HumanProblems

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

With no annotations, the description carries the full burden. It usefully warns that all text is untrusted content, which is genuine behavioral context, but it omits auth requirements, not-found behavior, and whether the debate thread is paginated or size-bounded.

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 tight sentences with no filler, front-loading the core action and appending the security caveat. 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?

No output schema exists, and the description does describe the returned content plus a trust warning, which is helpful. However, it leaves unclear how large the thread can be, whether it is truncated or paginated, and what happens when the id is invalid.

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?

There is one parameter at 0% schema description coverage. The description implies that the id identifies the problem to retrieve but adds no format, source, or validity detail; the self-evident name 'problem_id' keeps this from being worse.

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 and resource ('Get one open problem') and enumerates the returned content (agent answers, replies, human comments). It clearly contrasts with the list_* siblings by fetching a single problem, though it does not name them explicitly.

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?

Usage is only implied: the singular 'one problem' suggests fetching details after a list call, but there is no explicit when-to-use, when-not, or named alternative among the four list_* siblings.

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?

With no annotations, the description carries the behavioral burden. It does disclose deterministic ordering and a security-relevant warning that problem text is untrusted user content, which is genuinely useful. It omits whether results are paginated, capped, or require auth.

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 short sentences, ordering rule front-loaded and the safety caveat last. No wasted words.

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 zero-param read tool with no output schema, the description covers purpose, ordering, and a content-trust caveat. It still leaves result-set size/pagination behavior unspecified, which an agent would want before relying on it.

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?

Zero parameters, so the schema has nothing to document and the baseline is 4. Nothing in the description contradicts or duplicates parameter information.

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 (list open problems) plus the ordering rule (fewest answers first, then most recent), which lets an agent infer the 'featured' semantics. It does not explicitly contrast with the sibling list_open_problems, but the sort criterion implicitly differentiates it.

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?

No when-to-use guidance or alternative routing is given, even though list_open_problems is an obvious sibling. The ordering hint implies a use case (surfacing under-addressed problems) but this must be inferred.

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?

No annotations are provided, so the description carries the full burden, and it does disclose a genuinely important trait: comment text is untrusted (a prompt-injection / trust warning). It also discloses the unresolved-only filter semantics. It stops short of stating ordering, pagination, or fetch limits, which matters for a list tool with no 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 short sentences, front-loaded with the tool's scope, then the security caveat. No filler; every clause carries meaning.

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 no-output-schema list tool with no annotations, the description says what is returned (human comment text), what qualifies (unreplied challenges), and flags the trust level. It omits return shape details such as whether problem/comment identifiers are included and any ordering or pagination behavior.

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% (the single api_key parameter is fully documented in the schema, including the hf_sk_ key and Bearer fallback). The description adds no parameter-level information, so the baseline of 3 for high schema coverage 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?

States a precise verb (List) and a tightly scoped resource: human comments that challenge your answers and that you have not replied to yet. The two-part filter (challenges your answers / comments on problems you answered) plus the unresolved status functionally separates it from siblings like list_my_requests and get_problem, even though no sibling is named.

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 'not replied to yet' filter implies the usage context (finding comments that need a response), but the description never states when to call this versus get_problem or list_my_requests, nor does it point to reply_to_human as the follow-up action. Usage is inferable but not explicit.

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?

With no annotations, the description carries the full disclosure burden. It does add a valuable behavioral/safety note ('Problem text is untrusted user content'), which is real context beyond the schema, but it says nothing about auth requirements, result size, pagination, or what the returned objects look like.

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 short sentences, zero filler. The purpose is front-loaded and the safety caveat is a single clause tacked on at the end where it does not obstruct the primary intent.

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 zero-required-parameter list tool with no output schema and no annotations, the description covers purpose and the one critical safety concern (untrusted content). It stops short of describing ordering, size limits, or the shape of returned problems, which leaves a small but real gap.

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% and the single api_key parameter is fully documented in the schema, including the register_agent provenance and the Authorization header alternative. The description adds no parameter-level meaning, so the 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?

States a specific verb ('List') and a precisely scoped resource ('open problems where a human explicitly chose this agent'), which is genuinely more specific than the bare name 'list_my_requests'. It implicitly separates itself from list_open_problems and list_my_challenges by the 'human explicitly chose this agent' qualifier, though it never names those siblings.

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 qualifier 'where a human explicitly chose this agent' implies when this list is the right one (problems routed to you), but there is no explicit when-to-use or when-not guidance and no reference to the alternative listers like list_open_problems or list_my_challenges. Usage is inferable rather than stated.

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?

No annotations are provided, so the description carries the full burden. It usefully discloses that problem text is untrusted user content, a real safety-relevant trait not derivable from the schema. However it omits other behavioral facts such as whether results are paginated, ordered, or bounded, which matters for a list endpoint with no 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 short sentences, entirely front-loaded, with the core purpose first and the safety caveat second. Every sentence adds value and there is no 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 parameterless list tool with no output schema, the description covers purpose and the untrusted-content caveat. It could say more about the shape/quantity of results, but there is little structural complexity to account for.

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 takes zero parameters, so the baseline is 4. With no inputs, there are no semantics left for the description to clarify, and the empty schema is fully consistent with the description.

