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amazonbusiness

Amazon Business Integrations MCP Server

Official

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: 'search-documentation' is for finding relevant documentation through semantic search, while 'read-documentation' is for retrieving the full content of specific documents. There is no overlap in functionality, and the descriptions clearly differentiate their roles in the documentation workflow.

    Naming Consistency4/5

    Both tools follow a consistent verb-noun pattern ('search-documentation' and 'read-documentation'), which is clear and predictable. The minor inconsistency is the use of hyphens instead of underscores, but this is consistent across both tools, so it does not cause confusion.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for its purpose of Amazon Business API integrations. While the tools cover documentation search and retrieval, there are no tools for actual API operations (e.g., ordering, product search, user management), which are implied by the server name and documentation paths. This limited set may hinder agent effectiveness in performing integration tasks.

    Completeness1/5

    The tool surface is severely incomplete for an Amazon Business Integrations server. It only provides documentation tools, missing all core API operations like creating orders, searching products, managing users, or generating reports. This creates significant gaps that will cause agent failures when attempting to perform actual integration tasks beyond reading documentation.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • 0 of 1 community issues answered or closed 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 Apache 2.0.

  • This repository includes a README.md file.

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly describes what the tool does (retrieves complete documentation content) and provides usage context, but doesn't address potential behavioral aspects like error conditions, rate limits, authentication requirements (beyond the recommendation to read auth docs), or what happens with invalid references. It adds value beyond the minimal schema but doesn't fully compensate for the lack of annotations.

    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 (purpose, usage guidance, recommendations, common use cases) and front-loads the core functionality. While comprehensive, some sentences could be more concise (e.g., the second paragraph repeats similar information). Overall, it's appropriately sized for a tool with important usage context, though slightly verbose in places.

    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 single parameter with full schema coverage and no output schema, the description provides substantial contextual information. It explains the relationship with the sibling tool, gives specific usage examples, and recommends starting points. The main gap is the lack of output format description (what 'complete documentation content' actually returns), but otherwise it's quite complete for a read-only documentation retrieval tool.

    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?

    With 100% schema description coverage for the single parameter, the baseline would be 3. However, the description adds significant value by explaining that 'documentReference parameter is typically obtained from search-documentation results' and providing concrete examples of valid references like 'Guides and FAQs/amazon-business-apis-getting-started-guide.md'. This gives practical context beyond the schema's generic description of 'Reference to the document file from search results'.

    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 as 'Retrieve complete documentation content using a document reference' and 'fetches the full content of documentation files identified by documentReference'. It distinguishes from its sibling tool 'search-documentation' by explaining that this tool retrieves full content while search-documentation provides references. The verb+resource combination is specific and unambiguous.

    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 this tool versus alternatives. It states that 'documentReference parameter is typically obtained from search-documentation results' and gives specific examples of when to use it (when a document contains references to other docs). It also provides 'HIGHLY RECOMMENDED' starting points and lists common use cases, giving clear 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.

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: semantic search returning relevant documentation chunks with relevance scores and document references, explains how to handle referenced documents, and provides important implementation guidance (e.g., OAuth prerequisites, recommended starting documents). It doesn't mention rate limits or authentication requirements beyond OAuth, but covers most behavioral aspects well.

    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 comprehensive but verbose, with multiple sections (DOCUMENTATION PATH SELECTION, Helpful Hints, Common use cases, Note) that could be more streamlined. While all content is relevant, it could be more front-loaded and concise. Some redundancy exists in explaining the relationship with read-documentation tool.

    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 complexity (semantic search across documentation corpus with path filtering) and no output schema, the description provides substantial context: explains result format (snippets with relevance scores and references), relationship to sibling tool, implementation guidance, and extensive usage rules. It lacks explicit information about output structure details, but covers most aspects needed for effective use.

    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 100%, so the baseline is 3. The description adds significant value beyond the schema by explaining the semantics of documentationPath parameter through the detailed documentation path tree and helpful hints that guide parameter selection. It provides context about how to choose appropriate paths based on query intent, which goes beyond the schema's basic 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's purpose: 'Search through documentation using a natural language query' and 'provides semantic search capabilities across the entire API documentation corpus.' It specifically distinguishes from its sibling tool 'read-documentation' by explaining that search-documentation returns snippets while read-documentation fetches complete documents.

    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 extensive guidance on when to use this tool vs alternatives, including explicit rules for documentation path selection (e.g., 'If you are searching for code generation/building applications → API Swagger Model'), when to use the sibling read-documentation tool, and common use cases. It also specifies when NOT to provide a documentation path ('If nothing is relevant, search across all the documentation paths. DO NOT provide any documentation path').

    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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