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Get Float History

get_float_history
Read-only

Get point-in-time float history per trade date to verify past float values and track dilution events. Includes float, shares outstanding, and source; empty rows before mid-2026 are normal.

Instructions

Get the POINT-IN-TIME float history for a company — one sample per trade date (float shares, shares outstanding, and the source the float came from). Use to answer "what was the float on date X" or to see float expand across a dilution event, which the latest-only get_float cannot show. IMPORTANT: this series is FORWARD-ONLY — it began accumulating in mid-2026 and is NOT backfilled, so early/absent history is expected and an empty rows array is a normal result, not an error or a delisted company. Each row carries "source" ("polygon" | "computed" | "sec_10k" | "fmp") because float quality varies by provider — weigh rows accordingly rather than treating all sources as equal. Charged per your API tier.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLookback window in trade dates. Default 90, clamped to 1-730.
tickerYesStock ticker symbol (e.g., "AAPL", "TSLA")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.2

TDQS

A4.9/5.0
Behavior5/5

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

Description adds critical behavior beyond readOnlyHint: forward-only accumulation starting mid-2026, no backfill, empty rows as normal, source reliability variance, and API tier charging. Predicts expected data patterns and prevents misinterpretation.

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?

Single paragraph, front-loaded with purpose, then usage, then caveats, then cost. Every sentence provides unique value; no redundancy or 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?

Given historical complexity and presence of output schema, description covers all essential aspects: return fields, usage context, data gaps, source quality, and cost. No missing critical information for agent to decide and use tool correctly.

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% with clear parameter descriptions. The description adds context about the lookback window (implicitly tied to 'days') and the point-in-time nature, but does not significantly enhance parameter understanding beyond schema. Still above baseline due to schema covering parameters well.

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 the tool retrieves point-in-time float history per trade date, with specific fields (float shares, shares outstanding, source). It distinguishes itself from sibling get_float by highlighting that it shows history across time, not just latest.

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?

Explicitly states when to use (for historical float queries, dilution events) and when not (latest-only queries serve get_float better). Also advises weighing rows by source quality, guiding proper interpretation.

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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