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Ein auf MCP (Model Control Protocol) basierendes Tool zum Abfragen und Verwenden der GitHub GraphQL-API. Dieses Projekt bietet einen Server, mit dem Sie das GitHub GraphQL-Schema erkunden und GraphQL-Abfragen über MCP-Client-Tools (wie Claude AI) ausführen können.
Die GitHub GraphQL API bietet gegenüber herkömmlichen REST-APIs erhebliche Vorteile:
Präziser Datenabruf : GraphQL ermöglicht es Clients, genau die Felder anzugeben, die sie benötigen, und so überschüssige Daten zu vermeiden
Reduzierter Token-Verbrauch : Durch die Anforderung nur der erforderlichen Felder wird die Größe der API-Antwort erheblich reduziert, wodurch der Token-Verbrauch des KI-Modells gesenkt wird
Einzelne Anfrage für verwandte Daten : Eine Abfrage kann mehrere verwandte Ressourcen abrufen, wodurch die Anzahl der Anfragen reduziert wird
Selbstdokumentierend : Durch das integrierte Dokumentationssystem können Sie das API-Schema ohne externe Dokumentation direkt abfragen und verstehen
Starkes Typsystem : Bietet Typprüfung und reduziert Fehler
Dieses Projekt nutzt diese Vorteile, um Tools bereitzustellen, mit denen Sie das GitHub GraphQL-API-Schema effektiv erkunden und optimierte Abfragen ausführen können, wodurch KI-Assistenten effiziente Funktionen zum Abrufen von GitHub-Daten erhalten.
Related MCP server: @cloud9-labs/mcp-github
Mit diesem Tool können Sie die folgenden gängigen Vorgänge problemlos implementieren:
Abfrage grundlegender Repository-Informationen : Erhalten Sie Repository-Namen, Beschreibung, Anzahl der Sterne, Zweigstellenliste und andere grundlegende Informationen
Abrufen von Problemdaten : Abfragen von Problemlisten, Details oder Kommentarinhalten für bestimmte Repositories
Zugriff auf Benutzerprofile : Rufen Sie persönliche Profile, Beitragsstatistiken und andere öffentliche Informationen der Benutzer ab
Pull Request-Statusansicht : Rufen Sie den PR-Grundstatus, Kommentarinhalte und Zusammenführungsinformationen ab
Abfrage der Projektabhängigkeiten : Abrufen von Paketlisten und Versionsinformationen zu Projektabhängigkeiten
Mit den flexiblen Abfragefunktionen von GraphQL können Sie auch versuchen, die folgenden erweiterten Analysefunktionen zu implementieren:
Analyse des Repository-Beitragstrends : Analysieren Sie die Häufigkeit von Code-Updates und die Beteiligung der Mitwirkenden, indem Sie Commit-Daten aggregieren und die Projektaktivität auswerten
Problemmanagement und -klassifizierung : Organisieren Sie Problemdaten nach benutzerdefinierten Bedingungen, entdecken Sie Probleme, die vorrangig behandelt werden müssen, und verbessern Sie die Effizienz des Projektmanagements
Code-Review-Musteranalyse : Analysieren Sie PR-Kommentare und Review-Prozesse, identifizieren Sie häufige Problemmuster und optimieren Sie den Code-Review-Workflow
Visualisierung des Mitwirkendennetzwerks : Bauen Sie Zusammenarbeitsbeziehungen zwischen Projektmitwirkenden auf, entdecken Sie wichtige Mitwirkende und Fachgebiete
Bewertung der Abhängigkeitsintegrität : Bewerten Sie die Aktualisierungshäufigkeit und potenziellen Sicherheitsprobleme von Projektabhängigkeiten und geben Sie Vorschläge zum Abhängigkeitsmanagement.
