Arco Lexicon
Server Details
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.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.6/5 across 7 of 7 tools scored.
Each tool has a clearly distinct purpose: cite_term for citations, get_related_terms for relationships, get_sources for reading lists, list_terms for overview, lookup_term for definitions, suggest_terms for term detection, and verify_alignment for scoring alignment. Overlaps are explicitly addressed in descriptions.
All tool names follow a consistent verb_noun pattern using snake_case (e.g., cite_term, get_related_terms, suggest_terms). No mixing of conventions or unclear verbs.
With 7 tools, the server is well-scoped for its purpose. Each tool serves a distinct function without redundancy, and the count is appropriate for a lexicon reference service.
The tools cover the full lifecycle of interacting with the lexicon: listing, searching, defining, relating, citing, and analyzing text for alignment. No obvious gaps for a read-only reference server.
Available Tools
7 toolscite_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.
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes | The Lexicon term to cite. Accepts canonical name or slug. | |
| context | Yes | The publication context for the citation — for example "academic paper", "blog post", or "investor memo". Used to tailor the citation format where applicable. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description adds that it is read-only and access dates are injected at call time, disclosing key behavioral traits. Missing details on error handling or return structure but sufficient for a read-only tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four concise sentences, front-loaded with main purpose, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity and no output schema, description covers purpose, usage, and behavioral note. Lacks return format details but mentions the citation formats, which provides sufficient context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%; description does not add meaning beyond what is already in the schema for both parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool returns citation-ready references in specific formats, and distinguishes from sibling get_sources which returns reading references.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use (for academic papers, blog posts, etc.) and when not to use (use get_sources for reading references), with clear alternative named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes | The Lexicon term whose sources to retrieve. Accepts canonical name or slug. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states 'Read-only' and 'ordered by recommended reading sequence,' which are behavioral traits. With no annotations, it adds necessary context, though it could mention output format or pagination.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two clear sentences front-loaded with purpose and behavioral notes. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one required param, no output schema, few annotations), the description sufficiently covers purpose, usage, parameter semantics, and behavioral traits.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage, but description adds value by clarifying the parameter accepts 'canonical name or slug,' which is not in the schema description. This helps the agent format input correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: 'Returns all published Arco sources for a term' and lists specific source types (Lexicon entries, blog articles, etc.), plus ordering. It distinguishes itself from sibling tools by referencing 'reading list' vs formatted citation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance: '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.' Clearly indicates when to use and alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| pillar | No | Filter results to a single pillar. Valid values: "How We Think", "What We Observe", "What We've Learned". Omit to return all pillars. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description covers return format (grouped, slug, short definition) and scope (published terms). Does not mention side effects or auth, but as a read-only list tool, this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences: purpose, usage guidance, and alternative. No wasted words, information front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains return format and scope. One optional parameter is well-covered. Complete for a list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. Description mentions optional pillar filter, reinforcing schema but not adding new meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns all published Arco Lexicon terms grouped by pillar with slug and short definition, distinguishing it from sibling tools like lookup_term.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use this tool first when unsure which term to look up, and to use lookup_term once identified, providing clear when-to-use and alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes | The Lexicon term to look up. Accepts the canonical name, a slug, or a close variant. Fuzzy matching handles minor spelling differences and common synonyms. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses fuzzy matching behavior and what is returned. While it does not explicitly state read-only or idempotent nature, it's implied by 'returns'. Could mention response format but overall adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no extraneous information. Front-loaded with primary output. Every sentence serves a purpose: describing capability, example, and usage guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given single parameter, no output schema, and no annotations, description is complete. It covers input flexibility, fuzzy matching, return types, and differentiates from sibling. No gaps for agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. Description adds value by clarifying fuzzy matching handles minor spelling differences and common synonyms, and that input accepts canonical name, slug, or variant. This exceeds the schema's description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the canonical Arco definition, related terms, and source URL. It specifies the input (Lexicon term) and mentions fuzzy matching with an example. It also distinguishes from the sibling 'suggest_terms' by stating different use cases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance on when to use this tool ('when you need a precise definition') and when to use an alternative ('use suggest_terms instead when you have a block of text'). This helps the agent choose correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The article or text block to scan. Plain text or markdown. Maximum 10,000 characters. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden. It discloses deterministic matching (no LLM calls), return format (two lists), and character limit. It doesn't mention error behavior on overflow, but overall is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, no redundant words. The main purpose is stated first, then details, then usage guidance. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, no output schema, no nested objects), the description fully covers what an agent needs: input format, output description, and usage context. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter. The description adds value by specifying that text can be plain text or markdown and reinforces the character limit, which goes beyond the schema description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verbs ('scans', 'returns') and identifies the resource ('Arco Lexicon terms'). It clearly distinguishes from siblings by naming verify_alignment and stating its different purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool ('audit an article or passage') and when to use an alternative ('use verify_alignment instead'). This provides clear guidance for agent selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to analyse. Plain text or markdown. Maximum 5,000 characters. Trim or chunk longer inputs before calling. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behaviors: deterministic scoring (no LLM calls), output structure with specific verdicts, and a maximum character limit of 5,000. Since no annotations exist, the description carries full burden and does so adequately, though it omits implicit read-only nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with three focused sentences: function, output, usage. Every sentence adds value, and the structure is logical.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description captures all essential aspects: purpose, input constraints, output format, and usage guidance. It is fully sufficient given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%; the single parameter 'text' is well-documented in the schema. The description adds context about the tool's overall behavior but does not enhance parameter understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'analyses' and clearly identifies the resource 'Arco Lexicon'. It explicitly distinguishes this tool from the sibling tool 'suggest_terms' by contrasting their purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear guidance on when to use this tool: 'Use this to score and audit text for correct Arco terminology.' It also provides an explicit alternative: 'Use suggest_terms instead when you want to discover which terms apply to a text without scoring it.'
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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