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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

Screen Universe by Factor Scores

screen_universe
Read-onlyIdempotent

Rank companies by cross-sectional factor scores from factor_scores.parquet. Returns the underlying factors (roe, gross_margin, operating_margin, net_profit_margin, revenue_growth_yoy, fcf_to_assets, debt_to_equity, asset_turnover, current_ratio, piotroski_f_score) plus their percentile ranks (1.0 = best in universe, 0.0 = worst). composite_rank (the default sort) is a one-number multi-factor shortcut; sort by a specific *_rank column for a single factor. Two modes: full-universe (omit ticker) or single-entity (ticker set — spot-check ONE company's factor profile). Sector filter is SIC-derived (GICS-aligned, not licensed GICS — see get_pit_universe). Use this instead of get_financial_ratios when you want CROSS-SECTIONAL comparison (rank vs peers); use get_financial_ratios when you want one company's ratios over time. Supports survivorship-free POINT-IN-TIME screening via as_of_date (see the param). Full-universe screens omit rows that don't join to a company (null symbol); pass exclude_outliers=true to also drop shell-company rows with implausible factors. Available on every plan — sample returns the subset covered by the sample bucket.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results to return (1-100). Defaults to 25.
offsetNoZero-based row offset for paging within the requested `limit` window. At most 250 rows are inlined per call; if the response carries a `truncation` envelope, pass its `next_offset` here. Defaults to 0.
sectorNoFilter to a specific sector (case-insensitive partial match). E.g. 'Technology', 'Healthcare'.
tickerNoIf provided, show only this ticker's factor scores (single-entity mode). Omit to screen the full universe.
sort_byNoWhich factor rank to sort by (see the enum). Defaults to composite_rank. An unrecognized column is rejected with INVALID_ARGUMENT (no silent fallback).composite_rank
as_of_dateNoPoint-in-time cutoff (YYYY-MM-DD). When set, the screen is reconstructed as of this date via factor_scores.accepted_at — each entity ranked at its latest-knowable period, zero look-ahead, survivorship-free. Omit for the latest snapshot.
exclude_outliersNoOptional data-quality guard (default false). When true, additionally drops rows with implausible raw factor values (non-finite, or e.g. asset_turnover > 50x, |FCF/assets| > 10) from shell companies with near-zero denominators. Rows that do not join to a company (null symbol) are ALWAYS omitted in full-universe mode, regardless of this flag.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesRanked factor-score rows for the screened universe
noteNo
planYesCaller's data plan used to scope the screen
_metaYesProvenance envelope — data lineage for every MCP response
tickerNoPresent only when a single-ticker lookup was requested
lineageNoProvenance for pipeline-derived values (ratio.parquet / factor_scores.parquet): source table + pipeline computed_at, plus a pointer to the tools that return filing-level lineage. NOT point-in-time (recomputed on each pipeline run).
sort_byYes
pit_safeNoPresent (and true) only when as_of_date was supplied — the screen was filtered by factor_scores.accepted_at with zero look-ahead
as_of_dateNoPresent only when a point-in-time as_of_date was supplied
truncationNoPresent only when the inline-row cap withheld rows. Page with `next_offset` (keep the same `limit`) or pull the full set via get_compute_ready_stream.
sector_filterNoPresent only when a sector filter was applied
results_returnedYes

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds significant behavioral context: explains return format (factors + percentile ranks with scale 1.0=best, 0.0=worst), default sorting, sector filter origin (SIC-derived, not licensed GICS), point-in-time screening behavior (zero look-ahead, survivorship-free), null symbol omission, and outlier exclusion logic. This goes well beyond what annotations provide.

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 well-structured and efficient: it front-loads the main purpose, then covers returns, modes, sector, sibling comparison, point-in-time, and outlier handling in logical order. Every sentence contributes information without redundancy or fluff, making it concise for the tool's complexity.

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's 7 parameters, no required parameters, and existing output schema, the description covers all critical aspects: purpose, output details, two usage modes, sorting guidance, sector filter, point-in-time semantics, outlier handling, and plan availability. It even mentions pagination via truncation envelope (in offset param but also implied). No gaps remain for an informed selection.

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 100%, so the baseline is 3. The description adds value by explaining the meaning of composite_rank as a multi-factor shortcut, clarifying the rank scale, specifying error handling for unrecognized sort_by values, and contextualizing parameters like as_of_date (zero look-ahead) and exclude_outliers (shell company drop). This extra context justifies a 4.

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 the verb+resource: 'Rank companies by cross-sectional factor scores from factor_scores.parquet'. It specifies what is returned (underlying factors and percentile ranks) and distinguishes from sibling tool get_financial_ratios, making the purpose unambiguous.

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 advises when to use this tool versus alternatives: 'Use this instead of get_financial_ratios when you want CROSS-SECTIONAL comparison; use get_financial_ratios when you want one company's ratios over time.' It also describes two operational modes (full-universe vs single-entity) and when to use each, providing thorough 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
Disambiguation5/5

Each tool has a distinct purpose with detailed descriptions that clarify differences. Overlaps like get_peer_comparables vs screen_universe are well-differentiated by scope (single company vs cross-sectional). Similarly, get_insider_sentiment vs get_smart_money_flow are clearly distinguished by data sources and methodology.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., create_report, get_financial_ratios, delete_alert). No mixing of conventions or inconsistent verbs.

Tool Count2/5

With 69 tools, the count far exceeds the 25+ threshold for 'too many'. While the domain is broad, the sheer volume likely overwhelms agents and increases selection complexity.

Completeness4/5

The tool set covers a wide range of SEC filings, ratios, smart-money data, alerts, reports, and more. Minor gaps exist (e.g., no options or detailed debt data), but most analyst workflows are supported.