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. Annotations indicate read-only, non-destructive, non-idempotent, and open-world traits. The description elaborates on the two-stage process (search then LLM analysis), the graceful degradation to contributions-only if sampling is unavailable, and the specific outputs (sentiment, supporters, opponents, concerns). This provides operational transparency not covered by 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?

The description is appropriately sized and front-loaded, with the core purpose stated first. Each sentence adds value: the first defines the tool's goal, the second explains the two-step process, and the third covers fallback behavior. There is no wasted text, and the structure is logical and 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 (involving search and LLM analysis), the description is complete. It explains the process, outputs, and fallback behavior. With annotations covering safety and an output schema (implied by context signals) handling return values, the description provides sufficient context without redundancy. It addresses key aspects like degradation and analysis scope.

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%, with clear descriptions for both parameters ('policy_text' and 'topic'). The description doesn't add significant meaning beyond the schema, as it doesn't explain parameter interactions or usage nuances. However, it implies the parameters are used together for searching and analysis, maintaining the baseline score since the schema adequately documents them.

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 ('assess', 'searches', 'classify', 'extract') and resources ('parliamentary reception', 'policy proposal', 'Hansard', 'debate contributions'). It distinguishes from siblings like 'parliament_search_hansard' by specifying the additional LLM analysis and sentiment classification, not just searching.

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: to assess parliamentary reception of policy proposals. It implies usage by mentioning 'searches Hansard for relevant debate contributions' and 'returns contributions only' as a fallback. However, it doesn't explicitly state when NOT to use it or name alternatives among siblings, such as using 'parliament_search_hansard' for raw searches without analysis.

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