Build Custom MCP Server

Create custom MCP server exposing domain-specific tools to AI assistants.

When Use

• Need to expose custom functionality to Claude Code or Claude Desktop
• Building specialized tools beyond what mcptools provides
• Creating 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

Steps

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: YAML or markdown spec for each tool with name, description, parameters (types, defaults, required flags), return type documented before writing code.

If fail: Tool specifications unclear? Interview domain expert or review existing API documentation for 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: Working

server.js

imports MCP SDK, defines tools with Zod schemas, connects via stdio transport. Running

node server.js

starts server without errors.

If fail: Verify

@modelcontextprotocol/sdk

and

zod

installed (

npm install

). Check import paths match SDK version (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: Working

server.R

registers custom tools with

mcp_tool()

, starts server with

mcp_server()

. Running

Rscript server.R

starts MCP server.

If fail: Ensure

mcptools

installed from GitHub (

remotes::install_github("posit-dev/mcptools")

). Check handler function signatures match 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,

test/

for tests.

If fail: Directory structure doesn't match 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 Claude Code session, check custom tools listed and functional.

Got:

tools/list

JSON-RPC call returns all defined tools with correct names and schemas.

claude mcp list

shows server registered. Tools callable from Claude Code session.

If fail:

tools/list

returns empty array? Tools were not registered before

server.connect()

. Claude Code cannot find server? Verify command path in

claude mcp add

is absolute, 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 wrapped in try/catch. Invalid inputs return

isError: true

with descriptive message instead of crashing server process.

If fail: Server still crashes on bad input? Check try/catch wraps entire handler body including async operations. Ensure promises awaited within try block.

Step 7: Package for Distribution

Create

package.json

with 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 install and configure:

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

Got:

package.json

with

bin

entry pointing to server entry point. Users install globally with

npm install -g

, register with

claude mcp add

.

If fail: Bin entry doesn't work after global install? Ensure

server.js

has shebang line (

#!/usr/bin/env node

), is marked executable. Verify package name doesn't conflict with existing npm packages.

Checks

• 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 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 server. Always wrap tool handlers in try/catch.
• Schema mismatches: Tool parameter schemas must exactly match what handler expects
• stdio buffering: Using stdio transport? Ensure output flushed. Node.js buffers stdout by default.
• Security: MCP servers have same access as process. Validate inputs carefully, especially for shell commands or database queries.

See Also

• configure-mcp-server

  - connect built server to clients

• troubleshoot-mcp-connection

  - debug connectivity issues

• containerize-mcp-server

  - package server in Docker