HackerNews Mention Intelligence

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

With no annotations, the description carries the full burden and does disclose meaningful behavior: paid access at $0.03 USDC per keyword via x402 and a max of 50 keywords. It omits auth/payment flow details, failure/partial-failure behavior, and rate limits beyond the cap, so a mutation-adjacent paid tool remains only partially specified.

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?

Dense and front-loaded: cost and cap come first, then the core behavior, then use cases. No wasted sentences, though the trailing use-case list is a little long relative to the operational content.

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 paid, no-annotation, no-output-schema tool with one undocumented parameter, the description conveys cost, cap, and output shape (mentions per keyword over weekly/monthly windows). It still leaves the x402 payment/auth mechanics and any return-format specifics to be discovered elsewhere.

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% for the single targets parameter. The description partially compensates by explaining that targets are companies/terms and that the batch is capped at 50, effectively annotating the array's item semantics and max length, but it gives no format guidance (ticker vs. company name vs. search term).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: batch tracking of a set of companies/terms on HN with weekly/monthly mention counts. The 'batch'/'whole set' framing implicitly distinguishes it from the single-target siblings like mention_snapshot, but it never names them, so differentiation is left to inference.

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?

Offers concrete use cases (portfolio buzz tracking, market research across many startups, competitive scanning, trend discovery at scale), which implies when to reach for the batch variant. However, it never states when NOT to use it or names an alternative sibling for single-target or change-tracking needs.

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, so the description carries the burden. It usefully discloses the paid cost model ($0.05 USDC on Base via x402) and the 'hot' ranking heuristic, which are real behavioral facts. It does not cover rate limits, what the response contains, or how 'new' is determined relative to the last call.

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?

Pricing and core purpose are front-loaded in the first sentence, with use cases packed into the second. Dense and mostly non-redundant, though the use-case list runs slightly long.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no annotations and no output schema, the description supplies the essentials an agent needs: cost, what it detects, and when to invoke it. The main remaining gap is the meaning/format of 'target'.

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 single 'target' parameter has 0% schema description coverage, so the description must compensate. Its examples ('did X get posted on HN', 'about a product', 'tracking new threads') imply target is a keyword/product/entity, but never define accepted formats (handle, domain, keyword, URL).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific capability: change detection over mention history, surfacing new HN discussions and flagging hot ones by points/comments. The 'vs history' framing hints at how it differs from siblings like mention_snapshot, though no sibling is named explicitly.

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?

Gives concrete triggering scenarios: 'did X get posted on HN', discussion monitoring, launch buzz alerts, tracking new threads. Clear context for when to reach for it, but no explicit when-not or named alternative 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 but does disclose important behavioral context: the tool is paid, the cost is $0.50 USDC on Base via x402, and the report contents are listed. It still omits read-only status, latency, data freshness, and whether the target scope is limited, so the disclosure is useful but incomplete.

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 efficient sentences: payment/cost, report contents, and use cases. It is front-loaded with the paid nature and what the report provides, with no significant filler beyond a mild marketing phrase ('Highest-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 one-parameter paid report with no output schema, the description covers cost, report contents, and use cases reasonably well. It remains incomplete because the target parameter is undocumented and sibling differentiation is absent, leaving gaps an agent must resolve before calling 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?

The single required parameter 'target' has 0% schema description coverage, and the description never explains what target represents, what format it takes, or whether it is a keyword, handle, brand, or URL. The agent must infer its meaning from the tool name alone.

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 report and lists its contents (mention counts, momentum, top stories, hot-discussion takeaways), so the agent knows what the tool returns. However, it does not explicitly distinguish this tool from siblings such as mention_snapshot or mention_landscape, leaving the 'intel report' positioning somewhat ambiguous.

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 clear use-case contexts (developer-marketing, PR monitoring, competitive mindshare, launch and reputation tracking), which helps the agent decide when this tool is appropriate. It does not state when not to use it or name alternative siblings for narrower scenarios.

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, and it does disclose two important traits: a $5 USDC on Base via x402 payment requirement and a 10-keyword cap. It says nothing about whether the call is read-only, what happens on payment failure, latency, or whether results are cached, leaving meaningful behavioral gaps for a paid operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with the price/limit front-loaded before the capability, so the highest-risk fact is seen first. Slightly dense with three comma-separated use cases, but no wasted words.

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?

No annotations and no output schema, so the description is the only source of behavioral detail; cost and keyword cap are covered, but the shape of the returned ranking (fields, time window definition for 'weekly momentum') is not, leaving the agent under-informed about results.

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, and it adds the material constraint that targets accepts up to 10 keywords. It does not clarify the expected format of each string (brand name vs. handle vs. query), which the schema also leaves ambiguous.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a concrete verb and resource (ranks brands/projects by HN mentions and weekly momentum, flags risers), so an agent knows exactly what it produces. It does not distinguish itself from the four sibling mention_* tools, 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?

It gives clear selection context — competitive developer-mindshare mapping, market positioning, category buzz benchmarking — which tells the agent what class of task this fits. It offers no when-not conditions or explicit routing to mention_snapshot/mention_intel_report, so there is no exclusion 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 carries the full behavioral burden. It usefully discloses the cost model ('FREE') and the shape of the returned data, but omits freshness/rate-limit behavior, auth requirements, and what 'current' precisely means.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with the highest-value token ('FREE') and the resource front-loaded. The use-case list is a bit of a keyword dump but remains reasonably tight and 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 simple one-parameter read tool with no output schema, the description covers purpose, contents, cost, and use cases adequately. Only the lack of sibling routing logic and parameter format detail keeps it from being fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the single 'target' parameter is an undescribed string. The description does imply its semantics ('a company, product or keyword'), partially compensating for the schema gap, but gives no format, matching, or normalization guidance.

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: 'Current HackerNews presence for a company, product or keyword', and even enumerates the returned data (total mentions, last 7/30 days, recent discussions). This is clear, but it never names or contrasts with siblings like mention_changes or mention_landscape, so the agent gets no explicit 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?

Explicit 'Use for...' guidance is given ('are people talking about X on HN', developer mindshare research, brand awareness, startup/competitor buzz'), which is clear context for when to pick this tool. However, no exclusions or alternative tools are named, so routing among the five siblings is left to inference.

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