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Model Context Protocol home pagedark logo Search... ⌘K Python SDK TypeScript SDK Java SDK Kotlin SDK C# SDK Get Started Introduction Quickstart For Server Developers For Client Developers For Claude Desktop Users Example Servers Example Clients FAQs Tutorials Building MCP with LLMs Debugging Inspector Concepts Core architecture Resources Prompts Tools Sampling Roots Transports Development What's New Roadmap Contributing User Guide SDKs Specification GitHub Concepts Tools Copy page Enable LLMs to perform actions through your server Tools are a powerful primitive in the Model Context Protocol (MCP) that enable servers to expose executable functionality to clients. Through tools, LLMs can interact with external systems, perform computations, and take actions in the real world. Tools are designed to be model-controlled, meaning that tools are exposed from servers to clients with the intention of the AI model being able to automatically invoke them (with a human in the loop to grant approval). ​ Overview Tools in MCP allow servers to expose executable functions that can be invoked by clients and used by LLMs to perform actions. Key aspects of tools include: Discovery: Clients can list available tools through the tools/list endpoint Invocation: Tools are called using the tools/call endpoint, where servers perform the requested operation and return results Flexibility: Tools can range from simple calculations to complex API interactions Like resources, tools are identified by unique names and can include descriptions to guide their usage. However, unlike resources, tools represent dynamic operations that can modify state or interact with external systems. ​ Tool definition structure Each tool is defined with the following structure: Copy { name: string; // Unique identifier for the tool description?: string; // Human-readable description inputSchema: { // JSON Schema for the tool's parameters type: "object", properties: { ... } // Tool-specific parameters }, annotations?: { // Optional hints about tool behavior title?: string; // Human-readable title for the tool readOnlyHint?: boolean; // If true, the tool does not modify its environment destructiveHint?: boolean; // If true, the tool may perform destructive updates idempotentHint?: boolean; // If true, repeated calls with same args have no additional effect openWorldHint?: boolean; // If true, tool interacts with external entities } } ​ Implementing tools Here’s an example of implementing a basic tool in an MCP server: TypeScript Python Copy const server = new Server({ name: "example-server", version: "1.0.0" }, { capabilities: { tools: {} } }); // Define available tools server.setRequestHandler(ListToolsRequestSchema, async () => { return { tools: [{ name: "calculate_sum", description: "Add two numbers together", inputSchema: { type: "object", properties: { a: { type: "number" }, b: { type: "number" } }, required: ["a", "b"] } }] }; }); // Handle tool execution server.setRequestHandler(CallToolRequestSchema, async (request) => { if (request.params.name === "calculate_sum") { const { a, b } = request.params.arguments; return { content: [ { type: "text", text: String(a + b) } ] }; } throw new Error("Tool not found"); }); ​ Example tool patterns Here are some examples of types of tools that a server could provide: ​ System operations Tools that interact with the local system: Copy { name: "execute_command", description: "Run a shell command", inputSchema: { type: "object", properties: { command: { type: "string" }, args: { type: "array", items: { type: "string" } } } } } ​ API integrations Tools that wrap external APIs: Copy { name: "github_create_issue", description: "Create a GitHub issue", inputSchema: { type: "object", properties: { title: { type: "string" }, body: { type: "string" }, labels: { type: "array", items: { type: "string" } } } } } ​ Data processing Tools that transform or analyze data: Copy { name: "analyze_csv", description: "Analyze a CSV file", inputSchema: { type: "object", properties: { filepath: { type: "string" }, operations: { type: "array", items: { enum: ["sum", "average", "count"] } } } } } ​ Best practices When implementing tools: Provide clear, descriptive names and descriptions Use detailed JSON Schema definitions for parameters Include examples in tool descriptions to demonstrate how the model should use them Implement proper error handling and validation Use progress reporting for long operations Keep tool operations focused and atomic Document expected return value structures Implement proper timeouts Consider rate limiting for resource-intensive operations Log tool usage for debugging and monitoring ​ Security considerations When exposing tools: ​ Input validation Validate all parameters against the schema Sanitize file paths and system commands Validate URLs and external identifiers Check parameter sizes and ranges Prevent command injection ​ Access control Implement authentication where needed Use appropriate authorization checks Audit tool usage Rate limit requests Monitor for abuse ​ Error handling Don’t expose internal errors to clients Log security-relevant errors Handle timeouts appropriately Clean up resources after errors Validate return values ​ Tool discovery and updates MCP supports dynamic tool discovery: Clients can list available tools at any time Servers can notify clients when tools change using notifications/tools/list_changed Tools can be added or removed