Skip to main content
Glama

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
NameRequiredDescriptionDefault
queryYesThe SQL query to execute and export. Same query used with stock_data_query.
titleYesTitle for the spreadsheet header (e.g. "Mega-Cap Stock Comparison").

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A3.8/5.0
Behavior1/5

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

The annotations mark this tool as destructive (destructiveHint: true), but the description implies a read-only export by stating it runs the same SQL query as stock_data_query, which is typically a SELECT. This contradicts the annotation. The description does not mention any destructive side effects or file modifications, creating an inconsistency.

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?

Two concise paragraphs: the first sentence states the core purpose, and the second elaborates on the relationship to sibling and output features. No fluff, front-loaded, and every sentence adds value.

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?

The description effectively covers the tool's purpose, usage context, and differentiator. However, it fails to reconcile the destructive annotation with its implied read-only behavior, leaving a minor gap in behavioral completeness. The presence of an output schema reduces the need to describe return values, so the overall context is mostly sufficient.

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?

The schema already covers both parameters with clear descriptions (query and title). The description adds minimal extra semantic value, mostly reinforcing that the query is the same as stock_data_query and that the title is for a branded header. With 100% schema coverage, a baseline of 3 is appropriate.

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 states a specific verb (Export) and resource (query results to a branded Excel spreadsheet). It also explicitly distinguishes itself from its sibling stock_data_query by noting it returns a downloadable Excel file instead of raw data, providing clear differentiation.

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 explicitly frames when to use this tool: when you want the same SQL query as stock_data_query but in an Excel file ('instead of raw data'). This directly addresses the alternative tool and clarifies the decision boundary.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct aspect of stock data workflow: schema discovery, query patterns, raw data access, Excel export, and eight specialized analysis workflows. The load_*_workflow tools are clearly differentiated by domain (earnings, filing, insider, etc.) with explicit usage criteria. There is no real overlap or confusion between tools.

Naming Consistency4/5

Most tools follow a consistent verb_noun snake_case pattern (get_database_schema, get_query_patterns, load_*_workflow, export_to_excel). The only deviation is 'stock_data_query' which places the noun first rather than the verb, but it remains readable and fits the overall naming style. The consistent use of snake_case and descriptive prefixes makes the set predictable.

Tool Count5/5

With 12 tools, the server is well-scoped for its purpose of comprehensive US stock data analysis. Each tool earns its place: two prerequisite/metadata tools, one query executor, one export utility, and eight distinct workflow loaders covering major analysis types. There is no bloat or redundancy.

Completeness5/5

The tool set provides a complete lifecycle for stock data analysis: schema discovery → query patterns → data query → specialized workflows (backtesting, comparison, earnings, filings, fundamentals, insider, screening, technical) and export. It covers all major query types described in the schema tool and leaves no obvious dead ends; agents can handle a wide range of financial questions.

Resources