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get_investment_splits

Read-only

Retrieve stock split events with adjustment multipliers and security details from your local cache. Use for narrative or historical analysis.

Instructions

Get stock split events from the local Firestore cache. Returns one row per (security, effective_date) with the adjustment multiplier (e.g. 0.1 for a 10-for-1 split — multiply pre-split prices/quantities by this value to convert to the post-split equivalent). Joined with the securities collection so each row includes ticker and name. IMPORTANT: prices returned by get_investment_prices and get_investment_prices_live are ALREADY split-adjusted by Copilot. Use this tool only when you need the split events themselves (e.g., for narrative or historical-analysis purposes) — you do NOT need to apply these multipliers to the prices yourself. Securities that have never split are not included in the output. Coverage is limited to securities Copilot currently syncs in your local cache (typically currently-held or recently-held).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ticker_symbolNoOptional. Case-insensitive ticker filter (e.g. "NVDA").
start_dateNoOptional. Inclusive lower bound on effective_date (YYYY-MM-DD).
end_dateNoOptional. Inclusive upper bound on effective_date (YYYY-MM-DD).
limitNoMaximum number of rows. Default 100, max 10000.
offsetNoPagination offset, default 0.
Behavior5/5

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

Annotations already declare readOnlyHint=true, and the description adds details about output structure (one row per security/date), the meaning of the multiplier, the join with securities, and cache coverage. No contradictions.

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

Conciseness4/5

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

The description is front-loaded with purpose and details, then usage guidance. It is somewhat lengthy but all sentences add value. Could be slightly more concise, but structure is logical.

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?

Despite no output schema, the description fully explains the output structure, including the multiplier meaning and joined fields. All 5 parameters are well-documented. The tool's behavior is completely described.

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 good descriptions, but the description adds extra context (e.g., case-insensitive ticker, inclusive date bounds, effective_date field name). This enhances clarity beyond 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?

The description clearly states it retrieves stock split events from the local Firestore cache, specifying verb 'get', resource 'stock split events', and scope. It distinguishes from siblings like get_investment_prices by noting that those return already-adjusted prices.

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 tells when to use this tool ('when you need the split events themselves') and when not to ('you do NOT need to apply these multipliers to the prices yourself'). Also mentions coverage limitations, guiding the agent appropriately.

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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