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MoonEyes

google-ecommerce-mcp

Apply a GA4, Search Console, Merchant, GTM plan

google_apply
Destructive

Apply approved signed plans across GA4, Search Console, Merchant Center, and GTM; execute operations in order, stop on first error, and journal every step.

Instructions

WRITE: apply a signed plan for GA4, Search Console, Merchant, GTM, after the user approved it. Pass plan_id, confirmation_token and confirm exactly as the plan_* tool returned them. Every lock is re-checked; ops run in order and stop at the first error; everything is journaled.

Input Schema

NameRequiredDescriptionDefault
confirmYesConfirmation string '<client_id>:<plan_id>:<digest8>' returned with the plan
plan_idYesPlan id returned by the plan_* tool
client_idNoClient id from clients_list; empty uses the only client
confirmation_tokenYesconfirmation_token returned by the plan_* tool, unchanged

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.5.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already flag destructive/openWorld/non-idempotent, yet the description adds execution semantics beyond them: locks are re-checked, ops run in order, execution halts on first error, and everything is journaled. It also imposes a token-integrity constraint (pass values exactly as returned), which is real behavioral guidance not present in the structured fields.

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?

Front-loaded 'WRITE:' marker followed by two dense sentences; every clause earns its place by covering scope, precondition, parameter integrity, and execution/failure semantics with no filler.

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 destructive, non-idempotent mutation tool with no output schema, the description covers the critical facts (approval gate, exact token passing, lock re-check, ordered execution, stop-on-error, journaling). It could go further by noting how to verify the result (audit_log) or undo it (rollback_plan), which keeps it from a 5.

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, but the description adds a meaningful constraint beyond the schema: plan_id, confirmation_token and confirm must be passed 'exactly as the plan_* tool returned them,' which conveys a provenance/integrity requirement the field descriptions do not state.

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 with a specific verb and object ('WRITE: apply a signed plan') and enumerates the exact platforms it covers (GA4, Search Console, Merchant, GTM), which distinguishes it from the sibling apply tools (gads_apply, meta_apply, openai_ads_apply). An agent can route to it without opening any schema.

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?

States a clear precondition ('after the user approved it') and implies the plan_* -> apply workflow by requiring the exact values the plan_* tool returned. It does not name when NOT to use it nor point to rollback_plan/audit_log as follow-ups, so it stops short of explicit alternatives.

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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