Agent Tool Routing

Design intelligent systems for agents to select and use the right tools at the right time.

When to Use

• Agents need to choose between multiple tools
• Implementing MCP (Model Context Protocol) integrations
• Building agents with external API access
• Designing tool fallback strategies
• Optimizing tool usage for cost/performance

Tool Architecture

┌─────────────────────────────────────────────────────────────┐
│                         AGENT                                │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                     TOOL ROUTER                              │
│  ┌─────────────┐  ┌──────────────┐  ┌────────────────────┐ │
│  │ Classifier  │  │ Capabilities │  │ Cost/Latency       │ │
│  │             │  │ Matcher      │  │ Optimizer          │ │
│  └─────────────┘  └──────────────┘  └────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        ▼                     ▼                     ▼
   ┌─────────┐          ┌─────────┐          ┌─────────┐
   │ Tool A  │          │ Tool B  │          │ Tool C  │
   │ (Local) │          │  (API)  │          │  (MCP)  │
   └─────────┘          └─────────┘          └─────────┘

Tool Definition

interface Tool {
  name: string;
  description: string;
  category: string;

  // Schema
  inputSchema: JSONSchema;
  outputSchema: JSONSchema;

  // Capabilities
  capabilities: string[];
  limitations: string[];

  // Execution
  execute: (input: unknown) => Promise<ToolResult>;

  // Metadata
  metadata: {
    costPerCall?: number;
    avgLatencyMs?: number;
    rateLimit?: RateLimit;
    requiresAuth?: boolean;
    supportsBatching?: boolean;
  };
}

interface ToolResult {
  success: boolean;
  data?: unknown;
  error?: {
    code: string;
    message: string;
    retryable: boolean;
  };
  metadata: {
    durationMs: number;
    tokensUsed?: number;
  };
}

Tool Registry

class ToolRegistry {
  private tools = new Map<string, Tool>();
  private capabilityIndex = new Map<string, Set<string>>();

  register(tool: Tool): void {
    this.tools.set(tool.name, tool);

    // Index by capability
    for (const cap of tool.capabilities) {
      if (!this.capabilityIndex.has(cap)) {
        this.capabilityIndex.set(cap, new Set());
      }
      this.capabilityIndex.get(cap)!.add(tool.name);
    }
  }

  findByCapability(capability: string): Tool[] {
    const toolNames = this.capabilityIndex.get(capability) || new Set();
    return Array.from(toolNames).map(name => this.tools.get(name)!);
  }

  getAll(): Tool[] {
    return Array.from(this.tools.values());
  }

  // Generate tool descriptions for LLM
  getToolDescriptions(): string {
    return this.getAll()
      .map(t => `- ${t.name}: ${t.description}`)
      .join('\n');
  }
}

Router Strategies

Strategy 1: LLM-Based Selection

Let the model choose based on descriptions.

class LLMToolRouter {
  async route(
    task: string,
    availableTools: Tool[]
  ): Promise<RoutingDecision> {
    const response = await this.llm.complete({
      system: `You are a tool routing assistant.
Given a task and available tools, select the best tool to use.

Available tools:
${availableTools.map(t => `
- ${t.name}
  Description: ${t.description}
  Capabilities: ${t.capabilities.join(', ')}
  Cost: ${t.metadata.costPerCall || 'free'}
  Latency: ${t.metadata.avgLatencyMs || 'unknown'}ms
`).join('\n')}

Respond with JSON: { "tool": "tool_name", "reasoning": "why", "input": {...} }`,
      user: `Task: ${task}`
    });

    return JSON.parse(response);
  }
}

Strategy 2: Rule-Based Selection

Deterministic routing based on patterns.

class RuleBasedRouter {
  private rules: RoutingRule[] = [];

  addRule(rule: RoutingRule): void {
    this.rules.push(rule);
    this.rules.sort((a, b) => b.priority - a.priority);
  }

  route(task: string, context: Context): RoutingDecision {
    for (const rule of this.rules) {
      if (rule.matches(task, context)) {
        return {
          tool: rule.targetTool,
          reasoning: rule.description
        };
      }
    }

    return { tool: 'default', reasoning: 'No specific rule matched' };
  }
}

// Example rules
const rules: RoutingRule[] = [
  {
    priority: 100,
    description: 'Use web search for current information',
    matches: (task) => /current|latest|today|news|2024|2025|2026/.test(task),
    targetTool: 'web_search'
  },
  {
    priority: 90,
    description: 'Use code execution for calculations',
    matches: (task) => /calculate|compute|sum|average|math/.test(task),
    targetTool: 'code_interpreter'
  },
  {
    priority: 80,
    description: 'Use file reader for document analysis',
    matches: (task, ctx) => ctx.hasAttachments && /read|analyze|extract/.test(task),
    targetTool: 'file_reader'
  }
];

Strategy 3: Cost-Optimized Selection

Choose based on cost/performance trade-offs.

class CostOptimizedRouter {
  async route(
    task: string,
    capableTools: Tool[],
    budget: Budget
  ): Promise<RoutingDecision> {
    // Score each tool
    const scored = capableTools.map(tool => ({
      tool,
      score: this.calculateScore(tool, budget)
    }));

