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AutomateLab-tech

SEO Performance MCP

Unified per-URL snapshot

posts_snapshot
Read-only

Pull a 30/60/90-day snapshot for one post URL to combine GSC, Matomo, GA4, Clarity, and AI-citation metrics in a read-only view.

Instructions

Pull a 30/60/90-day snapshot across every configured signal source for one post: GSC clicks/impressions/CTR/position + top queries, Matomo visits + dwell, GA4 pageviews, Clarity scroll/rage clicks, AI-citation counts.

Each source is best-effort: if its env vars are missing the field is omitted. Returns whatever is available.

Read-only. No third-party writes. Optionally persists to the local DuckDB cache when persist=true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesCanonical URL of the post to snapshot.
windowNo
persistNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
ga4No
gscYes
bingNo
metaYes
matomoNo
clarityNo
citationsNo
window_daysYes
cannibalizationNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.2

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds genuinely useful behavior beyond that: each source is best-effort and silently omits fields when env vars are missing, and it clarifies the local DuckDB cache write under persist=true. Minor tension with idempotentHint=false and the cache write, but not a contradiction of the read-only claim.

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?

Three short, well-separated blocks that are front-loaded with the core purpose and then the caveats. No redundant sentences; each line (signal list, best-effort behavior, read-only + persist) carries distinct information.

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?

An output schema exists so return values need not be explained, and annotations cover the safety profile. The description fills the remaining gaps — which sources are queried, best-effort omission behavior, and the optional cache write — leaving nothing an agent needs to call it correctly.

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 only 33%, so the description must compensate. It documents the window values (30/60/90-day) and the persist semantics (local DuckDB cache), effectively covering the two undocumented parameters. The url parameter is left to its schema description.

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?

States a specific verb (pull) and resource (30/60/90-day snapshot for one post), and enumerates exactly which signals are aggregated (GSC, Matomo, GA4, Clarity, AI citations). This makes it disambiguable from aggregation siblings like posts_verdict and posts_refresh_brief, though it never names them explicitly.

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 by the content (raw per-URL data gathering vs the sibling report/verdict tools), but there is no explicit when-to-use or when-to-prefer-an-alternative guidance. An agent must infer routing from the tool name and the sibling set.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.