List Tracked Experiments

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds that the tool returns JSON with metadata and tracker stats, and notes it shows 'local library' and includes publication/derived file counts. This adds behavioral context beyond annotations without contradiction.

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 concise and well-structured: a one-sentence purpose, a usage section, args list, and returns statement. Every sentence adds value with no redundancy.

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?

Given the tool's simplicity (3 optional params, no required params, output schema exists), the description covers purpose, usage, parameter semantics, and return value. Annotations provide safety context. No gaps for effective agent invocation.

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?

Input schema has 0% description coverage, but the description provides clear semantic meaning for each parameter: assay_title, organism, and organ filters with partial match behavior. While it lacks format constraints or examples, it compensates for the schema gap.

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 explicitly states the tool lists all locally tracked experiments with optional filters. It distinguishes from siblings by specifying 'tracked experiments' and referencing related tools like encode_summarize_collection. The verb 'list' and object 'tracked experiments' are 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?

The description includes a 'WHEN TO USE' section advising to use for seeing all experiments in the local library and mentions filtering capabilities. It lists related tools (encode_summarize_collection, encode_export_data), providing context for alternatives. However, it does not explicitly state when not to use this tool.

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