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akibdabgar07

Akib Dabgar MCP Server

by akibdabgar07
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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 confusion between tools. The single tool 'query_profile' has a clearly distinct purpose.

    Naming Consistency5/5

    The sole tool uses a consistent verb_noun pattern ('query_profile'). With only one tool, naming is perfectly consistent.

    Tool Count3/5

    A single tool is at the lower end of the borderline range. For a personal profile server, a single query tool may suffice but feels slightly thin compared to typical MCP servers.

    Completeness4/5

    The dynamic context retrieval allows querying all aspects of the profile (skills, experience, projects, interests) in one tool, covering the read-only domain well. Minor gap: no tool to list all sections or get a full profile dump.

  • Average 4.2/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

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  • This repository includes a README.md file.

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

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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

  • Behavior3/5

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

    The description mentions 'Dynamic Context Retrieval' and that it returns only relevant sections, disclosing a key behavioral trait. However, with no annotations provided, the description does not cover other behavioral aspects like auth needs, rate limits, or potential side effects, so it's only partially transparent.

    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 two sentences long, with no redundant information. Every word contributes to clarity. It is front-loaded with the core purpose and efficiently adds behavioral detail.

    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?

    Given the tool's simplicity (one parameter, no output schema, no siblings), the description fully covers what the tool does and how it behaves. It explains the retrieval mechanism and the scope of results, making it complete for an AI agent to invoke correctly.

    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?

    The input schema already describes the 'query' parameter as a natural language question. The description adds concrete examples ('What databases does Akib know?', 'Tell me about ChainIT Pay'), which enhance understanding beyond the schema. Since schema coverage is 100%, the baseline is 3, and the examples justify a 4.

    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 specifies the verb 'Query' and the resource 'Akib Dabgar's professional profile'. It lists the specific aspects queried (skills, experience, projects, interests), making the purpose unambiguous and distinct from any potential siblings.

    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 for asking natural language questions about Akib Dabgar, but it provides no explicit guidance on when to use this tool versus alternatives or when not to use it. Since no sibling tools exist, the lack of exclusions is acceptable but still leaves room for improvement.

    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.

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