encode-toolkit

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

Annotations already provide comprehensive behavioral hints (readOnlyHint: true, destructiveHint: false, openWorldHint: true, idempotentHint: true). The description adds valuable context beyond this by explaining the tool's exploratory nature ('discover what data is available'), the type of output ('faceted counts'), and practical use cases through examples. No contradiction with annotations exists.

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 well-structured and front-loaded with the core purpose, followed by usage guidelines, related tools, examples, and parameter details. Every section adds value without redundancy, and the text is efficiently organized for quick comprehension by an agent.

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 complexity (5 parameters, 0% schema coverage) and the presence of annotations and an output schema, the description is complete. It covers purpose, usage, examples, parameters, and return values ('JSON with facet names and their term counts'), providing all necessary context for an agent to invoke the tool correctly without needing to rely solely on structured fields.

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 carries the full burden. It provides a clear 'Args' section listing all 5 parameters with brief explanations (e.g., 'Pre-filter by assay type'), and the examples illustrate how parameters are used in practice (e.g., organ='pancreas'). This adds significant meaning beyond the bare schema, though it doesn't detail parameter formats 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 clearly states the tool's purpose with specific verbs ('Get live filter counts', 'Returns faceted counts') and resources ('from ENCODE', 'experiments/files'). It distinguishes from siblings by focusing on exploration rather than searching or downloading, as evidenced by 'Useful for exploring what's available before searching' and naming related tools like encode_search_experiments.

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 explicitly provides usage guidance with a dedicated 'WHEN TO USE' section, stating 'Use to explore what data exists before searching' and 'Best first step for unknown datasets'. It distinguishes from alternatives by naming related tools (encode_get_metadata, encode_search_experiments), helping the agent understand when to choose this tool over others.

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