Abfrage der Stammtypen des GitHub GraphQL-Schemas (Abfrage/Mutation)
Erhalten Sie detaillierte Dokumentation für bestimmte Typen
Abfragedokumentation und Parameter für bestimmte Felder
Führen Sie GitHub GraphQL-API-Abfragen direkt aus, rufen Sie die benötigten Daten präzise ab und reduzieren Sie den Token-Verbrauch
Zweisprachiger Support (Englisch/Chinesisch)
Python 3.10 oder höher
Persönlicher GitHub-Zugriffstoken (für den Zugriff auf die GitHub-API)
Poetry (empfohlenes Tool zur Abhängigkeitsverwaltung)
Klonen Sie das Repository:
git clone https://github.com/wanzunz/github_graphql_api_mcp.git
cd github_graphql_api_mcpInstallieren Sie Abhängigkeiten mit Poetry:
# If you haven't installed Poetry yet, install it first:
# curl -sSL https://install.python-poetry.org | python3 -
# Install dependencies using Poetry
poetry install
# Activate the virtual environment
poetry shellWenn Sie keine Poesie verwenden, können Sie die traditionelle Methode verwenden:
# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate # Linux/MacOS
# or
.venv\Scripts\activate # Windows
# Install dependencies
pip install -e .Konfigurieren Sie Umgebungsvariablen:
Erstellen Sie eine .env Datei und fügen Sie Ihr persönliches GitHub-Zugriffstoken hinzu:
GITHUB_TOKEN="your_github_token_here"Sie können es erstellen, indem Sie die Datei .env.example kopieren:
cp .env.example .envBearbeiten Sie dann die .env Datei und ersetzen Sie your_github_token_here durch Ihr tatsächliches GitHub-Token.
Stellen Sie sicher, dass Sie die virtuelle Umgebung von Poetry ( poetry shell ) aktiviert haben, und gehen Sie dann wie folgt vor:
python github_graphql_api_mcp_server.pyNachdem der Server gestartet ist, können Sie über einen MCP-Client (z. B. Claude AI) eine Verbindung zu ihm herstellen.
Sie können diesen MCP-Server in der Claude-Desktop-App für den Start mit einem Klick konfigurieren:
Öffnen Sie die Claude-Desktop-App
Gehen Sie zu den Einstellungen und suchen Sie den Abschnitt zur MCP-Serverkonfiguration
Fügen Sie die folgende Konfiguration hinzu (ändern Sie sie entsprechend Ihrem tatsächlichen Pfad):
{
"mcpServers": {
"github_mcp": {
"command": "<your Python interpreter path>",
"args": [
"--directory",
"<project path>",
"run",
"github_graphql_api_mcp_server.py"
]
}
}
}Konfigurationsbeispiel:
{
"mcpServers": {
"github_mcp": {
"command": "/usr/bin/python3",
"args": [
"--directory",
"/home/user/projects/github_graphql_api_mcp/",
"run",
"github_graphql_api_mcp_server.py"
]
}
}
}Wenn Sie Conda oder andere Tools zur Umgebungsverwaltung verwenden:
{
"mcpServers": {
"github_mcp": {
"command": "/opt/miniconda3/bin/python",
"args": [
"--directory",
"/Users/username/github/github_graphql_api_mcp/",
"run",
"github_graphql_api_mcp_server.py"
]
}
}
}Nach der Konfiguration können Sie den MCP-Server direkt aus der Claude-Desktop-App starten, ohne ihn manuell starten zu müssen.
Der Server stellt die folgenden Tools bereit:
print_type_field : Abfragefelder der Stammtypen des GitHub GraphQL-Schemas
graphql_schema_root_type : Dokumentation für Stammtypen abrufen (Abfrage/Mutation)
graphql_schema_type : Abfragedokumentation für bestimmte Typen
call_github_graphql : GitHub GraphQL API-Abfragen ausführen
Nachdem Sie mit einem MCP-Client eine Verbindung zum Server hergestellt haben, können Sie:
Dokumentation zum Abfragestammtyp:
Use the graphql_schema_root_type tool, parameter type_name="QUERY"Abfragefelder bestimmter Typen:
Use the print_type_field tool, parameters type_name="QUERY", type_fields_name="repository"Abfragedokumentation für bestimmte Typen:
Use the graphql_schema_type tool, parameter type_name="Repository"Führen Sie GraphQL-Abfragen aus:
Use the call_github_graphql tool, parameter:
graphql="""
query {
viewer {
login
name
}
}
"""Unten sehen Sie ein Beispiel für die Verwendung der GitHub GraphQL API MCP mit Claude:
Stellen Sie vor der Verwendung sicher, dass Ihr GitHub-Token über die entsprechenden Berechtigungen verfügt
Das Token wird in der .env Datei gespeichert, die nicht an Versionskontrollsysteme übergeben werden sollte.
Abfragen sollten den Nutzungsbeschränkungen der GitHub-API entsprechen
Dieses Projekt ist unter der MIT-Lizenz lizenziert – einer sehr freizügigen Lizenz, die es Benutzern ermöglicht, diese Software frei zu verwenden, zu ändern, zu verteilen und zu kommerzialisieren, solange sie den Copyright-Hinweis und die Lizenzerklärung beibehalten.
Ausführliche Bedingungen finden Sie in der MIT-Lizenz .
