Skip to main content
Glama
arthurpanhku

DocSentinel

by arthurpanhku
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.2.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: assess_document analyzes security documents for risks, while query_knowledge_base searches an internal knowledge base. There is no overlap in functionality or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (assess_document, query_knowledge_base). The naming is predictable and readable across the set.

    Tool Count2/5

    With only two tools, the server feels thin for its apparent security document assessment domain. This limited set may force agents to work around gaps in functionality, such as missing operations for managing documents or updating the knowledge base.

    Completeness2/5

    The tool surface is severely incomplete for a security document assessment system. There are no tools for creating, updating, or deleting documents or knowledge base entries, and no way to manage assessment scenarios or reports beyond the basic assess_document operation.

  • Average 3/5 across 2 of 2 tools scored.

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

    • 2 of 2 community issues answered or closed in the last 6 months
    • 43 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • This repository includes a glama.json configuration file.

  • 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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It states the tool returns a risk report but doesn't disclose behavioral traits like what types of security documents are supported beyond 'PDF, Word, etc.', whether the assessment is read-only or modifies the file, authentication requirements, rate limits, or error conditions. The description is minimal and lacks critical operational context.

    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 appropriately sized and front-loaded, with the core purpose in the first sentence and parameter details in a structured format. Every sentence earns its place, though it could be slightly more concise by integrating the 'Args' and 'Returns' sections more fluidly.

    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 the tool's moderate complexity (assessing security documents), no annotations, and an output schema present (which handles return values), the description is partially complete. It covers the basic purpose and parameters but lacks context on usage scenarios, behavioral traits, and detailed parameter semantics, making it adequate but with clear gaps for effective agent use.

    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 0%, so the description must compensate. It adds meaning by explaining that 'file_path' is for 'the file to be assessed' and 'scenario_id' is for 'the assessment scenario ID', which clarifies the purpose of each parameter beyond their titles. However, it doesn't detail format constraints (e.g., valid file paths, scenario ID options) or provide examples, leaving gaps in parameter understanding.

    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 clearly states the tool's purpose: 'Assess a security document (PDF, Word, etc.) and return a risk report.' This specifies the verb ('assess'), resource ('security document'), and output ('risk report'), distinguishing it from the sibling tool 'query_knowledge_base'. However, it doesn't explicitly differentiate from that sibling beyond the different resource focus.

    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?

    The description provides no guidance on when to use this tool versus alternatives. It mentions a sibling tool 'query_knowledge_base' but gives no context about when to choose one over the other, nor any prerequisites or exclusions for using this tool.

    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?

    With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool queries a knowledge base and returns JSON with document chunks, but lacks critical details like authentication requirements, rate limits, error handling, or whether it's read-only (implied but not explicit).

    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 clear sections (Args, Returns) and uses minimal sentences. Each part earns its place, though the 'Returns' section could be slightly more detailed given the lack of annotations.

    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 the tool has an output schema (returns JSON string), the description doesn't need to detail return values. However, with no annotations and a sibling tool, it lacks guidance on usage context and behavioral traits. The parameter explanations help, but overall completeness is adequate with clear gaps.

    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 description coverage is 0%, so the description must compensate. It provides meaningful context for both parameters: 'query' is explained with an example ('password complexity requirements'), and 'top_k' specifies it controls the number of results. This adds value beyond the bare schema.

    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 clearly states the verb ('Query') and resource ('internal security knowledge base') with specific content scope ('policies, standards'). It distinguishes from the sibling 'assess_document' by focusing on retrieval rather than assessment. However, it doesn't explicitly contrast with the sibling tool.

    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?

    The description provides no guidance on when to use this tool versus the sibling 'assess_document' or any alternatives. It mentions the tool's purpose but offers no context about appropriate use cases, prerequisites, or exclusions.

    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

DocSentinel MCP server

Copy to your README.md:

Score Badge

DocSentinel 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/arthurpanhku/DocSentinel'

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