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Stealth-Labs-LTD

GovUK MCP Server

search_stops

Find London transit stops by name or postcode. Filter results by tube, bus, DLR, overground, Elizabeth line, or tram.

Instructions

Search for bus stops, tube stations, and other transit stops in London.

Args: query: Search term (station name, postcode, etc.) modes: Optional comma-separated transport modes (tube,bus,dlr,overground,elizabeth-line,tram)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
modesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

The description discloses key behavioral traits: the query accepts station names, postcodes, etc., and modes are comma-separated transport modes (e.g., tube,bus). With no annotations, the description carries the transparency burden and adequately covers the search scope and filter options. However, it does not mention error handling, pagination, or case sensitivity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: a single sentence defining the tool's purpose plus two lines for parameter details. Every sentence adds value, with the verb 'Search' front-loaded and no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given only two parameters, a simple required query, and the presence of an output schema (which relieves the description from explaining return values), the description is largely complete. It covers the tool's scope and parameter usage. Minor gaps (e.g., whether modes are case-sensitive) do not severely hinder an agent's ability to use the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% coverage (no descriptions for properties), but the description fully explains each parameter: 'query' is a search term (station name, postcode) and 'modes' is an optional comma-separated list with explicit examples (tube,bus,dlr,overground,elizabeth-line,tram). This adds significant meaning beyond the raw schema types.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches for 'bus stops, tube stations, and other transit stops in London,' specifying the resource and location. This verb+resource combination is distinct from all sibling tools, which cover courts, hospitals, companies, etc. No similar stops search exists among siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly indicates usage for finding transit stops via query and optional mode filters, but it does not explicitly state when to use this tool versus alternatives like lookup_postcode or nearest_postcodes. No when-not or exclusion criteria are provided, leaving some ambiguity for an AI agent.

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

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