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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Top Holders (composite, classified)

get_top_holders
Read-onlyIdempotent

Classification-aware UNION across insider transactions (latest post_transaction_shares per insider), 13F institutional holdings, and SC 13D / 13G blockholder filings for one issuer. Each row carries holder_class ∈ {insider, institutional, blockholder_13D, blockholder_13G}. Dedupes overlapping filers by precedence (13D > 13G > institutional > insider). One call, classified cap table — Bloomberg charges separately for INSIDER, OWNER, and HDS; this consolidates them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNoMaximum holders to return, ranked by shares. Default 25.
tickerYesIssuer ticker symbol.
as_of_dateNoPoint-in-time cutoff (YYYY-MM-DD): only filings ACCEPTED by SEC on or before this date are considered across all three sources (institutional via accepted_at, insider via accepted_at, blockholders via accepted_at). Excludes amendments/late filings accepted after this date (zero look-ahead). Omit for the latest knowable cap table.
period_endNo13F REPORTING period_end. Omit for latest. NOT a point-in-time cutoff — use as_of_date.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cikYes
rowsYes
_metaYesProvenance envelope — data lineage for every MCP response
tickerYes
stalenessYesEach source has its own as-of date and lag (13F ~45-day lag; 13D/G snapshots can be years old). Percentages from different-dated denominators are NOT directly comparable.
as_of_dateYesThe point-in-time cutoff actually applied (echo of the as_of_date input). Null when no PIT cut was requested. NEVER equal to period_end unless explicitly supplied — a reporting period is not a knowable-as-of date.
period_endYesThe institutional 13F REPORTING period — NOT a point-in-time cutoff.
company_nameYes
sources_breakdownYes

TDQS

A4.6/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, destructiveHint), the description adds critical behaviors: deduping by precedence order, per-source filtering via accepted_at, and exclusion of late filings. This provides essential transparency for agent decision-making.

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 a single paragraph that efficiently conveys purpose, data sources, dedup logic, and time filtering. While concise, it could be slightly restructured for readability, but every sentence earns its place.

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 complex tool with 4 parameters and an output schema, the description covers purpose, behavioral nuances, and parameter semantics thoroughly. No gaps remain for effective use.

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 coverage is 100%, so baseline is 3. The description adds meaningful context for as_of_date (point-in-time cutoff, zero look-ahead) and period_end (13F reporting period), which are not fully captured by schema descriptions alone.

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 it retrieves a 'classification-aware UNION' of insider, institutional, and blockholder holdings. It specifies the exact data sources and the composite nature, distinguishing it from sibling tools like get_insider_transactions or get_institutional_holdings.

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 explains it consolidates multiple sources into one call and compares to Bloomberg products, giving context for when to use it. It does not explicitly state when not to use it, but the purpose is clear enough to infer alternatives.

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.

Resources