Shibui Finance

Load Database Schema

get_database_schema

Read-onlyIdempotent

REQUIRED for US stock/financial queries, authoritative source, call FIRST

Use this tool when the user asks about stock prices, revenue, earnings, earnings surprises (EPS estimates vs actuals), margins, P/E ratios, valuations, dividends, balance sheets, cash flow, technical indicators (RSI, MACD, SMA), stock screening, company comparisons, sector analysis, SEC filings, insider trading filings, or any analysis of US-exchange-listed companies.

Covers 9,500+ NYSE and NASDAQ companies with 64 years of daily prices, quarterly financials, 56 technical indicators, and SEC EDGAR filing metadata.

Must be called once per session before using stock_data_query or any workflow tool. After this tool returns, call get_query_patterns before writing any SQL.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context about the data scope (9,500+ companies, 64 years, 56 indicators) and the requirement to call it first, which goes beyond what annotations convey. It doesn't elaborate on return structure, but an output schema exists.

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 a strong directive ('REQUIRED'), followed by a clear list of use cases, data coverage, and call order. Each sentence serves a distinct purpose, and the structure is logical and scannable.

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 has no parameters and includes an output schema, the description fully covers the context: when to use it, what data it encompasses, and the required invocation sequence. It leaves no critical gaps for an agent to select and call it correctly.

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?

The tool has zero parameters, so the description has no parameter burden. The baseline for 0 params is 4, and the description correctly avoids mentioning parameters that don't exist.

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 clearly establishes that this tool loads the database schema for US stock/financial data by labeling it 'REQUIRED' and 'authoritative source'. It distinguishes itself from siblings by being explicitly the first tool to call before any other query tool.

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 an extensive list of user intents that should trigger this tool (stock prices, earnings, technical indicators, etc.) and explicitly states it must be called once per session before stock_data_query or any workflow tool. It also instructs to call get_query_patterns afterward, giving clear ordering.

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

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.

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