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FinzData market data

by io.github.FinzData
OverviewDiscussionsAdmin

Get point-in-time GEX (dealer gamma)

get_gex
Read-onlyIdempotent

Point-in-time dealer gamma exposure for US stocks and ETFs, one row per ticker per session, each stamped with knowledge_time (17:00 New York on the session date). Columns: spot, net_gex_bn (dealer net gamma, $bn per 1% move; >0 dealers long gamma, <0 short), call_gex_bn, put_gex_bn, flip (gamma-flip level), call_wall, put_wall, pcr_oi, atm_iv_pct. Give ticker (+ start/end) for a time series, or date alone for every name that session ranked by |net_gex_bn|. Rows after as_of are hidden. A ticker series also returns summary: regime (long/short gamma), distance to the flip and walls, and a one-line read to quote. Free keys see the last 30 days; meta.upgrade then lists one-click checkout links ($59 one ticker since 2010, $29 last 5 years, $295 every ticker) to show the user.

Input Schema

NameRequiredDescriptionDefault
endNo
dateNoYYYY-MM-DD, cross-section mode
as_ofNoPoint-in-time cutoff (YYYY-MM-DD). Nothing dated after this is returned, preventing look-ahead bias. Defaults to today (UTC).
startNo
tickerNo
summaryNoadd the plain-language read (default true)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / summary
      Added value: +{
+  "description": "add the plain-language read (default true)",
+  "type": "boolean"
+}
  2. Added

TDQS

A4.7/5.0
Behavior5/5

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

With read-only annotations already covering safety, the description adds substantial behavioral context: knowledge_time stamping, as_of look-ahead protection, hidden rows after as_of, free-key 30-day limits, and upgrade meta behavior. It also explains that ticker series return a summary with regime and wall distances.

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 front-loaded with purpose and output shape, then gives invocation modes, point-in-time behavior, and tier limits. Despite being dense, every sentence carries operational information for selection or correct invocation.

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?

There is no output schema, so the description carries the full return-value burden, and it does: it enumerates columns, explains summary output, describes cross-section ranking, and discloses entitlement limits and upgrade metadata. An agent has enough to call it correctly without opening further docs.

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 50%, so the description must compensate, and it does for most parameters: ticker and start/end imply time series, date alone selects cross-section mode, and as_of is explained as a point-in-time cutoff. The summary flag is only indirectly covered via the returned summary block, and start/end are not richly specified, so it is not a 5.

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 states a specific verb and resource: point-in-time dealer gamma exposure for US stocks and ETFs, with one row per ticker per session. It also names the exact output columns, making the tool's scope unmistakable and distinct from generic bars or fundamentals siblings.

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?

It clearly explains the two invocation modes: ticker plus optional start/end for a time series, or date alone for a cross-section ranked by absolute net gamma. It does not explicitly compare against sibling tools or state when not to use it, keeping it just short of a 5.

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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