CLSTR

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

The annotations already signal read-only and open-world behavior, and the description adds substantial behavioral detail: exactly what fields are returned, the significance score scale with the 8+ exceptional threshold, the presence of source articles and links, and the authentication requirement. It also clarifies the deduplication behavior of clusters, which helps the agent set user expectations.

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 primary purpose and then efficiently covers return values, usage context, input sourcing, and authentication. Every sentence contributes useful information, though it is slightly longer than strictly necessary due to the explanatory cluster concept.

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 single-parameter read-only tool with no output schema, the description is remarkably complete. It covers what the tool returns, how to obtain the required identifier, when to use it, and the access requirements, leaving no critical gap for an agent to call it correctly.

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 schema already documents the cluster_id parameter well. The description adds extra semantics by telling the agent where to find a cluster id (search_situations, get_situation_timeline, or clstr.news URLs), which goes beyond the schema's basic type and format info.

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 identifies the operation (get one news event in detail), the resource (a cluster), and explains what a cluster is. It distinguishes itself from the sibling tools by emphasizing single-event granularity versus near-duplicate headlines and by positioning it as the tool for specifics of one event.

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 an explicit use case: 'Use this when the user wants the specifics of one event.' It also explains where to obtain the required cluster_id, which is actionable guidance. It does not explicitly state when not to use alternatives, but the context is clear enough that a competent agent can route correctly.

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 does not contradict these. The description further discloses what the tool returns (events with date, significance score, source count, link) and the pagination mechanism, providing transparency beyond the annotations. No side effects or mutations are implied.

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 verbose, repeating definitions of 'situation' and 'event' which adds length but also clarity. It is structured logically, starting with the primary action, then explaining concepts, then use cases, then parameter sources. Could be trimmed, but every sentence adds value; no 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?

The description covers the return content (timeline events with date, significance score, source count, link), use cases, how to obtain the ID, and pagination details. Even without an output schema, the description paints a complete picture of what to expect and how to use the tool effectively.

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 describes all three parameters, but the description enriches each with meaningful context: situation_id is said to come from other tools or URLs, timeline_limit has a default and maximum depending on API key access, and timeline_before is a cursor for pagination. This goes well beyond the schema's brief descriptions, fully clarifying parameter usage.

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: retrieving the full chronological timeline of a single situation. It distinguishes itself from siblings by focusing on timeline retrieval for a specific situation, as opposed to cluster retrieval, top situations, or searching. The verb 'get' and resource 'situation timeline' are explicit.

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 explicit use cases: 'Use this to brief on a story's history or answer how did this develop.' It also explains how to obtain the required situation_id from other tools or URLs, which is practical guidance. It doesn't state when not to use it, but the clarity of use cases 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?

Annotations declare readOnlyHint=true and openWorldHint=true, and the description enriches these with genuinely useful behavior: the intentional, non-obvious sort ('a lower significance score can rank above a higher one, and that ordering is intentional; do not re-sort'), the concept of a maintained summary with source count, and cursor-based pagination. No contradiction with annotations found.

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 well-organized paragraph that front-loads the core behavior and ends with formatting guidance. Every sentence earns its place, though the enum examples for window and category do get re-stated relative to the schema, creating slight redundancy.

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 tool with no output schema, the description skillfully covers the key return semantics: row identity (situation as storyline with summary and source count), the cursor for pagination, and that URLs should be cited. It is complete enough for an agent to use the tool end-to-end without needing the response format spelled out.

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 the baseline is 3. The description adds meaningful context beyond the schema: how the ranking should be treated on the client side, the workflow implication of the cursor ('When there are more, the result ends with a cursor to page further'), and that the sort must be preserved verbatim. This justifies a point above the 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?

The description immediately states the verb and resource ('List the situations developing right now') and gives a precise account of the ranking blend (significance, outlet count, recency), plus a crisp definition of what a 'situation' is. It stops short of a 5 because sibling differentiation is handled through usage hints ('when there is no specific topic yet') rather than explicitly contrasting with a sibling like search_situations.

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 an explicit trigger ('what is going on in the world' / 'what is happening in <category>'), specifies when NOT to use it ('when there is no specific topic yet'), and chains the next step ('then open any result with get_situation_timeline'). It could be a 5 only if it explicitly named a search/topic sibling for the alternative case.

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 only provide readOnlyHint and openWorldHint, so the description carries the behavioral burden. It adds substantial context: results are deduplicated, grouped under situations, relevance ordered, bounded to top matches, paginated via cursor, each page counts against a cap, and sign-in or API key is required.

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 well organized: it opens with the core action, defines key domain terms, lists result contents, gives the intended use case, names downstream tools, explains pagination and cap behavior, and closes with auth and citation guidance. Every sentence contributes information an agent needs.

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?

Without an output schema, the description adequately explains what the result looks like: situations, one-line summaries, significance scores, source counts, and canonical URLs. It also covers pagination, rate cap implications, auth requirements, and how to proceed to sibling tools, making it safe and complete to call.

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 value beyond the schema by explaining that results are grouped under situations so limit may produce fewer headings, and by framing query as a topic, entity, company, country, or event. Cursor semantics are also reinforced.

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: search the news by topic to find situations and events. It further distinguishes the tool by explaining that a situation is a storyline and an event is a deduplicated cluster, making it clear how search_situations differs from the 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 gives clear use context: use it to answer 'what is happening around X' for a topic, company, country, or event. It also routes to get_situation_timeline for history and get_cluster for event detail, though it does not explicitly contrast with get_top_situations.

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