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yeuxuan

Acemcp

by yeuxuan
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Server Quality Checklist

67%
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 with other tools. The tool's purpose is clearly defined as searching for code context, making it impossible for an agent to misselect between multiple options.

    Naming Consistency5/5

    A single tool inherently has consistent naming, as there are no other tools to compare it against. The tool name 'search_context' follows a clear verb_noun pattern, which would be consistent if more tools were added.

    Tool Count2/5

    One tool is too few for most server purposes, as it severely limits functionality and scope. While the tool is well-described for code context searching, a single tool feels thin and incomplete for broader operations, suggesting an extreme mismatch in tool count for typical server needs.

    Completeness2/5

    The server appears focused on code context searching, but with only one tool, there are significant gaps in coverage. For example, there are no tools for creating, updating, or managing code context, or for other related operations like indexing control or project management, leading to a severely incomplete surface.

  • Average 3.6/5 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 ISC 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.

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

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context beyond basic functionality: it mentions automatic incremental indexing before searching, ensures results are up-to-date, returns formatted text snippets with file paths and line numbers, and supports cross-platform paths. However, it lacks details on performance, rate limits, or error handling, preventing a perfect score.

    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 appropriately sized and front-loaded, with the core purpose stated first. Each sentence adds value: indexing behavior, result format, and platform support. There's minimal waste, though it could be slightly more streamlined by integrating some details more tightly.

    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 the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but has gaps. It covers purpose, behavior, and parameters well, but without an output schema, it doesn't fully explain return values (e.g., format of snippets or error cases). This leaves room for improvement in completeness.

    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%, meaning the input schema already documents both parameters thoroughly. The description adds some semantic context by mentioning 'cross-platform paths' for project_root_path and giving examples for query, but this doesn't significantly enhance understanding beyond the schema. This meets the baseline for high 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 tool's purpose: 'Search for relevant code context based on a query within a specific project.' It specifies the verb (search), resource (code context), and scope (within a specific project). However, without sibling tools, it cannot demonstrate differentiation from alternatives, so it doesn't reach the highest score.

    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 context through phrases like 'within a specific project' and 'Supports cross-platform paths,' but it doesn't explicitly state when to use this tool versus alternatives or provide any exclusions. Since no sibling tools exist, there's no opportunity for comparative guidance, leaving the guidance at an implied level.

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

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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.

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