UK Legal Research MCP Server

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

Annotations already indicate read-only, non-destructive, and idempotent behavior, but the description adds valuable context beyond this: it specifies the data source ('static lookup table current as of 22 Nov 2023'), return format ('rate category, effective date, and any relevant conditions or exceptions'), and a disclaimer about potential rate changes. This enhances transparency without contradicting 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 first sentence stating the core purpose. Each subsequent sentence adds necessary value: return details, data source, and disclaimer. There is no wasted text, making it efficient and well-structured for an AI agent.

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 low complexity (one parameter), rich annotations (readOnlyHint, idempotentHint, etc.), and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It covers purpose, usage context, behavioral details, and data limitations, providing sufficient information without needing to explain return values or parameter specifics redundantly.

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 input schema has 100% description coverage, with the parameter 'commodity_code' well-documented in the schema itself. The description does not add significant meaning beyond the schema's details, such as explaining parameter nuances or usage examples. With high schema coverage, a baseline score of 3 is appropriate as the schema carries the primary 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 with a specific verb ('Look up') and resource ('UK VAT rate for a commodity or service type'), distinguishing it from siblings like hmrc_check_mtd_status or hmrc_search_guidance. It precisely defines what the tool does without restating the name or title.

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 get VAT rates for commodities) and includes a caution about data currency ('Rates may have changed — always verify against GOV.UK for recent Budgets'). However, it does not explicitly mention when not to use it or name specific alternatives among siblings, such as hmrc_search_guidance for broader HMRC queries.

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