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GraphRAG TypeScript MCP Tools

Name: GraphRAG TypeScript MCP Tools
Author: Akakinad

by Akakinad

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GraphRAG TypeScript MCP Tools

Una implementación completa de un servidor MCP de GraphRAG construido con TypeScript, Neo4j y el SDK de MCP para TypeScript. Este proyecto demuestra cómo construir servidores MCP de calidad para producción que exponen herramientas basadas en grafos, recursos y características avanzadas como el muestreo de LLM y el autocompletado.

Construido como parte del curso Neo4j GraphAcademy — Building GraphRAG TypeScript MCP tools.

¿Qué es MCP?

El Model Context Protocol (MCP) es un estándar abierto de Anthropic que permite a los agentes de IA (Claude, Cursor, VS Code Copilot) conectarse a herramientas externas y fuentes de datos de manera estandarizada.

Related MCP server: CodeRAG

Estructura del proyecto

genai-mcp-build-custom-tools-typescript/ ├── server/ │ └── index.ts ← Main MCP server: 4 tools + 1 resource + sampling + completions ├── strawberry/ │ └── index.ts ← First MCP server: simple countLetters tool ├── solutions/ ← Course reference solutions ├── .vscode/ │ └── mcp.json ← VS Code MCP configuration └── README.md

Lo que se construyó

Paso 1 — Primer servidor MCP (`strawberry/index.ts`)

El servidor MCP más simple posible. Una sola herramienta, sin base de datos, transporte stdio.

server.registerTool("countLetters", {
  description: "Count occurrences of a letter in the text",
  inputSchema: {
    text: z.string().describe("The text to search in"),
    search: z.string().describe("The letter to count"),
  },
}, async ({ text, search }) => ({
  content: [{
    type: "text",
    text: String(text.toLowerCase().split(search.toLowerCase()).length - 1),
  }],
}));

Resultado de la prueba: countLetters("strawberry", "r") → 3

Probado con el MCP Inspector, una herramienta basada en navegador para explorar y probar servidores MCP.

Paso 2 — Conexión a Neo4j (Ámbito de Módulo)

A diferencia del administrador de contexto lifespan de Python, TypeScript utiliza variables de ámbito de módulo: el driver se crea una vez al principio del archivo y se comparte directamente con todas las herramientas.

// Created ONCE when file loads — shared by all tools
const driver: Driver = neo4j.driver(
  process.env["NEO4J_URI"] ?? "neo4j://localhost:7687",
  neo4j.auth.basic(
    process.env["NEO4J_USERNAME"] ?? "neo4j",
    process.env["NEO4J_PASSWORD"] ?? "password"
  )
);
const database = process.env["NEO4J_DATABASE"] ?? "neo4j";

Apagado elegante mediante SIGINT:

process.on("SIGINT", async () => {
  await driver.close();
  await server.close();
  process.exit(0);
});

Paso 3 — Herramienta 1: `graphStatistics`

Cuenta todos los nodos y relaciones en Neo4j.

Resultado: {"nodes": 28863, "relationships": 332522}

Paso 4 — Herramienta 2: `getMoviesByGenre`

Busca películas por género ordenadas por la calificación de IMDB. Usa console.error() para el registro — nunca uses console.log() en servidores stdio (corrompe el canal JSON-RPC).

server.registerTool("getMoviesByGenre", {
  description: "Get movies by genre from the Neo4j database",
  inputSchema: {
    genre: z.string().describe("The genre to search for (e.g., Action, Comedy, Drama)"),
    limit: z.number().default(10).describe("Maximum number of movies to return"),
  },
}, async ({ genre, limit }) => {
  const { records } = await driver.executeQuery(query,
    { genre, limit: neo4j.int(limit) },  // neo4j.int() for 64-bit integer compatibility
    { database }
  );
  ...
});

Paso 5 — Herramienta 3: `browse_movies_by_genre` (Paginada)

Paginación basada en cursor usando SKIP y LIMIT de Neo4j:

const skip = parseInt(cursor, 10) || 0;
// Cypher: SKIP $skip LIMIT $limit
const nextCursor = movies.length === pageSize ? String(skip + pageSize) : null;

Devuelve:

{
  "genre": "Action",
  "movies": [...],
  "nextCursor": "2",
  "page": 1,
  "pageSize": 2,
  "hasMore": true,
  "count": 2
}

