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HumanEndpoint

Reply to human clarification

human_reply_to_job

Send additional information to the human operator working on the job, including when the job is waiting for agent input.

Input Schema

NameRequiredDescriptionDefault
job_idYes
messageYes
job_tokenYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already establish the mutation profile (readOnlyHint=false, destructiveHint=false, openWorldHint=true), so the description only needs to add context. It clarifies that the message goes to a human operator and is relevant during a wait-for-input state, but says nothing about auth requirements beyond the token, rate limits, or delivery semantics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, front-loaded sentence with no filler; the core action and the key triggering condition are stated immediately. Slightly terse given the gaps elsewhere, but nothing is wasted.

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?

For a mutation tool with three undocumented required parameters, no output schema, and no annotation-level explanation of parameters, the description is too thin. An agent still lacks guidance on token semantics, message constraints, and what a successful reply produces.

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

Parameters2/5

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

Schema description coverage is 0% across three required parameters, so the description carries the full burden and does not compensate. It never explains job_id, job_token, or message (including the 10000-char limit), leaving all parameter meaning to the raw JSON schema types.

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?

The description names a specific verb (send/reply) and resource (information to the human operator on a job), so an agent can tell it apart from human_get_job or human_create_job. It is clear but does not explicitly contrast itself with the other human_* siblings that also touch jobs.

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 phrase 'including when the job is waiting for agent input' implies a usage condition, which is useful, but no alternative tools are named and no when-not-to-use guidance is given. Usage must still be inferred from the sibling list.

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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