Human For AI
Server Details
Hire a real human for real-world verification, product testing, AI output review, and errands.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.4/5 across 4 of 4 tools scored.
Each tool serves a distinct purpose: checking task status, fetching service manifest, messaging the operator, and submitting tasks. No overlap in functionality.
All tools follow a consistent verb_noun pattern in snake_case: check_task_status, get_human_services, message_human_operator, submit_human_task. Verbs are appropriate and uniform.
4 tools is well-scoped for a human-in-the-loop service covering discovery, submission, status checking, and communication. No excess or deficiency.
The set covers the core workflow: discover services, submit tasks, check status, and communicate. Missing explicit cancel or update task, but the messaging tool can fill that gap, making it highly functional.
Available Tools
4 toolscheck_task_statusCheck task statusARead-onlyInspect
Look up a submitted task by its task_id. Returns current status (submitted → under_review → accepted → in_progress → delivered, or rejected), status history with timestamps, seen_by_operator_at (the moment a human actually saw the task — usually well before the first status change), eta (operator-set delivery estimate, once accepted), and any operator notes.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | Task ID returned by submit_human_task, e.g. HFAI-2026-A1B2C3D4 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true. Description adds behavioral context by detailing the returned status flow, history with timestamps, and operator notes, which goes beyond what annotations provide.
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?
Two sentences, front-loaded with purpose, efficient. No unnecessary words; every sentence adds value.
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 no output schema, the description adequately explains return values (statuses, history, notes). Missing edge cases like invalid task_id, but sufficient for typical use.
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% with the parameter fully documented in the schema. Description only references 'task_id' generically without adding meaning beyond the schema's example and origin hint.
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 uses a specific verb ('Look up') and resource ('submitted task by its task_id'). It clearly distinguishes from sibling tools like submit_human_task (submission) and message_human_operator (messaging) by focusing on status retrieval.
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 states the tool is for looking up a submitted task, implying it should be used after submission via submit_human_task. Context is clear but no explicit when-not-to-use or alternatives are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_human_servicesList human servicesARead-onlyInspect
Fetch 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 parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that 'the catalog is examples, not limits — unlisted needs are welcome,' which aligns with openWorldHint and provides useful behavioral context.
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 two concise sentences. The first lists what is fetched, and the second provides usage guidance and an important qualifier. Every word earns its place.
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?
Despite no output schema, the description comprehensively covers the returned data (services, operator profile, etc.) and usage context. It is complete for a simple informational tool.
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 input schema is empty (0 parameters), so the baseline is 4. The description does not need to add parameter information and correctly focuses on what the tool returns.
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 fetches the 'Human For AI manifest' and enumerates its contents (services, operator profile, response times, etc.), distinguishing it from sibling tools like check_task_status or submit_human_task.
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 says 'Call this first to decide whether and how to hire the human,' providing clear usage guidance. It does not mention alternatives, but the context with sibling tools implies when to use others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
message_human_operatorMessage the human operatorAInspect
Send a free-form message to the human operator — questions, scoping, custom or recurring projects, anything that is not yet a ready-made task. Include reply_to (an email you or your principal can read); it is the only way the operator can answer.
| Name | Required | Description | Default |
|---|---|---|---|
| from | No | Your agent or system identifier | |
| message | Yes | The message. Plain language, English. | |
| subject | No | Short subject line | |
| reply_to | No | Email address for the reply — strongly recommended |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate non-read-only, non-idempotent, non-destructive behavior. The description adds valuable context: the message is sent to an operator, and reply_to is necessary for a reply. No contradictions; it enhances understanding beyond annotations.
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?
Two sentences convey all necessary information efficiently. The primary action and key parameter are front-loaded, with no extraneous 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 4-parameter tool with no output schema, the description covers the use case, required field, and critical workflow detail (reply_to). No gaps remain.
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?
With 100% schema description coverage, baseline is 3. The description adds semantic value by emphasizing reply_to's critical role and noting message constraints (English, min length). This goes beyond the schema's descriptions.
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 identifies the tool's purpose: sending free-form messages to a human operator for tasks not yet ready-made. It uses specific verbs and distinguishes from sibling tools like submit_human_task, which is for ready-made tasks.
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 lists when to use the tool (questions, scoping, custom projects) and implies when not (ready-made tasks). It also provides essential guidance on reply_to as the only communication channel for responses.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
submit_human_taskSubmit a task to the humanAInspect
Submit a task for the human operator to perform in the real world. Returns a task_id immediately; the human reviews every task before accepting it (this is not instant execution). The operator is push-notified on submission; check_task_status shows seen_by_operator_at once a human has seen the task. Free during the pilot. Include contact_email — it is how the deliverable reaches you.
| Name | Required | Description | Default |
|---|---|---|---|
| deadline | No | ISO 8601 datetime, e.g. 2026-07-10T12:00:00+03:00 | |
| requester | No | Your agent or system identifier, e.g. my-agent/1.0 | |
| task_type | Yes | Service category — see get_human_services for descriptions. The list is not exhaustive: use custom_human_in_the_loop for anything that fits no other category | |
| description | Yes | What to do, where, and what success looks like. Specific, self-contained tasks are accepted faster. | |
| contact_email | No | Where the deliverable and clarifying questions are sent. Strongly recommended. | |
| output_format | No | text_report (default), text_report_with_photos, structured_json, annotated_screenshots, or video | |
| location_detail | No | City, address, or area — required in practice when location_required is true | |
| location_required | No | true if the task needs physical presence (coverage is confirmed at review) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations: 'human reviews every task before accepting it (this is not instant execution)' and 'Free during the pilot'. Annotations (readOnlyHint=false, etc.) are minimal; the description provides useful caveats about timing and cost.
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: first states purpose, second explains async nature, third mentions free status and key parameter. Front-loaded with essential information, no redundant phrases.
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 8 parameters and no output schema, the description provides a solid overview (async, free, contact email importance). It mentions immediate task_id return but could elaborate on using task_id with check_task_status. Still, sufficiently complete for most use cases.
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?
With 100% schema description coverage, the baseline is 3. The description adds value by emphasizing 'Include contact_email — it is how the deliverable reaches you' and noting that the task_type list is not exhaustive (use custom_human_in_the_loop). This helps agents prioritize required fields.
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 'Submit a task for the human operator to perform in the real world', using a specific verb ('submit') and resource ('task'). It distinguishes from sibling tools (check_task_status, get_human_services, message_human_operator) by focusing on submission.
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 notes that human review is required before acceptance (not instant) and that it's free during the pilot, but it does not explicitly state when to use this tool versus alternatives like message_human_operator or check_task_status. The context of async execution is clear, but guidance on tool selection is absent.
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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