---

name: build-custom-mcp-server locale: caveman-lite source_locale: en source_commit: 82c77053 fence_basis_commit: 82c77053 translator: "Julius Brussee homage — caveman" translation_date: "2026-04-19" description: > Build a custom MCP (Model Context Protocol) server that exposes domain-specific tools to AI assistants. Covers server implementation in Node.js or R, tool definitions, transport configuration, and testing with Claude Code. Use when you need to expose custom functionality beyond what mcptools provides, when building specialized domain-specific AI integrations, or when wrapping existing APIs or services as MCP tools. license: MIT allowed-tools: Read Write Edit Bash Grep Glob metadata: author: Philipp Thoss version: "1.0" domain: mcp-integration complexity: advanced language: multi tags: mcp, server, custom-tools, node-js, protocol

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

Got: 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.

If fail: 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);

Got: 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.

If fail: 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()

Got: 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.

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

Step 4: 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

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

If fail: 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 5: 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.

Got: 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.

If fail: 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 6: 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,
    };
  }
});

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

If fail: 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 7: 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"

Got: 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.

If fail: 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

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