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tclp-mcp

by io.github.thechancerylaneproject

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
NameRequiredDescriptionDefault
nameYes
limitNo
include_full_textNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
metaYes
resultsYes

TDQS

A4.4/5.0
Behavior4/5

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

The description aligns with annotations (readOnlyHint=true, destructiveHint=false) by describing a lookup operation. It adds context about deterministic name matching (case-insensitive, exact match) and warns that `include_full_text` bodies are large, which goes beyond annotations. However, it doesn't mention any potential performance implications of large result sets or AI model context limitations, keeping it from a 5.

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 extremely concise and well-structured: a one-line purpose statement, a clear paragraph distinguishing from `search` with usage advice, a brief 'Args' section documenting parameters, and a concise 'Returns' summary. Every sentence adds value with no redundancy.

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 3 simple parameters (one required), annotations declaring it safe and open-world, and an output schema providing return structure, the description is complete. It covers the purpose, usage, parameter details, and return format comprehensively. No gaps are evident for this level of complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description fully carries parameter documentation. It explains `name` is an 'exact match, case-insensitive' search term, `limit` is a 'Maximum number of results' (1–50), and `include_full_text` governs including full body text (Markdown) with usage warnings. This adds significant value beyond the schema's bare types and defaults, but a 5 would require even more detail (e.g., default value re-stated for `name`).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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', using specific verbs ('Find') and resources ('Entity nodes in the knowledge graph'). It distinguishes itself from the sibling 'search' by highlighting deterministic vs. semantic matching, though it doesn't explicitly contrast with 'taxonomy_content' or 'taxonomy_facets'.

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 contrasts with 'search' ('Unlike `search`...'), explains when to use this tool ('when you have a specific term... and want to retrieve every clause or glossary entry that explicitly references it'), and provides detailed guidance for the `include_full_text` parameter ('request only when you need the content, and prefer a small `limit` when you do'). This is exemplary usage guidance.

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