social-pulse

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

No annotations are provided, so the description carries the full burden. It discloses that the tool is non-destructive (a read operation) and mentions output includes 'first_seen date + sample', but does not cover authentication needs, rate limits, or what happens with empty results. It provides basic behavioral context but lacks depth.

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 sentences with no fluff. First sentence defines the tool's source and action, second explains the use case, third notes output fields. It is front-loaded and every sentence earns its place, achieving maximum efficiency.

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 no output schema, the description mentions 'first_seen date + sample' which provides minimal return value context. For a two-parameter list tool with good schema coverage and no required params, this is sufficient. However, it could list additional output fields for completeness, but overall it is adequate for an agent to understand the tool's behavior.

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% (both 'days' and 'limit' have descriptions in the input schema). The description repeats those meanings (days for first-seen window, limit for max terms) without adding additional nuance like valid ranges or behavior at defaults. Baseline 3 is appropriate since the schema already fully documents the 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 clearly states the tool returns newly first-appearing terms from a specific novelty ledger, with explicit mention of sources (Reddit/HN, idea_intel loop) and use case (early-signal detection). The verb 'surfaces' combined with the resource 'Newly FIRST-APPEARING terms' makes the purpose specific and distinct from siblings like 'trending_topics' which likely focus on volume rather than novelty.

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 usage for early signal detection of new products, memes, etc., but does not provide explicit when-to-use versus alternatives. Siblings are listed but not contrasted, and no 'when-not' guidance or prerequisites are given. The context is adequate but lacks direct differentiation.

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 fully disclose behavior. It mentions data sources, adjustable time window, and types of returns. However, it omits details on rate limits, authentication, data freshness, or error handling, which are important for agentic usage.

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 sentences long, front-loads the core purpose and outputs, and uses no superfluous language. Every sentence adds value.

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?

Despite lacking an output schema, the description enumerates return data (total mentions, buckets, velocity, sentiment, top posts). It covers data sources, time window, and use cases. Could mention pagination or result limits, but overall complete for a simple tool.

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 all three parameters. The description adds marginal value by noting the default and max for 'days' and providing examples for 'term'. Baseline 3 is appropriate as the description does not significantly augment 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 clearly states the tool's function: counting mentions, velocity, and sentiment for a term across Reddit and Hacker News. It lists specific outputs and provides example use cases, effectively distinguishing it from sibling tools like emerging_terms and trending_topics.

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 explicitly recommends use cases such as brand-watch, ticker-buzz, product-launch tracking, and trend-confirmation. It implies when to use it but does not explicitly state when not to or provide direct alternatives.

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 fully convey behavioral traits. It mentions live data sources, recency-weighted engagement, and absence of stale snapshots. However, it does not disclose potential rate limits, authentication requirements, or what happens on errors. The description covers the core behavior adequately but lacks comprehensive transparency.

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 extremely concise: two sentences covering purpose, scope, use case, and data freshness. Every sentence is informative with no filler. It is front-loaded with the key action ('What is SURGING') and immediately states the output.

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 (2 optional params, no output schema), the description covers purpose, scope, use cases, and data sources. It explains the algorithm ('recency-weighted engagement') and data freshness. Missing details like pagination or response size limits are minor given the ranked list nature. Overall, it is quite complete for its 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 description coverage is 100%, so the baseline is 3. The description reinforces the param meanings (e.g., 'scoped to a subreddit or overall') and adds real-time context, but does not provide additional semantics beyond the schema's parameter descriptions. It marginally adds value but is not necessary for understanding the params.

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 verb (returns), resource (trending topics from Reddit and Hacker News), and scope (subreddit or overall). It provides specific use cases ('what's trending on r/<x>'), distinguishing the tool from generic search. The mention of 'real-time momentum' differentiates it from static lists, and the naming of data sources adds clarity.

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 usage context: answering questions about trending topics on Reddit/HN, with ability to scope to a subreddit. It implies when to use (for real-time trends) but does not explicitly state when not to use or list alternatives. However, the sibling tools (emerging_terms, mention_pulse, whats_being_said) suggest complementary functions, but no direct exclusion is provided.

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?

No annotations are provided, so the description must fully convey behavior. It states the tool returns live data and details per-post fields (score, comments, sentiment, link). It also implies a read-only operation. While it does not cover every aspect (e.g., rate limits, data freshness beyond 'live'), the description is sufficiently transparent for simple usage.

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 sentences, each serving a distinct purpose: stating the functional scope, providing a usage scenario, and detailing output fields. It is front-loaded with the essential information and contains no redundant or unnecessary text.

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 no output schema, the description provides a good overview of the return values (posts with scores, comments, sentiment, links, plus themes). It adequately covers the tool's complexity for an agent to understand what it returns. However, it lacks details on how themes are represented or structured, which could be helpful for full 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?

Schema coverage is 100%, so the baseline is 3. The description does not add significant meaning beyond the schema; it only provides an example query format. The parameters (days, limit, subreddit) are adequately documented in the schema, and the description offers no additional context or constraints.

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 fetches top recent posts and dominant discussion themes for a query across Reddit and Hacker News. The verb 'get' is implied, and the resource is well-defined. However, it does not explicitly distinguish this tool from its siblings (emerging_terms, mention_pulse, trending_topics), though the mention of both posts and themes across two platforms provides some implicit differentiation.

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 includes a usage phrase: 'Use to quickly understand what are people saying about X.' This gives a clear context for when to use the tool. However, it does not provide any explicit when-not scenarios or comparisons with alternative tools, leaving the agent to infer applicable use cases.

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