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ArmaghanRazaChaudhary

ContractAudit MCP Server

by ArmaghanRazaChaudhary
OverviewSchemaRelated ServersScoreDiscussions
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

    Each tool targets a clearly distinct aspect of the server: retrieving document content, listing source metadata, and reporting system status. No overlapping functionality.

    Naming Consistency4/5

    Most names follow a verb_noun pattern (get_document_context, list_sources), but corpus_status is a noun_noun pair without a verb, breaking full consistency. Overall pattern is clear and readable.

    Tool Count3/5

    Three tools is on the low end for a contract audit server, but could be acceptable if the server is narrowly scoped. It borders on feeling thin.

    Completeness2/5

    The tool set lacks essential operations like adding or deleting sources, searching across documents, or comparing contracts. Significant gaps impair full audit workflows.

  • Average 3.4/5 across 3 of 3 tools scored. Lowest: 2.7/5.

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

    • No community issues in the last 6 months
    • 2 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 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.

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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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden but only states 'Return ordered chunks,' implying a read operation. No behavioral traits (e.g., permissions, rate limits, idempotency) are disclosed, which is insufficient for a mutation-free analysis tool.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

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

    The description is a single sentence with no waste, but it is too brief for a tool with parameters and no schema descriptions. Front-loading the core action is good, but essential details are missing, making it minimally adequate.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the presence of an output schema, return values are not strictly required. However, the description fails to explain how the 'ordered chunks' are organized, how 'limit' affects results, or what 'document_id' refers to, leaving significant gaps for a tool with two parameters and no param descriptions.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, yet the description adds no meaning to the two parameters. 'document_id' and 'limit' are not explained, leaving the agent to infer their purposes without any additional context.

    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 returns ordered chunks from one source document, specifying the verb (Return) and resource (ordered chunks from one source document). It distinguishes from sibling tools like list_sources and corpus_status, which likely provide different functionality.

    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 is provided on when to use this tool versus alternatives. There is no mention of prerequisites, limitations, or exclusion criteria, leaving the agent without context for appropriate usage.

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

  • Behavior2/5

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

    No annotations provided; description carries full burden. Only states it returns status but does not disclose any behavioral traits like read-only nature, authentication requirements, or side effects.

    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?

    Single, concise sentence that directly conveys the tool's function with no excess words.

    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 zero parameters and presence of output schema, description is adequate but could mention if status is real-time or any prerequisites. Siblings provide enough differentiation.

    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?

    No parameters (coverage 100%), baseline 4. Description adds meaning by specifying the components of the status (corpus, vector collection, embedding model), which is helpful beyond the empty 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?

    Clearly states verb 'Return' and resource 'corpus, vector collection, and embedding model status'. Differentiates from sibling tools get_document_context and list_sources, which have different purposes.

    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?

    Implies usage for obtaining status but does not explicitly state when to use or when not to use. No mention of alternatives or exclusions.

    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?

    No annotations are provided, so the description carries the burden. It indicates a read operation ('list'), which is inherently safe, but does not disclose any additional traits like pagination, rate limits, or behavior when empty.

    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, front-loaded sentence with no extraneous words. Every word 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?

    Given no parameters and an existing output schema, the description adequately covers the basic purpose. It could mention scope (all sources) but is sufficient for a simple list operation.

    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?

    There are zero parameters, so baseline is 4. The description can add value but is not required to explain parameters.

    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 'list' and the resource 'ingested source documents, licenses, URLs, and processing states', and it distinguishes from siblings like get_document_context (single document) and corpus_status (overall status).

    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 vs alternatives or any exclusions. The description only states what it does without context.

    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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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.

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