A tool to execute GitHub GraphQL API queries. Before using, it's recommended to check the documentation first, and include ID fields in your queries for easier follow-up operations Args: graphql: The GraphQL query Returns: str: Execution result
| Name | Required | Description | Default |
|---|---|---|---|
| graphql | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions checking documentation and including ID fields, which adds some operational context, but fails to address critical traits like authentication requirements, rate limits, error handling, or mutation vs. query behavior. For a GraphQL API tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves in practice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose and usage recommendation in the first sentence. The Args and Returns sections are structured but could be more integrated. There's minimal waste, though the 'Returns: str: Execution result' is redundant given the output schema, slightly reducing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (GraphQL API execution) and the presence of an output schema (which handles return values), the description is partially complete. It covers basic purpose and some usage tips but lacks details on authentication, error cases, or integration with sibling tools. Without annotations and with low schema coverage, it doesn't fully equip an agent for reliable tool invocation in a GitHub context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal meaning beyond the input schema: it names the single parameter 'graphql' and states it's 'The GraphQL query', which the schema already indicates as a string type. With 0% schema description coverage, the description doesn't compensate by explaining query syntax, validation rules, or examples. The baseline is 3 because the schema covers the parameter's existence and type, but the description fails to enhance understanding significantly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'execute GitHub GraphQL API queries' with a specific verb (execute) and resource (GitHub GraphQL API queries). It distinguishes itself from sibling tools like graphql_schema_root_type and graphql_schema_type by focusing on query execution rather than schema exploration. However, it doesn't explicitly contrast with print_type_field, leaving some sibling differentiation incomplete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage guidance by recommending checking documentation first and including ID fields for follow-up operations, which suggests context for when to use this tool effectively. However, it lacks explicit when/when-not criteria or named alternatives to sibling tools, leaving the agent to infer optimal usage scenarios without clear boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A tool to query GitHub GraphQL schema root types. You need to provide the root type name (QUERY/MUTATION) Args: type_name: root type (QUERY or MUTATION) Returns: str: Documentation content
| Name | Required | Description | Default |
|---|---|---|---|
| type_name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral context. It states the tool returns documentation content as a string but doesn't disclose authentication requirements, rate limits, error handling, or what specific documentation format to expect. The 'query' verb implies read-only, but this isn't explicitly confirmed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with three sentences: purpose statement, parameter guidance, and return value. It's front-loaded with the core function. Minor improvements could include bullet points for Args/Returns, but overall it's efficient with minimal waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on authentication, error cases, the structure of returned documentation, or how this integrates with sibling tools. The return type 'str: Documentation content' is vague without examples or format specifications.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by explaining that 'type_name' should be 'QUERY or MUTATION' and represents the 'root type name.' This adds meaningful context beyond the schema's generic 'Type Name' title, though it doesn't detail format constraints or examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'query GitHub GraphQL schema root types' with specific resource (GitHub GraphQL schema) and verb (query). It distinguishes from sibling 'graphql_schema_type' by focusing on root types only, though it doesn't explicitly contrast with 'call_github_graphql' or 'print_type_field'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 versus alternatives like 'graphql_schema_type' for non-root types or 'call_github_graphql' for actual queries. The description mentions needing to provide root type name but doesn't explain use cases or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A tool to query specific type documentation in GitHub GraphQL schema. You need to provide the type_name
Args:
type_name: Type name like SecurityAdvisoryConnection
Returns:
str: Documentation content
| Name | Required | Description | Default |
|---|---|---|---|
| type_name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool queries documentation, implying a read-only operation, but doesn't specify if it's safe, requires authentication, has rate limits, or what happens on errors. For a query tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the purpose clearly. The 'Args:' and 'Returns:' sections are structured but slightly redundant with the schema. It's concise with no wasted words, though the formatting could be more integrated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the basic purpose and parameter semantics but lacks usage guidelines, behavioral details, and output explanation. For a simple query tool, it's adequate but not fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It explains the single parameter 'type_name' as 'Type name like `SecurityAdvisoryConnection`,' adding meaning beyond the schema's basic 'string' type. This clarifies the parameter's purpose and provides an example, though it could be more detailed about valid type names or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'query specific type documentation in GitHub GraphQL schema.' It specifies the verb ('query'), resource ('type documentation'), and domain ('GitHub GraphQL schema'). However, it doesn't explicitly differentiate from sibling tools like 'graphql_schema_root_type' or 'print_type_field' beyond mentioning 'specific type' documentation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance: 'You need to provide the type_name' and gives an example ('SecurityAdvisoryConnection'). It doesn't explain when to use this tool versus alternatives like 'graphql_schema_root_type' or 'print_type_field,' nor does it mention prerequisites or exclusions. The guidance is basic and lacks context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A tool to query GitHub GraphQL schema root type fields. You need to provide root_type_name and type_fields_name
