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owenwangmar

fja-mcp

by owenwangmar
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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

    The two tools have clearly distinct purposes: one evaluates a single response, the other compares two responses. There is no overlap in functionality, and an agent can easily select the appropriate tool based on whether it needs a single evaluation or a comparison.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern: evaluate_output and compare_outputs. The naming is predictable and aligns with their functions, making it easy to infer behavior from the name.

    Tool Count3/5

    With only two tools, the server feels minimal but covers the core evaluation and comparison tasks. However, it is on the thin side for a dedicated server, and additional tools like batch evaluation or criteria management could round it out.

    Completeness4/5

    The server provides essential single and comparative evaluation capabilities, which are the primary use cases. Minor gaps exist, such as lacking a tool to retrieve or modify FJA criteria, but these are not critical for basic evaluation workflows.

  • Average 2.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 3 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 MIT 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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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

  • Behavior1/5

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

    No annotations are provided, so the description must disclose behavioral traits, but it does not. It does not explain what FJA criteria are, what the output looks like, whether the operation is read-only, or any side effects. The single sentence offers no behavioral transparency beyond the bare action.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is one short sentence with no filler, which is concise, but it is under-specified for a tool with three parameters and a specific evaluation framework. It lacks structure or elaboration, though it is front-loaded with the core purpose.

    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?

    Despite the presence of an output schema, the description is incomplete for practical use. It does not explain the FJA criteria or provide any usage context, such as what the evaluation result contains or when this tool is appropriate. The sibling tool further underscores the lack of contextual guidance.

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

    Parameters1/5

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

    With 0% schema description coverage and no parameter details in the description, the agent receives no semantic context for 'prompt,' 'response,' or 'user_context.' The description fails to explain how these parameters relate to the FJA criteria, leaving the agent to guess parameter meaning.

    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 evaluates an LLM response, using the verb 'evaluate' with a specific resource. It references 'FJA criteria,' which adds specificity, though the acronym is unexplained. It is distinguishable from the sibling 'compare_outputs' in that this evaluates a single response rather than comparing two.

    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. There is no mention of 'compare_outputs' or any exclusions/prerequisites. The only implied usage is tautological—use it when you need to evaluate a response—which does not help an agent select between sibling tools.

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

  • Behavior2/5

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

    There are no annotations, so the description must carry the full burden of explaining behavior. It states the core function but does not disclose details such as whether the operation is read-only, how the recommendation is formatted, or what 'FJA' means, leaving significant behavioral ambiguity.

    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, focused sentence that conveys the essential purpose without unnecessary words. It is well-structured and front-loaded.

    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?

    For a tool with three parameters, no annotations, and a sibling that could be confused, the description is too minimal. It lacks context about FJA, parameter roles, when to use it, and what the output format is, despite having an output schema present.

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

    Parameters2/5

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

    The schema provides 0% description coverage for the three parameters. The description only hints at the roles of response_a and response_b ('two candidate responses') but does not explain the 'prompt' parameter or clarify how each parameter contributes to the comparison.

    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 a specific action ('Compare two candidate responses and recommend the one more aligned with FJA') with identifiable resources (two candidate responses). It is clear but does not explicitly differentiate from the sibling tool 'evaluate_output', so it misses the top score.

    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?

    No guidance is provided on when to use this tool versus the sibling 'evaluate_output'. The description gives no context for when this comparison is appropriate or what alternatives exist.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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