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kauzy7

F5 AI Security Docs MCP Server

by kauzy7
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: fetch_doc retrieves documentation content (catalog, TOC, or section), while search_docs finds relevant documents via search. There is no overlap.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern (fetch_doc, search_docs), using snake_case and parallel structure.

    Tool Count3/5

    With only 2 tools, the server is on the low end of the acceptable range. While they cover search and retrieval, a typical documentation server might include additional tools like list_docs or get_doc_metadata.

    Completeness5/5

    The tools cover the full lifecycle for documentation consumption: search to find documents, fetch_doc to browse catalog, get TOC, and read sections. There are no obvious gaps for the stated purpose.

  • Average 4.6/5 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 Apache 2.0.

  • This repository includes a README.md file.

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    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

  • Behavior4/5

    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 discloses the indexing scope (entire site), that results are ranked, and the return format. It does not mention rate limits or auth, but for a search tool these are less critical. The transparency is good.

    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 concise and well-structured. It starts with a one-sentence summary, then details indexed content, use cases, and parameter descriptions. Every sentence adds value, and it is front-loaded with the core purpose.

    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 (search with two parameters), the description adequately covers indexing scope, parameters, return format, and use cases. An output schema exists (as per context signals) and is described in text, making the documentation complete for an agent to use effectively.

    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?

    The input schema has 0% description coverage, so the description must add meaning. It does: for 'query', it provides example queries; for 'k', it explains 'Maximum number of results to return (default: 5)'. This adds value beyond the schema, though no parameter-level details about expected formats.

    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 F5 AI Security documentation and returns ranked results. It specifies the indexed content (entire docs site, including specific sections). This distinguishes it from the sibling 'fetch_doc', which likely retrieves a single document.

    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 says 'Use this to find relevant F5 AI Security documentation for any question about...' and lists example topics. While it doesn't explicitly say when not to use, the context of sibling tool 'fetch_doc' implies this is for searching, not retrieving. Clear usage guidance is provided.

    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 carries full responsibility. It discloses that the tool returns different payloads depending on mode, that small documents are returned fully, that the URI must be under a specific domain, and that errors are returned as dicts. It could mention potential rate limits or loading behavior, but overall it is quite transparent.

    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 for modes, workflow, args, and returns. It is slightly verbose but every sentence adds value. Front-loading with the main purpose is good. Minor room for trimming, but effective.

    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 (three modes) and the presence of an output schema, the description covers all necessary aspects: modes, workflow, parameter usage, small document handling, URL constraint, and error return. It is comprehensive and leaves no major gaps.

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

    Parameters5/5

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

    Despite 0% schema description coverage, the description explains both parameters in detail: what happens when uri is omitted (catalog), when section is omitted (TOC), and how section IDs like '3.2' work. It also specifies the URI constraint (must be under https://docs.aisecurity.f5.com/).

    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's purpose: 'Read F5 AI Security documentation pages with smart sectioning.' It describes three distinct modes (catalog, TOC, section) and provides a recommended workflow that distinguishes it from the sibling tool search_docs.

    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 explicitly explains when to use each mode (e.g., 'Catalog mode (omit uri)' and 'Section mode (uri + section)') and gives a recommended three-step workflow. It also clarifies edge cases like small documents being returned fully.

    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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