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

The description adds significant behavior beyond the readOnly/idempotent annotations: it details data source routing (CoinGecko vs Yahoo Finance with Stooq fallback), dividend/split adjustment behavior, keyless access, and optional proxy env handling for rate limits. This is exactly the kind of extra context that helps an agent anticipate side effects and configuration needs.

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?

The description is dense but well-structured: it starts with the core purpose, then output stats, routing behavior, adjustment, use cases, sources, and env config. Each sentence provides distinct operational information with no filler or redundancy. It's long but every segment earns its place.

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?

Given the tool's complexity (6 params, multiple asset classes, data-source selection, output statistics), the description covers all essential aspects: return content, statistics, ticker formats, sources, rate-limit workarounds, and use cases. The output schema exists, so exact return structure is handled outside the description. This is complete for an agent to decide when and how to invoke the tool.

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?

The input schema already describes all 6 parameters with 100% coverage, so the bar is lower. The description adds practical value with concrete ticker examples, clarifies that the 'metrics' parameter is informational (all fields are always returned), and explains the meaning of 'adjusted=true' in the context of dividends/splits.

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?

The description clearly states the tool fetches historical OHLCV price series, with specific examples (AAPL, SAP.DE, 7203.T, BTC-USD, GC=F). This distinguishes it from sibling tools like fx_rate, interest_rate, and economic_indicator, which cover other financial data types.

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?

Explicit use cases are listed (backtesting, factor analysis, performance attribution, charting, financial modelling), and it notes automatic crypto vs traditional asset detection. However, it does not explicitly mention when NOT to use this tool or name alternative tools, so it stops short of full exclusionary guidance.

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