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Short interest, short volume, fails-to-deliver and ATS volume of a stock

get_short_interest
Read-onlyIdempotent

Short-selling data for one stock, newest first, in four sections: short_interest (FINRA short interest per settlement date since 2021: short_shares, prev_short_shares, change_shares, change_pct, avg_daily_volume, days_to_cover, dtc_capped), short_volume (FINRA daily off-exchange short volume, last two years: short_volume, short_exempt_volume, total_volume, short_volume_pct), fails_to_deliver (SEC fails-to-deliver, last two years: quantity, price, value_usd) and ats_weekly (FINRA weekly ATS and OTC volume since December 2021, per tier: ats_shares, ats_trades, otc_shares, otc_trades, ats_pct). Without dates: about the last year of short interest (24 settlement dates), the last 30 trading days of short volume and of fails-to-deliver, and the last 12 weeks of ATS data; with from and/or to, each section covers that range, up to 200 rows. Short interest (FINRA) is the number of shares sold short and not yet covered on a settlement date, reported twice a month about 8 business days after the settlement date. Daily short volume (FINRA Reg SHO) is something else: the shares sold short on one day among off-exchange trades only (not short interest, and not total market volume); short_volume_pct is not a sentiment indicator, because market makers' hedging short sales are normally around half of off-exchange volume. Fails-to-deliver (SEC) are shares not delivered on the settlement date, with the SEC's price (the previous close). ATS weekly (FINRA) is volume on alternative trading systems (dark pools) and other off-exchange (OTC) trading, published 2 to 4 weeks late; ats_pct is ATS divided by ATS plus OTC, not a share of total volume. There is no short interest as a percent of float or shares outstanding (no reliable float data). Share classes are separate tickers (BRK.A, BRK.B).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoLatest date, YYYY-MM-DD, inclusive.
fromNoEarliest date, YYYY-MM-DD. With from or to, every section covers that range (up to 200 rows each).
tickerYesStock ticker, e.g. AAPL or BRK.B (share classes use a dot; BRK-B also works).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
nameYes
noteYes
rangeYes
tickerYes
ats_weeklyYes
short_volumeYes
short_interestYes
fails_to_deliverYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior5/5

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

Annotations cover the safety profile, and the description goes well beyond them: it explains default windows per section, the 200-row cap, publication lags (FINRA ~8 business days, ATS 2-4 weeks late), and disambiguates short interest vs. short volume vs. FTD vs. ATS. It also sets expectations on missing metrics (no float-based percentage).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

Front-loaded with the section inventory, which is good, but the body is a dense wall of comma-separated field lists and parenthetical caveats that is hard to scan. Every claim is useful, yet the structure sacrifices readability for completeness.

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 four heterogeneous datasets, no output schema detail needed beyond what exists, idempotent read-only annotations, and one required ticker, the definition covers the nuances an agent needs: what each section means, timing/lag, row caps, and data limitations. Nothing material is missing.

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 semantics by spelling out what from/to do per section (each section covers the range, up to 200 rows) and what defaults apply when they are omitted — information not fully present in 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?

Names a specific resource and enumerates all four data sections with their fields, distinguishing it from list_short_interest (which is presumably cross-stock). An agent can immediately tell this is the per-stock short-selling dataset.

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?

Implicitly scopes usage to a single ticker and clarifies date-range behavior, but never names the sibling list_short_interest or states when to use one over the other. Guidance is strong on domain semantics but weak on tool routing.

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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