Creating MCP Servers

Add a new MCP server to the Crow platform, following the same patterns used by the built-in memory, research, sharing, storage, and blog servers.

What is this?

An MCP server exposes tools that AI assistants can call. Crow's built-in servers handle memory, research, sharing, storage, and blogging. You can add your own server for any domain — task management, analytics, home automation, or anything else.

Why would I want this?

• Custom tools — Give your AI new capabilities tailored to your workflow
• Consistent architecture — Follow the same factory pattern so your server works with both stdio and HTTP transports
• Database access — Use the shared SQLite/Turso database for persistence
• Community sharing — Publish your server as an add-on for other Crow users

The Factory Pattern

Every Crow MCP server follows the same structure:

servers/your-server/
  server.js    # Factory function with tool definitions
  index.js     # Stdio transport binding

server.js

The factory function creates and returns a configured McpServer instance:

import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
import { z } from 'zod';
import { getDbClient, sanitizeFtsQuery, escapeLikePattern } from '../db.js';

export function createYourServer(dbPath) {
  const server = new McpServer({
    name: 'crow-your-server',
    version: '1.0.0',
  });

  const db = getDbClient(dbPath);

  server.tool(
    'crow_your_tool',
    'Description of what this tool does',
    {
      input: z.string().max(500).describe('What this parameter is for'),
      optional_param: z.string().max(200).optional().describe('Optional parameter'),
    },
    async ({ input, optional_param }) => {
      // Tool logic here
      const result = await db.execute({
        sql: 'SELECT * FROM your_table WHERE column = ?',
        args: [input],
      });

      return {
        content: [{ type: 'text', text: JSON.stringify(result.rows) }],
      };
    }
  );

  return server;
}

index.js

The stdio entry point is minimal:

import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import { createYourServer } from './server.js';

const server = createYourServer();
const transport = new StdioServerTransport();
await server.connect(transport);

Zod Schema Constraints

All string parameters must include .max() constraints to prevent abuse:

// Good
z.string().max(500).describe('Search query')
z.string().max(50000).describe('Content body')

// Bad — no size limit
z.string().describe('Search query')

Recommended limits:

• Short fields (names, IDs, queries): .max(500)
• Content fields (body text, notes): .max(50000)
• Numeric limits: use .min() and .max() on z.number()

Database Tables

If your server needs its own tables, add them to scripts/init-db.js:

await db.execute(`
  CREATE TABLE IF NOT EXISTS your_table (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    name TEXT NOT NULL,
    content TEXT NOT NULL,
    created_at TEXT DEFAULT (datetime('now')),
    updated_at TEXT DEFAULT (datetime('now'))
  )
`);

Then run npm run init-db to create the tables.

FTS5 Indexes

If your table needs full-text search, add an FTS5 virtual table and sync triggers:

await db.execute(`
  CREATE VIRTUAL TABLE IF NOT EXISTS your_table_fts USING fts5(
    name, content,
    content='your_table',
    content_rowid='id'
  )
`);

// Insert trigger
await db.execute(`
  CREATE TRIGGER IF NOT EXISTS your_table_ai AFTER INSERT ON your_table BEGIN
    INSERT INTO your_table_fts(rowid, name, content)
    VALUES (new.id, new.name, new.content);
  END
`);

// Update trigger
await db.execute(`
  CREATE TRIGGER IF NOT EXISTS your_table_au AFTER UPDATE ON your_table BEGIN
    INSERT INTO your_table_fts(your_table_fts, rowid, name, content)
    VALUES ('delete', old.id, old.name, old.content);
    INSERT INTO your_table_fts(rowid, name, content)
    VALUES (new.id, new.name, new.content);
  END
`);

// Delete trigger
await db.execute(`
  CREATE TRIGGER IF NOT EXISTS your_table_ad AFTER DELETE ON your_table BEGIN
    INSERT INTO your_table_fts(your_table_fts, rowid, name, content)
    VALUES ('delete', old.id, old.name, old.content);
  END
`);

Use sanitizeFtsQuery() from servers/db.js for any FTS5 MATCH queries:

import { sanitizeFtsQuery } from '../db.js';

const safeQuery = sanitizeFtsQuery(userInput);
const results = await db.execute({
  sql: `SELECT * FROM your_table WHERE id IN (
    SELECT rowid FROM your_table_fts WHERE your_table_fts MATCH ?
  )`,
  args: [safeQuery],
});

Use escapeLikePattern() for LIKE queries:

import { escapeLikePattern } from '../db.js';

const safePattern = escapeLikePattern(userInput);
const results = await db.execute({
  sql: `SELECT * FROM your_table WHERE name LIKE ? ESCAPE '\\'`,
  args: [`%${safePattern}%`],
});

Register in server-registry.js

Add your server to scripts/server-registry.js so npm run mcp-config includes it:

{
  name: 'crow-your-server',
  command: 'node',
  args: ['servers/your-server/index.js'],
  envVars: [],  // Required env vars (empty = always included)
}

If your server requires environment variables (e.g., API keys), list them in envVars. The server will only be included in .mcp.json when those vars are set.

Add to the Gateway

Import your factory in servers/gateway/index.js and wire it alongside the existing servers:

import { createYourServer } from '../your-server/server.js';
// ... then add HTTP transport binding

Create a Skill File

Write a skill file in skills/ that describes your server's capabilities and guides the AI on when and how to use the tools:

# Your Feature

## When to activate
- User asks about [your domain]
- User wants to [your use case]

## Available tools
- `crow_your_tool` — Does this thing
- `crow_your_other_tool` — Does that thing

## Workflow
1. Step one
2. Step two

Add a trigger row in skills/superpowers.md so the skill auto-activates.

Testing

Verify your server starts without errors:

node servers/your-server/index.js
# Should start and wait for stdio input (Ctrl-C to stop)

Run npm run mcp-config and check .mcp.json to confirm your server appears.