Ad Angle Map

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

Annotations already declare readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds genuinely new behavioral context beyond that: the tool is pollable (pending until a reply arrives, then answered) and carries an explicit trust boundary warning that reply content is data, not instructions — a meaningful prompt-injection disclosure.

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?

Three tight sentences: purpose first, then polling behavior, then the trust caveat. Every sentence carries distinct information and nothing is padded.

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

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

For a single-parameter read tool with an output schema and full annotation coverage, nothing an agent needs is missing. The polling semantics and the injection warning go beyond what structured fields provide, closing the remaining gaps an agent would otherwise have to discover by trial.

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?

Only one parameter exists and the schema already documents it fully (coverage 100%, including the ^fb_[0-9a-f]{32}$ pattern and the note that submit_feedback returned it), so the baseline of 3 applies. The description repeats the ticket-id linkage but adds no format or validation detail 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?

States a specific verb (Read) and resource (maintainers' reply to feedback), and ties it causally to the sibling tool submit_feedback via the ticket id. An agent can distinguish it from submit_feedback, screen_token, and wallet_identity without opening any schema.

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?

Clearly establishes the precondition: this is used after submit_feedback, with the ticket id that call returned. It does not state any exclusions or alternative ways to retrieve feedback status, but the usage context is unambiguous.

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

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), so the description adds real value beyond them: dedup semantics ("identical copy from one advertiser counts once"), the keyword-matching methodology, and the caveat that gaps are leads to check rather than proof. These are genuine behavioral traits an agent cannot infer from the annotations.

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?

Front-loaded with the usage trigger and result shape, and every clause carries information. It loses a point for mild redundancy: "does not fetch ads from any ad library" is stated in both the description and the ads parameter schema.

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 read-only analysis tool with an output schema, this covers everything an agent needs: when to call it, what to pass, how counts are derived, what the gaps mean, and the methodological limitation. No meaningful gap remains.

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

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

Schema description coverage is 100%, so the baseline is 3, but the description adds interpretation semantics the schema lacks: how the ads array is collapsed for counting (one advertiser's identical copy counts once) and how descriptions are consumed alongside text. It stops short of format details but meaningfully supplements 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?

States a specific verb and resource: it maps persuasion angles across a supplied set of ads and identifies which are missing or thin. Distinguishes itself from siblings like validate_angle_gaps and map_store_offers by naming the exact output (ranked angles with counts plus a gaps list) and the exact input (ads the user already has).

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 an explicit when-to-use trigger with a concrete user phrasing ("which angles are these competitor ads missing?") and a clear negative constraint ("does not fetch ads from any ad library"). It does not name an alternative tool such as validate_angle_gaps for the follow-up validation step, so the routing is clear but not fully comparative.

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

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

Annotations only declare read-only/idempotent/open-world safety, but the description adds real operational context: robots.txt is honored first, at most three pages of 250 products per store, the feed is read once, and a store that disallows the feed is reported as unavailable rather than fetched. These are non-obvious behaviors an agent would otherwise have to discover.

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?

Front-loaded with the trigger and example, then behavior, return shape, and boundaries in sequence. Every sentence carries information, though the return-value enumeration is slightly long for a tool that already ships an output schema.

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?

Covers trigger, inputs, fetch mechanics, return shape, scope exclusions, and the robots.txt failure mode. With an output schema present and 100% parameter coverage, nothing an agent needs to call this correctly 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 domains parameter, its 1-3 bounds and the /products.json fetch detail are already documented in the schema. The description's 'Pass one to three store addresses' restates rather than extends that, so the 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?

States a specific verb and resource ('map store offers', 'compare what competitor stores offer') with a concrete example, and explicitly separates itself from the ads-focused sibling by saying 'This is product pages and prices, not ads'. An agent can distinguish it from map_ad_angles and validate_angle_gaps without opening either schema.

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?

Opens with an explicit trigger condition ('Use this when the user wants to compare what competitor stores offer') plus a worked example, and adds a negative scope boundary ('not ads: there is no creative and no sales data') plus a failure condition for disallowed feeds. Both when-to-use and when-not are covered.

