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A1-x-Tech

mcp-google-merchants

Re-fetch a file feed now

fetch_data_source

Manually trigger an immediate re-fetch of a file-based data source to update it outside its regular schedule. Works for scheduled or Google Sheets feeds; API-type sources are not supported.

Instructions

Triggers an immediate re-fetch (re-read) of a file-based feed outside its regular schedule. Works only for data sources with a file input and fetch settings (scheduled fetch or Google Sheets); calling it on an API-type source is an error. Returns an empty object on success — the fetch itself runs asynchronously on Google's side.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accountNoMerchant Center account ID (digits, e.g. "123456"). Omit to use the GOOGLE_MERCHANTS_ACCOUNT_ID default.
data_sourceYesData source: numeric ID (e.g. "104628") or full name "accounts/{account}/dataSources/{id}". Product/promotion writes require an API-type data source (input: API), not a file feed.
Behavior4/5

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

Discloses key behavioral traits: the fetch runs asynchronously, returns an empty object on success, and errors on API-type sources. While annotations indicate non-read-only, non-idempotent, non-destructive, the description adds valuable context about async execution and the return contract, though it doesn't mention permissions or rate limits.

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 succinct sentences: purpose, usage condition, and return/async behavior. Every sentence provides unique, necessary information with no redundancy or fluff. The core action is front-loaded, making it easy to scan.

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?

Given the tool's moderate complexity (trigger action, async execution, error condition), the description covers all critical aspects: what it does, when it's valid, what it returns, and how it executes. No output schema exists, so the explicit 'empty object on success' is especially important. The sibling tools are many, but the description sufficiently differentiates this tool from them.

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% for both parameters, with detailed descriptions in the schema itself. The tool description does not add new parameter syntax or formats, but it does contextualize the data_source parameter by reinforcing the file-feed requirement, which the schema only implies in a separate sentence about product/promotion writes.

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?

The description uses a specific verb 'Triggers' and a clear resource 'file-based feed', immediately distinguishing this from read-only tools like get_data_source. It also explicitly mentions 'outside its regular schedule', clarifying the unique purpose.

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?

Explicitly states when the tool is applicable ('file input and fetch settings (scheduled fetch or Google Sheets)') and when it is not ('calling it on an API-type source is an error'). This provides clear usage boundaries and echoes the data_source schema description to reinforce the constraint.

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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