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GraphRAG TypeScript MCP Tools

Name: GraphRAG TypeScript MCP Tools
Author: Akakinad

by Akakinad

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TypeScript

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GraphRAG TypeScript MCP Tools

Eine vollständige Implementierung eines GraphRAG-MCP-Servers, erstellt mit TypeScript, Neo4j und dem MCP-TypeScript-SDK. Dieses Projekt zeigt, wie man produktionsreife MCP-Server baut, die graphgestützte Tools, Ressourcen und erweiterte Funktionen wie LLM-Sampling und Completions bereitstellen.

Erstellt im Rahmen des Kurses Neo4j GraphAcademy — Building GraphRAG TypeScript MCP tools.

Was ist MCP?

Das Model Context Protocol (MCP) ist ein offener Standard von Anthropic, der es KI-Agenten (Claude, Cursor, VS Code Copilot) ermöglicht, auf standardisierte Weise eine Verbindung zu externen Tools und Datenquellen herzustellen.

Related MCP server: CodeRAG

Projektstruktur

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

Was gebaut wurde

Schritt 1 — Erster MCP-Server (`strawberry/index.ts`)

Der einfachste mögliche MCP-Server. Ein Tool, keine Datenbank, 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),
  }],
}));

Testergebnis: countLetters("strawberry", "r") → 3

Getestet mit dem MCP Inspector — einem browserbasierten Tool zum Erkunden und Testen von MCP-Servern.

Schritt 2 — Neo4j-Verbindung (Modulbereich)

Im Gegensatz zum Lifespan-Kontextmanager von Python verwendet TypeScript Variablen im Modulbereich – der Treiber wird einmal am Anfang der Datei erstellt und direkt von allen Tools gemeinsam genutzt.

// 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";

Sanftes Herunterfahren per SIGINT:

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

Schritt 3 — Tool 1: `graphStatistics`

Zählt alle Knoten und Beziehungen in Neo4j.

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

Schritt 4 — Tool 2: `getMoviesByGenre`

Sucht Filme nach Genre, sortiert nach IMDB-Bewertung. Verwendet console.error() für die Protokollierung – niemals console.log() in stdio-Servern (dies korrumpiert den JSON-RPC-Kanal).

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 }
  );
  ...
});

Schritt 5 — Tool 3: `browse_movies_by_genre` (paginiert)

Cursor-basierte Paginierung mit Neo4js SKIP und LIMIT:

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

Rückgabe:

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

Schritt 6 — Resource: `movie://{tmdbId}`

Stellt vollständige Filmdaten anhand der TMDB-ID mithilfe von ResourceTemplate bereit:

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
  }
);

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

Schritt 7 — Fortgeschritten: Sampling (`explainMovieData`)

Tools, die während der Ausführung das LLM aufrufen, um rohe Neo4j-Daten in natürliche Sprache umzuwandeln:

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

Ohne Sampling: {'title': 'Toy Story', 'released': '1995', 'actors': [...]}

Mit Sampling (VS Code Copilot): „Toy Story – Ein kluges, lustiges Animationsabenteuer über Woody, eine eifersüchtige Cowboy-Puppe, die sich verdrängt fühlt, als Buzz Lightyear zum neuen Liebling wird …“

Hinweis: Erfordert das Setzen der Fähigkeit auf dem Low-Level-Server:
server.server["_capabilities"] = { ...server.server["_capabilities"], completions: {} };

Schritt 8 — Fortgeschritten: Completions

Echtzeit-Autovervollständigungsvorschläge für Genreparameter – fragt Neo4j ab, während der Benutzer tippt:

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: [] } };
});

