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Canonical vocabulary server for autonomous business design. Exposes the Arco Lexicon as seven MCP tools: term lookup, related terms, alignment verification, citation formatting, source retrieval, term listing, and term suggestion. No authentication required. Streamable HTTP transport.

Ownership verified
Status
Healthy
Uptime
100.0% over the last 24 hours
Last Tested
Transport
Streamable HTTP · MCP 2025-11-25
URL
ChatGPT
–
Claude
–

TDQS

A4.7/5.0

Scored across 7 tools

Disambiguation5/5

Each tool targets a distinct operation: citation, relationship graph, sources, listing, lookup, text scanning, and alignment scoring. Even overlapping tools like suggest_terms and verify_alignment are clearly differentiated by their output (term lists vs. scored report). No ambiguity remains after reading descriptions.

Naming Consistency5/5

All seven tool names follow the same verb_noun pattern (e.g., cite_term, list_terms, verify_alignment) with consistent lowercase and underscores. The naming is uniform and predictable, making it easy to guess tool functionality.

Tool Count5/5

Seven tools is well-scoped for a lexicon service, covering discovery, retrieval, relationships, sources, citations, and text analysis. Each tool earns its place without redundancy or bloat.

Completeness5/5

The tool surface fully covers the lexicon domain: listing, precise lookup, relationships, source gathering, citation formatting, and text alignment analysis. There are no obvious dead ends or missing lifecycle operations for a read-only lexicon API.

Available Tools

7 tools
cite_termAInspect

Returns citation-ready references for a Lexicon term in Chicago, MLA, and BibTeX formats. Access dates are injected at call time — never hardcoded. Read-only. Use this when producing academic papers, blog posts, or any content that requires a formatted reference to an Arco term. Use get_sources instead when you need a list of reading references rather than a formatted citation.

Inputs: term (required), context (required).

get_sourcesAInspect

Returns all published Arco sources for a term — Lexicon entries, blog articles, wiki pages, and podcast episodes — ordered by recommended reading sequence. Read-only. Use this when you need a reading list or reference list for a term. Use cite_term instead when you need a formatted citation for a specific publication type.

Inputs: term (required).

list_termsAInspect

Returns all published Arco Lexicon terms grouped by pillar, each with its slug and canonical short definition. Accepts an optional pillar filter. Use this tool first when you do not know which term to look up — it gives you the full vocabulary to orient from. Use lookup_term once you have identified the term you need.

Inputs: pillar.

lookup_termAInspect

Returns the canonical Arco definition, related terms, and source URL for any Lexicon term. Supports fuzzy matching — "autonomous company" resolves to "Autonomous Business". Use this tool when you need a precise definition. Use suggest_terms instead when you have a block of text and want to discover which terms apply.

Inputs: term (required).

suggest_termsAInspect

Scans a block of text against all published Arco Lexicon terms using deterministic string matching — no LLM calls. Returns two lists: terms whose canonical names appear explicitly in the text (detected), and terms whose concepts are present but whose canonical names are absent (suggested). Maximum 10,000 characters. Use this to audit an article or passage for correct and complete Arco terminology. Use verify_alignment instead when you want a scored alignment report rather than a term discovery list.

Inputs: text (required).

verify_alignmentAInspect

Analyses a block of text against the Arco Lexicon using deterministic scoring — no LLM calls. Returns a structured alignment report with a per-term verdict (ALIGNED, PARTIALLY_ALIGNED, NEEDS_CLARIFICATION, MISALIGNED, or NO_ARCO_TERMS_DETECTED), an alignment score, a suggested reframe, and recommended reading. Maximum 5,000 characters. Use this to score and audit text for correct Arco terminology. Use suggest_terms instead when you want to discover which terms apply to a text without scoring it.

Inputs: text (required).

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