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

With no annotations, the description carries the full behavioral burden. It discloses significant traits: the `date` parameter is silently ignored for non-enterprise tiers, `days` is tier-capped and silently capped above the cap, snapshots are returned oldest first, and response metadata includes `tier_cap`, `effective_days`, `start_date`, `end_date`, and `date_param_honored`. This is thorough and candid.

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-organized: purpose and usage in the first paragraph, parameter caveats in the second/third, and a clear return-value statement. Every sentence provides necessary information, and the structure makes the important caveats (tier caps, date ignoring) easy to spot.

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—tier caps, date behavior, return format—and the absence of an output schema, the description is complete. It explains what the response contains (array of daily snapshots with `snapshot_date` and standard `MarketSnapshot` fields, plus metadata fields) and covers all critical edge cases. It doesn't need to re-explain parameters already well-documented in the schema.

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%, so the baseline is 3. The description adds value beyond the schema by explaining the `date` parameter's enterprise-only behavior ('silently ignored' on other tiers) and by restating the `days` range and default in operational context. It relies on the schema for `version_info` and `snapshot_type`, which is acceptable given the schema's own detailed 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?

The description opens with a specific verb and resource: 'Fetch a historical time series of daily market-level snapshots.' It immediately clarifies scope ('overall market sentiment, not a single ticker') and explicitly distinguishes itself from a sibling by saying 'For a single ticker's history use get_ticker_history instead.' This makes the tool's purpose unmistakable.

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?

The description gives explicit when-to-use guidance: 'Call this when the user asks how the overall market mood/regime has trended over time, wants to chart market α-sentiment / z-score over a window, or needs a range of daily market snapshots to compute averages or momentum.' It also names the alternative tool for single-ticker histories, and explains tier-specific behaviors affecting usage.

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