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.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "queries": [
        "personalised dog food"
      ]
    },
    "name": "validate_angle_gaps"
  }
}

Input 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

Name	Required	Description
`as_of`	Yes	ISO 8601 time the data was published by its source or fetched from it. A cached answer keeps the time of its fetch.
`notice`	Yes
`source`	Yes	Who the data comes from, to cite and to check it against.
`results`	Yes
`window_days`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedOct 23, 2026

addedInput schema / examples

Added value: +[
+  {
+    "queries": [
+      "personalised dog food"
+    ]
+  }
+]

addedOutput schema / properties / as_of / description

Added value: +"ISO 8601 time the data was published by its source or fetched from it. A cached answer keeps the time of its fetch."

addedOutput schema / properties / source

Added value: +{
+  "description": "Who the data comes from, to cite and to check it against.",
+  "properties": {
+    "name": {
+      "type": "string"
+    },
+    "url": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "name",
+    "url"
+  ],
+  "type": "object"
+}

changedOutput schema / required

Previous value: -[
-  "as_of",
-  "window_days",
-  "results",
-  "notice"
-]New value: +[
+  "as_of",
+  "window_days",
+  "results",
+  "notice",
+  "source"
+]

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 mark it read-only, idempotent and open-world, and the description goes well beyond them: it names the sources searched, the one-year (or since_days) window, the per-phrase response shape, and the crucial limitation that this is a weak signal skewed toward technical/hobby topics and not a market-size estimate.

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?

Four front-loaded sentences: trigger, input constraint, source/window and return shape, then the honesty caveat. Every clause carries information an agent needs and nothing is repeated padding.

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?

Even though an output schema exists and return values need not be described, the description still summarizes the per-phrase output and flags reliability limits, which is exactly the kind of judgment an agent cannot infer from structured fields. Nothing needed to call it correctly or interpret it cautiously is missing.

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 coverage is 100%, so the baseline is 3; the description nevertheless adds behavioral meaning by stating that stack_site enables searching exactly one additional Stack Exchange site and by restating the 1–5 phrase limit alongside the default 365-day window. It stops short of explaining phrase syntax or how multi-phrase results are aggregated, so it is a modest upgrade over the schema rather than a full one.

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') plus a concrete user-facing example question. The named data sources (Hacker News, one Stack Exchange site) further pin down what the tool actually does, distinguishing it from the sibling map_* and get_* tools.

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 when-to-use trigger ('when the user wants to check whether people talk about a topic'), gives a natural-language example, and states an explicit exclusion ('it does not search Reddit') plus a caveat that it is not a market size. This tells the agent both when to call it and when its output should not be over-trusted.

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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