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joewilsonai

GitHub MCP Server

by joewilsonai
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

🐙 GitHub Meta MCP Server

Manage GitHub repositories with natural language via the Model Context Protocol.

An MCP server that lets Claude (or any MCP-compatible client) create and configure GitHub repositories from plain-English commands.

MCP Node License

What it does

Talk to Claude. Claude talks to this server. This server talks to GitHub.

"Create a private repo called 'lunchbox' with topics ai, agents, mcp and the homepage thelunchbox.app"

→ Repo created. Topics set. Homepage set. README initialized.

Related MCP server: GitHub MCP Server

Tools exposed

create_repo

Create or update a GitHub repository via natural language. Handles:

  • Repo name (auto-generated from description if not specified)

  • Visibility (public / private)

  • Description

  • Topics / tags (multiple, comma-separated)

  • Homepage URL

  • Auto-init with README

Install

git clone https://github.com/joewilsonai/github-meta-mcp-server
cd github-meta-mcp-server
npm install
npm run build

Configure in Claude Desktop

Edit your claude_desktop_config.json:

{
  "mcpServers": {
    "github-meta": {
      "command": "node",
      "args": ["/absolute/path/to/github-meta-mcp-server/build/index.js"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_personal_access_token"
      }
    }
  }
}

Restart Claude Desktop. The create_repo tool will appear.

Required token scopes

Your GitHub personal access token needs:

  • repo — to create and configure repos

  • delete_repo — only if you want to support deletion in future versions

Generate one at github.com/settings/tokens.

Stack

  • TypeScript + Node.js 18+

  • @modelcontextprotocol/sdk

  • @octokit/rest for the GitHub API

Related MCP servers

License

MIT

Available Tools

1 tool
create_repoC

Create or update GitHub repositories using natural language commands

ParametersJSON Schema
NameRequiredDescriptionDefault
commandYesNatural language command like "Create a repository for my machine learning project with tags python tensorflow" or "Update repository-name description to New description with tags updated ml"

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 carries the full burden of behavioral disclosure. It states the tool can 'create or update' repositories, implying mutation, but doesn't address permissions, rate limits, error handling, or what happens on updates (e.g., overwriting). For a mutation tool with zero annotation coverage, this is insufficient.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to understand quickly.

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 tool's complexity (mutation operation with no annotations and no output schema), the description is incomplete. It lacks details on behavioral traits, error conditions, or return values, which are critical for a tool that modifies GitHub repositories. The high schema coverage doesn't compensate for these gaps.

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 schema description coverage is 100%, with the parameter 'command' fully documented in the schema. The description adds minimal value beyond the schema by reinforcing the natural language aspect but doesn't provide additional syntax, format details, or examples beyond what's already in the schema. This meets the baseline for high 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 tool's purpose: 'Create or update GitHub repositories using natural language commands.' It specifies the verb (create/update), resource (GitHub repositories), and method (natural language commands). However, with no sibling tools mentioned, there's no explicit differentiation from alternatives, preventing a score of 5.

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 minimal guidance on when to use this tool. It mentions 'using natural language commands' but doesn't specify prerequisites, constraints, or when to prefer this over other methods. No explicit alternatives or exclusions are discussed, leaving usage context vague.

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.

  1. 1 tool updatev1.0.0
    • First observedcreate_repo
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TDQS

B3.1/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined as creating or updating GitHub repositories, making it distinct by default.

Naming Consistency5/5

The single tool name 'create_repo' follows a clear verb_noun pattern, and since there are no other tools to compare it to, consistency is inherently perfect. There are no deviations or mixed conventions to evaluate.

Tool Count2/5

A single tool for a GitHub server is too few for the typical scope, which usually involves multiple operations like listing repos, managing issues, or handling pull requests. This feels thin and incomplete for the domain.

Completeness2/5

The tool surface is severely incomplete for a GitHub server, as it only covers creating or updating repositories. Obvious gaps include retrieving repos, managing issues, pull requests, and other core GitHub functionalities, which will likely cause agent failures.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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