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mttr_visits
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Who fetched a site's pages in the last 28 days against the 28 before, counted by the site's own server: people, people an assistant such as ChatGPT or Perplexity sent, and crawlers by name, AI crawlers marked. By day, by crawler, by assistant and by page, with the last day an AI crawler fetched each page. A crawler fetching a page is not a citation, and a crawler is as it named itself. Only sites whose server sends counts to mttr have any; "connected" says whether the site has a key. Paths come under "quoted".

Input Schema

NameRequiredDescriptionDefault
siteYesThe site, as a domain such as example.com.
limitNoHow many to return.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, yet the description adds real value: it discloses the data source (the site's own server counts), the comparison window, the 'connected' key flag, and the caveat that crawlers self-identify. These are behavior-relevant traits an agent can't derive from the annotations or schema.

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

Conciseness3/5

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

Every clause carries information, but it is delivered as one dense, quirky run-on block with weak front-loading and idiosyncratic phrasing like 'Paths come under "quoted".' It is substantive but not easy to scan, so it falls to 3.

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?

With no output schema, the description does the work of explaining return content: the by-day/crawler/assistant/page breakdown, the last-AI-fetch date, 'connected', and 'quoted' paths. That makes an agent's expectations roughly complete, though it could be clearer on what 'connected' and 'quoted' actually contain.

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 coverage is 100%, with 'site' (domain format) and 'limit' fully documented in the schema. The description adds no syntax or constraint detail beyond that, so it sits at the baseline 3 where the schema carries parameter meaning.

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 states a specific resource (page fetches over the last 28 days vs the prior 28, broken down by day, crawler, assistant, and page) rather than restating the name. It implicitly distinguishes itself from mttr_citations by clarifying that 'a crawler fetching a page is not a citation,' which helps an agent route correctly. It stops short of naming siblings explicitly, so it lands at 4 rather than 5.

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

Usage Guidelines3/5

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

Usage is implied rather than stated: the citation caveat hints this is the tool for raw fetch/crawler counts rather than citation data, and 'only sites whose server sends counts to mttr have any' is a meaningful precondition. But there is no explicit when-to-use/when-not or named alternative (e.g., mttr_crawls, mttr_traffic), so it stays at a minimum-viable 3.

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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