Engagemii AI Visibility Data

See which AI engines are reading company pages

ai_readstream

Read-onlyIdempotent

Which AI engines are reading the business web right now. Server-side, IP-verified crawler logs across one of the largest networks of business pages online. Returns per-engine reads over the last ten minutes, the last 24 hours and all time, plus a daily series. Free, no auth. Answers questions such as which AI crawler is most active, how two engines compare, and whether AI reading is rising.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "ai_readstream"
  }
}

Input Schema

Name	Required	Description	Default
`days`	No	How many days of the daily series to return, 0 to 30. Default 7.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 23, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive. The description adds valuable context beyond annotations: server-side IP-verified logs, free access with no auth, and the time windows returned (10 minutes, 24 hours, all time, plus a daily series). It does not cover pagination or exact output format, but the added provenance and auth details are substantive.

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?

The description is front-loaded with the core purpose, followed by data source, return windows, access model, and example use cases. It is efficient and every sentence adds information, though it is slightly verbose and could tighten the final sentence.

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

Completeness4/5

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

For a simple read-only tool with one fully documented optional parameter and no output schema, the description supplies enough context: data source, return windows, auth model, and example questions. It does not detail output structure, but with no output schema that is a minor gap.

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

Parameters3/5

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

Schema description coverage is 100% and the single 'days' parameter is fully documented in the schema. The description mentions a daily series but adds no syntax, range, or default details beyond the schema, so baseline 3 is appropriate.

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

Purpose4/5

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

The description clearly states the resource: AI engine reads from crawler logs across business pages. It does not name a specific verb (e.g., 'get', 'list'), but the scope and data source are specific enough to distinguish it from siblings like aeo_audit or citation_watch. Sibling differentiation is implicit rather than explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides concrete example questions the tool answers, such as which AI crawler is most active and how engines compare. This gives clear usage context, but it does not name alternatives or say when not to use the tool, 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.

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