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tclp-mcp

by io.github.thechancerylaneproject

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
NameRequiredDescriptionDefault
sortNotitle
limitNo
scopeNoall
offsetNo
filtersYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
metaYes
resultsYes

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already mark the tool as read-only (readOnlyHint true) and non-destructive (destructiveHint false). The description reinforces this with a listing action and adds key behavioral details: multi-facet AND logic, 400 error for invalid facets, and the constraint that each facet appears at most once. No contradiction 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.

Conciseness4/5

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

The description is well-structured with a brief summary in the first sentence, followed by usage guidance and a clear parameter listing. It is relatively concise but could be slightly trimmed; however, the completeness justifies the length.

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 5 parameters (1 required), no enums, nested object input, and an output schema, the description fully covers parameter semantics, filtering behavior, error cases, and return format. The output schema is present, so the description of return values is sufficient (meta and results with key fields). No gaps remain.

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?

With 0% schema description coverage, the description must fully compensate, and it does. It explains `filters` as a mapping of facet name to value slug, gives an example, and notes the 'at most once' constraint. It defines valid values for `scope` (clause/guide/all) and `sort` (title, date_published_desc, date_modified_desc). It also provides default values and ranges for `limit` (1-100) and `offset`.

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 facet=value. It specifies the resource (TCLP content like clauses and guides), the action (list), and the filtering mechanism. This distinguishes it from siblings like `taxonomy_facets` (which returns available facets) and `search` (general search).

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 instructs to call `taxonomy_facets` first to learn valid facet names, and warns that using an invalid name returns a 400 error. It also explains the AND combination of multi-facet filters. This provides clear when-to-use and prerequisite guidance, differentiating from sibling tools.

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 deterministic name matching, search for semantic/BM25 fusion, taxonomy_content for faceted filtering, and taxonomy_facets for exploring available facets. There is no ambiguity or overlap between them.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with descriptive verb_noun pairs (e.g., entity_lookup, taxonomy_content). Even 'search' fits as a verb-based name. No mixing of conventions.

Tool Count5/5

With only 4 tools, the server is tightly scoped to querying a knowledge graph. Each tool earns its place, covering entity lookup, free-text search, and taxonomy browsing. Neither too few nor too many.

Completeness4/5

The tool set covers the main query paradigms for this knowledge domain. However, a direct fetch-by-ID tool is missing (users must use search or entity_lookup as workarounds), and taxonomy filtering is limited to TCLP content, not LRSF. Minor gaps exist but agents can still accomplish core tasks.

Resources