 1; i >= 0; i--) {
            const nextFn = chain;
            chain = (ctx, args) => mw(ctx, args, () => nextFn(ctx, args));
        }

        compiled.set(action.key, chain);
    }
    return compiled;
}

Result: At runtime, calling an action with 10 stacked middleware layers is a single function call — zero chain assembly, zero closure allocation per request.

Validation Schema Pre-Caching

Zod schemas are merged and cached once at build time in the ToolDefinitionCompiler, not recomputed per request:

// From: src/framework/builder/ToolDefinitionCompiler.ts
const validationSchemaCache = new Map<string, ZodObject<ZodRawShape> | null>();
for (const action of input.actions) {
    validationSchemaCache.set(
        action.key,
        buildValidationSchema(action, input.commonSchema),
    );
}

Each action's merged schema (commonSchema.merge(actionSchema).strict()) is computed once. At execution time, the pipeline reads from this cache with O(1) Map.get().

Action Map (O(1) Routing)

Action resolution uses a Map<string, InternalAction> built at compile time:

// From: src/framework/builder/ToolDefinitionCompiler.ts
const actionMap = new Map(input.actions.map(a => [a.key, a]));

When the LLM sends { action: "users.list" }, the pipeline resolves the handler with a single Map.get() call — O(1) regardless of how many actions exist.

Action Keys String Pre-Computation

Error messages listing available actions (e.g., "Available: list, create, delete") are computed once at build time as a pre-joined string:

// From: src/framework/builder/ToolDefinitionCompiler.ts
const actionKeysString = input.actions.map(a => a.key).join(', ');

No Array.join() on every error path.

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2. Freeze-After-Build Immutability

After buildToolDefinition(), the entire builder state is permanently frozen:

// From: src/framework/builder/GroupedToolBuilder.ts
buildToolDefinition(): McpTool {
    if (this._cachedTool) return this._cachedTool;
    
    const result = compileToolDefinition({ ... });
    
    this._cachedTool = result.tool;
    this._executionContext = result.executionContext;
    this._frozen = true;
    Object.freeze(this._actions);
    
    return result.tool;
}

Why it matters for performance:

• Object.freeze() allows the V8 engine to mark objects as constant, enabling deeper JIT optimizations.
• Subsequent calls to buildToolDefinition() return the cached McpTool object — zero recomputation.
• The _frozen flag prevents accidental mutation, guaranteeing deterministic behavior without defensive copies.

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3. Zero-Overhead Observability

The debug observer pattern in MCP Fusion is designed so that when disabled, the hot path has zero conditionals:

// From: src/framework/builder/GroupedToolBuilder.ts — execute()

// Fast path: no debug observer → zero overhead
if (!this._debug) {
    const disc = parseDiscriminator(execCtx, args);
    if (!disc.ok) return disc.response;

    const resolved = resolveAction(execCtx, disc.value);
    if (!resolved.ok) return resolved.response;

    const validated = validateArgs(execCtx, resolved.value, args);
    if (!validated.ok) return validated.response;

    return runChain(execCtx, resolved.value, ctx, validated.value);
}

The if (!this._debug) branch contains the entire pipeline inline — no Date.now(), no performance.now(), no object allocations. The debug path below only runs when explicitly enabled:

// Debug path: emit structured events at each step
const startTime = performance.now();
// ... timing, event creation, observer calls
debug({ type: 'execute', tool: this._name, action: actionName, 
        durationMs: totalDuration, isError: isErr, timestamp: Date.now() });

Result: Production deployments without createDebugObserver() run the pure fast path. Adding observability is a single line — no code changes, no conditionals in any handler.

