The Chancery Lane Project

taxonomy_content

Read-only

List TCLP content (clauses, guides) filtered by taxonomy facet=value.

Multi-facet filters are combined with AND. Call `taxonomy_facets` first
to learn which facet names and slugs exist; using a name not in that
list returns a 400.

Args:
    filters: Mapping of facet name to value slug, e.g.
             `{"sector": "real-estate", "practice_area": "commercial"}`.
             Each facet may appear at most once.
    scope: `clause`, `guide`, or `all` (default).
    limit: Maximum results to return (1–100, default 25).
    offset: Result offset for paging (default 0).
    sort: `title`, `date_published_desc`, or `date_modified_desc`.

Returns:
    JSON with "meta" (totals, scope, filters echoed back) and "results"
    (each hit has `id`, `url`, `title`, `content_type`, dates, and a
    `facets` map of all taxonomy arrays on the node).

Input Schema

TableJSON Schema

Name	Required	Default
`sort`	No	title
`limit`	No
`scope`	No	all
`offset`	No
`filters`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`meta`	Yes
`results`	Yes

TDQS

A5/5.0

Behavior5/5

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

The description explains that invalid facet names cause a 400 error, the AND combination logic for multi-facet filters, and that each facet may appear at most once. This goes well beyond the annotations (readOnlyHint=true, destructiveHint=false) by detailing error conditions and filtering semantics.

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 brief summary followed by detailed param and return sections. Each sentence serves a distinct purpose with no redundancy, and the critical usage sequence (call taxonomy_facets first) is front-loaded in the opening paragraph.

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 high parameter count (5), nested object filters, and 0% schema coverage, the description is complete. It explains the return format (with meta and results fields), prerequisites, error behavior, and all parameter semantics. The presence of an output schema further reduces the burden on the description, which still goes above and beyond.

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?

Schema coverage is 0%, so the description bears full burden to explain parameters. It provides detailed semantic info for all 5 parameters including example usage for filters, valid options for scope, limit range and default, offset default, and sort options. This fully compensates for the lack of schema descriptions.

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 lists TCLP content filtered by taxonomy facets, and distinguishes it from siblings by referencing the need to call taxonomy_facets first to get valid facet names. This differentiates it from the similar sibling tools like search and entity_lookup.

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 explicitly tells the agent to call taxonomy_facets first to learn valid facet names and slugs, warns that invalid names result in a 400 error, and explains that multi-facet filters are combined with AND. This provides clear when-to-use and when-not-to-use guidance relative to the sibling tool taxonomy_facets.

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

A4.7/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: entity_lookup for exact entity matching, search for semantic/full-text search, taxonomy_content for filtered browsing, and taxonomy_facets for discovering filter options. There is no overlap or ambiguity.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with descriptive, domain-specific names (entity_lookup, search, taxonomy_content, taxonomy_facets). The naming is predictable and self-explanatory.

Tool Count5/5

With 4 tools, the set is well-scoped for searching and browsing a legal content knowledge graph. Each tool serves a clear, necessary function without redundancy, and the count feels appropriate for the domain.

Completeness4/5

The tools cover key discovery use cases: exact lookup, free-text search, faceted browsing, and facet exploration. A minor gap is the lack of tools for creating, updating, or deleting content, but that aligns with a read-only knowledge graph server, so completeness is high.

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