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shubhamekapure

Social Search MCP

by shubhamekapure
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.1

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to confuse it with. The tool's purpose is clearly defined and distinct by default.

    Naming Consistency5/5

    The single tool name follows a consistent verb_noun pattern (search_social), and with only one tool, there is no inconsistency to evaluate. The naming is straightforward and predictable.

    Tool Count2/5

    A single tool is too few for a server named 'Social Search MCP', which implies a broader scope of social media search operations. This minimal set feels thin and inadequate for the apparent domain, limiting functionality.

    Completeness2/5

    The tool surface is severely incomplete for social media search, as it only offers a generic search without coverage for specific platforms, filtering, or advanced operations like trending topics or user lookups. This will likely cause agent failures due to significant gaps.

  • Average 2.9/5 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

  • Behavior2/5

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

    No annotations are provided, so the description must fully disclose behavioral traits. It states the tool performs a 'web search' but does not explain how results are retrieved, formatted, or limited (e.g., pagination, rate limits, authentication needs). The description lacks details on error handling, response structure, or operational constraints, leaving significant gaps in behavioral understanding.

    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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it easy for an AI agent to parse quickly. This optimal conciseness earns the highest score.

    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 (6 parameters, no output schema, no annotations), the description is incomplete. It lacks details on behavioral traits, usage context, and output expectations. Without annotations or an output schema, the description should provide more comprehensive guidance to help the agent understand how to invoke and interpret results, but it falls short.

    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 input schema has 100% description coverage, providing clear details for all 6 parameters. The description adds no additional parameter semantics beyond implying a focus on social media platforms, which is somewhat redundant with the schema's 'platforms' parameter. With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate with extra insights.

    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: 'Perform a web search focused ONLY on specific social media platforms.' It specifies the verb ('search'), resource ('social media platforms'), and scope ('web search focused ONLY on specific...'). However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, preventing a score of 5.

    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 exclusions. It mentions the focus on social media platforms but does not clarify use cases, limitations, or comparisons to other search tools. This lack of contextual guidance limits its utility for an AI agent.

    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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