Shibui Finance

Export to Excel

export_to_excel

Destructive

Export query results to a branded Shibui Finance Excel spreadsheet.

Runs the same SQL query as stock_data_query but returns a downloadable Excel file instead of raw data. The spreadsheet includes branded headers, smart number formatting, and auto-fitted columns.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	Yes	The SQL query to execute and export. Same query used with stock_data_query.
`title`	Yes	Title for the spreadsheet header (e.g. "Mega-Cap Stock Comparison").

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.4/5.0

Behavior3/5

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

Annotations already indicate destructiveHint: true, but the description does not explain what destructive behavior may occur. It adds output details (branded headers, auto-fitted columns) but does not address the side effects or permissions implied by the annotations. This leaves a transparency gap.

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 two sentences, front-loaded with the purpose, and every clause adds relevant information. No fluff or repetition of schema content.

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 two-parameter tool with a schema and output schema, the description is fairly complete. It mentions the output is a downloadable Excel file and describes formatting. However, given the destructiveHint annotation, a note on side effects or file handling would make it more complete.

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 baseline is 3. The description adds value by clarifying that the 'query' parameter is the same as used with stock_data_query, and provides a concrete example for 'title'. This goes beyond the schema descriptions.

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?

Description uses specific verb+resource: 'Export query results to a branded Shibui Finance Excel spreadsheet.' It distinguishes from the sibling tool stock_data_query by explicitly stating it runs the same SQL query but returns an Excel file instead of raw data.

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?

The description provides explicit guidance: 'Runs the same SQL query as stock_data_query but returns a downloadable Excel file instead of raw data.' This clearly contrasts with the sibling tool and indicates when to use this tool (when Excel output is desired) versus the alternative.

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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TDQS

A4.4/5.0

Disambiguation4/5

Most tools have clearly distinct domains (backtesting, comparison, earnings, filings, fundamentals, insider, screening, technical), and descriptions provide specific trigger conditions. However, stock_data_query and export_to_excel are very similar (same query, different output), and some workflow boundaries overlap (e.g., earnings vs. fundamental both mention revenue trends; filing vs. insider both involve SEC documents).

Naming Consistency3/5

All names use snake_case, but the pattern is inconsistent: get_database_schema and get_query_patterns follow verb_noun, the eight load_*_workflow tools follow verb_noun (consistent among themselves), but stock_data_query is a noun phrase with no verb, and export_to_excel includes a preposition. The mixed conventions are still readable but not uniform.

Tool Count4/5

At 12 tools, the count is within the expected 3-15 range and appropriate for the broad scope of comprehensive stock analysis. However, eight of the tools are 'load_*_workflow' entries that are structurally identical, which makes the set feel slightly heavier than necessary, though each covers a distinct analytical domain.

Completeness5/5

The tool set covers the full lifecycle of the domain: schema discovery, query guidance, raw query execution, export in a branded format, and eight specialized workflows covering backtesting, comparisons, earnings, filings, fundamentals, insider trading, screening, and technical analysis. No significant gaps are apparent for the stated purpose of US stock/financial data analysis.

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