Skip to main content
Glama

Agentic House Search

by io.github.asturksever

Get one registry dataset entry

postcode_get_dataset
Read-onlyIdempotent

Full registry entry for one dataset, by id.

Returns everything needed to call the source yourself: its home page, machine-readable endpoint, API documentation, format, licence, geographic coverage, update cadence, and the concrete questions it can answer.

Args:

  • id (string): registry id, e.g. "police-street-crime", "land-registry-ppd", "ea-flood"

  • response_format ('markdown' | 'json'): default 'markdown'

Examples:

  • "How do I call the police API?" -> id="police-street-crime"

  • "What licence is Price Paid under?" -> id="land-registry-ppd"

Errors:

  • Returns the closest matching ids when the id is not found.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesRegistry id, e.g. "police-street-crime" — find one with postcode_search_datasets
response_formatNomarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
apiYes
linkYes
formatYes
datasetYes
licenceYes
api_docsYes
categoryYes
coverageYes
publisherYes
questionsYes
update_frequencyYes

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds value by disclosing what is returned (home page, endpoint, API docs, format, licence, etc.) and the error behavior (closest matching ids when id not found). No contradictions with annotations.

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 and front-loaded with purpose. It includes only necessary sections: a concise summary, Args, Examples, and Errors. Every sentence contributes useful information without redundancy.

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

Completeness5/5

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

Given the tool's simplicity, the description is complete. It lists what the returned entry contains, accepts an optional output format, and explains error handling. The presence of an output schema means return value structure need not be detailed further.

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

Parameters4/5

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

The input schema has descriptions for only 1 of 2 parameters (50% coverage), so the description compensates by providing an Args section with examples for id and the default/options for response_format. This adds practical meaning beyond the schema's enum and type declarations.

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 'Full registry entry for one dataset, by id' with a specific verb ('Get'), resource ('registry dataset'), and scope ('by id'). It distinguishes itself from sibling tools like postcode_search_datasets by focusing on retrieval of a single entry rather than searching or comparing.

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 implies when to use the tool: when you have a specific registry id and need complete details to call the source directly. It also includes examples and error fallback behavior. However, it does not explicitly mention alternatives or when not to use it, though the context is clear.

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

All Agentic House Search toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.7/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: compare does side-by-side multi-postcode analysis, report does a single-postcode deep dive, lookup resolves geography, search_datasets finds datasets, and get_dataset retrieves a full dataset entry. The descriptions explicitly cross-reference when not to use each tool, eliminating any ambiguity.

Naming Consistency5/5

All tool names follow a consistent pattern: lowercase snake_case with a 'postcode_' prefix followed by a verb_noun (compare, get_dataset, lookup, report, search_datasets). No mixed conventions or vague verbs like 'process' or 'execute'.

Tool Count5/5

At 5 tools, the set is tightly scoped for a UK house-search data server. Each tool covers a distinct operation (lookup, report, compare, dataset search, dataset details) and neither feels redundant nor bloated. This is well within the ideal 3-15 range.

Completeness5/5

The tool set provides a full workflow: look up a postcode's geography, get a comprehensive neighbourhood report, compare multiple postcodes, search for external datasets to fill gaps (e.g., EPC, flood risk), and retrieve full dataset details. There are no dead ends; every tool has a clear follow-up and the coverage matches the stated purpose.

Resources