intelligent_analyze

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

With no annotations, the description must carry behavioral weight. It does reveal that the tool is a recommender/planner rather than an executor: output includes 'recommended tool, expected results, insights,' and it advertises token savings. It stops short of disclosing limitations, accepted data formats, or whether any external queries are made.

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?

Very compact: one purpose sentence plus labeled input/output lists. No redundant content; key information is front-loaded. Minor markdown-like labels are structurally acceptable.

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?

For a two-parameter router with an output schema, the core contract is present: input semantics, optional goal, and output categories. But in a 70-tool ecosystem, the description is thin on selection context and examples, so an agent might misuse it for direct analysis rather than planning.

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%, but the description maps user_input to a 'data string' and analysis_goal to an 'optional analysis goal.' This adds moderate meaning beyond generic property titles, but it gives no format, example, length, or constraints.

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 identifies a clear verb-object pair: 'intelligently analyze biological data' and 'recommend the best analysis plan.' The output list (data type identification, recommended tool, expected results, insights) distinguishes it as an advisor/router from data-access siblings, though it never names a sibling or explicit exclusion.

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 implies the use case: when a user provides a biological data string and optionally a goal, and the agent wants to save tokens by getting a recommended plan. However, it does not state when not to use it, nor does it compare it to dedicated analysis tools or get_analysis_template.

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