torneo

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It reveals substantial behavior: status meaning, fixture flag, freshness, expiry, explicit limits, conflicts, funding, errata chained to the served result hash, legal preflight verdicts for all tools, and a mandatory answer rule. It does not explicitly say the tool is read-only, but 'Returns' and the content list make the tool's behavior largely transparent.

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 one dense, front-loaded sentence that begins with the core purpose and then packs in many specific output components. Every listed item adds information, though the long comma-heavy list is harder to scan than a structured or shorter version would be.

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 thoroughly inventories the return content, but it leaves invocation semantics ambiguous: both parameters are optional in the schema and the description does not state whether one is required or what happens if both or neither are supplied. Since there is no output schema, the lack of any return-shape guidance also reduces completeness.

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 describes both parameters with 100% coverage, so the baseline is 3. The description adds only the category-vs-run framing, which is largely mirrored by the schema's 'alternative to category' note. It does not add meaningful semantics beyond what the schema already provides.

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 opens with a specific verb and resource: 'Returns what a category's (or run's) result can and cannot tell you' and then enumerates a detailed list of returned aspects. This clearly differentiates it from siblings like get_results, which retrieves raw results, and get_run, which retrieves run details.

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 the tool is for interpreting the limits and meaning of a result rather than retrieving the raw data itself. However, it never explicitly says when to use this tool instead of get_results or get_run, nor does it provide exclusions or alternative routing. Usage context is only implicit.

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 it delivers: it discloses the canonical answer rule, that the tool never names 'the best tool,' that it reports intervals/costs/conflicts/limits, and that stale, superseded, or indeterminate results yield INSUFFICIENT_EVIDENCE with no rank. This gives an agent accurate expectations for unusual 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?

Three dense sentences, each earning its place: purpose, behavioral rule, and CLI equivalence. The core action and the key edge case are front-loaded, with 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 one-parameter query tool with no output schema and no annotations, the description explains what the answer contains, when it is withheld, and what the result signals carry. An agent has enough context to call it correctly and interpret its unusual response semantics.

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 the schema already explains category as an id with an example and a pointer to list_categories. The description reinforces that the tool operates on one category but adds no parameter-level meaning beyond the schema.

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 clear verb ('Answers') and resource ('one category') and adds the canonical answer rule, so an agent knows what the tool produces. It is distinguishable from siblings like list_categories or get_run by the focus on per-category evidence, though it never names alternatives.

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 'for one category' implies when to use the tool, and the CLI equivalence gives a reference point, but there is no explicit guidance on when to prefer it over siblings such as get_run, explain_limits, or list_categories, and no stated exclusions.

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 provided, the description carries the full burden of behavioral disclosure. It explains the special PRE_REGISTERED state behavior: returns state PRE_REGISTERED, measured false, lock and frozen files, and no result because nothing has been measured. This goes well beyond a simple 'get' description, though it does not mention errors, permissions, or pagination.

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 dense but efficient: the main content is stated first, followed by a precise edge-case explanation. Every sentence adds value, and the special PRE_REGISTERED behavior is clearly separated without unnecessary prose.

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 one-parameter tool with no output schema and no annotations, the description is remarkably complete. It names the return schema, lists expected result components, and covers the pre-registered edge case. An agent has enough information to call the tool correctly and interpret its response.

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 provides 100% coverage of the single parameter run_id, including an example format. The description adds the context that it identifies 'one run' and connects to list_categories, but does not add substantial new meaning beyond the schema.

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 ('Returns'), a clear resource ('canonical result bundle of one run'), and enumerates the contents (protocol lock hash, provenance, reproduce command, per-participant outcomes). It also names the schema 'result.v1', making it easy to distinguish from sibling tools like get_results.

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 implies when to use the tool: to fetch the canonical result bundle for a single run, identified by run_id. It does not explicitly name alternatives or exclusion conditions, but the 'one run' scope and reference to list_categories for run_id provide sufficient context.

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 provided, the description carries the behavioral burden and does substantial work: it defines the meaning of OK, LOCAL_VALIDITY, and INDETERMINATE, and explicitly warns that 'fixture-' categories are synthetic demo data, preventing misinterpretation. It does not cover auth or error behavior, but for a read-only list that is a minor gap.

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 entire description is one dense sentence with high information density. Every clause earns its place, and the most important context—status semantics and the fixture-data caveat—is included without padding.

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 tool with no output schema and no annotations, the description covers the returned fields and the key interpretive caveats. It could mention ordering or pagination, but nothing critical is missing for correct invocation.

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 zero parameters and schema description coverage is 100%, so the baseline is 4. There is no parameter information for the description to add.

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 opens with a specific verb and resource ('Lists every category') and enumerates the returned attributes plus status values. This clearly distinguishes it from sibling tools like get_results or get_run, which target different data.

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 explains what the tool returns but gives no explicit guidance on when to use this tool versus the sibling tools. An agent must infer that listing categories is the intended use, with no exclusions or alternative routing provided.

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