HeatPulse

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does disclose the return contents and the default 'latest' behavior, but it does not mention error handling, invalid dates, or explicitly confirm that this is a read-only operation. It is adequate but not 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, tightly written sentence that front-loads the resource, includes the date format, states the default behavior, and summarizes the return contents. Every phrase earns its place with no 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 one optional parameter and no output schema, the description covers the essential information: what the digest contains, how to specify a date, and the default behavior. It omits minor details like invalid-date handling, 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?

Schema description coverage is 100%, and the schema already documents the date parameter as 'YYYY-MM-DD, default latest'. The description repeats this same information without adding new semantic detail, so it meets the baseline but does not exceed it.

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 resource (daily editorial digest), its scope (a specific date or the latest one), and its contents (intro paragraph, stats, featured events). It is easily distinguishable from sibling tools like get_event or search_news, though it lacks an explicit verb such as 'fetch' or 'get'.

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 no guidance on when to use this tool versus its siblings. It does not mention alternatives or exclusions, leaving the agent to infer that this is the tool for daily digests without any comparative 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 full burden of behavioral disclosure. It transparently states what the tool returns ('full editorial summary plus its outbound coverage list'), which is helpful. However, it does not mention failure modes (e.g., what happens if the slug does not exist), authentication requirements, or any side effects. For a read-only lookup, this is acceptable but not comprehensive.

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, tightly composed sentence that front-loads the primary purpose and the distinguishing output. Every word contributes to the meaning, and there is no filler or repetition. This is exemplary conciseness.

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 get-by-slug tool with one parameter and no output schema, the description covers the essential elements: what resource it retrieves, by what identifier, and what it returns. The only missing context is error handling or edge cases, but these are not critical for a straightforward lookup. The description is sufficiently complete for an agent to invoke 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 description coverage is 0%, so the description must compensate for parameter documentation. The description only says 'by slug', which clarifies that slug is the identifier but adds no meaning about its format, how to obtain it, or any constraints beyond the schema's maxLength. This is minimal added value over the raw 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-action ('One AI-news event by slug') and specifies the resource ('AI-news event') and the exact output (editorial summary plus outbound coverage list). This distinguishes it from siblings like search_news (search) and related_stories (related content) because it is a direct singular fetch by unique identifier.

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 makes it clear that this tool is for retrieving a single event when you already have a slug, which is a concrete usage context. However, it does not explicitly state when not to use it or mention alternative tools (e.g., use search_news when you need to find events by query). The absence of explicit exclusions is a minor gap, but the implied condition ('by slug') is sufficient for basic routing.

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 burden of behavioral disclosure. It states the output is a 'manifest' of datasets, which suggests a listing operation rather than returning the actual data, but it doesn't clarify whether this returns metadata only or the full JSONL content, nor does it describe pagination, size limits, or output format. The word 'manifest' adds some behavioral context but leaves important details unspecified.

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 sentence that front-loads the core purpose ('Bulk JSONL dataset manifest') and then adds the specific dataset categories. It is compact and every phrase adds information. It could arguably be slightly clearer about the output format, but as a one-liner it is efficient.

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, the description is mostly adequate: it names the resource and the categories. However, with no output schema and no annotations, an agent is left guessing whether the tool returns a list of dataset names, URLs, or the actual JSONL data. The term 'manifest' hints at metadata, but the description doesn't fully close that 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?

The tool has zero parameters, so the schema provides no parameter documentation. The description compensates by explaining what the returned manifest contains (events corpus, entities, leaderboard history, digests), which is the only meaningful semantic content an agent needs for a parameterless list operation. The baseline for 0 params is 4, and the description meets that by clarifying the resource scope.

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 verb ('list') and resource ('datasets'), and enumerates the dataset categories (events corpus, entities, leaderboard history, digests), which distinguishes it from the sibling tools that operate on individual events, topics, or digests. It doesn't explicitly name a sibling alternative, but the scope is clear enough to separate it from the other 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 implies this is the tool for bulk dataset pulls for RAG/fine-tuning, which gives some context for when to use it. However, it doesn't explicitly state when not to use it or name alternatives like get_event or daily_digest for single-item retrieval. The usage context 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.

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

With no annotations, the description must carry the behavioral burden. It discloses that results are ranked by coverage count, giving a sense of ordering, but it does not mention sorting direction, scope of the corpus, rate limits, or any side effects.

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 an eight-word sentence with no filler. It front-loads the resource ('extracted entities') and packs the ranking criterion into the same sentence.

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 the description gives the gist, but it omits the meaning of 'coverage count', how limit behaves, and what the return shape looks like (no output schema). With a sibling list like related_stories, an agent cannot confidently choose this tool based solely on the description.

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 has zero property descriptions and the description does not mention the 'limit' parameter at all. The parameter's name and min/max constraints imply it caps the number of returned topics, but the description fails to compensate for the missing schema coverage.

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 concrete output: extracted entities (companies, models, people, themes) ranked by coverage count. It identifies the resource and ordering, though it doesn't explicitly differentiate from sibling tools like related_stories or search_news.

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 guidance is provided about when to call list_topics versus the sibling tools. There are no exclusions, prerequisites, or alternative tool references.

