tw-market-data

List factor findings

list_factor_findings

Read-onlyIdempotent

Verdicts from the overnight factor search on YOUR namespace — including the rejections.

    The rejections are returned deliberately. A research log that keeps only the winners is the
    highlight reel overfitting lives in, and the acceptance RATE is the number that tells you
    whether the anti-overfitting gate is doing its job: a search that accepts most of what it
    tries has a broken gate, not a talent for finding alpha.

    Every verdict carries the trial count it was judged against, so it can be re-checked.
    `coverage.missing` names hypotheses that were proposed but never judged because a cost
    ceiling was reached — those are UNTESTED, not rejected.

    An accepted factor is a FINDING with a run_id, not an allocation. Nothing here trades.

    Args:
        limit: how many verdicts to return, newest first (max 100).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`hint`	No
`count`	No
`error`	No
`reason`	No
`blocked`	No
`accepted`	No
`coverage`	No
`rejected`	No
`execution`	No
`interpretation`	No
`trials_declared`	No
`not_investment_advice`	No

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses deliberate inclusion of rejections, trial counts, the meaning of coverage.missing as untested rather than rejected, and that accepted factors are findings with run_ids rather than allocations. These are meaningful behavioral details an agent could not infer from annotations alone.

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 is front-loaded and each paragraph covers a distinct semantic point. Some rationale prose is longer than strictly necessary for executing the call, but it is not filler and supports correct interpretation.

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 single-parameter list tool with an output schema already available, the description covers scope, ordering, limit cap, and the special semantics of rejections and missing coverage. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only defines an integer limit with a default, but the description adds that limit controls how many verdicts to return, that ordering is newest-first, and that 100 is the cap. Since schema description coverage is 0%, the description fully compensates.

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 states a specific resource and action: it returns factor-search verdicts for the caller's namespace, including rejections. This distinguishes it from sibling list/get tools by emphasizing the overnight factor-search scope and the deliberate inclusion of rejected findings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives clear context: use this when you want the full set of overnight factor-search verdicts, not just accepted ones, and when you care about the acceptance rate as a quality gate. It does not explicitly name alternatives or exclusion conditions, so it stops short of a 5.

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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