AICIA AI Signal

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

With no annotations present, the description carries the full burden of behavioral disclosure. It reveals that the attitude is a 'keyword heuristic only' (not reliable for deep analysis), that results are 'raw evidence for host-side synthesis' (not a final answer), and that it is 'point-in-time' and 'not a durable trust score.' This gives the agent important expectations about the tool's limitations and output nature. It could add more about how the tool handles edge cases, but it is transparent about its core 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?

The description is three sentences, with the primary purpose stated upfront in the first sentence. Each sentence adds value: the first defines what it does, the second clarifies its role and adds a comparison to a sibling, and the third adds a usage caveat. There is no wasted text, and the structure is efficient and front-loaded.

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 is fairly complete for a data-retrieval tool: it states purpose, limitations, and usage guidance. An output schema exists, so return values need not be explained. However, the lack of parameter semantics is a notable gap, and it does not distinguish itself from other HN-related siblings like hn_search or hn_tag_search, which may also return stories. Given the complexity of 4 parameters and the absence of schema descriptions, the description should provide more guidance on how the parameters shape the result.

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 provides no parameter descriptions (schema coverage 0%), so the description must compensate. However, the description does not explain what 'limit', 'since', or 'until' do. While 'query' is self-evident from context, the other three parameters are undocumented. The phrase 'HN stories + counts' implies limit affects counts, but it is not explicitly stated. This is a significant gap for an agent attempting to invoke the tool correctly.

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: 'Point-in-time community signal: HN stories + light attitude hint + counts.' It specifies the resource (HN stories) and what it returns (attitude hint and counts). It also distinguishes itself from hn_attitude by clarifying that this is a keyword heuristic, not fine-grained stance analysis, which helps differentiate from a key sibling.

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?

Explicit guidance is provided: 'call hn_attitude for finer stance work' gives a direct alternative for a different need. 'Re-query when asking again (not a durable trust score)' tells the agent when to re-use the tool and cautions against treating results as persistent. This covers both when to use and when not to use, with a clear fallback.

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 behavioral burden; it does add useful context by restricting comparisons to hard-metric trends and naming heat/selection score. However, it does not disclose constraints such as minimum entity count, window handling, or side-effect/read-only assumptions beyond what 'compare' implies; the output schema covers return shape.

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, front-loaded sentence with no filler. The parenthetical defining 'hard-metric' adds relevant meaning without bloating the 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?

For a tool with only two simple parameters and an output schema, this is close to minimally usable, but the agent must infer window formats and the relationship to singular/alternative trend tools. The output schema reduces the need to explain return values, but the description still lacks enough surrounding context to be fully self-sufficient.

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%, but the description partially compensates by indicating that the 'entities' parameter consists of the things being compared and that the metrics are heat/selection score. The 'window' parameter is left entirely to inference from its name and default value of '30d', so the compensation is incomplete.

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 action ('compare'), a resource ('hard-metric trends'), and a scope ('across entities'), and it clarifies the metrics being compared (heat / selection score). It does not explicitly distinguish itself from siblings like get_entity_trend or compare_model_costs, 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?

'Across entities' implies this is the tool for multi-entity comparisons, and the sibling get_entity_trend offers a likely singular alternative, but the description never spells out when to choose this tool over those alternatives or states any exclusions. The guidance is present only by implication.

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 disclosure burden. It usefully reveals default resolution behavior, expand_family effects, and that unresolved names appear in an `unresolved` field. It does not mention sorting direction or pricing caveats, but these are partially covered by the output schema.

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 concise and front-loaded with the core purpose, followed by only essential parameter usage notes. Every sentence adds value and there is 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?

The description covers the main purpose and the most behaviorally important parameters, and an output schema exists to clarify return values. However, with no annotation support and zero parameter descriptions in the schema, leaving min_coding_index and the token-volume parameters implicit makes the tool somewhat incomplete for fully 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 0%, so the description must compensate for parameter meaning. It explains model_list and expand_family, and implies the token-volume mix concept, but it does not explain min_coding_index, limit, or the daily token parameters, leaving important gaps for a 6-parameter tool.

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 a specific action ('Rank LLM models') and a specific resource/scope ('by estimated monthly API bill for a token-volume mix'). This distinguishes it from sibling tools like rank_models or get_model_benchmarks by focusing on cost estimation.

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 usage context: use this when comparing LLM costs under a token mix. It also explains how to restrict models with model_list and expand families with expand_family, but it does not explicitly name alternatives or say when not to use it.

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 behavioral burden and does well: it discloses that no profile is stored, the tool never writes a publish-ready article, formal cards need independent discussion, and single-primary events stay in the watchlist. This goes beyond a simple 'create' description.

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-paced: a one-line purpose, a useful profile example, then output and gating details. A few phrases could be tightened, but every sentence adds meaning and the most important scoping information is front-loaded.

