Build Custom MCP Server

Build Custom MCP Server

Create a custom MCP server that exposes domain-specific tools to AI assistants.

When to Use

• Need to expose custom functionality to Claude Code or Claude Desktop
• Building specialized tools beyond what mcptools provides
• Creating a domain-specific AI assistant integration
• Wrapping existing APIs or services as MCP tools

Inputs

• Required: List of tools to expose (name, description, parameters, behavior)
• Required: Implementation language (Node.js or R)
• Required: Transport type (stdio or HTTP)
• Optional: Authentication requirements
• Optional: Docker packaging needs

Procedure

Step 1: Define Tool Specifications

Before writing code, define each tool:

tools:
  - name: query_database
    description: Execute a read-only SQL query against the analysis database
    parameters:
      query:
        type: string
        description: SQL SELECT query to execute
        required: true
      limit:
        type: integer
        description: Maximum rows to return
        default: 100
    returns: JSON array of result rows

  - name: run_analysis
    description: Execute a predefined statistical analysis by name
    parameters:
      analysis_name:
        type: string
        description: Name of the analysis to run
        enum: [descriptive, regression, survival]
      dataset:
        type: string
        description: Dataset identifier
        required: true

Expected: A YAML or markdown specification for each tool with name, description, parameters (including types, defaults, and required flags), and return type documented before writing any code.

On failure: If tool specifications are unclear, interview the domain expert or review the existing API documentation to determine parameter types and return formats.

Step 2: Implement in Node.js (Using MCP SDK)

// server.js
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "my-analysis-server",
  version: "1.0.0",
});

// Define tools
server.tool(
  "query_database",
  "Execute a read-only SQL query against the analysis database",
  {
    query: z.string().describe("SQL SELECT query"),
    limit: z.number().default(100).describe("Max rows to return"),
  },
  async ({ query, limit }) => {
    // Validate read-only
    if (!/^\s*SELECT/i.test(query)) {
      return {
        content: [{ type: "text", text: "Error: Only SELECT queries allowed" }],
        isError: true,
      };
    }

    const results = await executeQuery(query, limit);
    return {
      content: [{ type: "text", text: JSON.stringify(results, null, 2) }],
    };
  }
);

server.tool(
  "run_analysis",
  "Execute a predefined statistical analysis",
  {
    analysis_name: z.enum(["descriptive", "regression", "survival"]),
    dataset: z.string().describe("Dataset identifier"),
  },
  async ({ analysis_name, dataset }) => {
    const result = await runAnalysis(analysis_name, dataset);
    return {
      content: [{ type: "text", text: JSON.stringify(result, null, 2) }],
    };
  }
);

// Start server with stdio transport
const transport = new StdioServerTransport();
await server.connect(transport);

Expected: A working server.js file that imports the MCP SDK, defines tools with Zod schemas, and connects via stdio transport. Running node server.js starts the server without errors.

On failure: Verify that @modelcontextprotocol/sdk and zod are installed (npm install). Check that the import paths match the SDK version (the SDK reorganized exports between versions).

Step 3: Implement in R (Using mcptools)

# server.R
library(mcptools)

# Register custom tools
mcp_tool(
  name = "query_database",
  description = "Execute a read-only SQL query",
  parameters = list(
    query = list(type = "string", description = "SQL SELECT query"),
    limit = list(type = "integer", description = "Max rows", default = 100)
  ),
  handler = function(query, limit = 100) {
    if (!grepl("^\\s*SELECT", query, ignore.case = TRUE)) {
      stop("Only SELECT queries allowed")
    }
    result <- DBI::dbGetQuery(con, paste(query, "LIMIT", limit))
    jsonlite::toJSON(result, auto_unbox = TRUE)
  }
)

# Start server
mcptools::mcp_server()

Expected: A working server.R file that registers custom tools with mcp_tool() and starts the server with mcp_server(). Running Rscript server.R starts the MCP server.

On failure: Ensure mcptools is installed from GitHub (remotes::install_github("posit-dev/mcptools")). Check that the handler function signatures match the parameter definitions.

Step 4: Decide Whether You Also Need Resources or Prompts

Tools are one of three server-side primitives, and reaching for a tool when the protocol already has the right shape is the most common design mistake here. The three differ by who decides to invoke them, and that difference is the whole basis for choosing:

• Tools are model-controlled. The model decides to call one, usually to do something with side effects. Everything above this step.
• Resources are application-driven. The host application decides what to pull into context — through a picker, a search, or its own heuristics. The spec's examples are files, database schemas, and application-specific data. If your "tool" only reads and returns data with no side effect, it probably wants to be a resource.
• Prompts are user-controlled: "exposed from servers to clients with the intention of the user being able to explicitly select them", typically surfaced as slash commands. A reusable instruction template belongs here, not in a tool.

Each must be declared as a capability during initialization or the client will never call it, and this is the usual reason a correctly implemented handler is never reached. Add resources and prompts alongside tools in the capabilities object. Both take optional listChanged, meaning the server will notify when its list changes; resources additionally takes subscribe, for per-resource change notifications. Declaring neither is valid — an empty resources: {} means the feature is supported with no optional extras.

