list_observations

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

With no annotations, the description carries the full behavioral burden. It does disclose a genuinely useful trait not inferable from schema: missing values are never silently converted and observed/missing status is preserved. However, it says nothing about pagination or truncation behavior despite a limit parameter defaulting to 100, nor about ordering or how large result sets are handled.

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?

Three short sentences, capability front-loaded before the filtering detail, with no filler. The clause about missing values is slightly terse to the point of being cryptic, but nothing is redundant.

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?

An output schema exists, so return-shape explanation is excused, and the null-handling note adds real value. Still, for a tool with three undocumented parameters and no annotations, the description leaves the agent guessing about matching semantics, the meaning of 'source', and limit behavior.

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 description coverage is 0%, so the description must compensate and it only partially does. It names source and system_name as filters but does not clarify matching semantics (exact vs partial), what 'source' refers to as a provenance value, or what values are valid, and the limit parameter is never mentioned at all.

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 ('List provenance-tracked observations') plus the filtering axis, which clearly distinguishes it from siblings like save_observation or get_experiment_result. It stops short of naming an alternative or stating scope boundaries, so an agent must infer that this is the read side of the observation workflow.

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?

'Filters by source and/or system name' implies how to narrow results and that both filters can be combined, which is implied usage guidance. There is no explicit when-to-use versus alternatives, no statement about prerequisites or result ordering, and no exclusion of any case.

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