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Stock Screening Workflow

load_screening_workflow
Read-onlyIdempotent

Load screening workflow to find, filter, scan, rank stocks, top N by.... REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks to find, screen, scan, rank, or filter stocks — "find stocks that...", "top 10 by...", "best dividend stocks", value/growth screens, sector ranking, or any multi-factor selection. Can be combined with other workflow tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds important behavioral context: the tool has a mandatory prerequisite sequence and should be invoked before writing SQL. This goes beyond annotation data and helps the agent understand the intended workflow.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact: three sentences covering purpose, prerequisites, and usage timing. It is front-loaded with the main purpose. Minor wording awkwardness ('top N by....') and a slight run-on in the second sentence prevent a perfect score, but it remains efficient.

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, an output schema, and complete annotations, the description provides all essential context: what it does, when to use it, prerequisites, and relationship to sibling tools. It even includes examples of user intents that should trigger this tool. No gaps are evident.

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 0 parameters and schema description coverage is 100%. Per the rubric, 0 params yields a baseline of 4. The description adds no parameter details (there are none), so the baseline is appropriate and no deduction is needed.

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 clear verb + resource: 'Load screening workflow to find, filter, scan, rank stocks, top N by...' and then enumerates specific use cases ('find stocks that...', 'top 10 by...', 'best dividend stocks'), which differentiates it from sibling workflow loaders.

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?

Explicitly states when to use: 'Call BEFORE writing SQL when the user asks to find, screen, scan, rank, or filter stocks.' It also lists concrete example queries and notes the required call order: 'REQUIRES get_database_schema then get_query_patterns to be called first (in that order).' The addition of 'Can be combined with other workflow tools' gives integration 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
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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