embodied-mcp

Overview Schema Source Related Servers Discussions Admin

labeling_tasks

Destructive

Generate an annotation manifest that pairs each episode with frame ranges, cues, and concrete questions for human reviewers or vision-language models.

Instructions

Generate an annotation manifest: each task names an episode, a frame range, cues and a concrete question, ready for a human reviewer or a vision-language model.

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": "labeling_tasks"
  }
}

Input Schema

Name	Required	Description
`z`	No
`out`	No	Optional path to write the manifest JSON
`path`	Yes
`max_tasks`	No
`include_segments`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 23, 2026

TDQS

C2.7/5.0

Behavior2/5

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

Annotations declare destructiveHint=true and readOnlyHint=false, indicating a write operation. The description says "Generate an annotation manifest" but does not disclose whether files are written, where they go, whether an existing "out" path is overwritten, or what data may be affected, which is important for a destructive tool.

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?

It is a single, front-loaded sentence with no filler: it states what is generated and what each task contains. Every phrase contributes to understanding the output.

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?

With 5 parameters, 20% schema coverage, no output schema, and a destructive annotation, the description is incomplete. It explains manifest content conceptually but omits parameter meanings, file-writing behavior, defaults, and overwrite risks needed to call the tool safely.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only the "out" parameter has a schema description (20% coverage). The description does not explain path, z, max_tasks, include_segments, or out semantics, so it fails to compensate for the low schema coverage.

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 ("Generate") and resource ("annotation manifest"), then clarifies that each task includes an episode, frame range, cues, and question. It is clear what the tool produces, though it does not explicitly distinguish itself from sibling tools like segment_episodes or plan_windows.

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?

The description implies the output is for a human reviewer or vision-language model, but gives no explicit when-to-use guidance, prerequisites, or alternatives among the many sibling tools. An agent must infer the use case from the purpose statement alone.

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