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 output composition, profile behavior, no-write guarantees, and card criteria, and an output schema exists for return-value details. However, the parameter semantics gap for since/until/limit and the ambiguous '1–10 vs at most five' maximum leave an agent under-equipped for a tool of this 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 0%, so the description must compensate. It does explain the profile parameter with an example and its scoping role, but limit, since, and until are never explicitly described; the 'default window' and 'at most five cards' hints are indirect and ambiguous against the earlier '1–10 cards' claim.

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: 'Create evidence-gated 14-day event cards for a Chinese AI-tech creator.' It clearly distinguishes the tool's editorial-card purpose, though it does not explicitly name or contrast a sibling tool such as get_tech_brief.

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 when the tool is useful—when an evidence-based editorial brief is needed—and gives context about profiles, output, and card criteria. However, it never names alternatives or states when not to use this tool, leaving the choice to inference.

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 and reasonably explains the core behavior: filtering HN stories by domain and by either story URLs or comment recommendations. It is clearly read-only in nature and adds useful surface semantics, though it does not define 'top' or time-window 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?

The description is compact and front-loaded, with two bullet points that each provide essential information. Every sentence earns its place and there is no redundant repetition of schema fields.

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?

Basic invocation with just a domain is clear and the output schema covers return values. However, the time-window parameters have no semantic guidance, and there is no explicit comparison with sibling signal tools, leaving the context slightly incomplete for an agent.

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 real meaning to the 'surface' parameter that the schema alone does not provide. However, 'since' and 'until' remain undocumented in both the schema and the description, so the description only partially compensates for the gap.

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 top HN stories tied to a specific domainverktyg, and then defines the two surfaces: story URLs and comment recommendations. This distinguishes it from broader HN search or community-signal tools even without explicitly naming 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?

The surface bullets give clear context for how to use the tool, including which mode to select for direct domain URLs versus comment-recommended links. It does not name alternative tools or explicitly say when not to use it, so it stops short of a 5.

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 full responsibility for behavioral disclosure. It only states the result criteria but does not mention whether the operation is read-only, how results are ordered, whether limits apply (apart from schema defaults), or any dependencies. The lack of actions like 'returns' or 'lists' obscures the 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?

The description is extremely concise at six words with no fluff, but it is structured as a noun phrase rather than a clear verb-initial sentence. This brevity is efficient, though the fragmentary form slightly reduces immediate comprehensibility.

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?

Since an output schema exists, return-value details are presumably covered elsewhere. However, the description gives no context about the tool's role in an analysis workflow, what the thresholds mean pragmatically, or why an agent would choose this over similar tools, making it incomplete for contextual decision-making.

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 already provides full descriptions and defaults for all three integer parameters (limit, min_posts, min_comments), achieving 100% coverage. The description adds no parameter-specific meaning beyond the schema, so the baseline score of 3 is appropriate.

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 (HN authors) and the selection criteria ('both post often and comment heavily'), which implies a distinct behavior from sibling tools like hn_prolific_commenters. However, it is a noun phrase without an explicit verb like 'Find' or 'List', so it could be clearer about the action performed.

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 given on when to use this tool versus alternatives. There is no mention of conditions, exclusions, or related sibling tools, leaving the agent to infer context from the name and description alone.

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 carries the full burden of behavioral disclosure. It only states what the tool does without revealing any behavioral traits such as whether it performs network calls, requires authentication, has rate limits, or returns partial data. The description is too minimal to inform the agent about side effects or operational characteristics, which is a significant gap for a tool with zero annotation coverage.

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, compact sentence that is front-loaded with the key action and scope. It avoids fluff and is easy to parse. However, it is arguably too brief for a tool with many siblings, but for conciseness and structure, it earns a high score because it is efficient and to the point.

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?

While the tool has an output schema (which relieves the description from explaining return values), the description lacks essential contextual information. It does not clarify how this tool is distinct from the many related siblings, does not state any operational constraints or prerequisites, and provides no guidance on what counts as 'factual signals.' Given the tool's moderate complexity and the rich sibling set, the description is incomplete for an agent to make an informed decision about when to invoke it.

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 single parameter already has a clear description ('entity name, handle, or id'). The tool description adds no additional meaning beyond what the schema provides, merely referencing 'an entity.' Since the parameter semantics are already well covered by the schema, a baseline score of 3 is appropriate; the description does not enrich the parameter understanding.

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 ('Fetch') and resource ('factual signals for an entity'), and enumerates the entity types it covers (repos, models, papers, leaderboards). This provides a specific purpose. However, it does not explicitly differentiate from siblings like get_entity_trend or get_model_benchmarks, which might also fetch entity-related data, leaving some ambiguity about what makes 'factual signals' distinct.

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 alternatives. It does not mention any conditions, prerequisites, or exclusions. Given the large number of sibling tools (e.g., get_entity_trend, get_tech_brief, compare_entity_trends), an agent has no explicit basis to choose this over others, making usage guidance effectively absent.

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 disclose behavioral traits. It only says 'Fetch' which implies a read operation, but it does not explicitly state it is non-destructive, does not require special permissions, or mention any rate limits or side effects. The description also fails to explain how results are aggregated or ordered. With no annotation safety net, this gap is significant.

