The Chancery Lane Project

search

Read-only

Search the TCLP knowledge graph using fusion search (semantic + BM25).

Args:
    query: Free-text search query (max 1000 characters).
    node_type: Content scope — "tclp" (clauses, glossary terms, guides),
               "lrsf" (laws, regulations, standards, frameworks), or "all".
    limit: Maximum number of results to return (1–50).
    rerank: Whether to apply RRF reranking when combining graph and text results.
    include_full_text: Include each hit's full body text (Markdown). Off by
        default — bodies are large; request only when you need the content,
        and prefer a small `limit` when you do.

Returns:
    JSON with "meta" (totals, timing) and "results" (ranked hits with title,
    url, content_type, scores, and optionally relationships and full_text).

Input Schema

TableJSON Schema

Name	Required	Default
`limit`	No
`query`	Yes
`rerank`	No
`node_type`	No	all
`include_full_text`	No

Output Schema

TableJSON Schema

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

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's safe. The description adds valuable behavioral context: mentions fusion search, RRF reranking, and warns that bodies are large, which goes beyond annotation hints. No contradiction.

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?

Description is well-structured with Args section and Returns section, each parameter documented succinctly. Every sentence adds value, and the warning about body size is concise yet crucial. No wasted words.

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 5 parameters, 0% schema coverage, and available output schema, the description fully explains all parameters, their defaults, and usage nuances. The return format is described (JSON with meta and results), and the output schema can handle details. Completely adequate.

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 description coverage is 0%, so the description must compensate fully. It does: defines query as free-text (max 1000 chars), node_type as content scope with specific vocab, limit as 1–50, rerank as RRF switching, and include_full_text with a usage warning. This adds significant meaning beyond the schema.

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 searches the TCLP knowledge graph using fusion search (semantic + BM25), which is a specific verb+resource combination. It distinguishes from siblings like entity_lookup and taxonomy queries.

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 provides clear context on when to use this tool (for full-text search) and includes a practical note on include_full_text (request only when needed, prefer small limit). However, it does not explicitly state when NOT to use it or point to alternatives for specific use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All The Chancery Lane Project tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?