Deduped AI use cases

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

Read-only and open-world behavior is already declared in annotations, so the description's job is to add context, and it does: it tells the agent exactly which content fields come back (prompt, requirements, risks, caveat, repo url) in the absence of an output schema. It stops short of noting error behavior for an unknown id or any freshness/source caveats.

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?

A single compact sentence with the subject front-loaded and the returned payload listed in a scannable comma series. It is a sentence fragment, but every item named is informative and nothing is repeated.

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 read tool with annotations covering safety and no output schema, the description gives the agent enough to know what it will get back. The missing piece is the relationship to search_use_cases as the id source, which the description leaves to 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?

With only one parameter and 100% schema description coverage, the schema already explains the id format and its origin in search_use_cases. The description adds no further meaning about the id, 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?

The description names the resource ('One use case') and enumerates what it contains (who it is for, steps, prompt, requirements, risks, caveat, repo url), which makes the singular-detail nature distinguishable from the sibling search_use_cases. It is a noun phrase rather than an explicit verb+resource, and it does not name the sibling to contrast with, keeping it 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?

There is no statement of when to call this versus search_use_cases or the other siblings; the agent must infer that this is the detail lookup for an id obtained from search. The only routing hint ('Use case id from search_use_cases') lives in the schema's parameter description, not the tool description.

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 and openWorldHint, so safety and external-source behavior are covered. The description adds genuinely useful context beyond them: a fixed 90-day recency window, deterministic best-first sorting, and the fact that each entry carries a plain-English summary line. It could still say more about ranking criteria or freshness of results.

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?

A single front-loaded sentence that packs scope, time window, ordering, and return shape with no filler. Nothing could be removed without losing information.

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 is the only source of return-value information, and it only says each repo comes with a 'plain-English line' — no mention of fields like stars, URL, or repo name. The missing limit parameter guidance leaves a second gap. Adequate to call correctly, but not fully 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?

There is one parameter (limit) with 0% schema description coverage, and the description never mentions it, its default of 10, or the maximum of 50. With low coverage the description should compensate, and it does not.

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?

Names a specific resource and scope: trending AI repositories on GitHub over the last 90 days, ordered best-first. It is clearly distinct from the sibling tools (use-case lookups and news) without needing to say so explicitly. It has no explicit verb, but for a list-style tool the resource definition is enough to identify 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?

The description states what the tool returns but never says when to reach for it instead of siblings such as today_news or search_use_cases. 'Right now' implies a trending/discovery use, but that is inference rather than guidance, and no exclusions or prerequisites are given.

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 and openWorldHint, so the safety profile is covered. The description adds real value by naming the return fields (ids, titles, summaries, page urls) and the chained follow-up call, though it is silent on ranking, pagination beyond the limit param, or result quality.

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: what it searches, what it returns, and what to do next. The workflow hint is front-loaded right after the return shape, 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?

With no output schema, naming the returned fields is important and present, as is the follow-up routing to get_use_case. It falls short only on result-ordering/relevance behavior and how category values are discovered, which an agent might still wonder about.

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%: query and category carry inline descriptions, while limit is documented only via min/max/default. The description frames the domain (practical AI use cases) but adds no syntax or filtering nuance beyond the schema, 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 (search) and resource (Deduped's catalog of practical AI use cases from hot GitHub AI repos), plus the exact fields returned. It also distinguishes itself from the sibling get_use_case, which handles steps and prompts.

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?

Explicitly says to call get_use_case afterward for steps and an agent prompt, giving a clear two-step workflow. It does not clarify when to prefer this over the other siblings (hot_repos, today_news), so the routing guidance is partial rather than complete.

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 and openWorldHint, so safety and network behavior are covered. The description adds genuine content-level context beyond that: results are deduped, each story appears once, and each carries a category and short summary — details absent from annotations and schema. It stops short of stating story count, source list, or the timezone/window defining "today".

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?

A single front-loaded sentence that describes scope and output shape with no filler. Every clause (deduped, top stories, each once, category, summary) carries information an agent would not otherwise have.

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-parameter read-only tool with no output schema, the description adequately conveys what comes back (stories with category and summary). It leaves minor gaps — how many stories, which sources, and where the cutoff for "today" falls — but nothing that would cause a mis-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 tool takes zero parameters, so there is nothing for the description to disambiguate; per the baseline this lands at 4. The description correctly implies a no-argument call by describing only the return content.

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 names a specific resource (a deduped AI news brief of today's top stories) and states the shape of each item: category plus short summary. It is immediately distinguishable from siblings like hot_repos or search_use_cases, though it does not explicitly contrast itself with them.

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 by the word "Today's" — an agent can infer this is the go-to for current AI news. There is no explicit when-to-use statement, no when-not-to-use condition, and no named alternative (none exists among siblings, which mitigates but does not eliminate the gap).

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