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, focused sentence that immediately states the main purpose and source scope. It is front-loaded with the core action ('Fetch recent discussions') and adds only relevant source details. There is no filler or redundant information, achieving efficient communication.

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 and the presence of an output schema, the description covers the basics. However, it does not mention that the tool aggregates across multiple sources, how the results are ranked or limited, or how the 'since'/'until' parameters interact with the 'recent' semantics. An agent might need more context to call it appropriately, but the schema and output schema fill some gaps.

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 all parameters (query, limit, since, until) are already documented in the input schema. The description adds marginal value by equating 'topic' with the query parameter, but it does not explain semantics beyond the schema, such as expected format for dates or limits. The baseline of 3 is appropriate given full 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 clearly states the tool fetches recent discussions about a topic, specifying sources (HN, GitHub, papers). The verb 'fetch' and resource 'discussions' are concrete. It does not explicitly differentiate from siblings like hn_search or search_reddit, but the mention of multiple sources and 'recent' provides enough distinction to be clear.

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 guidance on when to use this tool versus alternatives such as hn_search, search_reddit, or content_brief. The description only states what it does without any context on conditions that favor it over siblings. No exclusions or scenarios are given, leaving the agent to infer usage.

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 usefully discloses that comment bodies are returned verbatim, a judgment_prompt is included, and the host (not the tool) performs classification. This goes beyond a simple fetch description, though it does not address rate limits or failure 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?

Two sentences, front-loaded with the core purpose, followed by a concise behavioral note about the returned comment bodies and judgment_prompt. Every sentence 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?

Given the output schema exists and the schema covers most parameters, the description provides enough context for a capable agent: what the tool fetches, what it returns, and who performs classification. It is missing explicit usage boundaries versus siblings, but the core invocation context is 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?

The description does not add parameter-level meaning beyond the schema. The schema already documents entity, problem, issue_limit, and comment_limit; since/until are self-explanatory by name. With 67% schema coverage, the description does not need to compensate heavily, but it also adds no parameter detail.

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 ('Fetch'), a specific resource ('raw GitHub issue threads'), and the exact purpose ('for the host to judge workarounds / fixes'). This clearly differentiates it from sibling search/discussion tools by focusing on GitHub issue threads and workaround candidacy.

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 purpose implies when to use it — when raw GitHub issue threads are needed for workaround vs official-fix judgment — but it does not explicitly state when not to use it or name alternative tools. Usage context is clear but left to inference.

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 carries the full burden of behavioral disclosure. It only says 'find' and lists example relation types; it does not mention resolution behavior, whether anything is mutated, or any limitations. The behavioral detail that the name is 'resolved first' appears only in the parameter schema, not in the tool description.

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 tight sentence with no filler and front-loaded examples. It earns its place, but it is so brief that it leaves relevant usage and behavioral context unstated, so it is not a perfect 5.

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 query tool with a fully described input schema and an output schema, the description covers the basic purpose adequately. However, with 28 sibling tools and no usage differentiation, an agent may not reliably know when to invoke this tool versus other related-entity or research tools.

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 schema already documents all three parameters. The description's examples ('competitors, integrations, alternatives') loosely mirror the schema's relation_types examples, but they do not add meaningful new semantics beyond what the schema 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 states a specific verb ('find') and resource ('related AI tech entities'), and it gives concrete examples of what counts as related: competitors, integrations, alternatives. It does not explicitly distinguish itself from sibling tools like recommended_tools or resolve_tech_entity, so it does not earn 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 guidance on when to prefer this tool over sibling tools such as fetch_entity_facts, recommended_tools, or get_tech_brief, and no exclusions or prerequisites. The intended usage must be entirely inferred from the name and the examples in the 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?

With no annotations, the description carries the behavioral burden and does add valuable context: it discloses that the tool returns insufficient_data until enough snapshots exist. It also scopes results to hard metrics and excludes community attitude, giving the agent meaningful expectations beyond what the schema shows.

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 short sentences with no filler. The core purpose is front-loaded, followed by the exclusion/alternative and the edge-case note, so every sentence earns its place.

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 output schema covers return values, and the description covers purpose, an alternative, and an important edge case. However, it leaves the source parameter and window format undefined, and with no annotations this creates a noticeable gap for an agent trying to make a well-formed 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 description coverage is 0%, so the description must compensate for the bare parameter names. It only restates 'entity' and 'time window' in natural language, never explains the optional source parameter, and gives no format guidance for window beyond the schema default. This is minimal compensation for missing parameter documentation.

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 defines the tool as returning a hard-metric trend for an entity over a time window, which is a specific operation and resource. It also distinguishes itself from community-attitude tools by explicitly stating that community attitude is not included, making its purpose unambiguous even among many 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?

It gives explicit when-not-to-use guidance by stating community attitude is excluded and directing the agent to community_signal / hn_attitude instead. It does not cover every possible alternative such as compare_entity_trends, but the singular-entity scope and the named alternatives provide adequate usage direction.

