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RBPaaS MCP Server

RBPaaS MCP Server

by mm-gtm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_optimization_result

Retrieve the detailed outcome of an optimization run by providing its run ID.

Instructions

最適化結果の詳細を取得する。

Args: run_id: 最適化実行ID (UUID)

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": {
      "run_id": "<run_id>"
    },
    "name": "get_optimization_result"
  }
}

Input Schema

NameRequiredDescriptionDefault
run_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.8/5.0
Behavior1/5

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

With no annotations provided, the description must disclose behavioral details. It merely restates the tool's action ('get details') without describing return structure, side effects, access requirements, or limitations. This is essentially a tautology and provides no added transparency.

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 extremely short, with a single sentence and an Args block. It is concise and to the point, with no wasted words. However, the brevity comes at the cost of important context, but that is evaluated under completeness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema and annotations, the description should clarify what 'details' are returned and how run_id is obtained (e.g., from get_optimization_history). It lacks this context, making it insufficient for an agent to confidently invoke the tool correctly in a real workflow.

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 description includes an Args section that explains run_id as the '最適化実行ID (UUID)' (optimization execution ID), adding semantic meaning beyond the schema's bare title 'Run Id'. This helps the agent understand the parameter's purpose and format, though it does not cover any additional constraints or relationships.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool retrieves details of an optimization result, with a specific parameter run_id identifying the optimization execution. However, it does not distinguish this from the sibling get_optimization_history, which may also return optimization-related data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool compared to alternatives such as get_optimization_history or other getter tools. No prerequisites or typical invocation flow are mentioned, leaving the agent to infer usage from the parameter alone.

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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