get_human_servicesFetch the Human For AI manifest: available services, operator profile (location, languages, working hours), response times, accepted and rejected task types, and trust & safety policy. Call this first to decide whether and how to hire the human. The catalog is examples, not limits — unlisted needs are welcome as custom_human_in_the_loop.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, and the description complements these by detailing the manifest contents and elaborating on the open-world semantics ('catalog is examples, not limits'). It adds useful context beyond annotations without contradicting them.
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 compact: one sentence summarizes the manifest contents, and a second provides usage guidance and the open-world caveat. Every sentence earns its place, and the most critical instruction ('Call this first') appears prominently.
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 no-parameter read-only discovery tool, the description fully covers what the tool returns, when to use it, and how to interpret the results. Combined with strong annotations and no output schema, no additional context is needed.
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?
The tool has zero parameters and schema coverage is 100%, so there is no parameter burden for the description to carry. The baseline of 4 applies because no parameter documentation is needed.
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?
Uses a specific verb ('Fetch') plus a clear resource ('Human For AI manifest') and enumerates the exact contents (services, operator profile, response times, task types, policy). This clearly distinguishes it from sibling tools like submit_human_task or message_human_operator, which are action-oriented.
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?
Gives explicit guidance to 'Call this first' before deciding to hire the human, establishing a clear ordering. It also notes unlisted needs are welcome, but it does not explicitly name alternative tools or state when not to use this tool, so it falls just short of full exclusionary 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.
Each tool targets a distinct resource and action: discovery, structured task submission/status, free-form messaging, and threaded replies. Potential overlaps like submit vs. message vs. reply are clearly separated by input requirements and response types.
All names are snake_case and action-first, which makes them predictable as a set. The only minor deviation is 'reply_in_message_thread' including a preposition, but it remains clear and consistent in style.
Six tools is well-scoped for the service: one discovery tool, two task lifecycle tools, and three messaging/thread tools. There is no redundant surface area.
The core lifecycle is covered: discover, submit, poll, receive verification, and exchange follow-up messages. The main gap is the lack of an explicit cancel or amend task operation, though agents can work around it by messaging the operator.