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 carries the full burden. It discloses that the tool returns scores grouped by benchmark and specifies reasoning_mode and agent_framework as secondary dimensions, which is useful behavioral context. However, it does not mention potential side effects, authentication requirements, rate limits, or whether results are historical or filtered. As a read-only 'get' operation, risk is low, but more explicit disclosure of limitations would improve 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?

Two sentences that front-load the core function and include a concrete use case. No redundant words or vague phrasing. Highly efficient and well-structured.

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 two-parameter tool with an output schema, the description covers the main purpose, the grouping of results, and the use case. It does not mention pagination or error conditions, but given the simplicity and the presence of an output schema, it is sufficiently complete for an agent to call 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% and both parameters have meaningful descriptions in the schema. The description adds minimal value beyond the schema, only reinforcing that all scores are returned and that benchmark filtering is optional. It does not provide syntax or format details, so 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 states a specific verb+resource: 'Get all benchmark scores for a model across eval sources.' It lists concrete benchmark examples and clearly distinguishes itself from siblings like get_model_trend or rank_models by focusing on benchmark scores. The purpose is unambiguous.

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 states a use case: 'Use to compare a model's strengths across tasks.' This provides clear context for when to use the tool, though it does not explicitly mention alternatives or exclusion conditions. The stated purpose is sufficient for an agent to select it appropriately.

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 bears the full burden of behavioral disclosure, but it only mentions the default family rollup, which is already visible in the schema. It does not clarify read-only behavior, data granularity, time-range handling, or other operational traits.

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 terse and front-loaded, with no wasted words. However, the brevity comes at the cost of explanatory 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?

For a tool with four parameters and no annotations, this description is too minimal. The output schema may cover return structure, but the required parameter, relationship to sibling tools, and intended usage context remain unclear.

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 explain parameter meaning, but it only vaguely hints at 'metric' and 'family rollup'. The required 'name' parameter is not explained, and 'limit' is entirely undocumented.

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 tool as providing model metric time-series data, and the tool name reinforces the 'get' action. It is distinct enough from siblings like topic_trend or get_entity_trend, though it doesn't explicitly name those 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?

No guidance is given about when to use this tool versus siblings such as get_entity_trend or compare_entity_trends. The phrase 'family rollup by default' hints at behavior but doesn't help an agent decide between this and related trend tools.

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. It discloses staleness explicitly ('may be stale') and details the return structure including age_days and stale_warning, which are critical behavioral signals. It does not mention mutations (though 'get' implies read-only) or any access requirements, but the disclosure of staleness and freshness fields is valuable and above baseline for a read tool.

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 concise and well-structured: the first sentence states purpose, the second gives usage guidance, and the third lists return fields. It is front-loaded with the core purpose. The return field list is somewhat verbose but adds value by clarifying output shape. No wasted words, though the return list could be trimmed if an output schema were actually provided (but it's not, so it earns its place).

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 tool with a simple retrieval purpose, the description covers the essentials: what it does, when to prefer alternatives, and the exact shape of the return object. It lacks an explicit definition of 'entity' and does not describe error handling beyond the {found:False} case, but given the tool's simplicity and the presence of an output schema (even if not shown), it is reasonably complete. Missing the entity definition is the main 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 only parameter 'entity' receives no description in the input schema (0% coverage) and the description merely says 'for an entity' without defining what an entity is (e.g., a tech entity, a company, a model). This fails to adequately compensate for the schema's lack of information. The agent would have to infer semantics from sibling tools or external context, which is insufficient for correct invocation, especially given the parameter is required.

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 a 'short synthesis brief for an entity' and notes it may be stale. This is a specific verb and resource, and it distinguishes itself from live community/HN tools by explicitly marking itself as potentially stale, which is a functional differentiator from siblings like content_brief or live search 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 provides explicit guidance on when NOT to use the tool: 'Prefer live community / HN tools for current voice' and 'Model-compare playbooks should not depend on briefs for brand-new models.' This naming of alternatives and exclusion of use cases gives the agent clear routing information, exceeding the typical minimal guidance.

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 behavioral disclosure, and it does add one meaningful trait: the data is historical and not a live stream. However, it says nothing about data freshness, source scope, pagination, latency, or side effects, so behavioral coverage is only partial.

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 two short sentences with no filler. The core distinction is front-loaded, and the routing note immediately follows; every sentence earns its place.

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 full schema coverage, an output schema, and only one required parameter, the description supplies the key contextual information needed to choose and call the tool. It could add more detail about the historical time window or data sources, but nothing essential for a correct invocation is missing.

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 parameters (entity, limit, platform) are already fully documented. The description only reinforces that the entity is the subject; it adds no extra 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 opens with a specific resource and state: 'Historical mention snapshot for an entity,' and explicitly distinguishes it from 'a live firehose.' It also names sibling tools for current discussion, making the tool's purpose and scope clear.

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 a direct routing rule: 'For current discussion, use community_signal / hn_attitude.' This tells the agent when not to use this tool and which alternatives to pick, while the historical framing implies when it should be used.

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 disclosing behavior. It only states high-level actions ('discover', 'compare', 'attach') without explaining side effects, data sources, or limitations. It does not mention whether it performs read-only lookups or if it requires external API calls, leaving significant behavioral ambiguity.

