Skip to main content
Glama
master997

cuad-audit

by master997
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

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 purpose: audit_clause evaluates clauses, get_standard retrieves the standard playbook, search_clauses searches precedents. There is no overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern: audit_clause, get_standard, search_clauses. No mixing of conventions.

    Tool Count5/5

    Three tools is appropriate for the focused scope of clause auditing. Each tool serves a necessary role without being redundant or insufficient.

    Completeness5/5

    The tool set covers the full workflow: retrieving the standard, searching precedents, and auditing clauses. There are no obvious gaps for the stated purpose.

  • Average 4.1/5 across 3 of 3 tools scored. Lowest: 3.5/5.

    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
    • No stable releases found
    • 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.

  • 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 provided, so description carries burden. It discloses that the standard is data derived from the clause library, not legal advice, and references scope_claim in the result. However, it lacks details on idempotency, authentication needs, or rate limits.

    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 main action, no wasted words. The caveat is placed efficiently.

    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 (1 param, no output schema, no annotations), the description covers the key aspects: what is returned, disclaimers, and a reference. It is complete for its scope.

    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?

    Schema coverage is 100% for the single parameter, and description adds meaning by mentioning 'positions P1-P6 with provenance' and 'see scope_claim in the result', which goes beyond the schema's const definition.

    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?

    Description clearly states it returns the company standard (playbook) for a clause type, specifying positions P1-P6 with provenance. It distinguishes the tool's purpose but does not explicitly differentiate from sibling tools like audit_clause or search_clauses.

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

    Usage Guidelines2/5

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

    No guidance on when to use this tool versus alternatives, no exclusions or context provided. Sibling tools exist but no comparison is given.

    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?

    With no annotations, the description covers key behaviors: returns stable chunk IDs, source, offsets, scores; explains 'below_threshold' means abstention (not error); mentions top_scores. No destructive implications or 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?

    Four short sentences, front-loaded with the main purpose, each sentence adds specific value. No fluff or repetition.

    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 no output schema or annotations, the description is fairly complete but lacks explanation for the k parameter (only in schema with constraints) and does not fully describe the return structure beyond listing fields. Could be more thorough.

    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?

    Schema coverage is 67%, and the description adds value: warns against short paraphrases for query, clarifies clause_type constraint. The k parameter lacks description, but overall the description enriches the 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 clearly states the tool searches a precedent clause library for CUAD liability spans, and lists what it returns (chunks with IDs, source, offsets, scores). It distinguishes from sibling tools by being explicitly a search function.

    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?

    Provides strong usage guidance: warns against retrying on weak evidence, advises passing complete clause texts to avoid abstentions. Does not explicitly contrast with siblings, but gives actionable instructions.

    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?

    With no annotations, the description fully discloses behavior: possible verdicts, server resolution of citations, and instructions for handling special outcomes (insufficient-grounding, escalate-infra). It provides rich behavioral context 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.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured with a clear lead sentence followed by a bullet-like list of verdicts. Every sentence adds value, though slightly verbose. It is front-loaded and concise enough for an AI agent.

    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 the tool's complexity (multiple verdicts, no output schema), the description is highly complete. It explains each verdict, how citations work, and how to handle abstentions. It covers all necessary behavioral aspects for 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%, so baseline is 3. The description does not add additional meaning to the parameters beyond what the schema already provides (e.g., max chars for incoming_clause, const/default for clause_type). No extra value added.

    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 audits a clause against the company standard with grounded citations. It specifies the verb 'audit' and the resource 'clause', and distinguishes from siblings 'get_standard' and 'search_clauses' by emphasizing grounded citations and specific verdict types.

    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 provides explicit guidance on when to use the tool and how to handle each verdict: it warns against guessing for 'insufficient-grounding' and instructs to retry for 'escalate-infra'. It also instructs to relay abstentions verbatim, making usage clear and actionable.

    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

luminance MCP server

Copy to your README.md:

Score Badge

luminance 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/master997/luminance'

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