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Most Widely Held (13F)

get_institutional_consensus
Read-only

Which tickers the tracked 13F filers most widely hold in common. Returns {as_of_quarter, tickers_found, min_funds, consensus}; each row is {ticker, issuer_name, fund_count, total_value_usd, total_shares, funds}, ranked by fund_count then value. consensus is truncated to top_n (default 50, cap 200) while tickers_found is the FULL count meeting min_funds (default 2, cap 20). COMMON STOCK ONLY: an ownership tally, not exposure. Each fund contributes its own latest quarter, which may differ across funds. Answers 'who is buying X?'; get_13f_holdings answers 'what does fund X own?'. Source: SEC EDGAR 13F-HR, ~45-day quarterly delay.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNoMaximum number of consensus tickers to return (default 50, max 200).
min_fundsNoMinimum number of notable funds that must hold a ticker for it to appear (default 2, max 20).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior3/5

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

Annotations supply readOnlyHint=true, so the safety profile is covered externally. The description adds real operational context beyond that: common-stock-only scope, ownership-tally-not-exposure framing, per-fund differing latest quarters, SEC EDGAR 13F-HR source, and ~45-day quarterly delay. These are meaningful behavioral disclosures, though it stops short of noting rate limits or error conditions.

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?

Purpose is front-loaded, followed by return shape, parameter effects, scope caveats, sibling routing, and data source. It is dense but every sentence carries distinct information; no filler.

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?

With no output schema, the description fully carries the burden and delivers: it enumerates the return object and row fields, ranking order, truncation behavior, data provenance, and freshness delay. An agent has everything needed to call and interpret results.

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, but the description adds non-obvious meaning: top_n truncates only the `consensus` array while min_funds governs the FULL `tickers_found` count, with defaults and caps restated. This clarifies how each parameter interacts with specific output fields, which the schema alone does not convey.

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 opening sentence states a specific verb and resource: which tickers the tracked 13F filers most widely hold in common. It further distinguishes itself from sibling get_13f_holdings via the 'who is buying X?' vs 'what does fund X own?' contrast, so an agent can route without opening either schema.

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?

It explicitly names an alternative (get_13f_holdings) and the question each tool answers, giving a clear selection rule. The 'COMMON STOCK ONLY: an ownership tally, not exposure' note further bounds when its output is and isn't appropriate.

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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