Args:
type_name: root type like QUERY or MUTATION
type_fields_name: field name like repository based on root type documentation
Returns:
str: Documentation content for the specified field
| Name | Required | Description | Default |
|---|---|---|---|
| type_name | Yes | ||
| type_fields_name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool returns documentation content as a string, which is useful behavioral information. However, it doesn't mention whether this is a read-only operation, what happens with invalid inputs, if there are rate limits, authentication requirements, or error behavior. For a query tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is reasonably concise but has structural issues. The first sentence clearly states the purpose, but the parameter explanations are formatted as 'Args:' and 'Returns:' sections which are somewhat redundant with the schema. The information is front-loaded but could be more efficiently integrated. It's not excessively verbose but has minor organizational inefficiencies.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 parameters with 0% schema coverage and no output schema, the description provides adequate parameter semantics and specifies the return type as a string. However, it doesn't explain the format of the returned documentation content, error conditions, or authentication requirements. For a query tool with no annotations, this is minimally complete but lacks depth about operational behavior and integration context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides meaningful semantics for both parameters: 'type_name' is explained as 'root type like `QUERY` or `MUTATION`' and 'type_fields_name' as 'field name like `repository` based on root type documentation'. This adds substantial value beyond the bare schema, though it doesn't provide exhaustive examples or format details. With 2 parameters fully addressed, this exceeds the baseline expectation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'query GitHub GraphQL schema root type fields' with specific verbs ('query') and resources ('GitHub GraphQL schema root type fields'). It distinguishes from siblings like 'call_github_graphql' (which executes queries) and 'graphql_schema_type' (which might query non-root types), but doesn't explicitly contrast them. The purpose is specific but sibling differentiation is implied rather than explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives. It mentions 'based on root type documentation' but doesn't specify when this tool is appropriate compared to sibling tools like 'graphql_schema_root_type' or 'graphql_schema_type'. There are no explicit when/when-not instructions or named alternatives, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
call_github_graphqlgraphql_schema_root_typegraphql_schema_typeprint_type_fieldScored across 4 tools
The tools have overlapping purposes focused on GitHub GraphQL schema exploration, which could cause confusion. call_github_graphql is distinct for executing queries, but graphql_schema_root_type, graphql_schema_type, and print_type_field all retrieve schema documentation with subtle differences in scope (root types vs. specific types vs. root type fields). Descriptions help clarify, but an agent might struggle to choose between them for schema inspection tasks.
Tool names follow a consistent snake_case pattern with clear verb_noun structure (e.g., call_github_graphql, graphql_schema_type). Minor deviations exist, such as graphql_schema_root_type using 'root_type' while print_type_field uses 'type_field', but overall the naming is predictable and readable across all tools.
With 4 tools, the count is reasonable for a GitHub GraphQL API server, providing a focused set for query execution and schema exploration. It's slightly thin for comprehensive API coverage but well-scoped for its intended purpose, avoiding bloat while supporting core workflows like querying and schema lookup.
The tool set covers query execution and schema documentation retrieval, but has notable gaps for a GitHub API surface. It lacks CRUD operations for resources like repositories, issues, or pull requests, and doesn't support mutations or advanced query building. Agents can work around this by crafting GraphQL queries, but the surface is incomplete for typical GitHub automation tasks.
The Grafbase MCP server sits in front of a GraphQL API and exposes an MCP protocol-compliant interface that allows AI agents and LLMs to explore and query GraphQL APIs using natural language. It provides tools to search schemas, introspect types and fields, and execute GraphQL queries while minimizing context bloat by returning only relevant schema subsets, with built-in support for authentication, authorization, and configurable access control.
Repository knowledge graph MCP server for codebase understanding and debugging.
An MCP server that gives your AI access to the source code and docs of all public github repos
The Cortex MCP server provides read-only access to real-time engineering context from the Cortex developer portal, allowing AI coding assistants to answer natural language questions about your organization's catalog (microservices, libraries, domains, teams, infrastructure), scorecards (engineering standards and best practices), initiatives (goals and deadlines), and Engineering Intelligence metrics. It includes tools for querying documentation, tracking personal entities, and accessing AI-assisted insights across the entire Cortex ecosystem.
