correlation

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

With no annotations, the description must disclose behavioral traits but only states 'calculate correlation', implying a read-only computation. It does not mention performance implications, error conditions (e.g., missing tags), correlation method (e.g., Pearson), or any side effects, leaving significant gaps.

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 two sentences long and front-loaded with the core purpose. The second sentence is slightly redundant but not wasteful. Overall, it is concise and contains no extraneous information.

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?

The description is reasonably complete for a tool with a provided output schema (which can describe return values). However, it lacks specificity on the correlation method used and whether correlations are computed pairwise between all tags. This ambiguity prevents a higher score.

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 100%, so the parameters' meaning is already defined in the schema. The description adds minimal extra value, only clarifying that tags are correlated 'with other tags'. This meets the baseline of 3 without adding significant new semantics beyond the schema.

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 states the tool calculates correlation between scalar tags in a TensorBoard event file, specifying the action (calculate) and resource (scalar tags). This verb+resource combination distinguishes it from siblings like query or tag_stats, which likely fetch raw data rather than compute relationships.

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 does not provide explicit guidance on when to use this tool versus alternatives. It describes what the tool does but lacks any when-to-use, when-not-to-use, or alternative recommendations, leaving the agent to infer usage from purpose alone.

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