Search_petitions_by_art_unit

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

With no annotations provided, the description carries full burden and does well by explaining the tool's purpose, return format ('balanced field set for cross-referencing'), and integration patterns. It describes analysis patterns for interpreting results and cross-MCP workflows. However, it doesn't mention rate limits, authentication needs, or error conditions, leaving some behavioral aspects uncovered.

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 (purpose, use cases, examples, analysis patterns, integration, parameters) and every sentence adds value. It's appropriately sized for a complex tool with integration needs, though slightly longer than minimal. The front-loaded purpose statement immediately clarifies the tool's function.

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 (3 parameters, 0% schema coverage, no annotations, but with output schema), the description is remarkably complete. It covers purpose, usage guidelines, parameter semantics, analysis patterns, and cross-tool integration workflows. The output schema existence means return values don't need explanation, and the description provides everything else needed for effective use.

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?

With 0% schema description coverage, the description fully compensates by providing detailed parameter explanations: art_unit format examples ('2128', '3600'), date_range format specification ('YYYY-MM-DD:YYYY-MM-DD'), and limit details (default 50, max 200). It adds practical examples showing parameter usage, giving clear semantic 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 tool searches petitions by art unit number for examiner/art unit quality analysis, using specific verbs ('search', 'returns') and resources ('petitions', 'art unit number'). It distinguishes from siblings like Search_petitions_by_application by focusing on art unit rather than application, and from Search_petitions_balanced/minimal by specifying balanced field sets for cross-referencing.

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 'Use for:' section explicitly lists specific scenarios (art unit quality assessment, systematic petition patterns, examiner behavior analysis). It provides clear alternatives by mentioning cross-MCP integration with pfw_search_applications_minimal and PTAB MCP, and distinguishes from siblings by focusing on art unit analysis rather than application-based or minimal searches.

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