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

Annotations already declare readOnly/openWorld/idempotent hints and non-destructive behavior, so the bar is higher. The description still adds meaningful behavior beyond annotations: parallel facet decomposition, the findings packet structure (verbatim evidence + confidence + source + fetched_at + pipeworx:// citation), explicit gaps[], tier-specific hop behavior, semantic excerpting of large records, and latency expectations. Minor deduction for not covering failure modes or rate limits.

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 packs a lot of information but is dense and cluttered, with the auth note awkwardly embedded in the opening parenthetical. It is not well front-loaded for scannability, though every sentence does earn its place. A restructured version with clearer rhythm would score higher.

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 tool with 2 parameters, no output schema, and rich annotations, the description is unusually complete. It covers what the tool does, what it returns, how it handles unanswerable facets (gaps[]), timing expectations, tier differences, auth requirements, and alternatives to siblings. Nothing essential is missing.

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 baseline is 3. The description adds real value by explaining what the depth values mean behaviorally (quick=3 facets, standard adds gap-recovery + contradictions, thorough adds a full iterative hop) and clarifying that 'question' can be broad/multi-part. This goes beyond the schema's enum descriptions.

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 states a specific verb ('research') and a precise resource ('Pipeworx's 1517 STRUCTURED data sources'), explicitly distinguishes itself from open-web search, and gives concrete example questions. It clearly separates deep_research from siblings like ask_pipeworx and compare_entities.

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 states when to use this tool ('Best for broad/multi-part questions over structured data'), when not to ('For a single lookup use ask_pipeworx instead'), and even an auth-based fallback ('If you are not signed in, use ask_pipeworx instead'). This is direct routing guidance with named alternatives.

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