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ruya-grp

fusion-query-mcp

by ruya-grp

fusion_save_fixture

Persist validated expectations as a reusable regression test, enabling re-runs to catch unexpected changes after Fusion quarterly patches.

Instructions

Persist a validated set of expectations as a reusable regression test.

Worth doing every time a validation passes: Fusion quarterly patches change views and add columns, and a saved fixture turns "this was right in August" into something you can re-run in November.

For a report validation, record the report and its bind values in description (and the report's call descriptor in sql) so the fixture says what it was proved against.

Values in configured redacted columns are masked before the file is written.

Args: name: Fixture name; letters, digits, _ and - only. expectations: The expectation objects to persist. description: Where the ground truth came from -- record the source and date. datasource: Datasource (or report) the fixture applies to. sql: The validated query or report call, stored alongside for reference. overwrite: Replace an existing fixture of the same name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlNo
nameYes
overwriteNo
datasourceNo
descriptionNo
expectationsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

The description adds useful behavioral context beyond annotations: redacted-column values are masked before writing, and sql is stored alongside for reference. Annotations already indicate the operation is not read-only and not destructive; no contradiction is present.

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?

The description is front-loaded with the core purpose, gives a short rationale, and then uses a compact, labeled Args section. Every sentence contributes either to selection, usage timing, or parameter semantics.

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 6-parameter write operation with an output schema, the description is nearly complete: it covers all params, the masking behavior, and the persistence intent. It does not spell out what happens when overwrite is false and a fixture already exists, so there is a small but non-fatal gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the Args block fully compensates by explaining all six parameters, including the name character restriction, the purpose of expectations, and the overwrite replacement behavior. This is exactly what an agent needs to fill the arguments correctly.

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 opens with a specific verb and resource: 'Persist a validated set of expectations as a reusable regression test.' This clearly distinguishes fusion_save_fixture from retrieval siblings like fusion_get_fixture or fusion_list_fixtures.

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?

It gives an explicit trigger ('Worth doing every time a validation passes') and explains why with the quarterly patch example. It does not name a specific alternative or state when not to use this tool, so it stops short of a 5.

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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