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fc0web

rei-checker

by fc0web
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Python
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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

    stats and verify are completely distinct: one provides aggregate metrics, the other checks individual expressions. There is no overlap in purpose, so an agent can easily select the correct tool.

    Naming Consistency4/5

    Both tool names are short, lowercase single words. While stats is a noun and verify is a verb, the naming style is consistent and clear, with no mixed conventions or confusing patterns.

    Tool Count3/5

    With only 2 tools, the server feels slightly thin, but the domain appears narrow (validation and ledger statistics). The count is borderline below the typical 3-15 range but not extreme enough to be a major issue.

    Completeness4/5

    The tools cover the core functions: verifying expressions and retrieving statistics. Missing operations like listing ledger entries or resetting data are not obvious gaps given the stated purpose, though a 'list' or 'detail' tool could be useful.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 5 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 AGPL 3.0.

  • 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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  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior4/5

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

    With no annotations, the description carries the full behavioral burden. It discloses the three-valued return, conditional reason_code, the no-LLM guarantee, and the side effect of recording in an append-only refutation ledger. This is strong transparency, though it omits details like idempotency 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?

    Three dense sentences with no filler. The verdict behavior is front-loaded, and the side-effect and no-LLM guarantees are stated efficiently. Every sentence earns its place.

    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?

    There is no output schema, but the description adequately explains return values and side effects. The input schema covers parameters. The only notable gap is the lack of explicit routing guidance relative to the sibling tool, but the core information needed to call and interpret the tool is present.

    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 description coverage is 100%, so the schema already documents all three parameters. The description adds the fact that the expression is normalized, but it does not provide additional parameter-level guidance beyond what the schema already states.

    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 states a specific action ('Verify one expression') and clearly defines the output (VALID / INVALID / UNDECIDED with reason_code). It is easily distinguishable from the sibling 'stats' tool, though it does not explicitly name or contrast that sibling.

    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 this tool is for judging a single expression, but it gives no explicit guidance on when to choose it over 'stats' or any other alternative. There are no stated exclusions or conditions, leaving the usage context to inference.

    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, so description carries burden. It clearly states this is a read-style 'Return' operation with no side effects implied meetabst, and it specifies what data is included (decision_rate and reason_breakdown). It doesn't explicitly state it's non-destructive or need auth, but the context implies a read-only stats query. No contradictions.

    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, front-loaded with the purpose and two key outputs. Efficient.

    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?

    No output schema, so description explains the two things returnedches. It references spec sections, which is helpful. It's sufficient for the tool's simple scope, though it doesn't detail the exact breakdown structure. Minor gap.

    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?

    No parameters exist threre, so this dimension is trivially satisfied. The description explains what the output contains (decision_rate, reason_breakdown) which helps the agent understand what the tool will produce, but doesn't add parameter meaning since there are none.

    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?

    Clear verb+resource: 'Return aggregate stats', specifies two concrete outputs (decision_rate and reason_breakdown) with explicit formulas, and references project specs. It distinguishes from the sole sibling 'verify' by being stats-focused, not per-item verification.

    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?

    Justifies use by linking decision_rate to 'sole metric of project success' and reason_breakdown to 'drives what the next sprint implements,' giving clear contexts for calling. It doesn't explicitly say when not to use or contrast with the sibling 'verify', but the purpose is specific enough that an agent can decide.

    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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