Shibui Finance

Earnings Analysis Workflow

load_earnings_workflow

Read-onlyIdempotent

Load earnings workflow for EPS surprises, beat/miss, estimates, revenue. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks about earnings results, EPS surprises, beat/miss history, "did X beat estimates", quarterly earnings, revenue growth trends, earnings season, or estimates vs actuals. Can be combined with other workflow tools.

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 the tool as read-only, idempotent, and non-destructive, so the description doesn't need to repeat that. It adds valuable behavioral context about prerequisite calls and ordering, which goes beyond the annotations. However, it doesn't describe internal behavior of the workflow beyond loading.

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 three sentences, tightly packed with use cases, prerequisites, and combination notes. It is front-loaded with the core purpose and contains no redundant filler.

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?

For a parameterless workflow loader with an output schema, the description covers usage timing, prerequisites, and scope of queries. It fully equips an agent to decide when to call this tool, and the existence of an output schema handles return value expectations.

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 and schema description coverage is 100%, so the baseline for 0 params is 4. The description doesn't need to explain parameters, but it still provides useful context about the workflow's content.

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 states 'Load earnings workflow' with specific domain terms (EPS surprises, beat/miss, estimates, revenue). It distinguishes itself from sibling workflow tools by focusing on earnings analysis, making the purpose unmistakable.

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 states when to use the tool ('before writing SQL' and lists trigger phrases like 'did X beat estimates' and 'earnings season'). It also names required prerequisites in order: get_database_schema then get_query_patterns. This is clear, actionable usage guidance.

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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