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Swanand33

mcp-llm-behave

by Swanand33
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a distinct and clearly separated purpose: comparing outputs, listing built-in behaviors, and running a behavior test. No overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow the same verb_noun pattern in snake_case: compare_outputs, list_builtin_behaviors, run_behavior_test. Perfectly consistent.

    Tool Count4/5

    Three tools is small but sufficient for the server's focused domain of LLM behavior testing. The set covers the essential operations without being overly minimal.

    Completeness4/5

    The tools cover listing available behaviors, running a test, and comparing outputs. While additional features like adding custom behaviors could be useful, the core workflow is complete.

  • Average 3.9/5 across 3 of 3 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 is passing

  • 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

  • Behavior3/5

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

    With no annotations, the description carries full burden. It discloses the return format (list of dicts with 'name', 'method', 'description') adequately, but does not mention behavioral traits like read-only nature, performance, or error behavior.

    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?

    Two sentences, zero waste. The purpose is stated first, followed by concise return structure. Ideal length and structure for a simple tool.

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

    Completeness3/5

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

    Given zero parameters and an existing output schema, the description provides the return keys. However, it lacks usage context (e.g., when to list vs. run tests) and does not cover edge cases or failure modes. Adequate but not complete.

    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 has 0 parameters with 100% coverage. The description adds no parameter info because none exists, which is appropriate. Baseline of 4 for zero-parameter tools.

    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 verb 'Return' and the resource 'catalog of built-in behavioral checks.' It immediately differentiates from sibling tools 'compare_outputs' and 'run_behavior_test' which involve executing or comparing tests rather than listing.

    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?

    No explicit when-to-use or when-not-to-use guidance is provided. While the zero-parameter signature implies it is a simple retrieval tool, the description does not mention it as a prerequisite for other tools or give any context for decision-making.

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

  • Behavior4/5

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

    No annotations provided, so the description carries the full burden. It discloses that the tool uses 'embedding similarity' and handles long outputs via 'sentence-level max similarity'. This gives meaningful insight into behavior beyond basic expectations.

    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?

    Extremely concise: two short sentences plus a line. Each sentence provides distinct value: purpose, mechanism, and handling of long outputs. No wasted words. Front-loaded with the core action.

    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 three clearly documented parameters in the schema, an output schema (per context), and helpful behavioral details, the description is complete. It covers what the tool does, how it works, and a key edge case (long outputs). No obvious gaps.

    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?

    Schema coverage is 100% and each parameter already has a clear schema description. The tool description restates the purpose of 'prompt' (context/logging) and the nature of 'expected_behavior' (plain-language), but adds no new semantics beyond the schema. Baseline of 3 is appropriate.

    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?

    Clear verb and resource: 'Run a single behavioral assertion'. It explains the technique (embedding similarity) and goal (check semantic satisfaction). However, it does not explicitly differentiate from sibling tools 'compare_outputs' and 'list_builtin_behaviors', which would help selection.

    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 checking expected behavior semantically, but lacks explicit guidance on when to use this tool versus alternatives like 'compare_outputs', or when not to use it. No prerequisites or exclusions stated.

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

  • Behavior3/5

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

    No annotations are provided, so the description must convey behavioral traits. It describes the tool as performing semantic comparison (likely read-only) without side effects, but it does not disclose potential behaviors like auth needs, rate limits, or error handling. The description is adequate but not thorough.

    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 (two sentences) and front-loaded with the core purpose, followed by a usage hint. Every sentence adds value with no wasted words.

    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?

    Given the tool's simplicity, two parameters, and output schema existence, the description covers the essential context (purpose, usage scenario) without needing to explain return values. It is sufficiently complete for an agent to use correctly.

    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?

    Schema coverage is 100% with both parameters described. The description does not add additional meaning beyond the schema (e.g., expected format or constraints), so it meets the baseline of 3.

    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 purpose: compare two LLM outputs for semantic similarity (regression detection). The verb 'compare' and resource 'outputs' are specific, and it distinguishes from sibling tools like list_builtin_behaviors and run_behavior_test, which serve different functions.

    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 usage context: run in CI against a known-good baseline to detect drift when changing prompts or models. It implies when to use but does not explicitly state when not to use or name alternatives, though the sibling tools hint at different purposes.

    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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