Human For AI

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds useful behavioral context beyond that: the operator works at human speed, an empty replies list means no answer yet, and the agent should poll occasionally rather than in a tight loop. No contradiction with 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?

The description is three sentences with no wasted words. It front-loads the core purpose, then gives the required inputs, then provides behavioral guidance. Every sentence 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?

For a simple read-only tool with no output schema, the description adequately explains what will be returned, how to interpret an empty replies list, and how to poll appropriately. Nothing essential for correct invocation or interpretation is missing.

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%, and both parameters are already well described: message_id has its source and format, access_token is described as a one-time token and the only key to the thread. The description's mention that both come from the submission response adds only a small reminder rather than substantial new meaning.

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 ('Read') and identifies the exact resource: a message thread for a message you sent. It goes further by enumerating the thread contents (original text, replies oldest-first, whether the operator has answered), which makes the tool's function unmistakable and distinct from sibling tools.

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 clearly situates when to use the tool: after submitting a message, using the message_id and access_token from the submission response. It also gives practical polling guidance and explains that an empty replies list means no answer yet. It does not explicitly list alternatives or state when not to use it, but the context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint=true and openWorldHint=true, setting the safety baseline. The description adds valuable behavioral context beyond annotations: the status lifecycle, seen_by_operator_at semantics, ETA, operator notes, and the JWS receipt verification process. No contradiction with annotations exists.

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 front-loaded with the main purpose ('Look up a submitted task by its task_id') and then efficiently lists the response fields in three sentences. While information-dense, it contains no filler or redundant phrases, and the structure is logical. It could arguably be two sentences, but the detail justifies its length.

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?

With no output schema, the description carries the full burden of explaining return values. It thoroughly covers status, status history, seen_by_operator_at, eta, operator notes, receipt, and deliverable_sha256, including a verification URL. For a single-parameter lookup tool, this is comprehensive and complete.

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 has 100% coverage for the only parameter (task_id), including a description and example. The tool description does not add any extra parameter explanation, so it neither enhances nor detracts from the schema. Baseline 3 is appropriate given the schema's completeness.

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 function: 'Look up a submitted task by its task_id.' It specifies the resource (submitted task) and the action (look up), and the detailed return fields further define its scope. This distinguishes it from sibling tools like submit_human_task (submission) and message_human_operator (messaging).

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 implies usage after task submission (e.g., 'Look up a submitted task') and details what information is returned, providing clear context. However, it does not explicitly state when to use this tool over alternatives or mention exclusions, so it stops short of full guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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

Annotations already flag a non-read-only, non-idempotent, open-world operation, so the description carries the burden of richer context — and it delivers substantially: the MX-checked email/https webhook reply mechanism, the pollable-thread model (thread_url + access_token), and the security-critical disclosure that the token 'is shown only once.' This is exactly the kind of behavioral and operational context annotations cannot convey.

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 dense sentences, each earning its place: purpose and scope first, then the reply_to requirement with its two delivery mechanisms, then the thread/token return behavior with the one-time-display warning. No padding, no filler, and the most operationally critical detail (token shown only once) is placed in the final sentence without being buried.

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?

With no output schema, the description carries the burden of explaining return values — and it does: the response carries thread_url + access_token, every message is a pollable thread, and check_message_thread is named as the follow-up path even without a mailbox. For a flat 4-parameter message-sending tool, nothing an agent needs to call it correctly and retrieve the reply is missing.

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 the baseline is 3. The description adds genuine meaning beyond the schema by specifying what belongs in message (questions, scoping, custom/recurring projects) and by explaining why reply_to is the delivery path for the reply. It does repeat some reply_to details that the schema already covers nearly verbatim, but the message-content guidance is a real addition above baseline.

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?

States a specific verb+resource pair ('Send a free-form message to the human operator') and immediately scopes it with 'anything that is not yet a ready-made task,' which cleanly separates it from siblings like submit_human_task. The free-form versus ready-made distinction is precise and actionable, so an agent knows exactly what this tool is for without opening other schemas.

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?

Provides explicit when-to-use context ('questions, scoping, custom or recurring projects') and names check_message_thread as the companion tool for reading replies. However, the primary alternative for ready-made tasks (submit_human_task) is never named; the exclusion is implied by 'not yet a ready-made task' rather than stated as an explicit when-not-to-use rule, which keeps it just shy of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already indicate a non-read-only, non-idempotent, non-destructive write operation. The description adds context beyond annotations by specifying the provenance of required credentials (submission response) and the range of intended follow-up actions, including withdrawing a request. This is useful behavioral context, though it doesn't detail side effects or response behavior.

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 three sentences long, front-loads the core action, and wastes no words. Every sentence earns its place: what it does, when to prefer it, and what inputs are needed. Excellent structure for an AI agent.

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 write tool with full schema coverage and a clear purpose, the description is nearly complete. It covers the key prerequisites and the primary decision rule. It doesn't mention return values or error cases, but the absence of an output schema and the simplicity of the operation keep this gap minor. A 4 is appropriate.

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 description coverage is 100%, so each parameter is already documented in the schema. The description adds modest context by explaining that message_id and access_token come from the submission response, but the schema's parameter descriptions already convey this. With full schema coverage, the baseline of 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 states a specific action ('Add a follow-up to a thread you opened') on a clear resource, and enumerates concrete purposes (answer, add detail, correct, withdraw). It also differentiates from sending a brand-new message, which maps to the sibling message_human_operator. This is a precise and unambiguous purpose statement.

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 to prefer this tool over sending a brand-new message about the same subject, giving an alternative condition and a clear routing rule. It also states the required inputs (message_id and access_token from the submission response), which tells the agent when the tool is usable. This is strong usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations give only basic hints (not read-only, not idempotent, not destructive). The description adds substantial behavioral context: the human review means execution is not instant, the operator is push-notified on submission, contact_email is MX-checked and placeholder domains are rejected, and status_poll delivers results via operator_notes with a one-per-client-per-day limit. This exceeds the annotation burden meaningfully.

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 yet dense with useful information. It front-loads the most important behavioral caveat (not instant execution) and then flows naturally through tracking, pricing, and delivery options. Every sentence earns its place; there is no redundancy or filler.

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 9-parameter tool with no output schema, the description is remarkably complete. It covers the submission lifecycle, the asynchronous review model, how the deliverable reaches the agent, fallback behavior for mailbox-less agents, and the rate limit. An agent has enough context to call the tool correctly and set expectations about the async result.

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 schema already covers all 9 parameters (100% coverage), but the description adds crucial semantic detail beyond the schema: it clarifies the relationship between delivery and contact_email (no mailbox means use status_poll), explains that a task_id is the only key for tracking the deliverable, and notes that location_detail is 'required in practice' when location_required is true. This is valuable guidance the schema alone does not provide.

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 states a specific verb ('submit') and resource ('a task for the human operator to perform in the real world'). It immediately differentiates from the sibling status-check tools by noting it returns a task_id immediately while the human reviews before accepting. This clearly distinguishes the submission action from check_task_status and other sibling tools.

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 the agent when to use this tool (submit a real-world task) and when to use the alternative (check_task_status to track progress). It also routes the agent to get_human_services for task_type category descriptions, and explains the delivery-mode alternatives for agents with or without a mailbox, including the status_poll budget constraint.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.