get_history

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable behavioral context beyond this: the downsampler keeps the last trading day of each period, large daily requests exceed the agent-side response clamp forcing a costly year-end-extraction pattern, and the frequency alias 'year_end'. This is actionable information the agent 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 longer than average but every sentence carries important information: output definition, parameter alternatives, usage warning, downsampling behavior, and use-case context. It is well-structured with a clear progression, though it could be slightly tightened without losing value.

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?

For a tool with 5 parameters and an output schema, the description covers all necessary aspects: purpose, parameter semantics, behavioral quirks, cost implications, and use cases. Since an output schema exists, not detailing return values is acceptable. The description is fully adequate for an agent to select and invoke this tool correctly.

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?

Schema coverage is 100%, but the description adds meaning beyond the schema: explains that start_date/end_date override range, lists the exact range options, clarifies the 'annual' alias 'year_end', and links frequency choice to response size and credits. This goes well beyond the baseline of simply having parameter descriptions.

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 exactly what the tool returns: 'OHLC bars for a single ticker.' This clearly distinguishes it from siblings like get_quotes (current quotes), compute_returns (derived metrics), and get_fundamentals (fundamental data). The scope is explicit: single ticker, historical bars.

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?

Provides explicit when-to-use guidance: 'Use for ad-hoc time-series analysis the trained models don't already cover.' It also gives specific best practices, e.g., use coarser frequencies for multi-year analysis to avoid exceeding the agent-side response clamp and incurring extra credits/time. This helps the agent choose this tool over alternatives.

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