Hauptunterschiede zur Python-Version

Konzept	Python (FastMCP)	TypeScript (McpServer)
Tool-Registrierung	`@mcp.tool()`-Dekorator	`server.registerTool()`-Methode
Gemeinsamer Zustand	Lifespan-Kontextmanager	Variablen im Modulbereich
Treiberzugriff	`ctx.request_context.lifespan_context.driver`	`driver` (direkt)
Protokollierung	`await ctx.info()`	`console.error()`
Sampling	`ctx.session.create_message()`	`server.server.createMessage()`
Completions	`@server.completion()`	`server.server.setRequestHandler(CompleteRequestSchema)`
Dateistruktur	Separate Dateien pro Feature	Alles in einer `index.ts`
Zahlenparameter	Python-int-Typhinweise	`neo4j.int()`-Wrapper erforderlich
Prompt-Parameter	`int`, `str`, `float`	Immer `z.string()`, manuell parsen

Einrichtung

Voraussetzungen

Node.js 20+
npm
Neo4j Sandbox – Recommendations-Datensatz von sandbox.neo4j.com

Installation

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

Anmeldedaten konfigurieren

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

Einrichtung überprüfen

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

Ausführen

Testen mit dem MCP Inspector (Browser-Oberfläche)

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

Öffnen Sie die im Terminal angezeigte URL → Connect → Registerkarte „Tools“ → List Tools → wählen Sie ein Tool → Run Tool.

Server für die Verwendung mit KI-Editoren ausführen

cd server
npx tsx index.ts

VS Code-Konfiguration (`.vscode/mcp.json`)

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

Testen in VS Code Copilot

Erklären Sie den Film „Toy Story“ mit dem MCP-Tool movies-ts.
Suchen Sie Actionfilme mit dem MCP-Tool movies-ts.
Rufen Sie Graphenstatistiken mit dem MCP-Tool movies-ts ab.

Kurs

Lernpfad: Generative AI & GraphRAG

Kurs: Building GraphRAG TypeScript MCP tools

Building GraphRAG TypeScript MCP Tools

Begleit-Repository zum GraphAcademy-Kurs Building GraphRAG TypeScript MCP Tools.

Die Teilnehmenden bauen einen MCP-Server (Model Context Protocol), der eine Verbindung zu einer Neo4j-Graphdatenbank herstellt und Tools sowie Ressourcen für die Verwendung mit KI-Assistenten bereitstellt.

Erste Schritte

Kopieren Sie .env.example in .env und aktualisieren Sie die Werte mit Ihren Neo4j-Verbindungsdetails.
Installieren Sie die Abhängigkeiten:

npm install

Starten Sie den Server:

npm start

Inspizieren Sie den Server mit dem MCP Inspector:

npm run inspect

Lösungen

Das Verzeichnis solutions/ enthält den vollständigen Code für jeden Lektions-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.

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

Was ist MCP?

Projektstruktur

Was gebaut wurde

Schritt 1 — Erster MCP-Server (strawberry/index.ts)

Schritt 2 — Neo4j-Verbindung (Modulbereich)

Schritt 3 — Tool 1: graphStatistics

Schritt 4 — Tool 2: getMoviesByGenre

Schritt 5 — Tool 3: browse_movies_by_genre (paginiert)

Schritt 6 — Resource: movie://{tmdbId}

Schritt 7 — Fortgeschritten: Sampling (explainMovieData)

Schritt 8 — Fortgeschritten: Completions

Hauptunterschiede zur Python-Version

Einrichtung

Voraussetzungen

Installation

Anmeldedaten konfigurieren

Einrichtung überprüfen

Ausführen

Testen mit dem MCP Inspector (Browser-Oberfläche)

Server für die Verwendung mit KI-Editoren ausführen

VS Code-Konfiguration (.vscode/mcp.json)

Testen in VS Code Copilot

Kurs

Building GraphRAG TypeScript MCP Tools

Erste Schritte

Lösungen

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Schritt 1 — Erster MCP-Server (`strawberry/index.ts`)

Schritt 3 — Tool 1: `graphStatistics`

Schritt 4 — Tool 2: `getMoviesByGenre`

Schritt 5 — Tool 3: `browse_movies_by_genre` (paginiert)

Schritt 6 — Resource: `movie://{tmdbId}`

Schritt 7 — Fortgeschritten: Sampling (`explainMovieData`)

VS Code-Konfiguration (`.vscode/mcp.json`)