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Get site counts

get_site_overview
Read-onlyIdempotent

Use this tool when the user asks how a site is doing, or how many people are waiting on it: 「サイトの様子は?」「問い合わせは溜まってる?」「how's my site doing?」

Returns each site's counts: pages, live pages, and over the last 30 days visitors (visitors30d), AI reads (aiReads30d) and form entries (entries30d), with who is waiting (waiting: entries nobody has marked as handled from the last 14 days, and orders waiting on the owner). A count that couldn't be read is null, which isn't 0. stopped: true means the workspace is past its page view cap, so only pages and waiting are read.

  • host: one site, such as www.example.com; without it, every site. Counts only: get_form_submissions and get_orders hold who wrote and what they said. Read-only.

Input Schema

NameRequiredDescriptionDefault
hostNoOne of the workspace's sites, such as 'www.example.com'; leave out for all

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Goes well beyond the annotations by disclosing degradation behavior: a count that couldn't be read is null rather than 0, and 'stopped: true' means the workspace exceeded its page view cap so only pages and waiting are read. That is exactly the kind of edge-case context an agent needs to interpret results correctly, which annotations cannot convey.

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 trigger condition before the return details, and every sentence carries information. It is slightly dense with inline example utterances and a dash-lead host note, but nothing is genuinely wasted.

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?

With no output schema, the description carries the full burden of explaining return values and does so field by field, including the null-vs-zero and stopped-state semantics. An agent can interpret the response without any other source.

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 host parameter is already fully documented there; the description's line about host ('one site... without it, every site') largely restates it. No additional format or validation meaning is added, so baseline 3 applies.

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 ('Returns each site's counts: pages, live pages, visitors30d, aiReads30d, entries30d') and enumerates exactly what is included, so an agent can distinguish it from get_analytics_summary or get_site without opening a schema.

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 ('when the user asks how a site is doing, or how many people are waiting') including quoted example utterances, and closes with a clear exclusion routing content questions to get_form_submissions and get_orders.

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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