Skip to main content
Glama

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clearly defined and distinct purpose as a proxy for dynamic tool calls.

    Naming Consistency5/5

    There is only one tool name, so naming consistency is inherently perfect. The tool follows a clear snake_case pattern (proxy_tool_call), which is appropriate for its function.

    Tool Count2/5

    A single tool is generally too few for most server purposes, as it limits functionality and forces all operations through a proxy. This feels thin and may not provide a complete or intuitive interface for agents.

    Completeness1/5

    The server's purpose is unclear from the single tool, but as a proxy server, it lacks any direct operations or domain coverage. There are significant gaps, as it relies entirely on external tools without providing its own functionality, making it severely incomplete for any defined domain.

  • Average 2.4/5 across 1 of 1 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • This repository is licensed under Apache 2.0.

  • 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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the input format but fails to mention critical aspects like error handling, authentication requirements, rate limits, or what happens if the proxied tool fails. The description is insufficient for a mutation-capable tool with zero annotation coverage.

    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 brief and front-loaded with the core purpose, using only two sentences. While efficient, it might be overly concise given the tool's complexity, as it omits necessary details that would help the agent use it effectively.

    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 (dynamic tool calling with nested objects), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't cover return values, error cases, or operational constraints, making it inadequate for safe and effective use by an AI agent.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, and the description only partially compensates by specifying the input format as a dictionary with 'tool' and 'args' keys. However, it doesn't explain the semantics of 'tool' (e.g., valid tool names) or 'args' (e.g., expected structure), leaving significant gaps in parameter understanding beyond the basic schema.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool 'dynamically calls other server-side tools based on tool name', which provides a general purpose but lacks specificity about what types of tools or services it proxies. It doesn't distinguish from siblings since there are none, but the verb+resource combination ('proxy' + 'tool call') is somewhat vague about the actual operation.

    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 alternatives, prerequisites, or context for its application. It mentions the input format but doesn't explain scenarios where dynamic tool calling is preferred over direct invocation, leaving the agent with minimal usage direction.

    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

MCP-Server-For-Local MCP server

Copy to your README.md:

Score Badge

MCP-Server-For-Local 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/Dreamboat-Rachel/MCP-Server-For-Local'

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