Send a message to the human on an active job. Works on PENDING, ACCEPTED, PAID, STREAMING, and PAUSED jobs. The human receives email and Telegram notifications. Use get_job_messages to read replies. Rate limit: 10/minute. Max 2000 chars.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | The job ID | |
| content | Yes | Message content (max 2000 characters) | |
| agent_key | Yes | Your agent API key (starts with hp_) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the human receives email and Telegram notifications, which is a meaningful side effect. It also mentions the rate limit and applicable job states. However, it doesn't describe error handling or whether the message is logged/visible elsewhere, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, front-loaded with the primary action. Every sentence adds value: purpose, applicable states, notification behavior, alternative tool, rate limit, and max length. No redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 3-parameter tool with no output schema and no annotations, the description covers the essential context: what it does, when to use it, side effects, constraints, and a pointer to the related read tool. Missing details like return value or error behavior are minor for this tool type.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so all parameters already have descriptions. The description adds no new parameter-specific meaning beyond restating the 2000-character limit for content. It does tie job_id to active job states, but that's more about usage context than parameter semantics. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Send a message to the human on an active job.' It specifies the verb (send), resource (message), and recipient (human on a job), and lists the applicable job states (PENDING, ACCEPTED, PAID, STREAMING, PAUSED). This distinguishes it from sibling get_job_messages, which is for reading replies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use this tool (on active jobs in listed states) and provides an alternative: 'Use get_job_messages to read replies.' It also includes practical constraints like rate limit (10/minute) and max message length (2000 chars), which guide usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes (e.g., get_human vs get_human_profile differ by access level, get_listing vs get_listings by specificity). There is minor overlap between check_humanity_status and get_human (both return verification info), and the deprecated no-op claim_free_pro_upgrade adds slight clutter, but descriptions otherwise disambiguate well.
All 40 tools follow a consistent snake_case verb_noun pattern (e.g., create_listing, get_job_status, start_stream, submit_verdict). No camelCase or mixed conventions exist; even compound actions like make_listing_offer and leave_review fit the established pattern.
With 40 tools, this is far beyond the 16-25 'heavy' range and into the 'too many' category. While the platform covers a broad domain (hiring, listings, streams, escrow, activation), this many tools could be split into smaller focused servers for maintainability and agent selection clarity.
The surface covers the core lifecycle well: search, hire, pay, communicate, approve, review. However, there is no generic cancel_job tool for regular jobs (only stop_stream for streams) and no update_listing to edit an existing listing, leaving small but navigable gaps in lifecycle management.
