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SoshiHashi

Audit Findings MCP Server

by SoshiHashi
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

58%
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: listing all findings, filtering by status, and updating a status. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    All tools use snake_case and follow a verb-based pattern (list, find, update). The minor inconsistency is the use of 'find' vs 'list' for read operations, but the names are still predictable and readable.

    Tool Count4/5

    With 3 tools, the count is within the typical well-scoped range, but it is on the lower end. Each tool serves a functional purpose, though the scope feels minimal for an audit findings domain.

    Completeness2/5

    The server lacks fundamental operations like creating or deleting audit findings. It only supports reading (all or by status) and updating status, leaving obvious lifecycle gaps that would force agents to work around.

  • Average 4.2/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 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

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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 are provided, so the description carries the full burden. It discloses that the operation is read-only ('取得する') and lists the allowed statuses (Open/Closed), but it does not elaborate on pagination, response format, or error behavior. For a simple read tool, this is adequate but not rich.

    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 a single, front-loaded sentence with no redundant words. It efficiently communicates the tool's function and parameter constraints.

    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 simplicity (one required parameter, no output schema), the description covers the essential purpose and parameter values. It does not explicitly relate to sibling tools, but it is largely sufficient for a basic 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?

    Schema description coverage is 0%, so the description's explicit mention of 'Open / Closed' adds critical meaning to the status parameter. This compensates for the bare schema, though it does not explain default values or validation behavior.

    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 retrieves audit findings filtered by status, using the specific verb '取得する' (retrieve). It distinguishes from siblings by specifying the status filter, differentiating it from list_findings and update_finding_status.

    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 usage is implied: one would use this tool to get findings by status. However, the description does not explicitly mention when to use this tool over list_findings or update_finding_status, nor does it provide any exclusions or alternatives.

    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?

    No annotations are provided, so the description carries the full burden. It makes clear the operation is a read-only retrieval of all findings, which is the primary behavioral trait. However, it does not disclose additional details such as pagination, ordering, or potential performance implications of fetching all records.

    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 a single, short sentence in Japanese that directly states the tool's function. It is appropriately concise for a parameterless list operation, with no filler or redundant detail.

    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 low complexity (no parameters, no output schema), the description is sufficient: it names the resource and the action. It could be slightly more complete by hinting at what a 'finding' is or whether results are paginated, but for a simple list tool, this is adequate.

    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 tool has zero parameters, and the schema is empty. The baseline for 0 params is 4, and there is nothing additional the description needs to explain about parameters.

    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 action ('取得する' = retrieve) and the resource ('監査指摘' = audit findings), specifying the scope as '全件' (all). This distinguishes it from the sibling tools 'find_by_status' (filtered retrieval) and 'update_finding_status' (mutation).

    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 word '全件' (all) implies this tool is for retrieving the complete set of findings without filtering. However, it does not explicitly mention alternatives or state when not to use it. Usage guidance is largely implied from the sibling tool names.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

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

    With no annotations provided, the description takes full responsibility. It discloses that this is a database-modifying write operation and mandates user confirmation, which are crucial behavioral traits. It also specifies return behavior for success and failure.

    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 for description, args, and returns, and it is reasonably concise. It contains a slight redundancy in the warning, but overall each part serves a 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?

    For a simple two-parameter tool with no output schema, the description covers purpose, parameters, return values, and the necessary user-confirmation requirement. It provides enough information for an agent to decide and invoke correctly.

    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?

    Schema coverage is 0%, so the description must explain the parameters. It provides a clear definition for each: finding_id as the ID to update and new_status with the only allowed values 'Open' or 'Closed'. This extends meaningfully beyond the bare 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 the tool's purpose: updating the status of audit findings. The verb 'update' and resource 'status of audit findings' are specific, and the tool is distinguished from sibling tools (list_findings, find_by_status) by its write operation.

    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 explains when to use the tool (to change finding status) and emphasizes the critical requirement to get user confirmation before executing, which is an important guideline. It does not mention alternatives or when-not to use, 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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