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bayraak

Load Planner MCP

by bayraak
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Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    solve and explain have clearly separated responsibilities: solve is the only numeric-producing tool, while explain only consumes a stored result_id and never touches inputs or arithmetic. There is no overlap or plausible misselection.

    Naming Consistency5/5

    Both tool names are bare imperative verbs following the same style: solve and explain. Although the pattern is not verb_noun, it is entirely consistent across the server.

    Tool Count3/5

    With only two tools, the server is minimal and borderline thin. Each tool has a distinct role, but the surface feels light for a load-planning MCP.

    Completeness4/5

    The core solve-then-explain workflow is covered, and solve returns a result_id for later explanation. Gaps appear around stored result management, such as listing or retrieving prior results independently.

  • Average 4.2/5 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
    • 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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  ]
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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, the description carries the full burden. It discloses that it runs a solver and returns the numeric result verbatim with result_id and content hash, which is useful. However, it does not mention side effects, performance implications, error behavior, or prerequisites, leaving a moderate gap.

    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, front-loaded with the action, and contains no redundant or irrelevant information. Every sentence earns its place.

    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 rich schema for the input and no output schema, the description adequately describes the output format (numeric result, result_id, content hash) and provides context (only numeric tool). However, it omits details like failure handling or solver constraints, which could be important. Overall it is fairly complete for a simple tool.

    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 schema itself defines the problem structure thoroughly. The tool description adds no specific parameter guidance beyond 'run on a problem', so it meets the baseline of 3 but does not elevate it.

    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 (run the CP-SAT load-planning solver) and resource (a problem), and specifically notes 'This is the only tool that produces numbers', which differentiates it from the sibling 'explain'. This meets the standard of a specific verb+resource that distinguishes from siblings.

    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?

    It provides clear guidance on when to use this tool (when numeric results are needed) by stating it is the only tool that produces numbers. However, it does not explicitly mention when not to use it or alternative usage scenarios beyond that differentiation, so it falls short of a 5.

    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, the description carries the transparency burden. It explicitly discloses key behavioral constraints: no access to problem inputs, no arithmetic, and inability to state numbers the solver did not produce. It does not cover failure modes or detailed return structure, but the core limitation is clearly communicated.

    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, front-loaded with the primary action and followed by a concise constraint. Every sentence earns its place, and there is no repetition of schema fields or filler.

    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 single-parameter tool with no output schema, the description explains the input source, what the tool returns, and its limitations. It could specify the narration template's structure, but the tool's simplicity and clear boundaries make it adequately complete for selection and invocation.

    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 coverage is 100% and the single parameter is already described as 'A result_id returned by solve.' The description adds value by emphasizing that it takes 'a result_id and nothing else' and by clarifying the tool's isolation from problem inputs, reinforcing the parameter's purpose 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 opens with 'Return a narration template populated only from a stored solver result,' which is a specific verb and object. It clearly distinguishes this tool from the sibling solve by emphasizing that it consumes a stored result rather than producing one.

    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 implies use after solve by stating it takes a result_id and nothing else, and the schema's parameter description says the result_id is 'returned by solve.' It also provides a clear exclusion: it has no access to problem inputs and performs no arithmetic, so it should not be used to derive or verify numbers.

    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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