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tunde-alao

Social MCP

by tunde-alao
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Python
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusing it with others. The tool's purpose is clearly stated and unambiguous.

    Naming Consistency5/5

    The single tool name 'get_instagram_transcript' follows a clear verb_noun pattern, making it descriptive and predictable. Though there are no other tools to compare, the naming is well-formed and consistent with common conventions.

    Tool Count2/5

    The server name 'Social MCP' implies a broad social media toolkit, yet it contains only one tool for Instagram transcripts. This is far too few for the apparent scope, making the tool count feel inadequate and mismatched.

    Completeness1/5

    For a social media server, having only Instagram transcript extraction leaves enormous gaps. There is no support for other platforms, and even for Instagram, there are no other operations beyond transcripts. The surface is severely incomplete for the stated domain.

  • Average 4.3/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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

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  • This repository includes a README.md file.

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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?

    With no annotations, the description carries the full burden. It discloses the return format (timestamps and speaker labels) and the API key fallback to environment variable, but does not mention potential side effects, error conditions, or network/processing behavior. This is adequate but not rich.

    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 concise, well-structured with Args and Returns sections, and front-loaded with the main purpose. Every sentence provides useful information without unnecessary verbosity.

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

    Completeness4/5

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

    For a simple two-parameter tool with an output schema (even if not shown), the description covers the essential usage details and return format. It lacks some edge-case handling (e.g., invalid URLs, missing API key errors), but the core operational context is complete.

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

    Parameters5/5

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

    Schema coverage is 0%, and the description fully compensates by explaining both parameters: the 'url' parameter with concrete examples and the 'assemblyai_api_key' parameter with its optionality and environment variable fallback. This adds significant meaning beyond the bare schema.

    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's function: 'Extract transcript from Instagram video/reel using AssemblyAI.' It identifies a specific verb ('Extract') and resource (Instagram video/reel), and distinguishes the tool's purpose even without siblings.

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

    Usage Guidelines4/5

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

    The description provides clear context on how to use the tool, including URL formats and the optional API key behavior. It doesn't explicitly mention when not to use it or compare with alternatives, but since there are no sibling tools, the usage context is sufficiently clear.

    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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  • 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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