Resolves any UK postcode to its Local Authority (admin_district), ward, constituency, LSOA, NUTS region, and coordinates. Write-through to postcodes.io with 30-day Redis caching.
| Name | Required | Description | Default |
|---|---|---|---|
| postcode | Yes | UK postcode (e.g. SW1A 1AA) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It adds useful context about the postcodes.io dependency and 30-day Redis caching, and implies a read-only operation. However, it does not cover failure modes, error handling, or any auth/rate-limit considerations.
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?
Two concise sentences with no filler. The first sentence front-loads the core purpose and outputs; the second adds relevant caching/backend detail. Every word earns its place.
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?
For a low-complexity one-parameter tool, the description covers the essential behavior, lists the expected return fields, and notes the caching behavior. Since no output schema exists, enumerating admin_district, ward, constituency, LSOA, NUTS, and coordinates is valuable. It omits invalid-postcode behavior but is otherwise sufficiently complete.
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 already documents the single 'postcode' parameter with a format example, so description-level parameter explanation is unnecessary. The description adds only the UK scope and output-field context, which is a modest addition. Baseline 3 applies due to 100% schema description coverage.
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 states a specific verb ('Resolves') and resource ('any UK postcode'), and enumerates the output fields. The explicit 'UK' scope clearly distinguishes it from the sibling au-postcode-lookup and nz-place-lookup tools.
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 clearly identifies the intended context: UK postcode resolution. It does not explicitly name alternatives or when-not-to-use conditions, but the UK scope is unambiguous and sufficient for selecting this over country-specific siblings.
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.
Every tool maps to a clearly distinct dataset or lookup, with country prefixes and topic names separating overlapping domains. Even similar tools like au-abs-building-activity and au-abs-building-approvals are unambiguously differentiated by their descriptions.
The data tools follow a consistent country/topic hyphenated pattern (au-*, nz-*), making resource selection predictable. The meta tools (get_catalog, list_services, health) break this pattern with imperative/underscore names, but this is a minor and understandable deviation.
At 26 tools, the set is on the heavy side and slightly exceeds the typical comfortable range. However, each tool represents a genuinely distinct data service, and the clear grouping by country and topic keeps the surface navigable.
The server covers a broad range of common agent data needs for Australia and New Zealand: demographics, income, building, labour, weather, time, holidays, school terms, and place resolution. Minor gaps exist, such as no NZ building data or broader international coverage, but core workflows are well supported.