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Intrinsic Value Estimate

ol_intrinsic_value
Read-only

Per-share intrinsic value from SEC EDGAR XBRL -- three textbook models: a levered-FCF DCF, a Greenwald Earnings-Power-Value, and the Graham number. Returns {summary, ticker, available, dcf_per_share, epv_per_share, graham_number, inputs, assumptions, as_of}; all three values are USD PER SHARE. assumptions are FIXED model constants, not per-name (10% discount, 2.5% terminal growth, 10y horizon, 21% tax). NO current price, market cap or margin of safety is returned -- fetch a price yourself and compare. A model is null when its inputs are negative or missing (null_reasons names the input), never computed as if debt-free. reason 'fetch_error' is transient: retry. The DCF is FCFE-style, not comparable to an enterprise-value DCF. Source: SEC EDGAR company-facts XBRL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock ticker symbol (e.g. AAPL).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

With only readOnlyHint available, the description carries the burden and does so richly: full return shape, fixed assumption constants, null-on-negative/missing-input behavior with null_reasons, and transient 'fetch_error' retry guidance. This is far beyond what annotations provide.

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?

Front-loads the core purpose and packs return keys, assumptions, and caveats densely with little waste. Minor redundancy in restating 'USD PER SHARE' twice, but given no output schema the return enumeration earns its space.

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?

No output schema exists, so the description must explain returns — and it enumerates the return object, clarifies units, assumptions, null semantics, and error retry behavior. An agent has everything needed to call and interpret this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Single parameter with 100% schema description coverage, so the schema already documents ticker. The description adds no ticker-specific syntax or format detail, making the baseline 3 appropriate.

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?

Names a specific resource (per-share intrinsic value) and its exact provenance and method (SEC EDGAR XBRL; levered-FCF DCF, Greenwald EPV, Graham number). This clearly separates it from generic siblings like get_fundamentals or ol_fundamentals_screen without opening either schema.

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?

Explicitly states what the tool does NOT give you and what to do instead ('fetch a price yourself and compare'), plus a compatibility warning ('FCFE-style, not comparable to an enterprise-value DCF'). It gives clear usage context but does not name sibling tools or state when-not-to-call-it conditions.

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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