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infra-advisor-mcp

infra-advisor-mcp

by yangsong7
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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generate_followup_answer

Answer focused follow-up questions on GPU needs, costs, and TCO with calculator-backed data and glossary.

Instructions

Answer a specific follow-up question with calculator-backed data and an inline glossary.

Use this instead of generate_full_report when the user asks a focused follow-up (e.g. "what's the training cost?", "cloud vs on-prem for this?", "which GPU?"). Returns a concise answer: direct response, data table, recommendation, and jargon glossary.

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": {
      "original_query": "<original_query>",
      "followup_question": "<followup_question>"
    },
    "name": "generate_followup_answer"
  }
}

Input Schema

NameRequiredDescriptionDefault
original_queryYesThe original task description (provides context for scale, domain, token volumes, and constraints).
followup_questionYesThe specific follow-up question to answer.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses that the answer is 'calculator-backed' (implies computation) and returns specific components. However, it does not explicitly mention idempotency, permission requirements, or side effects. Still, the description gives a good overview of behavior without hiding critical traits.

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?

Four sentences, each serving a purpose: main function, usage guideline, examples, return components. No redundant text, well-structured and efficient.

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 moderate complexity (two string parameters, output schema exists), the description covers when to use, what to expect, and differentiates from sibling tools. It is complete enough for an agent to correctly select and invoke the tool.

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% with descriptions for both parameters. The tool description adds meaning by clarifying that the followup_question should be focused and provides examples. This goes beyond the schema by giving usage context, though the schema already adequately describes parameters.

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?

Description clearly defines that the tool answers specific follow-up questions with calculator-backed data and an inline glossary. It explicitly distinguishes itself from generate_full_report by stating when to use this tool instead, and lists the components of the return (direct response, data table, recommendation, jargon glossary).

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

Usage Guidelines5/5

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

Explicitly states when to use: 'when the user asks a focused follow-up' and provides concrete examples like 'what's the training cost?'. Also says to use generate_full_report instead for full reports, giving clear guidance on alternatives.

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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