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 short and front-loaded, with key actions in the first few words. However, it is more concise than informative, sacrificing necessary detail for brevity. The description is adequately brief but lacks substance, but it is not verbose or wasteful.

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 output schema exists, so return values might be self-evident, but the description does not explain what a 'tech_selection_report' contains or how the parameters affect it. There is no mention of required parameters or how to use 'window' effectively. For a complex tool with 3 parameters and no schema descriptions, this description is incomplete.

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%, meaning the schema provides no descriptions for parameters. The tool description does not compensate by explaining the meaning or format of 'domain', 'limit', or 'window'. The agent only sees defaults and types, which is insufficient for correct invocation, especially for domain (likely a URL or ID) and window (format like '30d' is unclear).

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 uses verbs like 'Discover' and 'compare' which indicate what the tool does, but it is vague about the specific resource ('tech_selection_report') and what constitutes a domain candidate. It mentions 'attach live community_signal' which hints at functionality, but the overall purpose is not clearly differentiated from sibling tools like 'domain_signal' or 'get_tech_brief'.

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 alternatives. It does not mention prerequisites, such as resolving a domain first, or when to choose 'get_tech_selection_report' over 'domain_signal' or 'get_tech_brief'. The agent is left to infer usage from the vague purpose.

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 disclosure burden. It usefully reveals that the tool provides only a 'light keyword stance hint' and that final stance synthesis is left to the host. However, it does not explain query matching behavior, filtering semantics, pagination, or any limitations beyond what the schema already shows.

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 concise and front-loaded with the core action, followed by a clear division of labor between tool and host. Every sentence contributes; there is no redundant filler. It could include more behavioral guidance, but as a concise statement it is well structured.

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 gives the essential purpose and the presence of an output schema helps cover return-value expectations. Still, since/until parameters are left semantically vague)Skip and no sibling tool is mentioned for routing. The definition is minimally viable for a retrieval tool but leaves the agent to infer several operational details.

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 60%, with query and limit documented in the schema and min_depth given a meaningful hint. The description adds no extra parameter detail)Skip that compensates for the undocumented since/until fields, so the agent still has to infer their format and behavior from names and context.

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 that the tool returns HN comments on a topic and is intended for host-side attitude synthesis. It also distinguishes the tool's role by saying the HOST synthesizes support/oppose/mixed, which helps separate it from analysis tools. It does not explicitly contrast with hn_search or hn_topic_painpoints, so it stops short of full sibling 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 communicates when to use it: when you need HN comments plus a light stance hint for the host to synthesize an attitude. It does not say when not to use it or name alternatives such as hn_search, hn_topic_painpoints, or hn_ideas. The intended usage is implied but not made 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 carries the burden and adds a useful behavioral boundary: the tool returns 'tagged clues' rather than synthesized ideas. However, it does not disclose filtering/aggregation behavior, how tags are structured, or any operational constraints.

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 short sentences front-load the purpose and use boundary without wasted prose. Minor redundancy ('Return' / 'returns') and the unexplained capitalized 'HOST' keep it from being flawlessly structured.

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 tool has no required parameters, so it can be called without further details, and an output schema is present. But the description leaves since/until semantics undefined and does not clarify how this tool relates to hn_topic_painpoints, making it merely adequate with clear gaps.

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 only 50%; limit and theme have brief descriptions, but since and until are bare string/null fields with no format or semantics. The description adds no parameter-level guidance and does not compensate for the undocumented date 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 states a specific resource ('HN pain points / unmet needs') and a clear purpose ('host-side startup-idea synthesis'). It is more informative than a tautology, but it does not differentiate hn_ideas from the similarly named sibling hn_topic_painpoints, so it stops short of 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 phrase 'for host-side startup-idea synthesis' tells the agent when this tool belongs, and 'the HOST generates ideas' warns against using the tool's output as final answers. It gives clear context but names no alternatives or explicit when-not-to-use conditions.

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 carries the full burden of behavioral disclosure. It does not state sorting order, data freshness, pagination, or any side effects (though 'list' implies read-only). The parenthetical 'active discussion participants' adds minimal interpretive value beyond the schema.

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, efficient sentence with no filler. The verb and resource are front-loaded, and every word earns its place.

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 simple listing tool with an output schema present, so return-value details are covered elsewhere. The description conveys purpose adequately, and the schema covers parameters. It lacks usage heuristics but is reasonably complete given its simplicity.

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% for both limit and min_comments, so the input schema fully documents parameter meaning. The description adds no extra parameter semantics, and the baseline of 3 applies because the schema does the heavy lifting.

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 ('List') and resource ('high-volume HN commenters'), with a parenthetical clarifying what counts as high-volume. It is clear, but it does not explicitly differentiate from sibling tools like hn_search or hn_attitude, though the purpose is distinct enough from the name alone.

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 guidance on when to use this tool versus alternatives. The description only states what it does, leaving the agent to infer from the name. No exclusions, alternatives, or contextual triggers are 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?