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

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), yet the description adds genuinely new behavior: it discloses the conditional path for an unclear note (returns two likely stages), the evidence block's fields, and the important caveat that the stage is derived from keyword rules and a public post rather than a model or test result.

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?

Front-loaded with purpose, then usage, then return shape and caveats; sentences are dense but each carries distinct information. The return-structure sentence partially duplicates the output schema, which is minor redundancy rather than 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?

With an output schema present, the detailed description of return values is somewhat redundant, but the conditional unclear-note behavior and the methodology disclaimer are completeness-relevant and not captured in structured fields. Nothing needed to call the tool correctly 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 coverage is 100%, so baseline is 3, but the description adds meaning beyond the schema by stating the precedence relationship: pass product and stage, OR an audience note to have the stage inferred, and by tying 'given/inferred/unclear' evidence back to how the inputs were resolved.

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 ('pick ad type') plus the qualifier 'by buyer stage', which cleanly distinguishes it from siblings like map_ad_angles and map_store_offers. An agent can tell what the tool produces without opening the schema.

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 an explicit trigger ('use this when the user wants to know which kind of ad to run for a product and an audience') with a concrete example query. It does not name or exclude sibling alternatives, so it falls 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?

Adds substantial context beyond the annotations: it returns a ticket id, feedback is explicitly not a chat and nothing in it is executed, and links/emails/phone numbers are stripped with nothing stored about the caller. That privacy and no-execution disclosure is genuinely useful given openWorldHint=true and readOnlyHint=false.

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?

Front-loads what the tool sends, then what to pass, then what it returns, in three dense sentences with no filler. Every clause carries actionable information.

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

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

Covers purpose, the enumerated kinds, the optional tool/error inputs, the 1000-character bound, the return value (ticket id), and privacy handling. With an output schema present, nothing further is required for an agent to call it correctly.

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

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

Schema description coverage is 100%, so the schema already documents all four parameters including the kind enum. The description restates the message length limit and the optionality of tool/error ('if you know them') but adds no syntax or format detail beyond the schema, making the baseline 3 correct.

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 ('Send feedback, a bug report or a tool request to the maintainers of the tools on this server') and enumerates the concrete situations (missing tool, stale data, broken/wrong answer). It is clearly distinguishable from the read-side sibling get_feedback_reply.

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 clear triggering conditions for each kind (need_tool, need_data, bug, other) and tells the agent to include the tool name and error text when known. It does not explicitly name the alternative get_feedback_reply or state when NOT to use this tool, so it falls short of full routing 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?

Annotations already establish read/idempotent/open-world safety, and the description goes well beyond them: it discloses the data sources (Hacker News, plus one Stack Exchange site when stack_site is given), the time window behavior, the shape of the result (count, top three with title/link/score/comment count, plain signal), and an honest bias caveat about technical/hobby skew and weak signal strength.

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?

Front-loaded with the trigger and example, then sources, return shape, and caveats. Every sentence carries information an agent needs to decide and call correctly; there is no restatement of the name 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?

Despite an output schema existing, the description usefully previews the return shape, and it covers sources, window, bias, and limitations. An agent has everything needed to invoke and interpret this tool without opening the schema.

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

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

Schema description coverage is 100%, so the baseline is 3, but the description adds meaning: it clarifies that 1-5 short phrases should be passed and that stack_site conditionally broadens the search to one Stack Exchange site ('Without it only Hacker News is searched'), which is the kind of interaction detail the schema alone does not spell out.

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 ('check whether people talk about a topic or an angle in public') and gives a concrete example query. This is clearly distinguishable from siblings like map_ad_angles or map_store_offers, which map ads or offers rather than probing public discussion.

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?

Opens with an explicit trigger ('Use this when the user wants to check whether people talk about a topic...') and names the primary alternative sources by exclusion ('it does not search Reddit') plus a scope caution ('not a market size'). The agent knows both when to use it and what it isn't for.

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