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klinikal

beanie-mcp

by klinikal
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

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 completely distinct purposes: one validates the ledger for errors, the other executes BQL queries. There is no ambiguity in what each tool does.

    Naming Consistency4/5

    Both tools use snake_case, but the naming pattern differs slightly: 'bean_check' is a compound noun or verb-object reversed ('check bean'), while 'run_query' is a clear verb-object. Still, the style is consistent and readable.

    Tool Count2/5

    Only 2 tools for a ledger management server is too few. Typical accounting workflows require more operations (e.g., insert transactions, list accounts, generate reports), making this set feel incomplete for its domain.

    Completeness1/5

    The tool surface is severely incomplete: there is no way to create, update, or delete ledger entries, no account management, and only basic validation and querying. Users cannot perform core accounting tasks.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 25 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • 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?

    With no annotations, the description carries full burden. It explains the output: a clean confirmation on success, or a list of errors with file path and line number on failure. This provides sufficient behavioral context for a validation tool.

    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 two sentences long, front-loaded with the action and followed by output details. No redundant phrases; 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 tool's simplicity (no parameters, no output schema), the description fully covers what the tool does and what it returns. It is complete for an agent to understand and invoke 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?

    The input schema has no parameters, so the description does not need to add parameter information. The baseline for 0 parameters is 4, and the description fulfills this without needing extra detail.

    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 validates the ledger using bean-check, which is a specific verb and resource. It distinguishes itself from the sibling tool run_query by focusing on validation rather than querying.

    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 usage for ledger validation, but does not explicitly state when to use it over run_query or provide exclusion criteria. The purpose is clear, but usage guidelines are not fully articulated.

    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?

    Details the return structure (columns, rows, truncated, total_rows, error), truncation behavior at 200 rows, and error handling for invalid BQL. Since no annotations are provided, the description fully covers behavioral aspects.

    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, with each bullet point serving a purpose. It efficiently communicates input, output, and error conditions without redundancy.

    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 single-parameter query tool with no output schema, the description is complete. It covers input format, output structure, truncation, and error handling. No gaps.

    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 schema already describes the 'query' parameter with an example. The tool description adds value by mentioning the 200-row cap and suggesting LIMIT usage. With 100% schema coverage, this extra context justifies a score above baseline.

    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?

    Clearly states it queries a Beancount ledger using BQL. The verb 'Query' is specific, and it distinguishes itself from 'bean_check' by focusing on querying rather than checking.

    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 usage context: it is used to query the ledger with BQL. Mentions adding LIMIT for smaller result sets, but does not explicitly discuss when to use this tool over alternatives or when not to use it.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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