UK Business Tools - Ledgerhall

Parse OSCOLA Citations

law_citations_parse

Read-only

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

Name	Required	Description	Default
`text`	Yes	Free 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.
`disambiguate`	No	Default 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

Name	Required	Description
`ambiguous`	Yes	Citations with confidence < 0.7; may have been partially disambiguated via sampling
`citations`	Yes	All successfully parsed citations (confidence >= 0.7)
`text_length`	Yes	Character length of the input text
`parse_duration_ms`	Yes	Time taken to parse, in milliseconds

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the readOnlyHint=true annotation, the description discloses important behaviors: pure-regex default, optional MCP sampling for disambiguation with implications (model shapes result), URL resolution to TNA/legislation.gov.uk, and the important limitation that the parser recognizes shape but does not confirm existence. The description also mentions it connects to the client's model, which is beyond the annotation.

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-structured with clear sections (use case, supported types, behavior, follow-up). It is longer than average but every sentence adds relevant information. The 'USE THIS TOOL WHEN' directive is front-loaded. Minor redundancy with schema description keeps it from a 5.

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 has an output schema, the description needn't explain return values. It covers input requirements, parsing behavior, optional disambiguation, URL resolution, and a recommended follow-up action. The combination of annotations, schema, and description leaves no significant gaps for an agent to use correctly.

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?

Schema coverage is 100% for both parameters (text and disambiguate). The description does restate some parameter context (e.g., disambiguate triggers MCP sampling), but it does not add meaning beyond what the detailed parameter descriptions already provide. Baseline 3 is appropriate because the schema carries the full burden.

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's purpose: extracting and classifying OSCOLA-style citations from free text. It explicitly lists supported citation types (neutral citations, law reports, legislation sections, SIs, retained EU law), which distinguishes it from sibling tools like law_citations_format_oscola and law_citations_resolve.

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?

The description opens with 'USE THIS TOOL WHEN you have free text...' establishing the exact trigger scenario. It also provides explicit post-condition guidance: 'AFTER calling, pass each citation through citations_resolve to verify' — this gives the agent a clear workflow and indirectly distinguishes this tool from the resolve 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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TDQS

A4/5.0

Disambiguation4/5

Tools are well-grouped by domain prefixes (dd_, gov_, law_, prop_) with clear descriptions that differentiate them. However, there is minor overlap, e.g., dd_search could be used instead of individual searches, and dd_fetch versus dedicated profile tools might cause confusion.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with domain-specific prefixes (dd_, gov_, law_, prop_). Names are descriptive and predictable, e.g., dd_charity_search, gov_govuk_search, law_bills_search_bills.

Tool Count4/5

70 tools is high but justified by the broad scope covering due diligence, government, legal, and property domains. Each domain has a reasonable number of tools (about 15-20 each). The count is on the upper end but still manageable.

Completeness5/5

The tool set is comprehensive across all domains: full CRUD for companies and charities, detailed legal research (cases, legislation, parliament, citations), property data (EPC, planning, price paid, rentals), and government information. No obvious gaps for the intended use cases.

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