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4. Railway-Oriented Execution Pipeline

The ExecutionPipeline uses the Result<T> monad for zero-exception error handling:

// From: src/framework/result.ts
export function succeed<T>(value: T): Success<T> {
    return { ok: true, value };
}

export function fail(response: ToolResponse): Failure {
    return { ok: false, response };
}

Each pipeline step returns Result<T>:

parseDiscriminator → resolveAction → validateArgs → runChain

On failure, the pipeline short-circuits immediately with a typed Failure — no exception throw, no stack unwinding, no try/catch overhead:

const disc = parseDiscriminator(execCtx, args);
if (!disc.ok) return disc.response;  // Short-circuit — zero cost

This is measurably faster than exception-based error handling for expected failures (missing discriminator, unknown action, validation errors).

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5. Zero-Copy Validation

After Zod validates args, the discriminator is re-injected via direct mutation instead of object spread:

// From: src/framework/execution/ExecutionPipeline.ts — validateArgs()

// Remove discriminator before validation
const { [execCtx.discriminator]: _, ...argsWithoutDiscriminator } = args;
const result = validationSchema.safeParse(argsWithoutDiscriminator);

// Mutate directly — zero-copy re-injection of discriminator
const validated = result.data as Record<string, unknown>;
validated[execCtx.discriminator] = resolved.discriminatorValue;
return succeed(validated);

Instead of creating a new object with { ...result.data, action: value }, the framework mutates the result.data reference directly. This avoids an extra object allocation on every validated call.

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6. State Sync Caching Architecture

Policy Resolution Cache (O(1) Repeat Lookups)

The PolicyEngine caches resolved policies per tool name. Glob pattern matching only happens once per unique tool name:

// From: src/framework/state-sync/PolicyEngine.ts
resolve(toolName: string): ResolvedPolicy | null {
    const cached = this._cache.get(toolName);
    if (cached !== undefined) return cached;

    const result = this._resolveUncached(toolName);

    // Bounded cache: evict all when hitting the cap
    if (this._cache.size >= MAX_CACHE_SIZE) {
        this._cache.clear();
    }

    this._cache.set(toolName, result);
    return result;
}

The cache is bounded to MAX_CACHE_SIZE = 2048 entries to prevent unbounded memory growth from adversarial input. In practice, MCP servers have 10–200 tools, so the cache hit rate approaches 100%.

Pre-Frozen Shared Policy Objects

Multiple tool names matching the same policy share a single frozen object reference:

// From: src/framework/state-sync/PolicyEngine.ts — constructor
// Pre-compute a frozen ResolvedPolicy for each policy entry.
// N tool names matching the same policy share one object.
this._resolvedByIndex = Object.freeze(
    this._policies.map(p => this._buildResolved(p)),
);

// Pre-frozen default resolution — reused for every unmatched tool name
this._defaultResolved = this._defaultCacheControl
    ? Object.freeze({ cacheControl: this._defaultCacheControl })
    : null;

No repeated object construction or property copying for the same policy.

Tool Description Decoration Cache

StateSyncLayer caches decorated McpTool objects per tool name. The regex + string concatenation + object spread only runs once per unique tool name, not per tools/list request:

// From: src/framework/state-sync/StateSyncLayer.ts
private _decorateToolCached(tool: McpTool): McpTool {
    const cached = this._decoratedToolCache.get(tool.name);
    if (cached) return cached;

    const decorated = decorateDescription(tool, this._engine.resolve(tool.name));
    this._decoratedToolCache.set(tool.name, decorated);
    return decorated;
}

Since tools/list is the hottest path (runs at the start of every LLM conversation), this cache ensures near-zero overhead.

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7. Bounded Glob Matching

The GlobMatcher for State Sync policies uses iterative matching with bounded backtracking to prevent exponential blowup on adversarial patterns:

// From: src/framework/state-sync/GlobMatcher.ts
const MAX_ITERATIONS = 1024;

function matchIterative(pattern: string[], name: string[]): boolean {
    let iterations = 0;
    while (ni < name.length) {
        if (++iterations > MAX_ITERATIONS) return false;
        // ... iterative matching with bookmark-based backtracking
    }
    return pi === pattern.length;
}

Why this matters: Recursive glob matching can be O(2^n) for pathological patterns like **.**.**.**. The iterative approach with a 1024-iteration cap guarantees deterministic worst-case CPU usage while being generous enough for any real-world MCP tool name hierarchy.

