Approve submitted work for a SUBMITTED job. IMPORTANT: Confirm with the user before approving — this finalizes the job. Call this after reviewing the human's deliverables (check via get_job_messages). Moves the job to COMPLETED. After approval, use leave_review to rate the human. If the work needs changes, use request_revision instead.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | The job ID | |
| agent_key | Yes | Your agent API key (hp_...) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full weight. It discloses that approval 'finalizes the job' and moves it to COMPLETED, and flags the need for user confirmation. It doesn't detail error handling or idempotency, but the key irreversible behavior is clearly stated.
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?
Three concise sentences plus an important callout. Every sentence earns its place: action, confirmation warning, sequencing with siblings. Front-loaded with the verb and scoped to the SUBMITTED state.
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?
Given the tool's state-transition nature and no output schema, the description provides the necessary workflow context: check messages first, approve, then leave_review, or request_revision instead. It doesn't cover error scenarios, but the core task and surrounding workflow are well covered.
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?
Input schema covers 100% of parameters with descriptions ('The job ID', 'Your agent API key (hp_...)'). The description adds no parameter-specific syntax or format details beyond the schema, so baseline 3 applies.
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 opens with 'Approve submitted work for a SUBMITTED job' — a specific verb+resource — and clarifies the state transition 'Moves the job to COMPLETED'. It distinguishes from siblings by explicitly referencing 'request_revision' for changes and 'leave_review' after approval.
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?
States exactly when to call: 'Call this after reviewing the human's deliverables (check via get_job_messages)'. Provides explicit alternative: 'If the work needs changes, use request_revision instead' and warns to 'Confirm with the user before approving'. This gives clear sequential and conditional guidance.
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.
