Deep Noodle AI Atlas MCP Server

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answering

Deep Noodle AI Atlas is answering right now. Last checked 5 min ago. It exposes 10 tools.

Read-only, sourced AI history, organizations, stories, and model facts.

Uptime history 7 hours of history · worst hour 25%

7 hours agonow

80.0%

Uptime 24h

20 of 25 checks

Tools

read from the server

338 ms

Response time

average over 24h

open, no key

Access

streamable-http

Deep Noodle AI Atlas does not always answer

Over the last week it answered 80.0% of our checks. We check every 15 minutes, so you hear about the next outage within the hour — not from your users.

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 5 min ago.

Claude Code Claude Desktop Codex Cursor VS Code

run in your terminal

claude mcp add atlas --transport http https://deepnoodle.ai/atlas/mcp

~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "atlas": {
      "url": "https://deepnoodle.ai/atlas/mcp"
    }
  }
}

~/.codex/config.toml

[mcp_servers.atlas]
url = "https://deepnoodle.ai/atlas/mcp"

.cursor/mcp.json

{
  "mcpServers": {
    "atlas": {
      "url": "https://deepnoodle.ai/atlas/mcp"
    }
  }
}

.vscode/mcp.json

{
  "mcpServers": {
    "atlas": {
      "url": "https://deepnoodle.ai/atlas/mcp"
    }
  }
}

Available tools 10

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

atlas

search_atlas

Find relevance-ranked events, briefs, and organizations. Default match=any; use match=all for every term; sort=most-linked ranks distinct published inbound neighbors before pagination. Event filters constrain events and organization facets; organization matches and briefs are separately labeled. Each result family has pagination metadata. Organization name/evidence matches are returned only with q; organizationFacets always describes filtered events. For what happened recently, use since/until here; for records published or edited recently, use get_changes.

changes

get_changes

Current-record feed of additions and revisions since a date. Uses publication timestamps, not occurrence dates. For what happened recently, use search_atlas with since/until instead. Publication backfills can include many older events here. This is not a historical diff or deletion log.

compare

compare_models

Compare two to four catalog IDs or unambiguous API identifiers in requested order. Defaults include compact release, lifecycle, specs and token prices, sourceRefs and a website link. fields selects facts such as pricing. Unknown values stay explicit. IDs must resolve to distinct records.

event

get_event

Read compact event facts, sources, date precision, revisions, contextual links and story connections. Request include=["explanation"] for the full narrative. hasExplanation and explanationScope describe availability. explanationCoverage counts published event explanations across the corpus; empty links can mean unwritten context, not an unconnected event.

events

get_events

Read 1–20 published event details in request order. Unknown or unpublished IDs are returned in missingIds. Explanations remain opt-in; each event has independently paginated incoming and outgoing context.

model

get_model

Read a model by exact catalog ID or unambiguous API identifier. Defaults to core facts; fields selects fact groups such as pricing. sourceRefs resolve through atlas://models/sources. Ambiguous or missing lookups return candidate IDs; surface disambiguates hosts.

models

list_models

Browse compact model facts with search, filters, sorting and nextOffset pagination. Read atlas://models/catalog for filter values and price units. Defaults to current models, newest documented release first. matchedUnknown reports unverified capability alternatives only when capability filters are used; includeUnknown=true includes them. undatedMatches reports relevant undated records; releaseDateKnown=false browses them. Unknown is not unsupported, free or older.

organization

get_organization

Read compact sourced organization facts and paginated events. Request include=["explanation"] for the full narrative; hasExplanation and explanationScope describe availability. Subsidiaries and successors are separate.

stories

list_stories

Enumerate published briefs with review dates, evidence cutoffs, and pagination.

story

get_story

Read narrative, labeled interpretation, citations, chronology, open questions and Markdown. Newer research leads use strict phrase matches and are separate from reviewed narrative.

Endpoints

URL	Transport	State	Latency	Checked
https://deepnoodle.ai/atlas/mcp	streamable-http	answering	107 ms	5 min ago

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Deep Noodle AI Atlas — questions

Answers built from our own checks of this server.

What can Deep Noodle AI Atlas do?

It exposes 10 tools, read directly from the server on our last check. Among them: compare_models, get_changes, get_event, get_events, get_model, get_organization and 4 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 Deep Noodle AI Atlas working right now?

We send a real MCP handshake every 15 minutes. Over the last 24 hours 20 of 25 checks got a reply (80.0%), average response time 338 ms. The bar chart above shows every period we have measured.

How do I connect Deep Noodle AI Atlas?

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 Deep Noodle AI Atlas need an API key?

No. Deep Noodle AI Atlas completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 10 of them are readable on this page. This is what we observed, not what the docs claim.

How fast is Deep Noodle AI Atlas?

It answers our handshake in 338 ms on average, which is faster than 49% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.