Valuein — SEC EDGAR Fundamentals & Smart-Money Data

Stock Price (as-of date)

get_stock_price

Read-onlyIdempotent

End-of-day closing price for a company AS OF any calendar date. Pass date to get the close on that day; if the date falls on a weekend or market holiday, it resolves backward to the most recent prior trading day's close (the price_date field tells you which day was actually used, and resolved_backward flags when it stepped back). Omit date for the latest available close. Closes are RAW (not split/dividend-adjusted); div_cash and split_factor carry the corporate-action factors for query-time total-return adjustment. This is EOD market data (not a SEC filing fact), so it carries a price_date rather than a fact_id. Coverage follows your plan's tier slice: full = all companies & all history, pro = all companies & last 15 years, sp500 = S&P 500 only, sample = S&P 500 & last 5 years. Available on all plans.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`date`	No	As-of calendar date (YYYY-MM-DD). Returns the close of the most recent trading day on or before this date — a weekend/holiday resolves to the prior trading close. Omit to get the latest available close.
`ticker`	Yes	Stock ticker symbol, e.g. AAPL, MSFT

Output Schema

TableJSON Schema

Name	Required	Description
`cik`	Yes
`note`	Yes
`plan`	Yes
`_meta`	Yes	Provenance envelope — data lineage for every MCP response
`close`	Yes
`ticker`	Yes
`currency`	Yes
`div_cash`	Yes
`price_date`	Yes
`company_name`	Yes
`split_factor`	Yes
`requested_date`	Yes
`resolved_backward`	Yes
`is_exact_date_match`	Yes

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds crucial behavioral details: backward date resolution, RAW closes with corporate action factors, price_date and resolved_backward fields, and plan-specific data 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?

Description is moderately long but well-structured, starting with main purpose then detailing nuances. Every sentence adds information without redundancy. Could be slightly tighter, but overall effective.

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 the tool's complexity (market data, backward resolution, plan tiers) and presence of output schema, the description covers all necessary context: return fields, corporate action adjustments, and data coverage limits. No gaps identified.

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. Description adds significant value by explaining how the date parameter handles weekends/holidays (backward resolution) and that omitting date returns latest close. This goes beyond schema descriptions.

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?

Description clearly states it provides end-of-day closing price for a company as of any calendar date. It specifies the verb 'get' and resource 'stock price', and includes unique behaviors like backward resolution on weekends/holidays, separate from sibling tools.

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?

The description explains when to omit date for latest close, and mentions plan tier coverage, but does not explicitly compare to siblings like get_price_history or get_valuation_metrics. However, it provides sufficient context for appropriate use.

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

A4/5.0

Disambiguation5/5

Each tool has a distinct purpose with detailed descriptions that clarify differences. Overlaps like get_peer_comparables vs screen_universe are well-differentiated by scope (single company vs cross-sectional). Similarly, get_insider_sentiment vs get_smart_money_flow are clearly distinguished by data sources and methodology.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., create_report, get_financial_ratios, delete_alert). No mixing of conventions or inconsistent verbs.

Tool Count2/5

With 69 tools, the count far exceeds the 25+ threshold for 'too many'. While the domain is broad, the sheer volume likely overwhelms agents and increases selection complexity.

Completeness4/5

The tool set covers a wide range of SEC filings, ratios, smart-money data, alerts, reports, and more. Minor gaps exist (e.g., no options or detailed debt data), but most analyst workflows are supported.

Stock Price (as-of date)

Input Schema

Output Schema

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