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paulieb89

UK Legal Research MCP Server

Parse OSCOLA Citations

citations_parse
Read-only

Extracts and classifies OSCOLA citations from free text, including cases, legislation, and statutory instruments. Optionally disambiguates ambiguous references using a connected model.

Instructions

USE THIS TOOL WHEN you have free text (a memo, an email, a clause) and want every OSCOLA-style citation it contains extracted and classified.

Identifies: neutral citations ([2024] UKSC 12), law reports ([2024] 1 WLR 100), legislation sections (s.47 Companies Act 2006), SIs (SI 2018/1234), retained EU law (Regulation (EU) 2016/679).

Parsing is pure regex by default. Ambiguous citations (e.g. bare [2024] EWHC without division) can OPTIONALLY be disambiguated by setting disambiguate=True, which asks the CONNECTED CLIENT's own model (not this server) to resolve the division via MCP sampling — off by default. Citations resolve to TNA / legislation.gov.uk URLs when possible.

AFTER calling, pass each citation through citations_resolve to verify it points at a real document before quoting or formatting it — the parser recognises the SHAPE of a citation but does not confirm the document exists.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesFree text containing OSCOLA citations to extract. Supported: neutral citations ([2024] UKSC 12), law reports ([2024] 1 WLR 100), legislation sections (s.47 Companies Act 2006), SIs (SI 2018/1234), retained EU law (Regulation (EU) 2016/679). Max 50,000 chars.
disambiguateNoDefault False — pure-regex parsing, no model in the loop. If True, ambiguous citations (e.g. bare EWHC without a division) are sent to the connected client's own LLM, via MCP sampling, to resolve the division. Opt in only when you want best-effort division resolution and accept that a model shapes the result.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
citationsYesAll successfully parsed citations (confidence >= 0.7)
ambiguousYesCitations with confidence < 0.7; may have been partially disambiguated via sampling
text_lengthYesCharacter length of the input text
parse_duration_msYesTime taken to parse, in milliseconds
Behavior4/5

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

Annotations indicate readOnlyHint=true and destructiveHint=false, which the description matches. It adds behavioral context: default pure-regex parsing, optional LLM-based disambiguation via MCP sampling, and URL resolution without existence confirmation. This goes beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-organized with clear sections and bullet points. It is slightly verbose but every sentence adds value. The structure aids readability and comprehension.

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 complexity (multiple citation types, optional disambiguation, output schema exists), the description is complete. It covers usage, behavior, parameter details, and post-processing steps, leaving no major gaps.

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 coverage is 100%, and the description adds meaningful detail beyond the schema. For 'text', it elaborates on supported citation formats; for 'disambiguate', it explains the trade-off and when to enable it. This provides practical guidance.

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 it extracts and classifies OSCOLA-style citations from free text. It lists specific citation types and distinguishes from sibling tools like citations_resolve (verification) and citations_format_oscola (formatting).

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

Usage Guidelines5/5

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

Explicitly says 'USE THIS TOOL WHEN' and gives clear use cases. It also advises 'AFTER calling, pass each citation through citations_resolve to verify', providing a clear workflow and excluding misuse. It explains when to use the optional disambiguation parameter.

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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