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Count the requests bots made, grouped

count_bot_visits
Read-only

The same requests as list_bot_visits, counted by the API rather than listed. groupBy picks the question:

Instructions

The same requests as list_bot_visits, counted by the API rather than listed. groupBy picks the question:

  • bot — which assistants read the site, and which never turn up

  • path — what they read, the nearest thing to knowing what they can quote

  • status — crawl health: every 4xx and 5xx is a page an assistant tried to read and could not

  • day — whether the attention is growing or fading

  • category — bots fetching for a waiting user against those building an index

A failing status is worth more than its count suggests: an assistant that cannot fetch a page does not retry it for the person waiting, it answers from something else. Each one is a citation that went elsewhere.

botId=chatgpt-user with groupBy=path is the sharpest reading here — that bot fetches because somebody has just asked ChatGPT something, so those paths are being read into answers as they are requested.

The answer is ranked, not paged: the limit largest groups come back and there is no cursor. partial is true when the period held more requests than could be read, so the counts then describe the newest ones only.

A bot visit is a machine fetching a page, not a person reading one. It is the supply side of visibility: an assistant can only quote a page its bot was able to fetch, so this says whether the site is reachable and readable to them at all. It is a different measurement from being named in an answer (list_prompts, list_competitors), from being cited as a source (list_sources), and from somebody arriving afterwards (get_ai_traffic). kind=ai is the assistants; kind=seo is classic search engines and SEO tools.

A request carries the name of the bot in its User-Agent, which is free text anybody can send, so each one is marked verified or not. The API has no filter for it and counts cannot be split by it, so any total here includes requests that only claimed to be that bot. Report a count as an upper bound and say so; never present it as measured reach without the caveat.

A project whose integration is not connected answers this with zeros and empty lists, which reads exactly like a site nobody visits. Call get_integrations_status before reporting a zero or an empty list as a finding: it says whether Search Console, Google Analytics, the bot tracker and the sitemap are connected, and a sync that is failing. Not connected means the figures say nothing about the site, never that it had no traffic; say the integration is missing and that it can be connected in the PromptEye app.

Input Schema

NameRequiredDescriptionDefault
kindNo`ai` for AI assistants and their bots, `seo` for search engines and SEO tools. Omit it for both.
pathNoOnly this exact path, without the domain and starting with `/`, e.g. `/pricing`.
botIdNoOnly this one bot, by the id the other traffic tools report — `chatgpt-user`, `gptbot`, `googlebot` and so on. An unknown id is rejected by the API rather than ignored.
limitNoHow many groups to return, at most 200.
statusNoAn exact HTTP status code, or a class such as `4xx` to see only the failures.
vendorNoOnly bots run by this company, spelled as the API spells it: OpenAI, Anthropic, Google, Perplexity, Meta, Amazon, Apple, Microsoft, ByteDance, Yandex, DuckDuckGo, Ahrefs, Semrush, Moz, CommonCrawl, Mistral, Cohere and others. This is not the `assistant` of get_ai_traffic, which matches a referrer instead.
endDateNoLast day to report on, inclusive. Defaults to today, and must be within 31 days of startDate — these endpoints read a month at a time, not a year.
groupByYesWhat to count by.
projectIdYesThe project to work on — its id from list_projects. Required on every call, so that parallel conversations never read or write each other's project.
startDateNoFirst day to report on, inclusive. Defaults to 30 days before today.

Output Schema

NameRequiredDescriptionDefault
dataYes
partialYestrue = the period held more requests than could be read, so the counts describe the newest ones only.
nextCursorYesAlways null: the answer is ranked, not paged.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.28
    • addedInput schema / properties / projectId
      Added value: +{
+  "description": "The project to work on — its id from list_projects. Required on every call, so that parallel conversations never read or write each other's project.",
+  "minLength": 1,
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "groupBy"
-]New value: +[
+  "projectId",
+  "groupBy"
+]
  2. Changed1 schema field changedv1.0.22
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "additionalProperties": false,
+  "properties": {
+    "data": {
+      "items": {
+        "additionalProperties": false,
+        "properties": {
+          "count": {
+            "description": "Requests in the group.",
+            "type": "number"
+          },
+          "key": {
+            "description": "A bot id, a path, an HTTP status code (unknown when none was reported), a UTC day or a category.",
+            "type": "string"
+          },
+          "label": {
+            "description": "Display name, set for bots. null for the other groupings.",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "lastAt": {
+            "description": "When the newest request of the group was made, ISO 8601 in UTC.",
+            "type": "string"
+          },
+          "uniquePaths": {
+            "description": "Distinct paths those requests asked for.",
+            "type": "number"
+          }
+        },
+        "required": [
+          "key",
+          "label",
+          "count",
+          "uniquePaths",
+          "lastAt"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "nextCursor": {
+      "description": "Always null: the answer is ranked, not paged.",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "partial": {
+      "description": "true = the period held more requests than could be read, so the counts describe the newest ones only.",
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "data",
+    "nextCursor",
+    "partial"
+  ],
+  "type": "object"
+}
  3. Addedv1.0.16

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only cover read-only/open-world; the description supplies the behavior that matters: results are ranked not paged with no cursor, `partial` means the period overflowed and counts describe only the newest requests, and UA spoofing means totals are an upper bound that must be caveated. It also warns that a disconnected integration returns zeros indistinguishable from a dead site.

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

Conciseness4/5

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

Front-loaded with the sibling comparison and a scannable bulleted enumeration of groupBy values, then caveats. It is long and carries some editorial flourish ('Each one is a citation that went elsewhere', 'the sharpest reading here'), but nearly every clause conveys a decision-relevant fact, so the length is defensible for a ten-parameter analytics tool.

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?

Given an output schema exists, the description still covers the things an agent cannot infer from fields: the `partial` and `verified` caveats, the 31-day window, ranked truncation, and the zero-result/integration-status trap. Nothing needed to call or interpret the tool safely 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 earns above that by giving interpretive meaning to each `groupBy` value and by clarifying that `vendor` is not the `assistant` referrer of get_ai_traffic and that `limit` is a top-N cutoff rather than a page size. Coverage of `path`, `status` and date-window semantics largely duplicates the schema, keeping it off a 5.

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?

Opens by naming the exact operation and its relationship to a sibling: 'The same requests as list_bot_visits, counted by the API rather than listed.' The grouping dimension and its five enum values are enumerated, so an agent knows precisely what the tool returns versus what list_bot_visits returns.

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?

Explicitly routes between tools: distinguishes bot-visit measurement from list_prompts, list_competitors, list_sources and get_ai_traffic, and gives a concrete preferred combination ('botId=chatgpt-user with groupBy=path is the sharpest reading here'). It also states a mandatory precursor (call get_integrations_status before reporting a zero) and what to say when that check fails.

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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