hunchful

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

Beyond the annotations (readOnly=false, openWorld=true, idempotent=false, destructive=false), it describes the mutation's effect on the model, visibility/editability/removability, and the auth path (owner OAuth or editToken). It also establishes data-fidelity constraints ('Never invent or infer a hunch they didn't confirm'). 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?

All five sentences carry distinct, decision-relevant information: purpose+effects, auth, data fidelity, naming, and the alternative tool. It is compact (~75 words) and front-loaded with the core purpose, with no filler or redundant 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 write tool with 8 parameters, this description covers the operational context fully: what qualifies as input, required authority, how to format names, and which sibling to use instead. Because an output schema exists, not detailing return values is acceptable. The only minor omission is not explicitly saying what happens if a hunch with the same name already exists, but idempotentHint=false and 'NEW' imply a new axis.

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 88% schema coverage, the baseline is high, and the description adds value by prescribing the naming convention for situational axes ('When <situation>') and reinforcing the meaning of pole ('what the person SAID') and editToken (owner-level alternative). It doesn't address 'confidence' or 'by', but the schema already documents those well.

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 opening sentence clearly identifies the operation: recording a NEW hunch/custom axis that the person just confirmed, with key traits (visible, revisable, removable). It differentiates from siblings by emphasizing 'just confirmed' and explicitly names suggest_question as the alternative, so the agent knows exactly what this tool does and how it differs.

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 the core precondition ('just confirmed in conversation') and the authority requirement (OAuth as owner or editToken). It gives an explicit exclusion and alternative: 'If they haven't answered yet, use suggest_question instead' and forbids inventing/inferring unconfirmed hunches, providing clear when-to-use and when-not-to-use 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 indicate readOnlyHint=false, so the write nature is clear. The description adds useful behavioral context by explaining that the operation is 'one logged revision', mentions authorization requirements (OAuth or editToken), and states that it appends evidence pointers and nudges confidence. It does not contradict the annotations and provides meaningful details beyond the structured fields.

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 sentences and gets straight to the point. It front-loads the core action, then adds necessary usage guidance without any filler or repetition.

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?

The tool has an output schema, so return values are already specified. The description covers the key operational context (auth, direct-write preference, logged revision) and is consistent with the moderately complex nested input schema. It is sufficiently complete for an agent to use correctly, though it could benefit from a note about max/min items, but those are already in the schema.

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 low at 33%, with only editToken described in the schema and nested item properties having descriptions. The description adds only minimal parameter context (mentions OAuth and editToken as authorization methods) but does not explain modelId or the items structure. Given the low coverage, the description should compensate but falls short.

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 action: 'Apply a refinement directly as one logged revision' with specifics on what it does ('append evidence pointers, nudge confidence'). It also distinguishes itself from the sibling tool propose_refinement by explicitly noting a directive to prefer that alternative unless direct writes are requested, making the purpose 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 description provides explicit guidance on when to use this tool versus the alternative: 'Prefer propose_refinement unless the owner asked for direct writes.' This clearly delineates the appropriate context and names the alternative, making it easy for an agent to decide between the two.

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, openWorldHint, and idempotentHint. The description reinforces 'Open read' but adds minimal behavioral detail beyond the annotations. It clarifies the conceptual approach but not operational aspects like output formatting or access requirements.

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, front-loaded with the core question and purpose. Every sentence adds value with no redundancy or fluff.

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 simplicity (one optional parameter, output schema present), the description covers purpose, use case, and read-only behavior. It is complete for an agent to understand when and how to invoke 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?

The single parameter modelId is fully documented in the schema with 100% coverage. The description adds no additional parameter-specific meaning, but the schema already provides adequate semantics, so the baseline 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 clearly states the tool's function: finding famous thinkers who complement a person via opposite poles, explicitly contrasting with similarity. This distinguishes it from sibling tools like find_matches.

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 usage context ('great for curiosity and for explaining what a complementary collaborator looks like') and implicitly contrasts with similarity-based approaches. It does not explicitly name alternative tools or give when-not-to-use conditions, but the guidance is sufficient.

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?

The description discloses the key side effect ('creates pending proposals'), the privacy behavior ('identities are never revealed here'), and authentication requirements (OAuth/editToken). It adds meaningful context beyond the annotations, which only indicate readOnly=false. It does not mention the 'queued' aspect from the title, but the description is otherwise rich.

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 a single dense sentence that front-loads the main purpose and packs in the rule, side effect, privacy, auth, and prerequisite without excess. It is slightly run-on but every part contributes value. Appropriate for the amount of context.

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?

