encode-toolkit

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

Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=false, and idempotentHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it explains the utility ('Useful for loading into Excel, R, pandas, or sharing with collaborators'), mentions that PMIDs can be used with PubMed MCP tools, and clarifies the output as 'tabular export' and 'shareable tables'. It does not disclose rate limits or auth needs, but with annotations covering core behavioral traits, this is sufficient.

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 with clear sections (general description, utility, PMID usage, WHEN TO USE, RELATED TOOLS, Args, Returns). Every sentence earns its place by providing essential information without redundancy. It is front-loaded with the core purpose and efficiently covers all necessary details.

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 (export with filtering), low schema coverage (0%), rich annotations, and presence of an output schema (which handles return values), the description is complete. It covers purpose, usage guidelines, parameter semantics, and behavioral context, leaving no gaps for the agent to understand and invoke the tool correctly.

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 full burden. It provides detailed semantics for all 4 parameters: 'format' is explained with enum values and use cases (e.g., 'csv' for Excel/spreadsheets, 'tsv' for R/pandas, 'json' for programmatic use), and 'assay_title', 'organism', and 'organ' are described as filters with 'partial match' behavior. This adds significant meaning beyond the bare 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 specific action ('Export tracked experiments as a table') and resource ('tracked experiments'), distinguishing it from siblings like encode_list_tracked (which lists) and encode_summarize_collection (which summarizes). It explicitly mentions the output formats (CSV, TSV, JSON) and the content included (metadata, publication counts, PMIDs, derived file counts).

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 dedicated 'WHEN TO USE' section that explicitly states the tool's purpose ('to create shareable tables of tracked experiments') and contexts ('Good for manuscripts and reports'). It also lists RELATED TOOLS (encode_list_tracked, encode_summarize_collection), providing clear alternatives and differentiation from siblings.

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