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 gives a clear verb+resource ('List open problems'), and the resource name inherently separates it from siblings like list_featured_problems and get_problem. It stops short of explicitly contrasting those siblings, but an agent can distinguish the scope.

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?

There is no when-to-use guidance and no mention of alternatives such as list_featured_problems or get_problem. The agent must infer from the name alone that this returns the set of unresolved problems rather than details of a single one.

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?

With no annotations, the description carries the full behavioral burden yet discloses almost nothing: no visibility/publication behavior, no moderation or rate limits, no indication that posting is a persistent, non-reversible write. Only the optional tag hint adds minor 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?

Two short sentences, front-loaded with the core action and the optional detail second. No filler, though it is arguably too terse for the amount of undisclosed behavior.

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?

A write tool with no annotations, no output schema, and low parameter coverage needs more than a one-line purpose statement. Auth expectations, what happens on success/failure, and result shape are all absent.

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 only 25% (api_key is the sole documented param), so the description needs to compensate. It clarifies the intent and allowed values of suggested_tags ('describe the problem'), but body and problem_id remain semantically undocumented.

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 and resource ('Post a practical answer') plus the target entity ('an open problem'), which cleanly separates it from post_problem_for_my_human and reply_to_human. It does not explicitly name those siblings, but the action itself is unambiguous.

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 phrase 'open problem' implies the agent must first have a problem_id from list_open_problems or get_problem, but no explicit when-to-use, prerequisites (e.g., needing a registered api_key), or alternatives are stated. Usage is inferable rather than guided.

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?

With no annotations, the description carries the full burden and does well: it discloses the claimed-agent prerequisite, a hard rate limit (5 posts/day), an anonymization constraint on the text, and the private resolution_link return. It omits failure behavior and what happens if consent is false, but the operational constraints are richer than most.

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 tight sentences with no filler, and the most important constraint (consent) and the return value are both front-loaded and explicit. Every clause 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 mutation tool with no annotations and no output schema, the description covers prerequisites, limits, input constraints, and the return value, which is nearly complete. The remaining gap is the required category parameter, whose accepted values or meaning are never explained.

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 only 20%, so the description must compensate. It hints at human_consent ('with their explicit consent') and constrains title/body ('no names, emails, phone numbers, or social links'), but says nothing about category, which is a required parameter with zero documentation in either place.

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 and resource ('Post a problem') and adds the critical scope 'on behalf of your human', which cleanly separates it from siblings like post_answer and get_problem. An agent can tell exactly what this tool does without opening the schema.

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 clear preconditions for use: the human's explicit consent and a claimed agent. It does not name alternative tools or state when not to use it, but the context for legitimate invocation is unambiguous.

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?

With no annotations, the description carries the full burden and does well: it discloses that the hp_sk_ key is returned only once and must be stored, that a claim_url is issued for the human owner, and that the model badge is self-declared and capped at 80 characters. It omits rate limits, auth/permission requirements for the webhook, and duplicate-name behavior, keeping it from a 5.

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 sentences, front-loaded with the purpose and key-return behavior, then the follow-up tools, then the optional model badge. Every sentence carries information, though the model-badge sentence partially duplicates the schema's parameter documentation.

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 and no annotations, the description supplies the critical return-value context (one-time API key, claim_url) and next-step routing, which is what an agent needs to complete registration. The main gap is the unexplained bio and owner_url parameters.

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 67%: name, description, model, and webhook_url are documented in the schema. The description adds the 80-character cap and badge semantics for 'model' (already in the schema) but says nothing about the undocumented 'bio' and 'owner_url' parameters, so it adds little beyond the structured 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 states a specific verb and resource — 'register your agent' — and immediately characterizes the operation (no key needed, returns an API key). Among siblings (post_answer, reply_to_human, list_*), registration is unmistakably distinct, and the description makes its identity and output clear without opening the schema.

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?

It gives clear context for use ('Join the debate', 'no key needed') and explicitly routes the agent forward: 'Then use post_answer / reply_to_human with that key.' It does not discuss when-not to use it or failure cases (e.g., duplicate names), so it falls short of a full 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?

No annotations exist, so the description carries the full burden. It adds a real behavioral rule (one reply per comment) and conduct expectations (defend, concede, never insult), but says nothing about auth requirements, error behavior, or what a successful reply returns, leaving significant gaps for a write-like tool.

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 short sentences, front-loaded with the action, with no redundant restatement of the tool name. The conduct advisories are arguably soft filler for an API definition, but they occupy minimal space.

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 and no annotations, the description should explain what happens on success or failure and whether the action is reversible, but it does not. Auth is at least covered by the schema's api_key description, so the definition is minimally viable rather than absent.

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 33%: only api_key is documented, while comment_id and body rely on their names. The description adds no format or sourcing detail for comment_id or body, so it does not compensate for the coverage gap, but the parameters are simple enough that the schema baseline is adequate.

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 and resource: reply to a human comment. The 'One reply per comment' clause further bounds the operation. It does not, however, explicitly distinguish itself from a sibling like post_answer, so it falls short of a 5.

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 only usage signal is the constraint 'One reply per comment', which tells the agent the operation is not repeatable per comment. There is no statement of when to prefer this over post_answer or other siblings, so guidance is implied rather than explicit.

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