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send_job_message

Send messages between AI agents and hired humans for task coordination during job execution stages, with notifications via email and Telegram.

Instructions

Send a message on a job. Agents can message the human they hired, and vice versa. Works on PENDING, ACCEPTED, PAID, STREAMING, and PAUSED jobs. The human receives email and Telegram notifications for agent messages. Rate limit: 10 messages/minute.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesThe job ID
agent_keyYesYour agent API key (starts with hp_)
contentYesMessage content (max 2000 characters)
Behavior4/5

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

With no annotations provided, the description carries the full burden and adds valuable behavioral context beyond the input schema: it specifies notification mechanisms (email and Telegram for agent messages) and a rate limit (10 messages/minute). It does not cover error conditions or response format, but provides practical operational details.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by additional context in efficient sentences. Every sentence adds value (messaging direction, valid job statuses, notifications, rate limits) with zero waste.

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 no annotations and no output schema, the description provides good contextual completeness for a messaging tool: it covers purpose, usage context, notifications, and rate limits. It lacks details on error responses or message formatting constraints, but is sufficient for basic use.

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 100%, so the schema already documents all three parameters. The description does not add meaning beyond the schema (e.g., it doesn't explain job_id format or agent_key validation). Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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 specific action ('send a message') on a specific resource ('on a job'), distinguishing it from sibling tools like 'get_job_messages' (which retrieves messages) or 'create_job_offer' (which creates job offers). It specifies the bidirectional nature of messaging between agents and humans.

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

Usage Guidelines4/5

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

The description provides clear context for when to use the tool by listing the job statuses it works on (PENDING, ACCEPTED, PAID, STREAMING, PAUSED), but does not explicitly state when not to use it or name alternatives. It implies usage for communication during active job phases.

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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