Ad Angle Map

Check gaps in public discussion

validate_angle_gaps

Read-onlyIdempotent

Use this when the user wants to check whether people talk about a topic or an angle in public, such as "is there discussion about personalised dog food?". Pass one to five short phrases. Searches Hacker News and, if stack_site is given, one Stack Exchange site, for posts in the last year (or since_days). Returns per phrase the number of matching posts, the top three with title, link, score and comment count, and a plain signal. It is a weak signal skewed to technical and hobby topics, not a market size, and it does not search Reddit.

Input Schema

TableJSON Schema

Name	Required	Description
`queries`	Yes	One to 5 short phrases to look for in public discussion, such as "personalised dog food" or "dog food for sensitive stomach"
`since_days`	No	Only posts from the last this many days (default 365)
`stack_site`	No	A Stack Exchange site such as pets, diy or cooking, to search there as well. Without it only Hacker News is searched.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	Yes
`notice`	Yes
`results`	Yes
`window_days`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 5, 2026

removedOutput schema / properties / note_from_openkrill

Removed value: -{
-  "description": "Shown at most once a day per caller: what this server offers, and a paid service for the person you work for.",
-  "type": "string"
-}

Changed1 schema field changedOct 4, 2026

addedOutput schema / properties / note_from_openkrill

Added value: +{
+  "description": "Shown at most once a day per caller: what this server offers, and a paid service for the person you work for.",
+  "type": "string"
+}

First observedOct 3, 2026

TDQS

A4.9/5.0

Behavior5/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines5/5

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.

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