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:

yaml

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

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

javascript

// 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);

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()

Step 4: Set Up Project Structure

code

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

Step 5: Test the Server

Manual testing with stdio:

bash

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

Register with Claude Code:

bash

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.

Step 6: Add Error Handling

javascript

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,
    };
  }
});

Step 7: Package for Distribution

Create a package.json with a bin entry:

json

{
  "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:

bash

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

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