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infra-advisor-mcp

infra-advisor-mcp

by yangsong7
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_available_gpus

Retrieve all GPU types with their specifications and pricing information for cost estimation.

Instructions

List all GPU types in the database with specs and pricing.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_available_gpus"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

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 provided, the description carries the full burden. It indicates a read operation ('list') but does not explicitly state that it is safe, idempotent, or free of side effects. For a listing tool, this is adequate but not rich—it lacks details on authentication or data freshness.

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 a single, well-structured sentence that front-loads the action ('List all GPU types') and adds context ('with specs and pricing'). It is concise and contains no unnecessary words.

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 that the tool has no parameters and an output schema exists, the description is mostly complete. It clearly states what is returned. However, it could mention that the list is unfiltered (all GPUs) and possibly include note about sorting or pagination, but overall it is sufficient.

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?

There are zero parameters, so the baseline is 4. The description adds no parameter-specific meaning, but none is needed. It correctly implies the tool returns all GPUs without filters.

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 tool lists all GPU types in the database with specs and pricing, which is a specific verb-resource pair. It distinguishes itself from sibling tools that focus on analysis, estimation, or reporting (e.g., estimate_training_cost, compare_cloud_vs_onprem).

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

Usage Guidelines3/5

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

The description implies the tool is used when the user wants to see available GPU options, but it does not provide explicit guidance on when to use it versus alternatives like compare_cloud_vs_onprem or recommend_model. No exclusions or prerequisites are mentioned.

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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