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ebenezer-isaac

llmconveyors-mcp

by ebenezer-isaac
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TypeScript
Remote

Server Quality Checklist

67%
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  • Latest release: v0.1.1

  • Disambiguation3/5

    Most tools have distinct purposes, but there is notable overlap in some areas. For example, health-check, health-live, health-ready, and health-root all serve health diagnostics with subtle distinctions that could confuse an agent. Similarly, session-get and session-hydrate both retrieve session data with different detail levels, which might lead to misselection if not carefully read.

    Naming Consistency4/5

    Tool names generally follow a consistent verb_noun pattern (e.g., agent-status, api-key-create, resume-parse), with clear and predictable naming. Minor deviations exist, such as ats-score (noun_verb) and byo-key-get (abbreviation-based), but overall the naming is coherent and easy to understand.

    Tool Count2/5

    With 64 tools, the count is excessive for a single server, making it overwhelming and difficult for an agent to navigate efficiently. While the domain (AI-powered job and sales automation) is broad, the toolset feels bloated with many specialized or overlapping tools that could have been consolidated.

    Completeness4/5

    The tool surface is highly comprehensive, covering CRUD operations for sessions, resumes, API keys, and more, with clear workflows for job hunting and B2B sales. Minor gaps exist, such as no direct tool for updating a session's metadata, but agents can work around these with existing tools like session-log or content-save.

  • Average 4.6/5 across 64 of 64 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • 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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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.

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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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