Paso 6 — Recurso: `movie://{tmdbId}`

Expone los detalles completos de la película por ID de TMDB usando ResourceTemplate:

server.registerResource(
  "movie",
  new ResourceTemplate("movie://{tmdbId}", { list: undefined }),
  { description: "Get detailed information about a specific movie", mimeType: "application/json" },
  async (uri, { tmdbId }) => {
    // uri.href = "movie://603"
    // returns: contents array with JSON movie data
  }
);

Ejemplos: movie://603 (The Matrix), movie://13 (Forrest Gump)

Paso 7 — Avanzado: Muestreo (`explainMovieData`)

Herramientas que llaman al LLM durante la ejecución para convertir los datos brutos de Neo4j en lenguaje natural:

const result = await server.server.createMessage({
  messages: [{
    role: "user",
    content: {
      type: "text",
      text: `Describe '${movieData.title}' (${movieData.released})...`,
    },
  }],
  maxTokens: 200,
});

Sin muestreo: {'title': 'Toy Story', 'released': '1995', 'actors': [...]}

Con muestreo (VS Code Copilot): "Toy Story — Una aventura animada inteligente y divertida sobre Woody, un muñeco vaquero celoso que se siente desplazado cuando Buzz Lightyear se convierte en el nuevo favorito..."

Nota: Se requiere configurar la capacidad en el servidor de bajo nivel:
server.server["_capabilities"] = { ...server.server["_capabilities"], completions: {} };

Paso 8 — Avanzado: Autocompletado

Sugerencias de autocompletado en tiempo real para los parámetros de género — consulta Neo4j mientras el usuario escribe:

import { CompleteRequestSchema } from "@modelcontextprotocol/sdk/types.js";

server.server.setRequestHandler(CompleteRequestSchema, async (request) => {
  if (request.params.argument.name === "genre") {
    const { records } = await driver.executeQuery(
      `MATCH (g:Genre)
       WHERE g.name STARTS WITH $prefix
       RETURN g.name AS name
       ORDER BY name ASC LIMIT 10`,
      { prefix: request.params.argument.value },
      { database }
    );
    return { completion: { values: records.map(r => r.get("name")) } };
  }
  return { completion: { values: [] } };
});

Diferencias clave con la versión de Python

Concepto	Python (FastMCP)	TypeScript (McpServer)
Registro de herramientas	decorador `@mcp.tool()`	método `server.registerTool()`
Estado compartido	Administrador de contexto lifespan	Variables de ámbito de módulo
Acceso al driver	`ctx.request_context.lifespan_context.driver`	`driver` (directo)
Registro	`await ctx.info()`	`console.error()`
Muestreo	`ctx.session.create_message()`	`server.server.createMessage()`
Autocompletado	`@server.completion()`	`server.server.setRequestHandler(CompleteRequestSchema)`
Estructura de archivos	Archivos separados por funcionalidad	Todo en un único `index.ts`
Parámetros numéricos	Anotaciones de tipo int de Python	Se necesita el envoltorio `neo4j.int()`
Parámetros de prompt	`int`, `str`, `float`	Siempre `z.string()`, parsear manualmente

Configuración

Requisitos previos

Node.js 20+
npm
Neo4j Sandbox — Conjunto de datos de recomendaciones de sandbox.neo4j.com

Instalar

git clone https://github.com/Akakinad/genai-mcp-build-custom-tools-typescript
cd genai-mcp-build-custom-tools-typescript
npm install

Configurar credenciales

cat > server/.env << EOF
NEO4J_URI=bolt://your-sandbox-ip:7687
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD=your-password
NEO4J_DATABASE=neo4j
EOF

Verificar la configuración

npx tsx client/test_environment.ts
# Expected: All checks passed!

Ejecución

Probar con MCP Inspector (interfaz de navegador)

cd server
npx @modelcontextprotocol/inspector npx tsx index.ts

Abre la URL mostrada en la terminal → Conectar → pestaña Herramientas → Listar herramientas → selecciona una herramienta → Ejecutar herramienta.

