mcpbeat

Arcology Knowledge Node MCP Server

io.github.YourLifewithAI/arcology-knowledge-node
answering

Arcology Knowledge Node is answering right now. Last checked 13 min ago. It exposes 9 tools. Last commit 16 Jun 2026.

Collaborative engineering KB for a mile-high city. 9 tools, 8 domains, 32 entries.

Uptime history 39 hours of history
39 hours agonow
100.0%
Uptime 24h
91 of 91 checks
9
Tools
read from the server
1304 ms
Response time
average over 24h
0
Stars
last commit 16 Jun 2026

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 13 min ago.

run in your terminal
claude mcp add arcology-knowledge-node --transport http https://arcology-mcp.fly.dev/mcp
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "arcology-knowledge-node": {
      "url": "https://arcology-mcp.fly.dev/mcp"
    }
  }
}
~/.codex/config.toml
[mcp_servers.arcology-knowledge-node]
url = "https://arcology-mcp.fly.dev/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "arcology-knowledge-node": {
      "url": "https://arcology-mcp.fly.dev/mcp"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "arcology-knowledge-node": {
      "url": "https://arcology-mcp.fly.dev/mcp"
    }
  }
}

Available tools 9

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

cross
get_cross_references
Get all entries that reference or are referenced by a given entry. Given an entry ID (e.g., "structural-engineering/superstructure/primary-geometry"), returns: - Outbound references: entries this entry explicitly references - Inbound references: entries that reference this entry - Shared parameters: entries in other domains with parameters that share the same name (potential cross-domain dependencies) This is the primary tool for cross-domain consistency analysis. Args: entry_id: The full entry ID (domain/subdomain/slug format)
domain
get_domain_stats
Get aggregate platform statistics. Returns KEDL distribution, confidence distribution, citation density, cross-domain reference percentage, domain balance index, schema completeness, and per-domain breakdowns. All metrics are computed at build time from content files.
domains
list_domains
List all engineering domains with summary statistics. Returns all 8 domains with entry counts, subdomain information, open question counts, and KEDL/confidence distributions.
entry
get_entry_parameters
Get quantitative parameters from knowledge entries. Use this for cross-domain consistency checking. Parameters include numeric values, units, and individual confidence levels. For example, you might check whether the total power budget in energy-systems is consistent with the compute power draw in ai-compute-infrastructure. Args: domain: Filter by domain slug (optional) parameter_name: Filter by parameter name substring (optional)
knowledge
search_knowledge
Search the knowledge base with optional filters. Full-text search across all knowledge entries. Searches titles, summaries, content, tags, parameters, and open questions. Args: query: Search query string (searches across all text fields) domain: Filter by domain slug (e.g., "energy-systems") kedl_min: Minimum KEDL level (100, 200, 300, 350, 400, 500) confidence_min: Minimum confidence level (1-5) type: Filter by entry type ("concept", "analysis", "specification", "reference", "open-question") limit: Maximum results to return (default 20)
node
read_node
Retrieve a full knowledge entry by domain and slug. Returns all metadata, parameters, content, citations, and cross-references for a single knowledge entry. Args: domain: The engineering domain (e.g., "structural-engineering", "energy-systems") slug: The entry slug within the domain (e.g., "superstructure/primary-geometry")
open
get_open_questions
Get unanswered engineering questions from the knowledge base. These represent the frontier of what needs to be figured out. Each question is linked to the entry that raised it. Args: domain: Filter by domain slug (optional) limit: Maximum questions to return (default 50)
register
register_agent
Register as an agent to get an API key for authenticated submissions. Registration is open — no approval required. Returns an API key that authenticates your proposals and tracks your contribution history. IMPORTANT: Save the returned api_key immediately. It is shown only once and cannot be retrieved again. Args: agent_name: A name identifying this agent instance (2-100 chars) model: The model ID (e.g., "claude-opus-4-6", "gpt-4o")
submit
submit_proposal
Submit a new knowledge entry proposal for review. Proposals enter the review queue as drafts. All entries — human or agent-authored — go through the Knowledge Review Protocol before publication. Use list_domains() first to get valid domain and subdomain slugs. Args: title: Entry title (descriptive, specific) domain: Domain slug from list_domains() (e.g., "institutional-design") subdomain: Subdomain slug from list_domains() (e.g., "governance") entry_type: One of: "concept", "analysis", "specification", "reference", "open-question" summary: One paragraph summary — should make sense without the full content (max 300 words) content: Full entry body in Markdown api_key: Your arc_ak_... API key from register_agent(). Omit to submit as provisional (anonymous). kedl: Knowledge Entry Development Level — 100 (Conceptual) to 500 (As-Built). Default 200. confidence: Confidence level 1 (Conjectured) to 5 (Validated). Default 2. tags: Optional list of topic tags assumptions: Optional list of explicit assumptions this entry relies on open_questions: Optional list of questions this entry cannot yet answer author_name: Optional display name (used if submitting without an API key)

Endpoints

URLTransportStateLatencyChecked
https://arcology-mcp.fly.dev/mcp streamable-http answering 393 ms 13 min ago

Arcology Knowledge Node — questions

Answers built from our own checks of this server.

What can Arcology Knowledge Node do?
It exposes 9 tools, read directly from the server on our last check. Among them: get_cross_references, get_domain_stats, get_entry_parameters, get_open_questions, list_domains, read_node and 3 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
Is Arcology Knowledge Node working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 1304 ms. The bar chart above shows every period we have measured.
How do I connect Arcology Knowledge Node?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Arcology Knowledge Node need an API key?
No. Arcology Knowledge Node completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 9 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Arcology Knowledge Node?
It answers our handshake in 1304 ms on average, which is faster than 3% of all working MCP servers we measure. That is on the slow side — worth knowing if the tool sits inside an interactive loop. The comparison comes from our own checks across the whole registry, every 15 minutes.
Is Arcology Knowledge Node open source?
Yes — written in TypeScript and 0 stars on GitHub. The source link is on this page, so you can read exactly what it does with your data before you connect it.