The description covers purpose, rule, side effect, privacy, auth, and a prerequisite, which is substantial for a tool with 3 parameters and side effects. An output schema exists, so omitting return values is fine. It lacks explicit sibling differentiation and the queued behavior, but is otherwise 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 schema covers 67% of parameters with descriptions (direction and editToken), but the description itself adds no parameter semantics. It mentions editToken only in the auth context, not its parameter usage, and completely ignores modelId and the direction enum. With modelId remaining undocumented, the description fails to compensate for the schema gap.

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 for' with the resource 'complementary PEOPLE' and adds the rule 'agents propose, humans accept', making the core purpose clear. However, it does not explicitly distinguish itself from the sibling tool 'find_complementary_thinkers', despite mentioning 'identities are never revealed here' as a potential differentiator.

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 provides important prerequisites (OAuth/editToken, must have joined introductions) and workflow context (creates pending proposals for humans to decide), implying when it should be used. However, it does not name alternatives or clearly exclude other tools, especially the near-identical sibling 'find_complementary_thinkers'.

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?

The description adds important behavioral traits not present in the annotations: the queued confirmation workflow ('nothing changes until they accept') and authentication requirements (OAuth or contributionToken). It also restricts inputs to provenance pointers, which is 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?

The description is three concise sentences, each adding distinct information: purpose, queued behavior, and authorization. It is front-loaded with the action verb and contains no redundant wording.

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?

The description covers purpose, queue behavior, and authorization, which is sufficient for an agent to choose the tool correctly. Given that an output schema exists and annotations are present, return values and safety profile are already available. However, a brief note on how to identify modelId would make it more 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?

Schema description coverage is only 33%, and the description does not compensate. It mentions 'provenance pointers only' which adds some meaning to the items array, but modelId is completely unexplained and the structure of items is only partially clarified by the schema's nested 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 states a specific verb and resource: 'Propose a refinement', and adds the scope constraint 'provenance pointers only'. It clearly distinguishes itself from the sibling apply_refinement by explaining that the proposal lands in a confirm queue and nothing changes until accepted.

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 gives clear context that this tool is for proposing refinements without immediate effect, contrasting with apply_refinement. It doesn't explicitly name alternatives or give when-not-to-use guidance, but the queue behavior makes the usage context clear.

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, openWorldHint, idempotentHint, and destructiveHint=false, so safety is established. The description adds behavioral nuance: the OAuth-dependent default behavior of modelId and the fact that it returns 'how you're connected'. This adds value 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, front-loaded with the core action and output, then a targeted OAuth note. Every word earns its place; 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?

With a single optional parameter and an output schema present, the description covers what the tool does, what it returns, and the OAuth alternate usage. Lacks explicit statements about prerequisites (e.g., OAuth scopes), but that is beyond the needed scope for a simple read operation.

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%: modelId is described as optional, omitted when connected via OAuth. The description repeats this exact guidance without adding new parameter meaning. Baseline 3 applies since the schema does the heavy lifting.

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?

Description uses specific verb 'Read' and resource 'collaboration model', enumerates returned contents (hunches with axis/pole/confidence, axis ids, connections) and marks itself as the entry point ('Start here'). Clearly distinguishes from sibling tools like add_hunch or propose_refinement.

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 clear context: 'Start here' signals this is the initial read action. Also gives specific instruction for OAuth (omit modelId to read the connected person's own model). Does not explicitly state alternatives or exclusions, but the tool's role as read-only entry point is clear.

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?

The description adds substantial behavioral context beyond annotations: queuing ('lands in their Hunchful inbox'), default emailing, deferred mutation ('nothing is added until they answer'), and auth via OAuth or contributionToken. This enriches the annotation set (readOnlyHint: false, openWorldHint: true) without contradiction.

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 and front-loaded with the core purpose. Every sentence adds distinct information: purpose, behavior, auth, and usage frequency. No redundant or vague phrasing.

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 an output schema present and high schema parameter coverage, the description provides the key missing context: the queued/email side-effect, auth requirements, naming guidance, and usage restrictions. It is fully adequate for an 8-parameter tool with moderate complexity.

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 high (88%), so the baseline is 3. The description adds value by providing a naming convention for 'name' ('situational axes welcome: name it 'When <situation>'') and clarifying that auth is via OAuth (modelId) or contributionToken, which helps interpret those parameters.

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 the exact purpose: 'Suggest a NEW question/axis for the person — a dimension their model doesn't capture'. The verb ('Suggest'), resource ('question/axis'), and scope ('their model doesn't capture') are specific and distinguish it from siblings like add_hunch or propose_refinement.

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 provides clear context: 'It lands in their Hunchful inbox... nothing is added until they answer' explains the flow, and 'Use sparingly — about once a week at most' gives rate guidance. It also mentions auth prerequisites. However, it doesn't explicitly name sibling alternatives, relying on the 'NEW' framing to differentiate.

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