Ejecutar el servidor para uso con editores de IA

cd server
npx tsx index.ts

Configuración de VS Code (`.vscode/mcp.json`)

{
  "servers": {
    "movies-ts": {
      "type": "stdio",
      "command": "npx",
      "args": ["tsx", "/absolute/path/to/server/index.ts"]
    }
  }
}

Probar en VS Code Copilot

Explica la película "Toy Story" usando la herramienta MCP movies-ts Busca películas de acción usando la herramienta MCP movies-ts Obtén estadísticas del grafo usando la herramienta MCP movies-ts

Curso

Ruta de aprendizaje: Generative AI & GraphRAG

Curso: Building GraphRAG TypeScript MCP tools

Building GraphRAG TypeScript MCP Tools

Repositorio complementario para el curso de GraphAcademy Building GraphRAG TypeScript MCP Tools.

Los estudiantes construyen un servidor MCP (Model Context Protocol) que se conecta a una base de datos de grafos Neo4j, exponiendo herramientas y recursos para su uso con asistentes de IA.

Primeros pasos

Copia .env.example a .env y actualiza los valores con los detalles de conexión de tu instancia de Neo4j.
Instala las dependencias:

npm install

Inicia el servidor:

npm start

Inspecciona el servidor con el MCP Inspector:

npm run inspect

Soluciones

El directorio solutions/ contiene el código completo para cada hito de la lección.

Available Tools

4 tools

browse_movies_by_genreC

Browse movies in a genre with pagination support

ParametersJSON Schema

Name	Required	Description	Default
`genre`	Yes	Genre name (e.g. Action, Comedy, Drama)
`cursor`	No	Pagination cursor - position in the result set	0
`pageSize`	No	Number of movies to return per page

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must disclose behavioral traits. It mentions pagination support but does not state whether this is a read-only operation, whether it requires authentication, or what happens with invalid genres. The mutation safety profile is unclear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that conveys the core purpose efficiently. It contains no unnecessary words, but could benefit from a second sentence on when to use this vs getMoviesByGenre.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description does not explain return values (e.g., movie details format, total result count). For a paginated browsing tool with three parameters and sibling overlap, more context is needed to guide correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds minimal value beyond listing genres and pagination, but it does not clarify cursor semantics (e.g., whether it is a page number or token). Baseline 3 is appropriate given full schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Browse' and resource 'movies in a genre' with pagination support. While it differentiates from siblings like getMoviesByGenre (similar purpose) and explainMovieData (different purpose), it could be more explicit about how browsing differs from getting movies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool vs getMoviesByGenre, which appears to have overlapping functionality. It does not specify exclusions or alternatives, leaving the agent to infer usage from the description alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

explainMovieDataA

Get a natural language explanation of movie data using LLM sampling

ParametersJSON Schema

Name	Required	Description	Default
`movieTitle`	Yes	The title of the movie

TDQS

A3.5/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description bears full responsibility for behavioral disclosure. It mentions 'using LLM sampling', which implies non-deterministic generative behavior, but omits details on authorization, rate limits, or potential side effects. The minimal context is acceptable for a read-like tool but lacks depth.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence of 10 words, front-loading the core functionality. Every word contributes meaning, and there is no redundant or irrelevant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given low complexity (1 required parameter, no output schema), the description adequately states the tool's purpose but fails to specify what aspects of movie data are explained (e.g., plot, cast, ratings) or the nature of the 'natural language explanation'. The output format is left entirely to inference.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage with a single parameter 'movieTitle' described as 'The title of the movie'. The description adds no additional parameter semantics beyond the schema, so it meets the baseline for well-documented parameters without extra value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get', the resource 'movie data', and the method 'natural language explanation using LLM sampling'. It distinguishes itself from siblings like getMoviesByGenre and graphStatistics, which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus its siblings. It does not mention when-not-to-use, prerequisites, or alternatives, leaving the agent to infer based solely on the tool name and sibling list.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

getMoviesByGenreC

Get movies by genre from the Neo4j database

ParametersJSON Schema

Name	Required	Description	Default
`genre`	Yes	The genre to search for (e.g., Action, Comedy, Drama)
`limit`	No	Maximum number of movies to return

