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alexsu1212

freebazi-mcp

Server Quality Checklist

75%
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  • Latest release: v1.0.1

  • Disambiguation5/5

    The two tools have completely distinct purposes: one searches for cities and the other computes a Bazi chart. There is no overlapping functionality, and the reference from compute_bazi_chart to search_cities makes the intended workflow clear.

    Naming Consistency5/5

    Both tool names follow a consistent snake_case verb_noun pattern: search_cities and compute_bazi_chart. The names are descriptive, predictable, and aligned with their actions.

    Tool Count4/5

    Two tools is slightly below the typical 3-15 range, but the narrow domain of Bazi chart computation is well served by a single core computation tool plus a supporting city search utility. The count feels minimal but not inadequate for this purpose.

    Completeness5/5

    The tool surface fully covers the stated purpose: resolving a birth location via search_cities and computing a comprehensive Bazi chart via compute_bazi_chart. The returned chart includes the major Bazi components, luck pillars, and current year, leaving no obvious dead ends or missing core capabilities.

  • 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
    • 2 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 MIT License.

  • 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 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?

    No annotations are provided, so the description carries the burden. It states the tool searches a built-in, finite list and names the returned fields: country, longitude, and IANA time zone. This makes the read-only, bounded nature clear, though matching behavior and result limits are not detailed.

    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 sentences deliver the core purpose, result contents, and downstream use case with no filler. The most useful guidance (returns fields and compute_bazi_chart integration) appears early and compactly.

    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 search tool, the description is complete enough: it names the input language options, the source list size, the return fields, and the intended downstream workflow. Since there is no output schema, listing the return fields in prose is especially valuable.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, and the query parameter already documents types and examples. The tool description adds contextual value by explaining the purpose of the query but does not substantially extend parameter semantics 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 clearly states the action ('Search the built-in city list') and the resource, with specificity about the ~490 cities and name input. It also distinguishes itself from sibling compute_bazi_chart by positioning itself as the lookup step before that chart computation.

    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 connects usage to compute_bazi_chart: 'so you can pass an exact city to compute_bazi_chart for accurate True Solar Time.' This gives clear context for when to use the tool, though it does not explicitly mention 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.

  • Behavior5/5

    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, and it delivers: it specifies True Solar Time correction, historical daylight-saving handling, and the tentativeness of the hour pillar when birth time is unknown via the schema. It also makes clear that gender is needed for luck-pillar direction. This is a rich, honest account of how the computation behaves.

    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 three focused sentences: the first states the core purpose and outputs, the second explains computation specifics, and the third gives coordinate/city guidance. It is front-loaded with the tool's primary function and every sentence contributes meaningful information without redundancy.

    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 tool with 13 parameters, no annotations, and no output schema, the description is strong: it names the full set of return fields and explains the key computational approach. It does not describe the exact structure of the return object or what happens when neither city nor longitude/utcOffset is supplied, but the input schema covers the parameter rules and the listed outputs are broad enough for an agent to understand the result.

    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, and the description goes beyond it by explaining the relationship between city and longitude/utcOffset and linking these to true-solar-time accuracy. It adds cross-parameter context that the schema alone does not fully convey, though much of the parameter detail is already present in the schema descriptions.

    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 names a specific action ('Compute a full Chinese Bazi chart'), identifies the required inputs (birth date, time, place), and enumerates the detailed output components. It clearly distinguishes this tool from its sibling search_cities, which is only referenced as a helper for resolving city names, not as an alternative computation path.

    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 gives actionable usage guidance: use search_cities to resolve a birth city for accurate true solar time, or pass longitude and utcOffset directly. It does not explicitly state when not to use the tool, but there is no competing sibling tool, so the context is clear enough for an agent to select and invoke it correctly.

    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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