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ruya-grp

fusion-query-mcp

by ruya-grp

fusion_author_report

Convert a SQL query into a persistent BI Publisher report registered for future runs, eliminating repeated catalog round trips. Verify binds before registration to reject broken reports.

Instructions

Mint a PERSISTENT report from SQL and register it for future runs.

Use this when a question will be asked again: an ad-hoc query costs catalog round trips every time, a registered report only once. The SQL becomes a BI Publisher data model + report under /Custom/MCP/, and the entry lands in the dynamic registry file (not config.yaml), immediately runnable via fusion_run_report / fusion_validate_report.

Same statement rules as fusion_adhoc_query: single guarded SELECT, every projection aliased, parameters as :bind names (discovered from the SQL automatically). Write description for the NEXT agent: say what the report returns and what each parameter means -- it is read through fusion_list_reports.

Strongly recommended: pass verify_binds with values that should return rows. The report is then run once through the normal pipeline (echo check included) before being registered, so a broken report is refused instead of registered.

Gated behind fusion.allow_report_authoring in config.yaml. A name that exists in config.yaml is never touched; force=True only overwrites reports this tool itself created.

Args: name: Registry key, lower_snake_case, e.g. open_pos_by_bu. sql: The SELECT with :bind parameters, every projection aliased. description: For the next agent -- returns what, parameters mean what. defaults: Values used for binds a caller omits, e.g. {"p_type": "%"}. verify_binds: Bind values for a one-off verification run after upload. force: Overwrite this tool's own earlier report of the same name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYes
nameYes
forceNo
defaultsNo
descriptionYes
verify_bindsNo

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

A5/5.0
Behavior5/5

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

The description discloses important behaviors beyond annotations: the report is persisted under /Custom/MCP/, registered in a dynamic registry file rather than config.yaml, gated behind a config flag, and verified through a normal pipeline before registration. It also clarifies that force only overwrites reports created by this tool and never touches pre-existing config.yaml entries, which fully informs the agent of side effects.

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?

Although detailed, every sentence serves a clear purpose: purpose, when-to-use, SQL rules, description guidance, verification recommendation, gating, and overwrite semantics. The content is logically organized and front-loaded with the core purpose, making it easy for an agent to parse quickly.

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 complexity and an existing output schema, the description covers all operational context: prerequisites, configuration gating, failure behavior, parameter meaning, verification, and overwrite rules. An agent has everything needed to call this tool correctly and avoid common mistakes.

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 description carries the full burden, and it succeeds by explaining every parameter: name's naming convention, sql's SELECT and bind rules, description's purpose for the next agent, defaults' role, verify_binds' verification run, and force's overwrite scope. This goes far beyond the bare type information in the schema.

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 precise verb and resource: 'Mint a PERSISTENT report from SQL and register it for future runs.' It clearly distinguishes this tool from the sibling fusion_adhoc_query by contrasting one-time query cost vs. reusable registered reports, so an agent can select it without ambiguity.

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?

It explicitly states when to use this tool: 'Use this when a question will be asked again,' and contrasts it with ad-hoc queries that cost catalog round trips. It also names related run/validate tools and notes that the report is immediately runnable via fusion_run_report / fusion_validate_report, giving clear guidance on the workflow.

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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