UK Legal Research MCP Server

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

The description adds valuable behavioral context beyond annotations: it specifies that it 'raises ValueError if no recognised citation is found in the input,' which is crucial error-handling information not covered by annotations. Annotations already indicate read-only, non-destructive, idempotent, and closed-world behavior, so the bar is lower, but the description enhances this with resolution outcomes and error conditions.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by supporting details (supported types, return values, error handling) in a logical flow. Every sentence earns its place with no wasted words, making it efficient and easy to parse.

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 (single-parameter parsing/resolution), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (which handles return values), the description is complete. It covers purpose, supported types, outcomes (parsed fields and resolved_url), and error behavior, providing all necessary context for an AI agent to use the tool effectively.

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 schema description coverage is 100%, with the parameter 'citation' well-documented in the schema (including examples and constraints). The description does not add significant parameter semantics beyond what the schema provides, such as formatting details or edge cases. With high schema coverage, the baseline is 3, and the description meets this without extra value.

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 with specific verbs ('parse and resolve') and resource ('a single OSCOLA citation'), and distinguishes it from siblings like 'citations_parse' and 'citations_network' by emphasizing resolution to a canonical URL rather than just parsing or network analysis. It explicitly lists supported citation types (neutral citations, SIs, legislation sections, retained EU law).

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 clear context for when to use this tool: for parsing and resolving a single OSCOLA citation to a canonical URL. It implicitly suggests alternatives by mentioning sibling tools like 'citations_parse' (for parsing without resolution) and 'citations_network' (for network analysis), but does not explicitly state when not to use this tool or compare directly with those alternatives.

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