Skip to main content
Glama
mattshuttle

gristmill-mcp

by mattshuttle
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation4/5

    The two tools have clearly distinct purposes: one is for static analysis of code, the other for documentation of rules. An agent would not confuse them.

    Naming Consistency3/5

    The naming is inconsistent: 'verify' uses a plain verb while 'explain_rule' uses verb_noun pattern. Both are clear in isolation, but the lack of a unified pattern (e.g., 'verify_code' vs 'explain_rule') makes the set feel ad-hoc.

    Tool Count2/5

    With only 2 tools, the server feels thin for a tool suite called 'gristmill-mcp'. A code analysis server typically needs more tools like listing rules or scanning for specific categories to feel properly scoped.

    Completeness2/5

    The server only provides a scan tool and a rule lookup tool, but is missing operations like listing all rules, skipping specific rules, or generating reports. Users cannot discover available rules without knowing their IDs, creating a dead end.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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.

  • Add a glama.json file to provide metadata about your server.

  • 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.

  • 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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It correctly implies a read-only operation ('Look up') and lists the content returned. However, it does not explicitly state that the tool has no side effects, nor does it mention authorization requirements, rate limits, or error handling for invalid rule IDs. A 3 is adequate but leaves gaps.

    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 sentence that is front-loaded with the action and resource, includes concrete examples in parentheses, and conveys the full return intent. There is no wasted text; every part 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?

    The description covers the tool's purpose, the parameter, and the core return values. An output schema exists to handle return type details, so the description does not need to reiterate those. However, it omits mention of what happens if the rule ID is invalid or missing (e.g., error or null response), which would make it more complete.

    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 0% (no parameter descriptions in the input schema), so the description must compensate. It adds value by specifying the parameter is a 'rule id' and provides examples (SEC001, STR002, CMT004), hinting at a consistent format. However, it does not fully specify the pattern or acceptable formats, leaving ambiguity for the agent.

    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 'Look up', identifies the resource as a 'gristmill rule', and specifies exactly what information is returned: rationale, what it catches, what it misses, and how to suppress it. This distinguishes it from the sibling tool 'verify', which likely performs a different function.

    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 when you need details about a specific rule (e.g., by its ID), but it does not explicitly state when to use this tool versus the sibling 'verify', nor does it provide guidance on when not to use it or any prerequisites. More specific exclusions or comparisons would improve this dimension.

    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?

    Without any annotations, the description must disclose behavioral traits fully. It states that findings are 'deterministic' and include 'file paths and line numbers,' which adds value. However, it does not address permissions, side effects (though likely read-only), rate limits, or what happens when no issues are found.

    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 efficiently structured sentences: purpose, output nature, and usage timing. No redundant or extraneous content. Every sentence earns its place.

    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?

    The presence of an output schema partially offsets the need to describe return values. However, the description does not explain how the three parameters interact or provide examples for common use cases, leaving gaps for a tool invoked after code generation.

    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?

    Schema description coverage is 0%, so the description must compensate. It only implicitly refers to 'paths' via 'source files' and does not explain 'checks' (the enum options) or 'severity_floor' at all. This forces the agent to rely solely on parameter names, which are insufficient.

    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 ('inspect') and resource ('source files') and enumerates three concrete issue types (secrets, structural problems, low-quality comments). With only one sibling tool 'explain_rule', the purpose is clearly distinct.

    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 explicitly tells when to call this tool: 'after generating or editing code, before presenting it as finished.' It provides clear context but does not mention when not to use it or compare to alternatives.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

gristmill-mcp MCP server

Copy to your README.md:

Score Badge

gristmill-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mattshuttle/gristmill-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server