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 disclosure burden. It adds useful behavioral detail: the data is 'live', rows relate to the previous daily snapshot, and agent-board scoring is defined. However, it does not cover defaults, sorting, pagination, or how fresh the data is beyond the word 'live'.

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 terse sentences front-load the resource and then pack row fields and the agent-board exception efficiently. Every sentence earns its place 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?

Without an output schema or annotations, it still gives the row shape and one board-specific exception, which is enough for basic use. Missing default behavior for `board` and `limit`, response ordering, and any pagination caveats keep it from fully standing alone.

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 0%, so the description must compensate. It does map the board values to six domains and explains the agent-board score/votes semantics, but it never explains the `limit` parameter (beyond naming it) or what happens when `board` is omitted.

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 concrete resource (LLM arena leaderboards) and enumerates the six board categories plus the metrics each row carries, making the tool's function clear. It stops short of an explicit verb like 'fetch' or 'list', so it is not maximally instructive.

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 intended usage is clear from context: any request for LLM arena rankings, arena scores/votes, rank changes, or model context/pricing. It does not explicitly say when not to use it or name alternatives, but the subject matter is distinct enough from the unrelated 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 the algorithm ('centroid cosine KNN') and the result cap ('max 5'), which are meaningful. However, it does not mention output format, ordering, errors, or the meaning of the slug parameter, leaving several behavioral aspects unspecified.

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 compact sentence that front-loads the core purpose and adds the algorithm and maximum result count in a parenthetical. There is no fluff; every word contributes useful 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?

For a simple one-parameter read tool, the description is mostly adequate: it conveys the operation, the similarity method, and the result cap. But it lacks explicit parameter guidance, return-value description, and usage boundaries, and with no output schema or annotations, those gaps make it only minimally 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 0%, so the description must compensate. It only says 'a given event' without explicitly explaining that the required 'slug' parameter identifies that event. The connection is inferable but not stated, so the description adds little beyond the schema's bare parameter name.

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 indicates the tool returns events most similar to a given event, and the algorithm/cap add specificity. It does not use an explicit verb like 'find' or 'retrieve', and it does not explicitly distinguish itself from siblings such as search_news or get_event, so it stops 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 description implies use when you have an event and want similar events, but it provides no explicit when-to-use guidance, no exclusions, and no comparison with alternatives like search_news or get_event. The agent must infer the appropriate 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 at all, the description carries the full burden of behavioral disclosure, and it only covers the output shape (endpoints, sources, 'why'). It does not disclose how ambiguous intents are handled, whether the tool performs live lookups or returns cached mappings, latency/cost implications, or failure modes. For a router that brokers access to other endpoints, this is a substantial 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?

A single sentence with no filler; the core mechanism, target resource, and return value are all present. Slight deductions for the ambiguous terms 'knowledge' and 'why' (quoted but undefined) and for the semicolon structure mixing imperative with outcome 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?

This is a complex routing tool with no output schema and no annotations, so the description must explain return behavior and result semantics. It does not describe the structure of returned endpoints, how many can be returned, the format of the 'why', or behavior for unknown intents. An agent would have to call the tool blindly to learn these.

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 0%, so the description must compensate, but it only glosses over the intent parameter ('Describe what you need in natural language') and says nothing about 'limit' — its range, meaning, or whether it caps endpoints, sources, or both. With two parameters at zero schema coverage, this is insufficient.

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 identifies a concrete function: taking a natural-language knowledge need and returning the fitting HeatPulse endpoint(s) plus external sources with a rationale. It clearly differentiates this from direct-access siblings like search_news or list_datasets by positioning it as a routing/discovery layer. It loses a point because the verb phrase is informal ('Describe... get back') and it doesn't explicitly name which sibling it replaces or complements.

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 implied: an agent would call this when it knows what knowledge it wants but not which HeatPulse endpoint fits. However, there is no explicit when-to-use statement, no 'use instead of X when...' routing condition, and no mention of when to prefer direct siblings like search_news. The guidance is inferable but not 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?

With no annotations provided, the description carries the behavioral disclosure burden. It reveals output behavior that schema cannot: returns editorial summaries that are safe to quote and canonical /event/{slug} URLs. This is substantive, though it omits details like auth, rate limits, 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?

Two sentences with no filler. The primary verb and resource are front-loaded, the mode distinction follows immediately, and the output note earns its place by telling the agent what results are safe to quote.

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 read-oriented tool with no required parameters and no output schema, the description covers the core invocation modes and return format. It leaves minor gaps around ordering behavior and filter combinations, but an agent has enough to call it correctly for the two primary use cases.

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 60%, and the description adds meaningful semantics for query ('semantic search') and the no-query default ('heat-ranked feed'). However, it does not add value for limit, order=recent, region, or category beyond what the schema already names or minimally describes, leaving those parameter interactions implicit.

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-resource pair ('Search or list AI news events') and immediately clarifies two distinct modes: semantic search with query versus the heat-ranked feed without it. This clearly distinguishes the tool from news-related siblings like daily_digest and related_stories.

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 explicit internal guidance: 'Pass query for semantic search, or omit it for the heat-ranked feed.' This is clear context for how to choose between the tool's own modes, though it does not explicitly contrast with sibling tools or state when not to use search_news.

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