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rwestergren

inbody-api-mcp

by rwestergren

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 has a clearly distinct purpose: get_profile returns user identity and baseline metrics; get_scan returns full metrics for a specific scan; get_scan_count returns the total number of scans; list_scans returns a summary list of scans. No overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case: get_profile, get_scan, get_scan_count, list_scans. The convention is uniform and predictable.

    Tool Count5/5

    With 4 tools, the server is well-scoped for an InBody API: it provides profile retrieval, scan listing, scan detail, and scan count. No unnecessary tools, and the count is appropriate for the domain.

    Completeness4/5

    The tool set covers the essential read operations for InBody scans and user profile. A minor gap is the lack of filtering or searching scans by date range, but the core workflow (list, get detail, count) is fully supported.

  • Average 4.3/5 across 4 of 4 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
    • 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.

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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?

    Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by specifying return blocks (BCA, MFA, IMP) and the default behavior (most recent scan if no parameter provided), which goes beyond annotation data. No contradictions.

    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 extremely concise: a one-line purpose, a two-line summary of return data, and a one-line parameter description. Every sentence adds value, with no redundancy or fluff.

    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 has a single parameter and an output schema (not shown), the description covers all necessary context: what the tool does, what data it returns (blocks), and the parameter semantics. It is fully sufficient for correct invocation.

    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?

    The single parameter raw_datetime is not described in the input schema (0% coverage), but the description fully compensates by detailing its format (YYYYMMDDHHMMSS) and default behavior (most recent scan). This provides complete semantic meaning beyond the 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 tool retrieves the full metric set for a single InBody scan, listing specific blocks (BCA, MFA, IMP). While it doesn't explicitly differentiate from sibling tools like list_scans, the purpose 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 Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies the tool is for retrieving details of a single scan, but provides no explicit guidance on when to use it vs. alternatives (e.g., list_scans for browsing, get_scan_count for counting). No 'when-not' or alternative suggestions are given.

    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?

    Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, fully covering safety and idempotency. The description adds minimal behavioral context beyond 'available for the account', which is already implied. No contradictions or additional traits disclosed.

    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, direct sentence with no extraneous words. Every word serves a purpose, making it highly efficient.

    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?

    For a simple count tool with no parameters and an existing output schema, the description is complete. It states the core function, and the output schema covers return value details. Minor gap: does not explicitly state the return type, but it's implied.

    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 no parameters, so the description does not need to add parameter semantics. The baseline for zero parameters is 4, and the description is sufficient for a parameterless tool.

    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 'Get', the resource 'total number of InBody scans', and the scope 'for the account'. It distinctly differentiates from sibling tools like get_scan (single scan) and list_scans (list of scans).

    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?

    While no explicit when-to-use or when-not-to-use guidance is given, the sibling tool names imply distinct purposes: this tool is for getting a count, while others retrieve specific scans or lists. The context is clear enough for an agent to infer 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?

    Annotations provide readOnlyHint and idempotentHint; description adds value by stating return values are 'useful for interpreting scan results', enhancing context beyond annotations.

    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?

    Two concise sentences, front-loaded with main action, no wasted words.

    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?

    Fully adequate for a zero-parameter tool with output schema; describes purpose and return value utility.

    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, so baseline 4; description doesn't need to add param info, and schema coverage is 100%.

    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?

    Description clearly states 'Get the InBody user profile' and lists specific returned data (name, gender, age, height, weight, email), distinguishing it from sibling scan tools.

    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?

    No explicit when-to-use or alternatives, but purpose is clear from description and sibling names imply this is for profile info.

    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?

    Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, which inform safety. The description adds that it returns a slim summary per scan with specific fields and is ordered newest first, but does not disclose additional behavioral traits like permissions or side effects beyond what annotations provide.

    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 very concise: two short paragraphs and a bulleted list for arguments. It front-loads the main action and includes necessary details without fluff. Every sentence adds value.

    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 simple nature of the tool (list with pagination), the description covers the purpose, output summary, pagination, and pointer to the sibling tool for full data. The annotations confirm read-only and idempotent behavior. An output schema exists, so the description does not need to detail return values. All relevant context is provided.

    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?

    The input schema has 0% description coverage, so the description compensates fully by explaining each parameter: 'limit: Maximum number of scans to return (default 20)' and 'offset: Pagination offset into the scan history (default 0).' This adds clear semantics beyond the raw schema types.

    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 'List InBody scans with headline metrics, newest first.' It identifies the verb (list), resource (scans), scope (headline metrics), and ordering (newest first). It also distinguishes from sibling tools by mentioning get_scan for full metrics.

    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 guides the agent to use get_scan for full metric set, providing an alternative. It explains the limit and offset parameters for pagination. However, it does not explicitly state when not to use this tool, 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.

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