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Glama

Get Legislation

get_legislation
Read-onlyIdempotent

Get metadata for one specific piece of UK legislation by type + year + number (e.g. ukpga/2010/15 = Equality Act 2010). Returns title, type, year, number, status, extent, enactment date, a long-title summary, and the full-text URL. Content is XML; fields are best-effort parsed and a raw excerpt is included.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesDocument type, e.g. "ukpga". One of: ukpga (UK Public General Acts), uksi (UK Statutory Instruments), asp (Scottish Parliament Acts), anaw/asc (Wales), nia (Northern Ireland), ukla (UK Local Acts).
yearYesYear, e.g. 2010.
numberYesItem number within that year/type, e.g. 15.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds further behavioral details: return fields (title, type, year, status, extent, enactment date, long-title summary, full-text URL), content format (XML), and a quality caveat (fields are best-effort parsed with a raw excerpt). This enriches the agent's understanding beyond the 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 two sentences with no wasted words. The first sentence states the tool's action and purpose with an example. The second lists return fields and notes the format and caveat. Every sentence adds essential information.

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?

For a simple tool with three required parameters, no output schema, and rich annotations, the description covers all necessary aspects: what it does, how to call it (example), what it returns (field list), and a quality note (best-effort parsing). There are no gaps in helping an agent decide when to use it and what to expect.

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?

Schema description coverage is 100%, so the schema already documents each parameter. The description adds value by providing a concrete example of valid parameter combinations (ukpga/2010/15) and explains how the three parameters together identify a unique document, which aids an agent in constructing a valid call.

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?

Description begins with a specific verb ('Get metadata') and identifies the resource (UK legislation) by type, year, and number. It includes a concrete example (Equality Act 2010) which distinguishes it from the sibling search_legislation tool that searches across multiple documents.

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 explicitly states the required parameters (type, year, number) and format, making it clear that this tool is for retrieving a single, known piece of legislation. It does not explicitly state when not to use it or name alternatives, but the context of siblings implies search_legislation for broader queries, so it's sufficiently directive.

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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TDQS

A3.8/5.0
Disambiguation2/5

The legislation-specific tools are distinct, but the server bundles dozens of unrelated Pipeworx/prediction-market/AI-visibility tools, making the set's purpose unclear. Several near-identical pairs exist: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ai_visibility_check overlaps heavily with scan_competitor_ai_presence. An agent would struggle to know which tool is the right entry point.

Naming Consistency2/5

Naming is mixed: snake_case dominates, but camelCase appears in ask_pipeworx, ask_pipeworx_grounded, generate_llms_txt, and pipeworx_feedback. There is also inconsistency in verb style — get_/search_/list_ coexist with bare verbs like remember, recall, forget, and subscribe. The legislation tools themselves follow a clean get_legislation* pattern, but the wider set does not.

Tool Count2/5

35 tools is too many for a server named 'Legislation Uk' where only 4 tools actually relate to UK legislation. The remaining 31 tools appear to belong to a broader data/prediction-market platform, which suggests severe scope creep or a mislabeled assembly. This bloats the surface area and makes the server harder for an agent to navigate.

Completeness4/5

For the stated UK-legislation purpose, the core read-and-search workflow is covered: search_legislation, get_legislation, get_legislation_section, and get_legislation_text together support discovery, metadata, targeted section lookup, and full-text retrieval with version selection. Obvious gaps remain, such as full-text content search and amendment/change history, but agents can complete the primary task of finding and reading legislation. The unrelated tools neither help nor complete this domain.