With no annotations, the description must disclose behavioral traits itself, but it only says 'search.' It does not explain how results are ordered or returned, how limit applies to both stories and comments, how scope behaves by default, or how since/until constrain the search.

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 clear sentence with no wasted words. It is concise and front-loaded, though it could provide more useful detail without becoming verbose.

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 five parameters, no annotations, and many similar sibling tools, the one-sentence description is under-specified. It omits date format expectations, default behavior details, and any disambiguation from other HN-related tools, making full understanding dependent on inspecting the schema and sibling tools.

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 only 60%, and the since and until parameters lack descriptions in the schema. The tool description adds no parameter semantics beyond the word 'topic,' so it does not compensate for the gaps in date formatting or limit/scope behavior.

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 action ('Search') and resource ('Hacker News stories and comments') with a topic parameter. It is clear and informative, though it does not explicitly differentiate itself from similar sibling search tools like hn_tag_search.

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 no guidance on when to use this tool versus alternatives such as hn_tag_search, hn_attitude, or fetch_recent_discussions. The intended use is implied by the verb 'search' but no conditions, exclusions, or comparative context are 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?

With no annotations provided, the description carries full responsibility for behavioral disclosure. It does not mention what the tool returns (e.g., list of tags, counts), whether it is read-only, or any limitations like pagination or rate limits. The special ask_kind dimension is disclosed, but other behavioral aspects are absent.

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 concise, consisting of two sentences that front-load the primary purpose and then highlight the special dimension. Every sentence adds information without fluff, earning a high score for 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 the tool has five optional parameters and an output schema, the description does not need to explain return values. However, it omits guidance on date-range parameters (since/until) and does not mention how tags are structured or aggregated. For a tool with many siblings and no annotations, more context would help, but the description is adequate for basic usage.

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 60% (limit and value have descriptions; since, until, dimension do not). The description adds value by explaining the dimension parameter's role, especially the ask_kind special value, but it does not clarify the semantics of since and until. This partial compensation merits a 3.

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 queries structured HN post tags, with a specific focus on dimensions like pain points, unmet needs, and categories. It also highlights the special ask_kind dimension. However, it does not explicitly distinguish itself from sibling tools like hn_search or hn_ideas, though the focus on tags provides 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 implies usage for tag-based queries and mentions a special dimension for Ask HN question types, but it does not explicitly state when to use this tool over alternatives. No exclusions or alternative tools are named, leaving the decision partially to inference.

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. It usefully discloses that phrase hints are coarse and that the HOST is expected to do semantic clustering, and it states the return includes raw comments. However, it omits practical constraints such as rate limits, default time ranges, sorting, or any limitations on which HN comments are included.

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 compact sentences with no filler: the first states the core function and output, and the second delivers an important caveat about phrase quality. The description is front-loaded and every sentence earns its place.

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 an output schema present and 5 of 6 parameters either described or self-explanatory, the minimal information needed to call the tool is available. However, the description does not clarify what distinguishes 'painpoints' from related HN analysis tools, nor does it explain default filtering or how results are ordered. It is usable but not comprehensively specified.

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 67%, covering topic, limit, ask_kind, and min_depth, while since/until are self-explanatory by name. The description itself adds no parameter-level detail, but the schema already carries most of the semantic weight, so this is adequate but not enhanced.

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 action ('Mine') and resource ('HN comments mentioning a topic') and states the outputs ('phrases + raw comments'). It is clear enough to distinguish the tool's core function, though it does not explicitly contrast it with close siblings like hn_ideas or hn_search.

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 guidance on when to use this tool versus alternatives such as hn_search, hn_ideas, or hn_attitude. The only usage note instructs the HOST to cluster raw_comments semantically, which is post-hoc output handling rather than tool-selection guidance.

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 carries the full burden. It discloses several meaningful behaviors: scenario-specific benchmark sources, the fact that benchmarks are distinct rather than a composite index, family_rollup collapsing variants, and include_community attaching a live community_signal. This goes well beyond what the schema shows, though it leaves 'cheap' undefined and does not mention output 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?

The description is compact and front-loaded: the first sentence states the core action and scenario options, and each subsequent sentence adds a distinct, necessary clarification. No redundant phrases or 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?

An output schema exists, so return shape is covered. But with six parameters and zero schema descriptions, the description leaves important gaps: what 'cheap' means, how max_input_price interacts with ranking, and what open_weights_only does. It is adequate for basic usage but not fully self-sufficient.

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 undocumented parameters. It adds meaning for scenario (lists valid values and their benchmark mappings), family_rollup, and include_community. However, limit, max_input_price, and open_weights_only remain unexplained, leaving a partial gap.

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?

Description opens with a specific verb and resource: 'Rank models by scenario: coding | agent | intelligence | cheap.' It clarifies that coding and agent map to specific benchmarks (AA Terminal-Bench 4.0, AA Apex Agents) and explicitly disclaims a composite capability index, which helps distinguish it from siblings like get_model_benchmarks. It doesn't explicitly name a contrasting sibling, 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 explains tool mechanics—scenario options, benchmark mappings, family_rollup behavior—but gives no guidance on when to pick this over sibling tools or what conditions make it inappropriate. There is no 'use X instead' routing, no exclusions, and no mention of 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 provided, the description must carry the behavioral burden. It discloses that the HOST should cluster raw tool mentions into canonical names, which is a key behavioral trait (the tool doesn't return raw mentions but canonicalized ones). It also implies it is a read-only analysis tool, though it doesn't explicitly state that. It doesn't mention any side effects or limitations, but the given trait is important and non-obvious.