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8. Tag Filtering with O(1) Set Lookups

The ToolFilterEngine pre-converts filter arrays to Set objects for O(1) tag membership tests, and uses single-pass iteration to avoid intermediate array allocations:

// From: src/framework/registry/ToolFilterEngine.ts
export function filterTools<TContext>(
    builders: Iterable<ToolBuilder<TContext>>,
    filter: ToolFilter,
): McpTool[] {
    // Pre-convert filter arrays to Sets for O(1) lookup
    const requiredTags = filter.tags?.length > 0
        ? new Set(filter.tags) : undefined;
    const excludeTags = filter.exclude?.length > 0
        ? new Set(filter.exclude) : undefined;

    const tools: McpTool[] = [];
    for (const builder of builders) {
        const builderTags = builder.getTags();
        // O(1) Set.has() instead of O(n) Array.includes()
        if (excludeTags) {
            for (const t of builderTags) {
                if (excludeTags.has(t)) { excluded = true; break; }
            }
        }
        tools.push(builder.buildToolDefinition());
    }
    return tools;
}

Early break on first match/exclusion avoids unnecessary iterations.

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9. TOON Token Compression (30-50% Fewer Tokens)

Description Compression

.toonDescription() encodes action metadata using TOON (Token-Oriented Object Notation) pipe-delimited format, reducing description token count by 30-50% compared to markdown:

// From: src/framework/schema/ToonDescriptionGenerator.ts
function encodeFlatActions<TContext>(actions): string {
    const rows = actions.map(a => buildActionRow(a.key, a));
    return encode(rows, { delimiter: '|' });
}

Standard description (~100 tokens):

Manage projects. Actions: list, get, create

Workflow:
- 'list': List all projects
- 'get': Get project details. Requires: id
- 'create': Create a new project. Requires: name [DESTRUCTIVE]

TOON description (~55 tokens):

Manage projects

action|desc|required|destructive
list|List all projects||
get|Get project details|id|
create|Create a new project|name|true

Column headers appear once. Values are pipe-delimited. Zero JSON key repetition per row.

Response Compression

toonSuccess() compresses list/tabular response data by ~40% vs JSON.stringify():

// From: src/framework/response.ts
export function toonSuccess(data: unknown, options?: EncodeOptions): ToolResponse {
    const text = encode(data, { delimiter: '|' });
    return { content: [{ type: "text", text }] };
}

For a 100-row user list, this saves thousands of tokens per response, translating directly to lower API costs.

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10. Cognitive Guardrails (Context DDoS Prevention)

The Presenter's .agentLimit() truncates large collections before serialization, preventing context overflow:

// From: src/framework/presenter/Presenter.ts — make()
if (isArray && this._agentLimit && data.length > this._agentLimit.max) {
    const omitted = data.length - this._agentLimit.max;
    data = data.slice(0, this._agentLimit.max);
    truncationBlock = this._agentLimit.onTruncate(omitted);
}

Impact on token costs:

Scenario	Rows	Tokens	Cost (GPT-5.2)
No guardrail	10,000	~5,000,000	~$8.75
.agentLimit(50)	50	~25,000	~$0.04

Truncation happens before Zod validation, so the schema only processes the capped set — saving CPU on large datasets.

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11. Zod .strict() Security Boundary

Every action's validation schema is compiled with .strict():

// From: src/framework/builder/ToolDefinitionCompiler.ts
function buildValidationSchema(action, commonSchema) {
    const merged = base && specific ? base.merge(specific) : (base ?? specific);
    return merged.strict();
}

.strict() rejects all undeclared fields from the LLM's payload with an actionable error message naming the invalid fields. This is both a security measure (no undeclared data reaches handlers) and an agent experience improvement — the LLM learns which fields are valid and self-corrects on retry.

