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Load Planner MCP

Load Planner MCP

by bayraak
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Solve a load plan

solve

Runs a load-planning optimization using CP-SAT, returning numeric cost and delivery risk figures.

Instructions

Run the CP-SAT load-planning solver on a problem and return its numeric result verbatim, with a result_id and a content hash. This is the only tool that produces numbers.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "problem": "<problem>"
    },
    "name": "solve"
  }
}

Input Schema

NameRequiredDescriptionDefault
problemYesThe load-planning problem.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
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.

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