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

With annotations only declaring readOnlyHint, the description carries the behavioral burden and does so richly: fixed model constants, FCFE-style DCF that is not EV-comparable, null-on-negative/missing-inputs semantics, and caveats surfaced in tool_notes. This is exactly the kind of beyond-schema disclosure that matters.

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

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

Front-loaded with the core purpose, then packed with high-value caveats; nearly every clause earns its place. It is dense and long-ish, but not padded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, yet the description enumerates the return keys, units (USD per share), null semantics, and explicitly warns that price/market cap/margin of safety are absent. An agent has everything needed to call and interpret the result.

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

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

Only one parameter (ticker) with 100% schema description coverage, so the schema already defines it fully. The description adds no ticker-specific syntax, format, or lookup behavior, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource (per-share intrinsic value from SEC EDGAR XBRL) and enumerates the three distinct models used, which clearly separates it from generic siblings like get_fundamentals or ol_value_investing_fact.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives actionable usage context: fetch a price yourself to compare, retry on transient 'fetch_error', and interpret nulls via null_reasons. It does not, however, explicitly position itself against sibling valuation tools like ol_value_investing_fact or ol_peer_fundamentals.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.