For resources, implement resources/list and resources/read. A resource is identified by a uri and carries a name, plus optional title, description, mimeType and size. resources/read returns a contents array whose entries carry either text or a base64 blob — never both, and binary data must be base64-encoded. For parameterized access add resources/templates/list, returning entries with a uriTemplate in RFC 6570 syntax such as file:///{path}. If you declared listChanged, emit notifications/resources/list_changed; if you declared subscribe, honour resources/subscribe and emit notifications/resources/updated. Return -32002 for a URI you do not recognise, which is a resource-specific code rather than the generic invalid-params one.

For prompts, implement prompts/list and prompts/get. A prompt has a name and an optional arguments list whose entries carry name, description and required. prompts/get returns a messages array; each message has a role of user or assistant — note there is no system role — and a content object of type text, image, audio, or resource for embedding server-managed content directly. Validate arguments before processing and return -32602 for both an unknown prompt name and a missing required argument.

Both list operations support cursor pagination, so return nextCursor when your list is long and accept cursor in the request.

Expected: The capabilities object names every primitive the server actually implements, and a client's resources/list or prompts/list returns entries rather than a method-not-found error.

On failure:

• Handler never invoked — the capability was not declared at initialization; capability negotiation happens once, before any request
• Client shows a resource but reading it fails — check the URI is valid per RFC 3986 and that you return -32002 rather than an empty result for unknown URIs
• Binary resource renders as garbage — blob must be base64, and mimeType must be set; a binary payload placed in text will not be decoded
• A prompt renders with the roles collapsed — system is not a valid role; fold system-level instruction into the first user message

Step 5: Set Up Project Structure

my-mcp-server/
├── package.json          # Node.js dependencies
├── server.js             # Server implementation
├── tools/                # Tool implementations
│   ├── database.js
│   └── analysis.js
├── test/                 # Tests
│   └── tools.test.js
├── Dockerfile            # Container packaging
└── README.md             # Setup instructions

Expected: Project directory created with server.js (or server.R), package.json, tools/ directory for modular tool implementations, and test/ directory for tests.

On failure: If the directory structure doesn't match your implementation language, adjust accordingly. R servers may use R/ instead of tools/ and tests/testthat/ instead of test/.

Step 6: Test the Server

Manual testing with stdio:

echo '{"jsonrpc":"2.0","method":"tools/list","id":1}' | node server.js

Register with Claude Code:

claude mcp add my-server stdio "node" "/path/to/server.js"

Verify tools appear:

Start a Claude Code session and check that custom tools are listed and functional.

Expected: The tools/list JSON-RPC call returns all defined tools with correct names and schemas. claude mcp list shows the server registered. Tools are callable from a Claude Code session.

On failure: If tools/list returns an empty array, the tools were not registered before server.connect(). If Claude Code cannot find the server, verify the command path in claude mcp add is absolute and the binary is executable.

Step 7: Add Error Handling

server.tool("risky_operation", "...", schema, async (params) => {
  try {
    const result = await performOperation(params);
    return {
      content: [{ type: "text", text: JSON.stringify(result) }],
    };
  } catch (error) {
    return {
      content: [{ type: "text", text: `Error: ${error.message}` }],
      isError: true,
    };
  }
});

Expected: Each tool handler is wrapped in try/catch. Invalid inputs return isError: true with a descriptive message instead of crashing the server process.

On failure: If the server still crashes on bad input, check that the try/catch wraps the entire handler body including any async operations. Ensure promises are awaited within the try block.

Step 8: Package for Distribution

Create a package.json with a bin entry:

{
  "name": "my-mcp-server",
  "version": "1.0.0",
  "bin": {
    "my-mcp-server": "./server.js"
  },
  "dependencies": {
    "@modelcontextprotocol/sdk": "^1.0.0",
    "zod": "^3.22.0"
  }
}

Users can then install and configure:

npm install -g my-mcp-server
claude mcp add my-server stdio "my-mcp-server"

Expected: A package.json with a bin entry pointing to the server entry point. Users can install globally with npm install -g and register with claude mcp add.

On failure: If the bin entry doesn't work after global install, ensure server.js has a shebang line (#!/usr/bin/env node) and is marked executable. Verify the package name doesn't conflict with existing npm packages.

Validation

• Server starts without errors
• tools/list returns all defined tools with correct schemas
• Each tool executes correctly with valid input
• Tools return appropriate errors for invalid input
• Server works with Claude Code via stdio transport
• Tools are discoverable and usable in Claude sessions

Common Pitfalls

• Blocking operations: MCP servers should handle requests asynchronously. Long-running operations block other tool calls.
• Missing error handling: Unhandled exceptions crash the server. Always wrap tool handlers in try/catch.
• Schema mismatches: Tool parameter schemas must exactly match what the handler expects
• stdio buffering: When using stdio transport, ensure output is flushed. Node.js buffers stdout by default.
• Security: MCP servers have the same access as the process. Validate inputs carefully, especially for shell commands or database queries.

Related Skills

• configure-mcp-server - connect the built server to clients
• troubleshoot-mcp-connection - debug connectivity issues
• containerize-mcp-server - package the server in Docker