Skip to main content
Glama
steipete

Claude Code MCP Server

by steipete
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Local

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.10.2

  • Disambiguation5/5

    With only one tool, there is no risk of confusion between tools. The single tool's description is broad but unambiguous in its role as a versatile assistant.

    Naming Consistency5/5

    There is only one tool, so naming consistency is perfect by default. No inconsistencies exist.

    Tool Count2/5

    The server has only one tool despite covering a very wide scope (file ops, code, git, terminal, web, GitHub). This monolithic approach is too few tools for the apparent scope, making it hard for agents to understand capabilities and potentially leading to oversimplification.

    Completeness4/5

    The single tool claims to handle file operations, code analysis, git workflows, terminal commands, web search, and GitHub integration, covering a broad range of development tasks. Minor gaps may exist (e.g., detailed permissions or specific API endpoints), but overall the surface is comprehensive for a developer assistant.

  • Average 4.6/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

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

    The description covers capabilities (file ops, code, git, terminal, web search, multi-step, GitHub) and hints at timeouts with a tip to split tasks. No annotations exist, so the description carries the full burden; it is mostly transparent but lacks explicit mention of destructive actions or permissions.

    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 long but well-organized with bullet points, sections for file ops, code, git, terminal, and prompt tips. It front-loads a summary and is structured logically, though every sentence earns its place.

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

    Completeness5/5

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

    The description is highly comprehensive given the tool's complexity. It covers all major operations, includes prompt tips, and provides enough context for an AI agent to understand and invoke the tool correctly without additional documentation.

    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 coverage is 100% for the two parameters. The description adds value by providing example prompts for the 'prompt' parameter and explaining when 'workFolder' is mandatory and how to use it with relative paths.

    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 tool is a versatile multi-modal assistant for code, file, Git, and terminal operations, with specific examples for each category. It is distinct and concrete.

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

    Usage Guidelines5/5

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

    The description includes extensive prompt tips, such as being concise, splitting long tasks, using workFolder for context, and explicitly stating when to seek analysis only. These provide clear guidance on when and how to use the tool.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

claude-code-mcp MCP server

Copy to your README.md:

Score Badge

claude-code-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/steipete/claude-code-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server