search_predefined_metrics

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 not disclose matching semantics (exact, substring, case sensitivity), whether an empty query returns everything, or any limits on results. Only the fact that it is a read/search operation can be inferred from the verb.

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?

It is a single front-loaded sentence with no filler, which is good. But the brevity comes at the cost of under-specification rather than efficiency, so it does not earn a high conciseness score.

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-value shape need not be described. Still, for a search tool with zero annotation coverage and an entirely undocumented query parameter, the description omits information an agent needs to invoke it correctly (matching rules, result limits).

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 explain the single 'query' parameter. It never says what the query is matched against (metric name, description, keywords) or what format it expects, leaving the only required parameter undocumented in both places.

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 names a verb (searches) and resource (predefined metrics), and scopes it to the einsteinpy.symbolic.predefined module, which does separate it from the create_predefined_metric sibling. However, it never explains what a 'predefined metric' is or what aspect of it the query matches, so the purpose is only minimally clear.

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?

There is no guidance on when to use this versus create_predefined_metric or create_custom_metric, no mention of prerequisites, and no statement of what a search returns when nothing matches. The agent must infer usage entirely from the tool name.

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