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equibles

by io.github.daniel3303

Short Squeeze Scores

GetShortSqueezeScores
Read-only

Rank primary operating-company stocks by a peer-relative 0-100 short-squeeze score using short interest, capped days to cover, price versus trailing VWAP, short-volume trend, short-interest change, fails-to-deliver pressure, and bounded price/volume/earnings catalyst boosts. Optional liquidity floors filter the board without changing scores. Pass ticker for one stock's factor breakdown and universe rank. Exchange-traded products are excluded because issuer shares outstanding and earnings are not product-level facts; use GetShortInterest for an ETF's exact FINRA series.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
offsetNoNumber of ranked results to skip before returning rows — pass the previous call's last rank to page past the maxResults cap (default: 0; ignored for a single-ticker lookup)
tickerNoOptional stock ticker (e.g. GME): returns that one stock's score, factor breakdown, and rank within the scored universe instead of the board. The liquidity floors do not apply to a single-ticker lookup.
maxResultsNoMaximum number of stocks to return (default: 25, highest score first; clamped to 1-200).
minMarketCapNoMinimum market capitalization in US dollars (e.g. 300000000 = $300M; default 0 = no floor). Stocks with an unknown market cap are excluded when set.
minDollarVolumeNoMinimum average daily dollar volume in US dollars, approximated as the FINRA average daily share volume times the market-cap-implied share price (e.g. 5000000 = $5M/day; default 0 = no floor). Stocks with unknown volume or market cap are excluded when set.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / properties / offset
      Added value: +{
+  "default": 0,
+  "description": "Number of ranked results to skip before returning rows — pass the previous call's last rank to page past the maxResults cap (default: 0; ignored for a single-ticker lookup)",
+  "type": "integer"
+}
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already mark it readOnly and non-destructive, and the description adds substantial behavioral context beyond that: the universe is limited to operating companies, liquidity floors 'filter the board without changing scores,' and passing a ticker switches to a single-stock mode returning 'factor breakdown and universe rank.' The ETP exclusion comes with a rationale (issuer shares outstanding and earnings are not product-level facts). No contradiction with the readOnlyHint.

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?

Four sentences, each carrying distinct information: purpose plus factors, floor semantics, ticker mode, and ETF exclusion with routing. The core purpose is front-loaded in the first sentence, and the factor list earns its place by defining what the composite score means. No repetition of what the schema already documents.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description covers the single-ticker return shape (score, factor breakdown, universe rank) and the score's 0-100 peer-relative meaning, while the schema documents ordering and caps. The missing piece is the board mode's exact return columns and rank assignment semantics, which the agent must infer. Overall, annotations, schema, and description combine sufficiently for selection and 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 coverage is 100%, so the baseline is 3; the description adds meaning by grouping minMarketCap/minDollarVolume as 'liquidity floors' and stating the key semantic that they 'filter the board without changing scores.' The ticker-mode behavior ('factor breakdown and universe rank') reinforces, though partly duplicates, the schema's ticker description. This is incremental but genuine added semantics.

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?

States a specific verb ('Rank'), a bounded universe ('primary operating-company stocks'), and a precise output ('peer-relative 0-100 short-squeeze score') with its formula inputs enumerated. Explicitly excludes exchange-traded products and routes ETF queries to GetShortInterest, separating it from that sibling. The title and name align with the stated purpose.

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?

Gives one explicit when-not-to-use rule with a named alternative: ETPs are excluded and 'use GetShortInterest for an ETF's exact FINRA series.' Does not, however, contrast this tool against other potentially confusing siblings (GetShortVolume, GetLargestShortVolume, ScreenStocks), so usage context is clear but not exhaustive.

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

B3.4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with detailed descriptions that cross-reference related alternatives. A few near-duplicate names could cause misselection, notably SearchDocument versus SearchDocuments and GetCftcPositioning versus GetLatestCftcPositioning.

Naming Consistency5/5

Tool names consistently follow a VerbNoun camelCase pattern: Get for retrievals, Search for discovery, List/Read for document access, and Add/Close/Remove/Update/Watch/Create/Delete for portfolio mutations. Despite the large count, there is no mixing of naming conventions or unpredictable verb styles.

Tool Count1/5

108 tools is an extreme surface area, far beyond the 3-15 well-scoped range and well past the 25+ threshold. Even for a broad financial data platform, this creates a heavy selection burden and substantial context overhead for agents.

Completeness4/5

The server covers an unusually wide domain: prices, fundamentals, SEC filings, options, insider activity, 13F holdings, short interest, macro data, funds, IPOs, and full portfolio lifecycle management. Notable gaps remain, such as a basic company profile/ticker-resolution tool, dividend history, and analyst estimates, so it is not a perfect 5.

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