embodied-mcp

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validate_dataset

Read-only

Validates embodied-AI datasets by checking metadata, state/action vocabulary, episode indices and lengths, fps consistency, and format rules; returns severity-coded issues, hints, and a 0-100 score.

Instructions

Run structural validation: required metadata, feature vocabulary (state/action), episode indices and lengths, fps consistency, format-specific layout rules. Returns issues with severity, stable code, hint and a 0-100 score. Emits MCP progress notifications when the request carries _meta.progressToken.

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": {
      "path": "<path>"
    },
    "name": "validate_dataset"
  }
}

Input Schema

Name	Required	Description
`path`	Yes
`checks`	No	Subset of checks; omit for defaults
`include_frames`	No	Also scan numeric frames for NaN/inf (slower)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 23, 2026

TDQS

A3.8/5.0

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 useful behavioral details beyond annotations: the returned issue structure, a 0-100 score, and MCP progress notifications when _meta.progressToken is present.

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?

Two dense sentences with the validation scope front-loaded, followed by return behavior and progress notification semantics. There is no filler or repetition.

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?

With no output schema, the description usefully explains the return shape (severity, stable code, hint, score) and progress-notification behavior. It is nearly complete, though it could say more about supported formats and path expectations.

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 67%; checks and include_frames are documented in the schema, while path is not. The description adds meaning to 'checks' by naming the validation categories, but it does not explain path semantics or clarify include_frames beyond what the schema already says.

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?

States a specific verb and resource ('Run structural validation') and enumerates the validation categories covered, so the agent knows precisely what it does. It does not explicitly distinguish itself from close siblings such as quality_report, quality_gate, or detect_dataset.

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 when to use it by framing it as structural validation, but it does not say when to prefer it over quality_report or quality_gate, nor does it state prerequisites or exclusions. Usage is inferable but not guided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.