    // Sort by score (higher is better)
    scored.sort((a, b) => b.score - a.score);

    return {
      tool: scored[0].tool.name,
      reasoning: `Best cost/performance ratio within budget`
    };
  }

  private calculateScore(tool: Tool, budget: Budget): number {
    const cost = tool.metadata.costPerCall || 0;
    const latency = tool.metadata.avgLatencyMs || 1000;

    // Can't use if over budget
    if (cost > budget.remaining) return -Infinity;

    // Score: lower cost and latency = higher score
    const costScore = 1 - (cost / budget.max);
    const latencyScore = 1 - Math.min(latency / 5000, 1);

    return costScore * budget.costWeight + latencyScore * budget.latencyWeight;
  }
}

MCP Integration

MCP Server Connection

interface MCPServer {
  name: string;
  transport: 'stdio' | 'http' | 'websocket';
  config: MCPConfig;
}

class MCPToolProvider {
  private clients = new Map<string, MCPClient>();

  async connect(server: MCPServer): Promise<void> {
    const client = new MCPClient(server.transport, server.config);
    await client.connect();

    // Discover tools
    const tools = await client.listTools();

    // Register each tool
    for (const tool of tools) {
      registry.register({
        name: `${server.name}:${tool.name}`,
        description: tool.description,
        inputSchema: tool.inputSchema,
        execute: (input) => client.callTool(tool.name, input),
        metadata: {
          source: 'mcp',
          server: server.name
        }
      });
    }

    this.clients.set(server.name, client);
  }

  async disconnect(serverName: string): Promise<void> {
    const client = this.clients.get(serverName);
    if (client) {
      await client.close();
      this.clients.delete(serverName);
    }
  }
}

MCP Configuration

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@anthropic/mcp-github"],
      "env": {
        "GITHUB_TOKEN": "${GITHUB_TOKEN}"
      }
    },
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@anthropic/mcp-filesystem"],
      "env": {
        "ALLOWED_PATHS": "/Users/dev/projects"
      }
    },
    "database": {
      "transport": "http",
      "url": "http://localhost:3001/mcp",
      "auth": {
        "type": "bearer",
        "token": "${DB_MCP_TOKEN}"
      }
    }
  }
}

Tool Execution

With Retry Logic

async function executeWithRetry(
  tool: Tool,
  input: unknown,
  options: ExecutionOptions = {}
): Promise<ToolResult> {
  const maxRetries = options.maxRetries || 3;
  const backoff = options.backoffMs || 1000;

  let lastError: Error;

  for (let attempt = 1; attempt <= maxRetries; attempt++) {
    try {
      // Check rate limit
      await rateLimiter.acquire(tool.name);

      // Execute
      const result = await tool.execute(input);

      if (result.success) {
        return result;
      }

      if (!result.error?.retryable) {
        return result;
      }

      lastError = new Error(result.error.message);
    } catch (error) {
      lastError = error as Error;
    }

    // Wait before retry
    if (attempt < maxRetries) {
      await sleep(backoff * Math.pow(2, attempt - 1));
    }
  }

  return {
    success: false,
    error: {
      code: 'MAX_RETRIES_EXCEEDED',
      message: `Failed after ${maxRetries} attempts: ${lastError.message}`,
      retryable: false
    },
    metadata: { durationMs: 0 }
  };
}

With Fallback Chain

async function executeWithFallback(
  task: string,
  tools: Tool[]
): Promise<ToolResult> {
  for (const tool of tools) {
    try {
      const result = await tool.execute({ task });

      if (result.success) {
        return result;
      }

      console.log(`Tool ${tool.name} failed, trying next...`);
    } catch (error) {
      console.log(`Tool ${tool.name} threw error, trying next...`);
    }
  }

  return {
    success: false,
    error: {
      code: 'ALL_TOOLS_FAILED',
      message: 'All fallback tools failed',
      retryable: false
    },
    metadata: { durationMs: 0 }
  };
}

Monitoring Tool Usage

interface ToolUsageMetrics {
  toolName: string;
  invocations: number;
  successRate: number;
  avgLatencyMs: number;
  totalCost: number;
  errorCounts: Map<string, number>;
}

class ToolMetricsCollector {
  private metrics = new Map<string, ToolUsageMetrics>();

  record(toolName: string, result: ToolResult): void {
    const m = this.getOrCreate(toolName);

    m.invocations++;
    m.avgLatencyMs = (m.avgLatencyMs * (m.invocations - 1) + result.metadata.durationMs) / m.invocations;

    if (result.success) {
      m.successRate = (m.successRate * (m.invocations - 1) + 1) / m.invocations;
    } else {
      m.successRate = (m.successRate * (m.invocations - 1)) / m.invocations;
      const errorCode = result.error?.code || 'UNKNOWN';
      m.errorCounts.set(errorCode, (m.errorCounts.get(errorCode) || 0) + 1);
    }
  }

  getReport(): ToolUsageMetrics[] {
    return Array.from(this.metrics.values());
  }
}

Best Practices

1. Clear tool descriptions – LLMs select based on descriptions
2. Appropriate granularity – Not too broad, not too specific
3. Handle failures gracefully – Always have fallbacks
4. Monitor usage – Track costs, latency, errors
5. Version tool schemas – APIs change over time
6. Rate limit appropriately – Respect external service limits
7. Cache when possible – Avoid redundant calls