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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Institutional Holdings (by issuer)

get_institutional_holdings
Read-onlyIdempotent

Returns top-N institutional holders of a US public company at a specific period_end (latest by default), with aggregate institutional shares, total market value, holder count, and HHI concentration (sum of squared share-of-total percentages). Sourced from Form 13F-HR via the by-issuer partition. Institutional tier only. 13F filings carry a ~45-day reporting lag — staleness_warning fires when latest data is older than 90 days.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNoMaximum holders to return, ranked by market_value_usd. Default 25, max 200.
tickerYesStock ticker symbol of the issuer.
as_of_dateNoPoint-in-time cutoff (YYYY-MM-DD): only 13F filings ACCEPTED by SEC on or before this date are considered, applied BEFORE the latest period is resolved. A 13F/A amendment or late filing accepted after this date is excluded (zero look-ahead) — use this for survivorship-free backtests. Omit for the latest knowable book.
period_endNoQuarter-end of the 13F reporting period (YYYY-MM-DD). Omit to use the latest period available. This is a REPORTING period, NOT a point-in-time cutoff — use as_of_date for that.
lineage_detailNoPer-row provenance envelope. compact / full / off.compact

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cikYes
rowsYes
_metaYesProvenance envelope — data lineage for every MCP response
tickerYes
aggregateYes
as_of_dateYesThe point-in-time cutoff actually applied (echo of the as_of_date input). Null when no PIT cut was requested — never confuse this with the reporting period_end.
period_endYesThe 13F REPORTING period the rows belong to — NOT a point-in-time cutoff.
company_nameYes
data_age_daysYes
holders_countYes
hhi_concentrationYes
staleness_warningYes
total_market_value_usdYes
options_positions_countYesOption positions (put_call set) excluded from totals/HHI/rows. rows[] are common-stock 13F holdings only.
total_institutional_sharesYes

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds significant behavioral context beyond these: data source (Form 13F-HR via by-issuer partition), staleness warning (45-day lag, fires if older than 90 days), and what the output contains (HHI concentration calculation). 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences with no wasted words. The first sentence immediately states the core function, followed by specific details (source, lag) in subsequent sentences. It is front-loaded and efficient.

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 that an output schema exists, the description does not need to detail return fields. It covers data source, reporting lag, staleness condition, and the computation of HHI. For a tool with five parameters (one required) and moderate complexity, the description is complete and adds necessary context for correct invocation.

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. However, the description adds meaning beyond the schema by explaining the overall output (aggregate shares, market value, HHI) and the staleness context, which helps the agent understand the tool's purpose and data quality. This 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 specifies the action ('Returns top-N institutional holders') and the resource ('US public company'), with details on what is included (aggregate shares, market value, holder count, HHI concentration). It distinguishes from siblings by stating 'Institutional tier only' and referencing the by-issuer partition, which differentiates it from tools like get_blockholders or get_top_holders.

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?

The description provides clear context on when to use the tool (e.g., for institutional holdings data from 13F filings) and includes a staleness warning about reporting lag. However, it does not explicitly exclude alternatives or provide 'when not to use' guidance, leaving the agent to infer from sibling tool names.

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.

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