during runtime Tool definitions can be updated (though this should be done carefully) ​ Error handling Tool errors should be reported within the result object, not as MCP protocol-level errors. This allows the LLM to see and potentially handle the error. When a tool encounters an error: Set isError to true in the result Include error details in the content array Here’s an example of proper error handling for tools: TypeScript Python Copy try { // Tool operation const result = performOperation(); return { content: [ { type: "text", text: `Operation successful: ${result}` } ] }; } catch (error) { return { isError: true, content: [ { type: "text", text: `Error: ${error.message}` } ] }; } This approach allows the LLM to see that an error occurred and potentially take corrective action or request human intervention. ​ Tool annotations Tool annotations provide additional metadata about a tool’s behavior, helping clients understand how to present and manage tools. These annotations are hints that describe the nature and impact of a tool, but should not be relied upon for security decisions. ​ Purpose of tool annotations Tool annotations serve several key purposes: Provide UX-specific information without affecting model context Help clients categorize and present tools appropriately Convey information about a tool’s potential side effects Assist in developing intuitive interfaces for tool approval ​ Available tool annotations The MCP specification defines the following annotations for tools: Annotation Type Default Description title string - A human-readable title for the tool, useful for UI display readOnlyHint boolean false If true, indicates the tool does not modify its environment destructiveHint boolean true If true, the tool may perform destructive updates (only meaningful when readOnlyHint is false) idempotentHint boolean false If true, calling the tool repeatedly with the same arguments has no additional effect (only meaningful when readOnlyHint is false) openWorldHint boolean true If true, the tool may interact with an “open world” of external entities ​ Example usage Here’s how to define tools with annotations for different scenarios: Copy // A read-only search tool { name: "web_search", description: "Search the web for information", inputSchema: { type: "object", properties: { query: { type: "string" } }, required: ["query"] }, annotations: { title: "Web Search", readOnlyHint: true, openWorldHint: true } } // A destructive file deletion tool { name: "delete_file", description: "Delete a file from the filesystem", inputSchema: { type: "object", properties: { path: { type: "string" } }, required: ["path"] }, annotations: { title: "Delete File", readOnlyHint: false, destructiveHint: true, idempotentHint: true, openWorldHint: false } } // A non-destructive database record creation tool { name: "create_record", description: "Create a new record in the database", inputSchema: { type: "object", properties: { table: { type: "string" }, data: { type: "object" } }, required: ["table", "data"] }, annotations: { title: "Create Database Record", readOnlyHint: false, destructiveHint: false, idempotentHint: false, openWorldHint: false } } ​ Integrating annotations in server implementation TypeScript Python Copy server.setRequestHandler(ListToolsRequestSchema, async () => { return { tools: [{ name: "calculate_sum", description: "Add two numbers together", inputSchema: { type: "object", properties: { a: { type: "number" }, b: { type: "number" } }, required: ["a", "b"] }, annotations: { title: "Calculate Sum", readOnlyHint: true, openWorldHint: false } }] }; }); ​ Best practices for tool annotations Be accurate about side effects: Clearly indicate whether a tool modifies its environment and whether those modifications are destructive. Use descriptive titles: Provide human-friendly titles that clearly describe the tool’s purpose. Indicate idempotency properly: Mark tools as idempotent only if repeated calls with the same arguments truly have no additional effect. Set appropriate open/closed world hints: Indicate whether a tool interacts with a closed system (like a database) or an open system (like the web). Remember annotations are hints: All properties in ToolAnnotations are hints and not guaranteed to provide a faithful description of tool behavior. Clients should never make security-critical decisions based solely on annotations. ​ Testing tools A comprehensive testing strategy for MCP tools should cover: Functional testing: Verify tools execute correctly with valid inputs and handle invalid inputs appropriately Integration testing: Test tool interaction with external systems using both real and mocked dependencies Security testing: Validate authentication, authorization, input sanitization, and rate limiting Performance testing: Check behavior under load, timeout handling, and resource cleanup Error handling: Ensure tools properly report errors through the MCP protocol and clean up resources Was this page helpful? Yes No Prompts Sampling github On this page Overview Tool definition structure Implementing tools Example tool patterns System operations API integrations Data processing Best practices Security considerations Input validation Access control Error handling Tool discovery and updates Error handling Tool annotations Purpose of tool annotations Available tool annotations Example usage Integrating annotations in server implementation Best practices for tool annotations Testing tools Tools - Model Context Protocol