tw-market-data

Screen the market

screen

Read-onlyIdempotent

Turn a spoken shortlist description into explicit numeric cut-offs, and show the cut-offs.

★ THE THRESHOLDS COME BACK WITH THE SHORTLIST. "低本益比" becomes `per < 15`, and that 15 is
printed in `applied[]` so you can disagree with it. A filter that hands over thirty names
without saying where it drew the line cannot be checked by anyone — and whether the line was
15 or 20 completely changes which thirty.

★ PHRASES IT CANNOT MAP COME BACK IN `unparsed[]`. It will not quietly invent a boundary for
a wording it did not recognise, because a list the caller believes they defined and actually
did not is worse than a shorter list.

★ NOT `query_dataset` AND NOT `compare`. `query_dataset` returns rows for symbols you already
chose; `compare` puts a handful of named symbols beside each other. This one is for when you
have no symbols yet, only a description of what you are looking for.

⚠️ A shortlist is not a recommendation. Rows whose value is absent are excluded rather than
assumed to pass — being unmeasured is not the same as qualifying.
Args: conditions (plain words), optional as_of, optional pre-fetched rows.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`rows`	No
`as_of`	No
`conditions`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`note`	No
`as_of`	No
`applied`	No
`results`	No
`unparsed`	No
`rows_examined`	No
`not_investment_advice`	No
`thresholds_are_choices`	No

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already mark the tool readOnly and idempotent, and the description adds valuable behavioral detail: thresholds are echoed in applied[], unrecognized phrases surface in unparsed[] rather than being silently invented, and missing values are excluded rather than treated as passing. This goes well beyond the annotations and sets accurate expectations.

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 the core purpose and uses bold markers to structure each distinct piece of guidance. Though longer than a minimal description, every block earns its place by adding behavioral disclosure, missing-data semantics, or sibling disambiguation.

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 annotations, output schema, and sibling list, the description covers the invocation path, return behavior via applied[] and unparsed[], missing-value handling, and alternative routing. No critical information needed to select or call the tool correctly is absent.

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 description coverage is 0%, so the description carries the parameter-meaning burden. It defines conditions as 'plain words' / spoken shortlist description, as_of as optional, and rows as optional pre-fetched rows. It does not detail as_of format, but it provides enough semantic grounding for correct invocation.

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 first sentence names a specific transformation: turn a spoken shortlist description into explicit numeric cut-offs and show them. It also explicitly distinguishes itself from query_dataset and compare by describing what those do and when this tool is appropriate, so an agent can tell them apart.

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 this tool is for when you have no symbols yet, only a description of what you are looking for. It names query_dataset as the alternative when symbols are already chosen and compare for named symbols, giving clear when-to-use and when-not-to-use 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

A4/5.0

Disambiguation4/5

The tools are largely distinct: querying, searching, backtesting, risk reads, alerts, memory, and audit functions each have clear homes. A few adjacent pairs (risk_read vs risk_assess, company_health_check vs positioning_read) could be confused, but the descriptions draw explicit boundaries.

Naming Consistency3/5

There are strong consistent clusters like list_*, get_*, run_*, and memory_*, but the *_read suffix alternates with noun-first names like company_health_check, and bare-verb tools like ask, calendar, chart, and screen break the pattern. The naming is readable but not uniform.

Tool Count2/5

Forty tools is well past the 25+ threshold and makes the surface heavy for an agent to navigate, even though the breadth reflects a genuinely wide platform. Several clusters could plausibly be consolidated without losing capability.

Completeness4/5

The tool surface covers the main lifecycle well: discovery, point-in-time querying, filings search and full text, backtesting, research, risk assessment, alerts, memory, approvals, and provenance verification. Minor gaps exist—no strategy management tools, no memory deletion, no bulk export—but agents can work around them.

Resources

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