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chuk-mcp-her

Official
by IBM
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Python
Remote

her_search_designations

Search across all designation types including listed buildings, scheduled monuments, parks, gardens, battlefields, protected wrecks, and World Heritage Sites. Filter by type, name, description, location, or bounding box.

Instructions

Search across all designation types in the NHLE.

Generic search covering listed buildings, scheduled monuments, registered parks and gardens, battlefields, protected wrecks, and World Heritage Sites.

Args: designation_type: Filter by type — listed_building, scheduled_monument, park_and_garden, battlefield, protected_wreck, world_heritage_site name: Name search (partial match) description: Description search bbox: Bounding box as "xmin,ymin,xmax,ymax" in BNG lat: WGS84 latitude for radius search lon: WGS84 longitude for radius search radius_m: Search radius in metres max_results: Maximum results (1-2000, default 50) offset: Pagination offset output_mode: Response format — "json" (default) or "text"

Returns: Matching designations across all types

Tips for LLMs: - Leave designation_type empty to search all types - Use her_count_features first for quick totals - Results include designation_type so you can see the mix

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNo
lonNo
bboxNo
nameNo
offsetNo
radius_mNo
descriptionNo
max_resultsNo
output_modeNojson
designation_typeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.3

TDQS

A4.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses search behavior, parameter formats (BNG vs WGS84), and that results include designation_type. However, it omits details like rate limits, error conditions, or how bbox and radius modes interact.

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 well-structured with a clear intro, parameter list, return info, and LLM-specific tips. Each section serves a purpose; the formatting makes it easy to scan despite its length.

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?

The description covers all parameters and offers practical usage tips, which is strong for a 10-parameter tool with no output schema. However, it lacks a description of the response structure beyond the designation_type field; a sample or field list would increase completeness.

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?

All 10 parameters are described with types, formats, defaults, and allowed values (e.g., bbox format in BNG, designation_type options). The schema has 0% description coverage, so this detailed documentation is essential and fully compensates.

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 opens with a specific verb and resource ('Search across all designation types in the NHLE') and enumerates the covered types, clearly distinguishing it from specialized sibling searches like her_search_listed_buildings and her_search_monuments.

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 'Tips for LLMs' section gives explicit guidance on using this tool for all types, suggests her_count_features for quick totals, and notes that results include designation_type. It implies this is the cross-type search alternative but does not explicitly state when to prefer a sibling tool.

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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