Which integrations are available for this server?

Provides tools for querying and analyzing movie data in a Neo4j graph database, including graph statistics, movie search by genre, paginated browsing, and detailed movie retrieval.

How do I use GraphRAG TypeScript MCP Tools?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@GraphRAG TypeScript MCP Tools Can you show me the highest rated action movies?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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GraphRAG TypeScript MCP Tools

by Akakinad

Overview Schema Related Servers Score Discussions

TypeScript

Local

GraphRAG TypeScript MCP Tools

A complete implementation of a GraphRAG MCP server built with TypeScript, Neo4j, and the MCP TypeScript SDK. This project demonstrates how to build production-quality MCP servers that expose graph-backed tools, resources, and advanced features like LLM sampling and completions.

Built as part of the Neo4j GraphAcademy — Building GraphRAG TypeScript MCP tools course.

What is MCP?

The Model Context Protocol (MCP) is an open standard by Anthropic that allows AI agents (Claude, Cursor, VS Code Copilot) to connect to external tools and data sources in a standardized way.

Related MCP server: CodeRAG

Project Structure

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

What Was Built

Step 1 — First MCP Server (`strawberry/index.ts`)

The simplest possible MCP server. One tool, no database, stdio transport.

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),
  }],
}));

Test result: countLetters("strawberry", "r") → 3

Tested using the MCP Inspector — a browser-based tool for exploring and testing MCP servers.

Step 2 — Neo4j Connection (Module Scope)

Unlike Python's lifespan context manager, TypeScript uses module-scope variables — the driver is created once at the top of the file and shared by all tools directly.

// 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";

Graceful shutdown via SIGINT:

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

Step 3 — Tool 1: `graphStatistics`

Counts all nodes and relationships in Neo4j.

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

Step 4 — Tool 2: `getMoviesByGenre`

Searches movies by genre ordered by IMDB rating. Uses console.error() for logging — never console.log() in stdio servers (it corrupts the JSON-RPC channel).

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 }
  );
  ...
});

Step 5 — Tool 3: `browse_movies_by_genre` (Paginated)

Cursor-based pagination using Neo4j's SKIP and LIMIT:

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

Returns:

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

Step 6 — Resource: `movie://{tmdbId}`

Exposes full movie details by TMDB ID using 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
  }
);

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

Step 7 — Advanced: Sampling (`explainMovieData`)

Tools that call the LLM during execution to convert raw Neo4j data into natural language:

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

Without sampling: {'title': 'Toy Story', 'released': '1995', 'actors': [...]}

With sampling (VS Code Copilot): "Toy Story — A clever, funny animated adventure about Woody, a jealous cowboy doll who feels displaced when Buzz Lightyear becomes the new favorite..."

Note: Requires setting capability on the low-level server:
server.server["_capabilities"] = { ...server.server["_capabilities"], completions: {} };

Step 8 — Advanced: Completions

Real-time autocomplete suggestions for genre parameters — queries Neo4j as the user types:

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: [] } };
});

Key Differences from Python Version

Concept	Python (FastMCP)	TypeScript (McpServer)
Tool registration	`@mcp.tool()` decorator	`server.registerTool()` method
Shared state	Lifespan context manager	Module-scope variables
Driver access	`ctx.request_context.lifespan_context.driver`	`driver` (direct)
Logging	`await ctx.info()`	`console.error()`
Sampling	`ctx.session.create_message()`	`server.server.createMessage()`
Completions	`@server.completion()`	`server.server.setRequestHandler(CompleteRequestSchema)`
File structure	Separate files per feature	Everything in one `index.ts`
Number params	Python int type hints	`neo4j.int()` wrapper needed
Prompt params	`int`, `str`, `float`	Always `z.string()`, parse manually

Setup

Prerequisites

Node.js 20+
npm
Neo4j Sandbox — Recommendations dataset from sandbox.neo4j.com

Install

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

Configure credentials

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

Verify setup

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

Running

Test with MCP Inspector (browser UI)

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

Open the URL shown in terminal → Connect → Tools tab → List Tools → select a tool → Run Tool.

Run server for AI editor use

cd server
npx tsx index.ts

VS Code Configuration (`.vscode/mcp.json`)

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

Test in VS Code Copilot

Explain the movie "Toy Story" using the movies-ts MCP tool Search for Action movies using the movies-ts MCP tool Get graph statistics using the movies-ts MCP tool

Course

Learning Path: Generative AI & GraphRAG

Course: Building GraphRAG TypeScript MCP tools

Building GraphRAG TypeScript MCP Tools

Companion repository for the GraphAcademy course Building GraphRAG TypeScript MCP Tools.

Students build an MCP (Model Context Protocol) server that connects to a Neo4j graph database, exposing tools and resources for use with AI assistants.

Getting Started

Copy .env.example to .env and update the values with your Neo4j connection details.
Install dependencies:

npm install

Start the server:

npm start

Inspect the server with the MCP Inspector:

npm run inspect

Solutions

The solutions/ directory contains the completed code for each lesson checkpoint.

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. Dates show when Glama detected each change.

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

Install Server

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TDQS

C2.9/5.0

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

ActivityMaintained

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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GraphRAG TypeScript MCP Tools

What is MCP?

Project Structure

What Was Built

Step 1 — First MCP Server (strawberry/index.ts)

Step 2 — Neo4j Connection (Module Scope)

Step 3 — Tool 1: graphStatistics

Step 4 — Tool 2: getMoviesByGenre

Step 5 — Tool 3: browse_movies_by_genre (Paginated)

Step 6 — Resource: movie://{tmdbId}

Step 7 — Advanced: Sampling (explainMovieData)

Step 8 — Advanced: Completions

Key Differences from Python Version

Setup

Prerequisites

Install

Configure credentials

Verify setup

Running

Test with MCP Inspector (browser UI)

Run server for AI editor use

VS Code Configuration (.vscode/mcp.json)

Test in VS Code Copilot

Course

Building GraphRAG TypeScript MCP Tools

Getting Started

Solutions

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Latest Blog Posts

MCP directory API

Step 1 — First MCP Server (`strawberry/index.ts`)

Step 3 — Tool 1: `graphStatistics`

Step 4 — Tool 2: `getMoviesByGenre`

Step 5 — Tool 3: `browse_movies_by_genre` (Paginated)

Step 6 — Resource: `movie://{tmdbId}`

Step 7 — Advanced: Sampling (`explainMovieData`)

VS Code Configuration (`.vscode/mcp.json`)