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12. Pure-Function Module Architecture

Critical performance modules are implemented as pure functions with no state and no side effects:

Module	File	Pattern
MiddlewareCompiler	execution/MiddlewareCompiler.ts	Pure function, stateless
ExecutionPipeline	execution/ExecutionPipeline.ts	Pure pipeline steps
ToolFilterEngine	registry/ToolFilterEngine.ts	Pure function, no state
GlobMatcher	state-sync/GlobMatcher.ts	Pure function, iterative
DescriptionGenerator	schema/DescriptionGenerator.ts	Pure function
ToonDescriptionGenerator	schema/ToonDescriptionGenerator.ts	Pure function
SchemaGenerator	schema/SchemaGenerator.ts	Pure function
AnnotationAggregator	schema/AnnotationAggregator.ts	Pure function
PostProcessor	presenter/PostProcessor.ts	Pure function
ValidationErrorFormatter	execution/ValidationErrorFormatter.ts	Pure function

Why pure functions matter for performance:

• V8 can inline and optimize them aggressively (no hidden state to track)
• No garbage collection pressure from instance allocation
• Thread-safe by construction (no shared mutable state)
• Deterministic output enables internal caching

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13. Minimal Dependency Footprint

MCP Fusion ships with only 2 runtime dependencies:

{
    "dependencies": {
        "@toon-format/toon": "^2.1.0",
        "zod-to-json-schema": "^3.25.1"
    }
}

zod and @modelcontextprotocol/sdk are peer dependencies (already in your project). This means:

• Tiny install size — no dependency tree bloat
• No duplicated code — Zod is shared with your app
• Fast npm install — two packages to resolve
• Reduced attack surface — fewer transitive dependencies

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14. Self-Healing Error Responses (Reduced LLM Retry Loops)

While not a CPU optimization, toolError() and the ValidationErrorFormatter dramatically reduce total system cost by eliminating unnecessary LLM retries:

// From: src/framework/execution/ValidationErrorFormatter.ts
// Instead of: "Validation failed: email: Invalid"
// Produces:
// ❌ Validation failed for 'users.create':
//   • email — Invalid email format. You sent: 'admin@local'.
//     Expected: a valid email address (e.g. user@example.com).
//   💡 Fix the fields above and call the action again.

Each retry is a full LLM round-trip (~$0.01-0.10 depending on context size). Self-healing errors make the LLM succeed on the second attempt instead of cycling through 3-5 retries, saving 60-80% of error-path costs.

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Summary

Optimization	Where It Lives	Impact
Middleware pre-compilation	MiddlewareCompiler.ts	Zero chain assembly per request
Validation schema cache	ToolDefinitionCompiler.ts	O(1) schema lookup per action
O(1) action routing	ToolDefinitionCompiler.ts	Map.get() instead of linear scan
Freeze-after-build	GroupedToolBuilder.ts	V8 JIT optimization + cache reuse
Zero-overhead debug	GroupedToolBuilder.ts	No conditionals when disabled
Railway-oriented pipeline	ExecutionPipeline.ts	No exceptions for expected errors
Zero-copy validation	ExecutionPipeline.ts	Direct mutation instead of spread
Policy resolution cache	PolicyEngine.ts	O(1) repeat lookups, bounded
Pre-frozen policy objects	PolicyEngine.ts	Shared references, no allocation
Tool decoration cache	StateSyncLayer.ts	Cache 100% hot path
Bounded glob matching	GlobMatcher.ts	O(n) worst case, iterative
Set-based tag filtering	ToolFilterEngine.ts	O(1) membership test
TOON compression	ToonDescriptionGenerator.ts	30-50% fewer prompt tokens
Cognitive guardrails	Presenter.ts	100x cost reduction on large sets
Zod .strict()	ToolDefinitionCompiler.ts	Unknown field rejection, cleaner payloads
Pure-function modules	10+ files	V8 inlining, no GC pressure
2 runtime dependencies	package.json	Minimal install, tiny bundle
Self-healing errors	ValidationErrorFormatter.ts	60-80% fewer LLM retries