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uk-schools-mcp

uk-schools-mcp

by mkb-123
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

find_schools_near_postcode

Find schools near a UK postcode within a specified radius. Geocodes the postcode and lists nearby schools by distance, helping you identify schools in a catchment area.

Instructions

Find schools within a specified distance of a UK postcode. Geocodes the postcode using Postcodes.io, then searches GIAS data for nearby schools sorted by distance. Useful for finding schools in a catchment area.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "postcode": "<postcode>"
    },
    "name": "find_schools_near_postcode"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum results (default: 20)
phaseNoFilter by phase (e.g., 'Primary', 'Secondary')
postcodeYesUK postcode (e.g., 'MK9 3BZ', 'SW1A 1AA')
radius_kmNoSearch radius in kilometres (default: 3, max: 10)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. It reveals internal steps (geocoding via Postcodes.io, searching GIAS data, sorting by distance), which is useful context. However, it does not disclose potential limitations like invalid postcode handling, rate limits, or maximum radius constraints beyond what's in the schema.

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 two sentences, front-loaded with the primary purpose, then details the method and use case. Every sentence adds value with no redundant or filler content.

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?

For a straightforward search tool with fully described parameters and no output schema, the description covers the main workflow (geocoding and searching) and expected sort order. It could mention result format or edge cases, but the current information is sufficient for a typical invocation.

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

Parameters3/5

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

The schema already provides 100% coverage with descriptions for all parameters, including defaults and max. The tool description adds a little nuance by mentioning distance and catchment, but it does not substantially enhance understanding beyond the schema fields.

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 finds schools within a distance of a UK postcode, using a specific verb ('Find') and resource ('schools near a postcode'). It distinguishes itself from sibling tools like search_schools by emphasizing location-based search and catchment area use.

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

Usage Guidelines4/5

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

The description provides a clear usage context ('Useful for finding schools in a catchment area'), but does not explicitly mention when to avoid this tool or point to alternatives. This meets 'clear context, no exclusions' but lacks the explicit alternative naming seen in the highest benchmarks.

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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