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ibrahimmian36

leanscreen

by ibrahimmian36
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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 are clearly distinct: check_fast is deterministic and free, suitable for frequent checks; check_deep includes LLM judges and costs money, appropriate for final verification. No overlap in purpose.

    Naming Consistency5/5

    Both tools use a consistent check_ prefix followed by a descriptive adjective (fast, deep), making their purposes clear and following a predictable pattern.

    Tool Count4/5

    With only 2 tools, the server is minimal but well-scoped for its purpose: one fast/deterministic screen and one deep/expensive screen. Could benefit from a medium option, but current count is reasonable.

    Completeness4/5

    The tools cover the essential workflow: fast frequent checks and deep pre-release verification. A minor gap is the lack of a certification tool, but the descriptions explicitly state neither tool certifies faithfulness, making this intentional.

  • Average 4.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 16 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

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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

  • Behavior5/5

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

    Since no annotations are provided, the description fully details behavioral traits: deterministic, no API calls, ~0.1s speed, REPL dependency, and crucially, its limitations with false pass rates (17% and 35.6%). This level of disclosure is exceptional and helps the agent set correct 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?

    The description is well-structured with three focused paragraphs: functionality/speed, limitations/false pass rates, and parameter details. Every sentence adds value, no redundancy, and key information is front-loaded. It wastes no space while being comprehensive.

    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 existence of an output schema, the description appropriately omits return value details. It covers the tool's purpose, limitations, parameter semantics, and operational context. However, it lacks specificity on the exact input format for 'lean' (e.g., full file vs. snippet) and 'informal', which could be clarified for seamless use.

    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?

    With 0% schema description coverage, the description adds significant meaning: it explains that 'kind' is 'theorem' or 'definition' and can be omitted for inference, and it contextualizes 'informal' and 'lean' as a pair. It does not fully detail the expected format or constraints for the two required strings, but provides enough for basic use.

    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 defines the tool as a deterministic faithfulness screen for informal-to-Lean 4 pairs, listing specific checks (lints, vacuity, triviality) and emphasizing its speed and cost-free nature. It distinguishes itself from the sibling 'check_deep' by implying a fast, shallow check suitable for frequent use during drafting.

    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 strong usage context: 'call it constantly while drafting' and notes it is fast and free. However, it does not explicitly mention when to use the sibling 'check_deep' instead, leaving the comparison implicit rather than giving clear when-not guidance.

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

  • Behavior5/5

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

    The description discloses all relevant behavioral traits: cost range, time, required API key, the meaning of a 'passed' outcome (not a certification), false pass rates for theorems and definitions, and the evidence ranking. Since no annotations are provided, the description carries the full burden and fulfills it thoroughly.

    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 appropriately sized for the complexity of the tool. It is front-loaded with the core purpose and then provides additional details. Every sentence adds value without redundancy, and it maintains a clear structure.

    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 complexity and that an output schema exists (but is not described here), the description covers most necessary aspects: behavior, cost, time, constraints, and interpretation of results. It could include more details on error conditions, but overall it is thorough for an AI agent to understand usage.

    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 description adds meaning for the 'kind' parameter by specifying it can be 'theorem' or 'definition' and if omitted it is inferred. For 'lean' and 'informal', the description does not elaborate beyond the tool's purpose, but given the context of faithfulness checking, their roles are deducible. Schema description coverage is 0%, so the description compensates partially.

    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: a full faithfulness screen that includes everything in check_fast plus two independent LLM judges and an adversarial counterexample probe. It distinguishes itself from the sibling tool check_fast by specifying the additional components.

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

    Usage Guidelines5/5

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

    The description explicitly advises when to use this tool: 'call it deliberately, before something ships.' It contrasts with check_fast by noting the added thoroughness and cost, and warns about the time and expense (17-27¢, 30-60 seconds). This provides clear guidance on when to choose this tool over the sibling.

    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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