Katzilla MCP

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

The description adds valuable behavioral context beyond annotations: it specifies the source ('CourtListener RECAP Archive / Free Law Project'), update frequency ('updates daily'), return format ('Katzilla envelope'), and quality metrics ('freshness/uptime/confidence'). Annotations cover read-only, non-destructive, idempotent, and open-world hints, but the description enriches this with operational details like data source and audit features (SHA-256 hash).

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 context, return details, and source information. It is moderately concise but includes some dense technical details (e.g., 'Katzilla envelope') that are necessary for completeness. Minor room for tightening exists, but overall it's efficient.

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 (search with multiple parameters), rich annotations, and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is highly complete. It covers purpose, usage scenarios, behavioral traits, source, update frequency, and return format, leaving no significant gaps for an AI 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 100%, so the schema already fully documents all parameters. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain 'party_name' matching behavior beyond the schema's 'case-insensitive substring match'). Baseline score of 3 is appropriate as the schema carries the full burden.

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: 'Search RECAP for federal dockets where a named party appears' with specific verbs ('search', 'returns') and resources ('federal dockets', 'party'). It distinguishes from sibling tools like 'crime__recap-docket' or 'crime__recap-search' by focusing on party-based docket searches rather than general docket/document retrieval or broader searches.

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: 'adverse-party checks, conflict-of-interest review, tracking a litigant across jurisdictions.' It also clarifies what it does not do ('Returns one hit per docket (not per filing)'), helping differentiate it from tools that might return per-filing results. No explicit alternatives are named, but the context signals its specialized use case effectively.

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