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Server Quality Checklist

83%
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  • Latest release: v0.2.0

  • Disambiguation5/5

    scan_repository performs actual repository scanning and returns findings, while audit_pricing provides fixed pricing and checkout information for a paid audit. There is no overlap or ambiguity between the two tools.

    Naming Consistency5/5

    Both tool names follow a clear verb_noun pattern: scan_repository and audit_pricing. The naming convention is consistent and immediately indicates what each action does.

    Tool Count3/5

    With only two tools, the server feels thin for a scanning service, especially since audit_pricing is a sales/marketing endpoint rather than a scanning operation. The count is borderline but not unreasonable.

    Completeness5/5

    scan_repository is self-contained: it clones, scans locked dependencies for advisories, checks secrets and config lint, and returns the full JSON report. audit_pricing provides all necessary pricing and checkout details, so there are no obvious missing operations for the stated purpose.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

  • Add related servers to improve discoverability.

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?

    Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds useful context about what the tool returns: price, turnaround, manual verification details, and a Stripe checkout URL. It also states 'No arguments,' clarifying invocation behavior beyond the schema.

    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?

    A single dense sentence communicates the audit scope, review process, output contents, and parameter requirements without waste. Every phrase adds meaning, and the most important information is front-loaded.

    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?

    For a zero-argument informational tool with read-only, idempotent annotations and no output schema, the description is fully sufficient. It tells the agent what the tool will describe and what content to expect, leaving no missing invocation details.

    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 zero parameters and 100% schema description coverage, there is little for the description to add. It explicitly confirms 'No arguments,' which fully resolves parameter ambiguity and aligns with the empty input schema.

    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 uses a specific verb ('Describe') and names a concrete resource ('Project Feldspar's paid code audit'), then enumerates exactly what is covered: scope, price, turnaround, and Stripe checkout URL. It clearly distinguishes itself from the sibling scan_repository, which would perform scanning rather than provide audit-pricing information.

    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 title and description make the intended use clear: retrieve information about the paid audit offering and how to order it. However, it does not explicitly mention when not to use it or contrast it with scan_repository, so the guidance is implied rather than stated.

    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 adds substantial behavioral context beyond the annotations: it clones a repository, runs a deterministic scan with no LLM involvement, reports secret findings with redacted evidence, and may take 2–90 seconds. It also explains the output format, which is essential since there is no output schema. No contradiction with readOnlyHint=true or openWorldHint=true.

    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 information-dense yet well-structured: it front-loads the core action, provides a parenthetical list of supported lockfiles, and covers output, determinism, and timing in a single compact paragraph. Every sentence contributes value.

    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?

    For a single-parameter tool with no output schema, the description is fully complete. It explains inputs, scanning scope, output structure, determinism, lack of LLM involvement, supported repository hosts, secret redaction, and expected runtime. There is no gap that would prevent correct invocation.

    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% because the sole parameter url includes a description with supported host examples. The description does not add meaning beyond the schema—it only mentions the URL implicitly through 'public git repository'. Baseline 3 is appropriate.

    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 uses a specific verb and resource: 'Clone a public git repository and run feldspar-scan' with a clear enum of what it scans and what it returns. It is distinguishable from the sibling audit_pricing tool because it uniquely mentions repository scanning, lockfile dependency advisories, secrets, and config lint.

    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 clearly establishes what the tool does and its operational context: public git repositories from supported hosts, deterministic behavior, and 2–90s runtime. It does not explicitly state when not to use it or mention alternative tools, but the context is sufficient for an agent to decide when it applies.

    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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