tw-market-data

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Read-onlyIdempotent

Lay two to five named companies side by side on the same measures, gaps marked as gaps.

★ AN ABSENT FIGURE STAYS ABSENT, AND THE COMPANY STAYS ON THE TABLE. A blank cell is reported
as `available: false`, never filled with a zero, a previous period, or by quietly dropping the
column. Dropping is the worst of the three: it converts "we do not hold this measure for that
company" into "you did not ask about that company".

★ EACH CELL NAMES ITS OWN SOURCE. Margins and institutional flows come from different datasets,
so one citation for the whole table would imply every figure came from one query.

★ NOT `screen` AND NOT `find_related`. `screen` finds symbols from a description when you have
none; `find_related` walks supply-chain links. This one needs you to already know which two to
five companies you mean.

⚠️ Placing figures next to each other is not ranking them. A measure one company publishes and
another does not is a coverage difference, not evidence about either.
Args: tickers (2-5), metrics, optional pre-fetched rows_by_ticker, optional as_of.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	No
`metrics`	Yes
`tickers`	Yes
`rows_by_ticker`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already establish read-only, open-world, idempotent, and non-destructive behavior. The description adds genuinely useful behavior beyond that: absent figures are reported as `available: false`, never filled with zero/previous period/dropped, and each cell names its own source. This goes well beyond the structured hints.

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 core purpose sentence is front-loaded, and the starred notes each carry meaningful behavioral guidance. The description is longer than minimal, but the added length is mostly purposeful, with only mild formatting weight.

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 an output schema exists and annotations cover safety, the description completes the picture: when to use it, what data gaps mean, per-cell source behavior, and argument constraints. An agent has enough to select and call the tool correctly.

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?

Schema description coverage is 0%, so the description must carry parameter meaning. It does name all four arguments and clarifies the ticker count constraint and optionality of rows_by_ticker/as_of. However, metric names/formats and as_of semantics remain implicit, so the compensation is only partial.

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 uses a specific verb and resource: 'Lay two to five named companies side by side on the same measures, gaps marked as gaps.' It clearly defines the tool's scope and distinguishes it from siblings by noting it requires companies to be specified upfront.

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 says 'NOT `screen` AND NOT `find_related`' and explains when those alternatives apply: screen finds symbols when you have none, find_related walks supply-chain links. It also warns that side-by-side figures are not ranking, which helps prevent misuse.

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.

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