get_observation_history

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

With no annotations, the description bears the burden. It discloses a prerequisite ('Requires ATLAS_API_URL') and explains key types. However, it does not explicitly state the operation is read-only, describe error behavior, or mention rate limits or pagination beyond the limit parameter.

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 three sentences long, with no redundant information. The first sentence states purpose, the second explains key types, and the third gives the environment requirement. Every sentence adds 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?

With no output schema, the description could specify the response structure, but 'time series' implies a sequence of snapshots. The tool has few parameters and no annotations, and the description covers purpose, key semantics, and environment needs, making it reasonably complete.

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 schema already covers both parameters, but the description adds valuable examples ('TSLA' for price snapshots, 'chain-tvl', contract address for supply/holders), enriching the key parameter semantics beyond the schema's brief description.

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 identifies the tool as providing 'The ATLAS agent's recorded observation time series' with a specific use case ('trend detection and day-over-day comparison'). This distinguishes it from sibling tools that focus on current market data, token supply, holders, and TVL.

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 states the tool is for 'trend detection and day-over-day comparison', giving clear context for when to use it. It does not explicitly name alternatives or exclusions, but the purpose statement implicitly differentiates it from sibling tools.

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