analyze_products

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

With no annotations, the description carries the full burden. It discloses key behavioral traits: no live network requests, requires configured AI provider keys, computes a page fingerprint, and runs either a single persona or full roundtable. It also clarifies the 'auto' behavior. This is good disclosure for a complex analysis tool, though it does not mention side effects or return format, which would be helpful.

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 two sentences, front-loaded with the core purpose and key constraints. It packs in the input format, behavior, use cases, and requirements without unnecessary filler. Every phrase earns its place, making it concise and well-structured.

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?

The tool has 7 parameters, no output schema, and no annotations. The description covers its purpose, inputs, behavior, use cases, and prerequisites thoroughly. The only notable gap is the absence of information about the return value or result structure, but given the tool's clear analysis function and the lack of an output schema, this is a minor omission. Overall, it is sufficiently complete for an agent to understand when and how to invoke it.

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 coverage is 100%, so the baseline is 3. The description adds some context (e.g., 'computes a page fingerprint' to explain the persona selection), but it largely mirrors what the schema already describes for products, facets, and persona. No additional parameter-level details beyond the schema are provided, which is acceptable given the schema's thoroughness.

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 opens with a specific verb and resource: 'Run full persona analysis on pre-scraped product data.' It clearly distinguishes this from live-scraping siblings like scrape_pdp and acquire by emphasizing 'without making any live site requests.' The mention of offline analysis and persona roundtables makes the tool's purpose unmistakable and differentiates it from other tools in the list.

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 provides explicit use cases: 're-analyze acquire payloads offline' and 'analyze data from external sources (CSV exports, internal APIs, DTC Research server handoffs).' It also states a prerequisite (AI provider configuration). However, it does not explicitly name an alternative tool for live-scraping scenarios, though siblings like scrape_pdp imply those cases. This is clear context but lacks an explicit 'if you need live data, use X instead' statement.

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