Skip to main content
Glama

tclp-mcp

by io.github.thechancerylaneproject

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
NameRequiredDescriptionDefault
limitNo
queryYes
rerankNo
node_typeNoall
include_full_textNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
metaYes
resultsYes

TDQS

A4.8/5.0
Behavior5/5

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

The description adds significant behavioral context beyond the annotations (readOnlyHint, openWorldHint, destructiveHint): it explains the fusion search approach, RRF reranking toggle, full-text body size considerations, and the return structure (meta and results with scores, relationships, full_text). The annotations already indicate a safe, read-only operation, and the description aligns with and enriches that understanding.

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 concise and well-structured, using an Args section with bullet-point-like formatting and a Returns section. Every sentence provides necessary information without redundancy. The key purpose is front-loaded in the opening sentence.

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 complexity (5 parameters, 1 required, fusion search with reranking, large result bodies), the description covers all important aspects: parameter semantics, usage trade-offs, output format, and performance considerations. The presence of an output schema further reduces the need to describe return fields, and the description still provides enough context for an agent to use the tool effectively.

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 fully compensates by explaining each parameter's semantic meaning. It clarifies query as free-text (max 1000 chars), node_type with enumerated values ('tclp', 'lrsf', 'all'), limit as a numeric range (1–50), rerank as a toggle for RRF combining graph and text, and include_full_text with a clear trade-off explanation. This adds essential meaning beyond the schema's basic types and defaults.

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's purpose with a specific verb ('Search'), a defined resource ('TCLP knowledge graph'), and the method ('fusion search (semantic + BM25)'). It distinguishes itself from sibling tools like entity_lookup and taxonomy_facets by focusing on full-text semantic search across multiple content types.

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 parameter usage guidance (e.g., when to enable include_full_text, acceptable limit range, node_type options) but does not explicitly state when to prefer this tool over sibling tools or when not to use it. The context is clear enough from the name and description that an agent can infer appropriate usage, but explicit exclusions are missing.

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

All tclp-mcp toolsInspect
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 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