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embodied-mcp

embodied-mcp

by DDDB479
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_formats

Read-only

Discover supported dataset formats, how each is detected, optional dependencies, and available validation checks for embodied-AI data.

Instructions

List supported dataset formats, how each is detected, which optional dependency it needs, and the available validation checks.

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_formats"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds real context beyond that by disclosing the four categories of information returned, including the fact that some listed formats depend on optional packages — useful for anticipating install requirements.

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?

A single front-loaded sentence that enumerates the returned content with no filler. Every clause (formats, detection, dependency, validation checks) earns its place.

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?

With no output schema and no parameters, the description carries the full burden of describing the return value, and it does so by naming all four pieces of information an agent will receive. Nothing needed to call this static reference tool correctly is missing.

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 tool takes zero parameters, so the baseline of 4 applies. There is nothing for the description to disambiguate beyond the empty input object.

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?

Specific verb ('List') plus resource ('supported dataset formats') with an enumerated payload: detection method, optional dependency, validation checks. It implicitly separates the reference lookup from siblings like detect_dataset and validate_dataset, which perform those actions, though no sibling is named outright.

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 reference/lookup nature of the tool implies when it applies (before choosing a format or configuring validation), but the description never states a when-to-use condition or points to detect_dataset/validate_dataset as the action-oriented alternatives. Usage must be inferred.

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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