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description alone must disclose behavioral traits. It only states 'Get movies by genre' without confirming read-only behavior, authentication needs, or pagination behavior (though the schema reveals a default limit of 10). This minimal disclosure leaves significant behavioral ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence of 8 words, with no superfluous information. It is front-loaded with the core action. However, it could be slightly expanded with additional context (e.g., limit behavior) without becoming verbose, so it does not achieve a perfect score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema, the description should explain what the tool returns (e.g., list of movie objects, format). It does not. Additionally, it fails to differentiate from the sibling tool 'browse_movies_by_genre', making the overall context incomplete for an agent to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for both parameters, so the baseline is 3. The description does not add any extra meaning beyond what the schema provides (e.g., it does not clarify whether genre matching is exact or fuzzy). It scores neither higher nor lower than the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get movies by genre') and the data source ('Neo4j database'). It is specific and provides a direct understanding of the tool's function. However, it does not differentiate itself from the sibling tool 'browse_movies_by_genre', which appears to have a very similar purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description offers no guidance on when to use this tool versus alternatives like 'browse_movies_by_genre'. It lacks any context about prerequisites, limitations, or typical use cases. Without such guidance, an agent may select the wrong tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

graphStatisticsB

Count the number of nodes and relationships in the graph

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

B3.4/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the full burden. It states a read operation (count), but doesn't disclose whether the count is real-time, cached, or if it requires permissions. For a zero-parameter tool, more context on performance or scope would help.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence, perfectly sized for the tool's simplicity. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters, no output schema, and simple purpose, the description is largely adequate. However, it doesn't mention the format or granularity of the counts (e.g., separate numbers for nodes vs. relationships), leaving minor ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has zero parameters with 100% coverage, so the description doesn't need to add param details. The baseline is 4 due to schema fully covering the (empty) parameter list.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Count') and the resources ('nodes and relationships in the graph'). It differentiates from siblings (which filter by genre or explain data) by being a global count tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for getting graph size, but it doesn't explicitly say when to use this vs. siblings (e.g., 'Use this for an overview; use getMoviesByGenre for filtering'). No when-not or alternatives mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

4 tool updatesv1.0.0Aug 18, 2026
- First observedbrowse_movies_by_genre
- First observedexplainMovieData
- First observedgetMoviesByGenre
- First observedgraphStatistics

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

C2.9/5.0

Scored 2026-08-18 05:10 across 4 tools

Disambiguation2/5

The two tools for movies by genre (getMoviesByGenre and browse_movies_by_genre) have overlapping purposes; the only distinction is pagination support, which may cause an agent to misselect. Other tools are distinct.

Naming Consistency2/5

Mixes camelCase (getMoviesByGenre, explainMovieData, graphStatistics) and snake_case (browse_movies_by_genre) with different verb conventions ('get' vs 'browse'), showing inconsistency.

Tool Count3/5

With only 4 tools, the surface is minimal but perhaps appropriate for a read-only movie graph query server. It borders on being too few but is not extreme.

Completeness2/5

Missing basic operations like fetching a specific movie by ID, listing all genres, or querying actors/relationships. The tool set covers only a small subset of expected graph queries, leaving significant gaps.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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GraphRAG TypeScript MCP Tools

¿Qué es MCP?

Estructura del proyecto

Lo que se construyó

Paso 1 — Primer servidor MCP (strawberry/index.ts)

Paso 2 — Conexión a Neo4j (Ámbito de Módulo)

Paso 3 — Herramienta 1: graphStatistics

Paso 4 — Herramienta 2: getMoviesByGenre

Paso 5 — Herramienta 3: browse_movies_by_genre (Paginada)

Paso 6 — Recurso: movie://{tmdbId}

Paso 7 — Avanzado: Muestreo (explainMovieData)

Paso 8 — Avanzado: Autocompletado

Diferencias clave con la versión de Python

Configuración

Requisitos previos

Instalar

Configurar credenciales

Verificar la configuración

Ejecución

Probar con MCP Inspector (interfaz de navegador)

Ejecutar el servidor para uso con editores de IA

Configuración de VS Code (.vscode/mcp.json)

Probar en VS Code Copilot

Curso

Building GraphRAG TypeScript MCP Tools

Primeros pasos

Soluciones

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Paso 1 — Primer servidor MCP (`strawberry/index.ts`)

Paso 3 — Herramienta 1: `graphStatistics`

Paso 4 — Herramienta 2: `getMoviesByGenre`

Paso 5 — Herramienta 3: `browse_movies_by_genre` (Paginada)

Paso 6 — Recurso: `movie://{tmdbId}`

Paso 7 — Avanzado: Muestreo (`explainMovieData`)

Configuración de VS Code (`.vscode/mcp.json`)