[Legal Intelligence] Search public court records by name, case number, or state. Returns case details, filing dates, and parties involved.
Args: name: Person or entity name to search (e.g. 'John Smith') case_number: Specific case number to look up state: State to search in (e.g. 'CA', 'NY')
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | ||
| state | No | ||
| case_number | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the description does not need to repeat these. However, the description adds no further behavioral context such as rate limits, data freshness, or jurisdiction coverage. Minimal additional value beyond annotations.
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?
Description is concise: a one-line purpose, a brief return summary, and parameter explanations. Front-loaded with key information. A bit more structure could improve readability, but no waste.
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 3 optional parameters, no output schema, and openWorldHint annotation, the description covers basic usage but lacks details on parameter interaction, pagination, or result limits. Adequate but not fully specifying agent behavior.
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?
The description includes an explicit 'Args:' section that explains each parameter with examples (e.g., 'e.g. John Smith' for name). Since schema description coverage is 0%, this adds crucial meaning beyond the bare schema types and types.
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?
Description clearly states 'Search public court records by name, case number, or state' and specifies return values (case details, filing dates, parties). This distinguishes it from sibling tools like search_arxiv or search_github which cover different domains.
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?
No explicit guidance on when to use this tool versus alternatives. Usage is implied by the domain name and purpose, but no when-not-to-use or alternative suggestions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools have overlapping functionality across categories (e.g., multiple search_arxiv, search_google_scholar, real estate tools, DNS/WHOIS checks). An agent would struggle to differentiate between similar tools from different categories, leading to ambiguity.
Tools follow a 'category__verb_noun' pattern mostly, but verbs vary (get, search, screen, check, etc.) and some categories use different orders (e.g., 'get_repo_stats' vs 'search_repos'). The consistency is acceptable but not uniform across the entire set.
With 152 tools, the server is excessively large for a single MCP server. While it aims to be an all-in-one gateway, the sheer number overwhelms the agent and likely exceeds practical limits for coherent selection.
The server covers a wide range of domains (finance, real estate, news, developer tools, etc.) but has notable gaps (e.g., social media APIs, CRM tools). Coverage is broad but not exhaustive, and some niche areas (e.g., global stock exchanges) are over-represented.