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 concise, with three sentences covering purpose, pairing, and a behavioral note. It's front-loaded with the main purpose, and each sentence adds value. No redundant content. However, it could have integrated the pairing hint more smoothly, but overall it's well-structured.

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 tool has an output schema, so return value details are covered there. But the description lacks explicit guidance on parameter usage (e.g., how `since`/`until` work, what `topic` should be more precisely, how `limit` behaves). It also doesn't mention any rate limits or required permissions. An agent might not know whether to fill in the date parameters or what to do with the canonical clustering behavior. It's sufficient for a basic call but leaves some ambiguity.

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 50%, with `topic` and `limit` having descriptions, while `since` and `until` lack any schema description. The description doesn't compensate for the missing semantics of `since` and `until`—it doesn't explain that they are date filters or what format they expect. For a topic-based tool, the parameter meanings are relatively clear from context, but the gap means the score stays at baseline 3.

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 that this tool returns tools recommended by the HN community for a given topic, and it pairs with hn_topic_painpoints. It is distinct from siblings like hn_topic_painpoints and hn_ideas, though it doesn't explicitly name all alternatives. A minor gap is that it doesn't specify the exact output format, but the core purpose is clear.

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 tells the agent to pair it with hn_topic_painpoints for a dual pain+recommendation view, giving clear context on when to use it. It doesn't explicitly say when NOT to use it or list other alternatives, but the pairing hint is valuable. The sibling set includes hn_topic_painpoints, which is a strong complement, and the description effectively guides the agent toward a combined workflow.

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 must disclose behavior on its own. It does disclose the lookup outcome shape, including a failure mode ({found:False, query}), but it does not explicitly state whether the operation is read-only, what happens on ambiguous matches, or whether it requires special permissions. This 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 compact and front-loaded: first the core purpose, then the usage context, then the return shape. Every sentence adds useful information, and there is no filler or repetition of schema details.

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 simple single-parameter interface and the presence of an output schema, the description is nearly complete. It explains the disambiguation role, provides usage timing, and summarizes the return shape. It could be more complete by explicitly noting the operation is safe/read-only, 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 schema only defines 'query' as a string with no description, so the tool description carries the burden. It compensates by clarifying that the query is likely an informal or abbreviated AI tech/model name, such as compact model spellings, which adds meaning beyond the raw schema. A more explicit 'query is the name/alias to resolve' would make it fully unambiguous.

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 uses a specific verb ('Disambiguate') and resource ('an AI tech or model name to a canonical entity'), making the tool's core function immediately clear. It also frames the tool as a prerequisite step before cost/trend/facts/workaround tools, which helps distinguish it from sibling tools like fetch_entity_facts or get_entity_trend.

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 says when to use it: before cost/trend/facts/workaround tools when the user's name may be informal or abbreviated. It gives a concrete example ('compact model spellings') but does not state when not to use it or name specific alternative tools, so it stops short of a full 5.

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 carry the behavioral disclosure burden. It does add a meaningful behavior note—matches tagline/description, not just the name—but it does not disclose return format, ordering, pagination behavior, or any side effects. For a read-only search tool, this is minimal but not contradictory; the description is more helpful than a tautology 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?

Two short, focused sentences with no fluff. The core action and resource are front-loaded, and the second sentence clarifies the matching semantics. Every word serves a purpose, making it easy to scan.

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 search tool with 5 parameters and an output schema, the description is fairly complete. It covers the tool's purpose and matching behavior. The main omissions are the format/meaning of since/until and any notes on defaults or limits, but the schema covers limit and min_votes defaults. Given the output schema exists, the description need not explain return values. It is adequate but could add a sentence on date filtering.

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 description adds semantic value to the query parameter by clarifying that it matches functional descriptions (tagline/description), which is not explicit in the schema ('free-text'). The schema already documents limit, query, and min_votes; the description compensates somewhat for the undocumented since/until parameters by giving the overall search intent. Coverage is 60%, and the description does add meaning beyond the schema for the key query parameter.

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 ('Search'), a resource ('Product Hunt products'), and a clear qualifier ('by functional description'). It also distinguishes the tool from siblings like search_reddit or search_x by tying it to Product Hunt and specifying the matching mechanism (tagline/description vs. name).

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 when to use this tool: when you need to find Product Hunt products by what they do rather than by name. It clarifies the focus on functional description, but it does not explicitly exclude alternatives or mention when other search tools might be better. However, the platform specificity is clear from the name and the first sentence.

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. It clearly states the tool is a read/search operation, filters results by relevance, and has deployment-specific limitations (realtime_unavailable). This goes beyond a bare 'search' and provides useful behavioral context without contradicting any annotations (none exist). It could mention the return format or pagination, but the output schema exists, which reduces the need.

