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Joseph19820124

AWS Documentation MCP Server

by Joseph19820124
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Python
Remote

Server Quality Checklist

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

  • Latest release: v1.1.1

  • Disambiguation5/5

    Each tool has a distinct purpose: reading a specific URL, getting recommendations, and searching across documentation. There is no overlap or ambiguity.

    Naming Consistency4/5

    Tool names mostly follow a verb_noun pattern ('read_documentation', 'search_documentation'), but 'recommend' is a verb without a noun, making it slightly inconsistent.

    Tool Count5/5

    With 3 tools covering read, search, and recommend, the count is well-scoped for a documentation server. Neither too few nor too many.

    Completeness4/5

    Core functionality is covered, but a tool to navigate documentation structure (e.g., list service guides) is missing, though not critical.

  • Average 4.4/5 across 3 of 3 tools scored.

    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 status not available

  • This repository is licensed under Apache 2.0.

  • 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 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.

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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

  • Behavior3/5

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

    No annotations provided, so description carries full burden. It covers conversion to markdown, chunking behavior, and URL constraints. However, lacks details on error handling, rate limits, or authentication.

    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?

    Well-structured with headings, bullet points, and examples. Every section serves a purpose without unnecessary fluff. Could be slightly shorter but remains clear.

    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?

    Covers key aspects: URL requirements, output format, handling long documents. Missing some edge cases but sufficient for typical usage.

    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?

    100% schema coverage provides baselines. Description adds value by explaining how to use start_index for continuation, which is beyond the schema's description.

    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 fetches and converts an AWS documentation page to markdown, with specific URL requirements and examples. It distinguishes from sibling tools (recommend, search_documentation) by focusing on retrieving a specific page.

    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 clear guidance on handling long documents with start_index and continuation strategies. Lacks explicit 'when not to use' but the context and sibling names imply appropriate use cases.

    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?

    No annotations are provided, so the description carries full burden. It describes four recommendation types (Highly Rated, New, Similar, Journey), how to interpret results (url, title, context), and specific usage context (e.g., using welcome pages). It does not disclose side effects or rate limits, but the behavior is well-explained.

    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 sections (Usage, Recommendation Types, When to Use, Finding New Features, Result Interpretation) and bullet points. It is somewhat long but every sentence adds value and 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 no output schema, the description explains the return structure (url, title, context) and the four recommendation categories. The tool has only one parameter and is simple, so the description is sufficiently complete for an agent to understand and invoke it correctly.

    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% (only param 'url' with description). The description reinforces the param's meaning by specifying it as an 'AWS documentation page URL' and provides usage examples (e.g., welcome page). It adds value beyond 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 explicitly states 'Get content recommendations for an AWS documentation page' and distinguishes from sibling tools (read_documentation, search_documentation) by focusing on recommendations based on a URL.

    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 provides a 'When to Use' section with concrete scenarios (after reading a page, exploring a new service, finding new features) and includes a step-by-step guide for finding new features. It does not explicitly mention when not to use or compare to alternatives, but the context is clear.

    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?

    No annotations, so description carries the burden. It describes the API source and result structure but does not explicitly state it's read-only or idempotent. However, as a search tool, this is implied. Adds value with result interpretation.

    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?

    Well-structured with sections (Usage, Search Tips, Result Interpretation). Each section is meaningful. Slightly lengthy but no wasted sentences.

    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 across all docs) and no output schema, the description fully explains input parameters, result format, and usage tips. Sufficient for correct invocation.

    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 covers 100% of parameters, but the description adds search tips for search_phrase and explains limit's default and range. Also describes return fields, adding meaning beyond 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 it searches AWS documentation using the official API. It distinguishes from read_documentation (which likely uses a URL) and recommend. The verb 'search' and resource 'documentation' are specific.

    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?

    Explicitly says 'Use it to find relevant documentation when you don't have a specific URL.' Provides search tips (specific terms, service names, quotes) that guide effective use.

    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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  • 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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