The Chancery Lane Project

entity_lookup

Read-only

Find TCLP content nodes (clauses, glossary terms) associated with a named concept.

Unlike `search`, this performs a deterministic name match against Entity nodes in
the knowledge graph rather than a relevance-ranked semantic search. Use it when
you have a specific term or concept (e.g. "scope 3 emissions", "net zero") and
want to retrieve every clause or glossary entry that explicitly references it.

Args:
    name: The entity or concept name to look up (exact match, case-insensitive).
    limit: Maximum number of results to return (1–50).
    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" and "results" where each hit includes the source content
    node and the entity names that matched.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes
`limit`	No
`include_full_text`	No

Output Schema

TableJSON Schema

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

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already provide readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds valuable behavioral context: deterministic match, case-insensitive exact matching, and a warning that include_full_text bodies are large. It does not contradict annotations and provides useful extra details for the agent.

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: a concise purpose statement, a clear differentiation from search, usage guidance, and a bullet-like parameter list. Every sentence adds value without redundancy. It is front-loaded with the most important information.

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 three parameters, the presence of annotations, and an output schema, the description is complete. It explains the deterministic nature, the return format (JSON with meta and results), and provides parameter usage advice. The output schema is not detailed in the description, but that is acceptable since it exists separately.

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 carry the burden. It does so excellently: for 'name' it specifies 'exact match, case-insensitive'; for 'limit' it states 'Maximum number of results to return (1–50)'; for 'include_full_text' it explains the default off, the size concern, and recommends using a small limit when requesting full text. 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 finds TCLP content nodes (clauses, glossary terms) associated with a named concept. It distinguishes itself from the sibling 'search' by specifying it performs a deterministic name match rather than a relevance-ranked semantic search, which provides clear differentiation.

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 explicitly says 'Use it when you have a specific term... and want to retrieve every clause or glossary entry that explicitly references it' and contrasts with 'search'. However, it does not mention when to use or avoid the other sibling tools (taxonomy_content, taxonomy_facets), leaving some ambiguity for those alternatives.

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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