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 concise at three sentences. It front-loads the core purpose in the first sentence, then adds important behavioral notes. No fluff or redundancy. Each sentence adds value: purpose, data source behavior, and deployment limitation. It could be slightly more structured (e.g., bullet points) but is appropriately sized.

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 moderate complexity (3 params, all optional, no required params, and an output schema), the description covers essential aspects: what it searches, how it behaves (corpus vs live), and a key limitation. The output schema likely describes the return structure, so the description doesn't need to repeat that. It lacks explicit examples or edge cases, but the description is sufficient for an agent to call it correctly in most 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 100%, so the schema already documents each parameter (query, subreddit, limit). The description adds minimal parameter-level detail, but it does clarify that search is by 'query and/or subreddit,' implying that either or both can be used, which aligns with all optional parameters. Since the schema is thorough, the description doesn't need to repeat details; a score above baseline is justified for the implicit relationship.

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 purpose: 'Search Reddit discussions by query and/or subreddit.' It specifically names the resource (Reddit discussions) and the two dimensions of search (query, subreddit). However, it doesn't explicitly differentiate from siblings like search_x or search_producthunt, though the resource is implied by the name and query structure.

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 that the tool prefers a local corpus and may supplement with live fetch when configured, giving context on data source behavior. It also warns that on some deployments only the corpus path is available (realtime_unavailable), which helps agents set expectations. However, it doesn't explicitly state when to use this vs a specific sibling (e.g., search_x for Twitter), so guidance is clear but not exhaustive.

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 carries the burden. It discloses a key behavioral trait: the tool may be unavailable on some deployments. However, it doesn't mention rate limits, authentication needs, or what happens when the handle doesn't exist or the fetch fails. The availability caveat is valuable but incomplete for a network-fetching tool.

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, front-loaded with the core action, and the availability caveat is placed second. Every sentence earns its place; no filler or repetition.

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 tool has an output schema, so return values are covered elsewhere. The description covers the main usage context and a critical deployment caveat. It could add a note about error behavior or authentication, but for a simple fetch tool with a rich schema, it is largely 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 100%, so the schema already documents all four parameters. The description adds no extra parameter-level meaning beyond the schema, but the schema is self-sufficient. Baseline 3 is appropriate.

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 ('Fetch') and resource ('recent posts for an X (Twitter) handle'), which clearly distinguishes it from generic search tools. It doesn't explicitly name a sibling alternative, but the mention of community_signal / hn_attitude as alternatives helps differentiate it from those social-voice 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 context: use this for recent posts by a specific handle, and prefer community_signal / hn_attitude for broad community voice when realtime social fetch is disabled. It doesn't explicitly say 'when not to use' beyond the availability caveat, but the alternative routing is useful and specific.

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. It discloses one-shot behavior, that it returns structured evidence plus a synthesis prompt, and that the HOST writes the final brief. However, it does not explain how evidence is gathered, what sources are used, or any limitations or failure 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?

The description is two sentences with no filler. Purpose is front-loaded, and the second sentence adds the key behavioral detail about the HOST writing the final brief, earning its place.

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?

An output schema exists, so return value details are covered elsewhere. The description explains the tool's role, one-shot nature, and relationship to the final brief. It is slightly less complete because it does not distinguish itself among many siblings, but overall enough 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%, so the schema already documents both parameters. The query description adds useful automatic time-phrase behavior, and context is described as optional prior-conversation data. The tool description itself does not need to repeat parameter details.

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: 'gather evidence for a selection question.' It further differentiates the tool from siblings by explicitly saying the HOST writes the final brief, clarifying that this tool only produces evidence and a synthesis prompt.

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 a clear use context: use this for a one-shot tech-decision/selection question. It does not name alternatives or explicitly state when not to use it, but the context is specific enough for an agent to recognize the intended scenario.

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 burden of behavioral disclosure. It reveals the read-only nature (trend calculation), the title-mention filter, and monthly/weekly aggregation. However, it does not disclose edge cases, rate limits, or the exact calculation method, which is acceptable for a simple query tool 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?

Two concise sentences with the core purpose front-loaded. No fluff or redundancy; every word earns its place.

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 omits mention of the since/until date-range parameters and does not explain the trend calculation details. While an output schema exists, the description fails to convey the full capability set of a 4-parameter tool, leaving gaps for an agent.

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 only 50% (topic and bucket have descriptions). The description does not elaborate on the since/until parameters or their expected formats. It merely echoes the bucket granularity without adding value beyond the schema, failing to compensate for the missing parameter descriptions.

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 it computes a post-count trend for a topic on HN, filtered to title mentions. It uses a specific verb ('trend') and resource ('topic on HN'), which distinguishes it from some siblings, though it doesn't explicitly name 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 description implies use for trend analysis ('heating up or cooling down') but offers no explicit when-to-use guidance or differentiation from sibling tools like get_entity_trend or compare_entity_trends. No conditions or exclusions are mentioned.

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