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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

Point-in-Time Universe

get_pit_universe
Read-onlyIdempotent

Use this tool to answer questions about historical index membership — e.g. "Was Company X in the S&P 500 on date Y?" or "Which companies were in the Russell 2000 on 2010-01-01?" Use this INSTEAD OF search_companies when the question involves a specific historical date or whether a company was an index member in the past — search_companies only returns current membership and cannot answer historical questions.

Returns a survivorship-free universe valid on a given as_of_date (only companies that existed and were members on that exact date — no hindsight). Supports SP500, RUSSELL1000, RUSSELL2000, RUSSELL3000 via index_membership.parquet (accurate join/leave dates, [) interval semantics). To check one company, pass its ticker + the target date: present = was a member, absent = was not.

Returns per company: CIK, ticker, name, sector, industry, SIC code, and per-row confidence (high/medium/low). _meta.pit_safe is true only when every matched row is high-confidence — treat low-confidence rows with caution. sector is SIC-derived (GICS-aligned, not licensed GICS) — a screening bucket, not an authoritative label.

Use as the first step of a quantitative backtest before get_compute_ready_stream. Returns an empty array (with error detail) if the date is out of range or has no coverage. Available on every plan — sample returns the subset covered by the sample bucket.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
indexNoIndex filter. 'sp500' (~500 large caps), 'russell1000' (~1000 large/mid), 'russell2000' (~2000 small caps), 'russell3000' (~3000 broad market). Omit for no index filter (sector-only or full universe queries).
limitNoMaximum companies to return (1–3500). Defaults to 100. Universe is deduped to one row per CIK, so set near the index size (SP500 ~505, Russell 3000 ~3050).
offsetNoZero-based row offset for paging a large universe. At most 250 rows are inlined per call; when more match, the response carries a `truncation` envelope — pass its `next_offset` here (keeping the same `limit`) to fetch the next page. Defaults to 0.
sectorNoSector filter (case-insensitive substring) over the SIC-derived, GICS-aligned label (not licensed GICS — see tool description). E.g. 'Technology', 'Energy'.
is_activeNoFilter to active (currently trading) companies only. Omit to include all. WARNING: setting this to true on a HISTORICAL query reintroduces survivorship bias — companies that were active on as_of_date but later went bankrupt or got acquired will be filtered out. Leave unset for true PIT backtests.
as_of_dateNoHistorical date (YYYY-MM-DD) for survivorship-free construction. Index queries use index_membership join/leave dates (entrants after the date excluded, later-removed members kept); sector queries use security valid_from/valid_to. Omit for the current universe.
as_of_basisNoWhich date column drives historical construction. 'effective' (default) = effective_date/removal_date (first trading day; passive replication). 'announcement' = announcement_date/removal_announcement_date (S&P's public-announcement day; for inclusion-arb backtests) — rows with NULL announcement_date (mostly pre-2015) are skipped.effective
include_share_classesNofalse (default) collapses to one row per CIK (index-provider convention — BRK counts once, not BRK-A + BRK-B). true returns every share-class row (GOOG and GOOGL separately) — for security-level analysis only.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteYes
_metaYesProvenance envelope — data lineage for every MCP response
indexYes
sectorYes
coverageYes
companiesYes
as_of_dateYes
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.
as_of_basisYes
coverage_gapYes
universe_sizeYes
survivorship_freeYes
confidence_summaryYes

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate read-only and idempotent behavior. The description adds essential behavioral details: survivorship-free construction, index membership interval semantics, confidence levels, the `_meta.pit_safe` field, and a warning about `is_active` causing survivorship bias. No contradictions with annotations.

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 dense but well-organized, front-loading the purpose and usage. Every sentence adds value. However, it could be slightly restructured (e.g., bullet points or separate sections) for easier scanning.

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 complexity (8 parameters, output schema exists), the description covers all necessary aspects: purpose, alternatives, parameter semantics, return fields, confidence handling, survivorship bias warnings, and integration with other tools. No gaps identified.

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?

Schema coverage is 100%, but the description enriches each parameter with practical context: explains enum values for `index`, paging mechanism for `offset`, the case-insensitive substring behavior for `sector`, the historical vs current usage for `as_of_date`, the differences between `effective` and `announcement` basis, and the dedup vs multi‑share-class behavior of `include_share_classes`. Goes well beyond the schema.

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 identifies the tool's purpose: answering historical index membership questions. It provides concrete examples (e.g., "Was Company X in the S&P 500 on date Y?") and explicitly distinguishes it from the sibling tool `search_companies`.

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 ('for historical questions') and when not to ('INSTEAD OF search_companies'). Provides context for quantitative backtesting workflow, and notes plan restrictions. No ambiguity.

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.