Is the Aicomputeradar Ai Compute Radar MCP server down?

mcp-registry/dev.aicomputeradar/ai-compute-radar · v1.2.1

healthy
Last checked 2026-10-23 00:56 UTC · handshake 238ms

As of 2026-10-23 00:56 UTC, the Aicomputeradar Ai Compute Radar MCP server is up with a Glimind reliability score of 97/100 (grade A). Over the last 30 days it answered 100% of Glimind's liveness checks.

uptime (30d checks)
100%
checks observed
4,316
outages (90d)
0
tools
5

Which open models fit your GPU or Mac, measured. Model momentum, GPU rental prices, weekly pick.

Install Aicomputeradar Ai Compute Radar MCP server

Add to Cursor Add to VS Code

Claude Code

claude mcp add --transport http aicomputeradar-ai-compute-radar-mcp-serv https://aicomputeradar.dev/api/mcp

Claude Desktop · Windsurf · Cline (mcpServers JSON)

{
  "mcpServers": {
    "aicomputeradar-ai-compute-radar-mcp-serv": {
      "type": "http",
      "url": "https://aicomputeradar.dev/api/mcp"
    }
  }
}

Third-party server, listed as discovered. Review its source before installing.

Agent-readiness: B (85/100)

B (85/100): 0 error(s), 1 warning(s) across 5 tool(s); ~858 tokens of tool definitions.
Scores higher than 47% of 15,575 MCP servers Glimind has audited.
Its tool list costs an agent ~858 tokens per session — lighter than 80% of servers.
84 → 85 since 2026-10-17
no tool-poisoning patterns
Schemas100
Descriptions100
Safety hints30
Token cost100
Client compatibility70
Protocol100
Reliability100
Stability100

What to fix (2)

5 of 5 tool(s) carry no readOnlyHint/destructiveHint annotations.

Annotate read-only tools with {"readOnlyHint": true}. Agents and gateways can then run them without a confirmation prompt, and route around destructive ones. How to fix →

Tools: find_fit, gpu_prices, list_hardware, trending_models, weekly_pick

No tool declares an outputSchema.

Declare outputSchema and return structuredContent (MCP 2025-06-18+). Agents can then parse results without guessing, and chain tools reliably. How to fix →

Auto-fix: 2 corrected tool definitions

Paste-ready definitions with the problems above fixed. Changes marked verify need a human look. All of them as JSON: /v1/readiness/mcp-registry%2Fdev.aicomputeradar%2Fai-compute-radar/fixes

find_fit 1 change 
{
  "name": "find_fit",
  "description": "Which tracked models run on a given GPU or Mac: measured GGUF weights + computed context cache + runtime overhead versus usable memory. Returns the best recommendation and every verdict (EXCELLENT/GOOD/TIGHT/OFFLOAD_REQUIRED/NOT_RECOMMENDED/UNKNOWN) with plain-language reasons. Get hardware ids from list_hardware.",
  "inputSchema": {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "required": [
      "hardware"
    ],
    "properties": {
      "kv": {
        "enum": [
          "f16",
          "q8_0",
          "q4_0"
        ],
        "type": "string",
        "description": "KV-cache quantization (default f16)."
      },
      "model": {
        "type": "string",
        "description": "Restrict to one model slug."
      },
      "context": {
        "type": "integer",
        "maximum": 1048576,
        "minimum": 512,
        "description": "Context length in tokens (default 8192)."
      },
      "hardware": {
        "type": "string",
        "description": "Hardware id or page slug, e.g. rtx-4090, mac-studio-m3-ultra-96gb."
      }
    }
  },
  "annotations": {
    "readOnlyHint": true
  },
  "title": "Find models that fit a machine"
}
list_hardware 1 change 
{
  "name": "list_hardware",
  "description": "Curated GPU and Mac profiles the fit engine knows — ids, memory, usable memory after margins, bandwidth. Use an id with find_fit.",
  "inputSchema": {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "properties": {}
  },
  "annotations": {
    "readOnlyHint": true
  },
  "title": "List hardware profiles"
}

Get alerted

Get an email or webhook when this server's readiness drops by 10+ points, tool-poisoning text appears, or a tool breaks:

curl -X POST https://glimind.com/v1/subscriptions -H 'content-type: application/json' -d '{"tools":["mcp-registry/dev.aicomputeradar/ai-compute-radar"],"email":"[email protected]"}'

Agents: watch_tool({"toolId": "mcp-registry/dev.aicomputeradar/ai-compute-radar"}) over MCP.

Passed

Largest tool definitions

Static checks of the advertised tools/list (schema, descriptions, annotations, tool-poisoning patterns, names, size) plus observed protocol, uptime, latency and contract changes. Read-only: no tool is ever called. Weights: schema 15, docs 20, safety 15, efficiency 15, compatibility 15, protocol 5, reliability 10, stability 5. Token counts are estimates (~3.6 characters per token).

Operator? Fix these, then re-check live. Embed the readiness badge:

Markdown 
[![MCP agent-readiness](https://glimind.com/v1/readiness/badge/mcp-registry%2Fdev.aicomputeradar%2Fai-compute-radar.svg)](https://glimind.com/server/mcp-registry%2Fdev.aicomputeradar%2Fai-compute-radar?ref=badge)
HTML 
<a href="https://glimind.com/server/mcp-registry%2Fdev.aicomputeradar%2Fai-compute-radar?ref=badge"><img src="https://glimind.com/v1/readiness/badge/mcp-registry%2Fdev.aicomputeradar%2Fai-compute-radar.svg" alt="MCP agent-readiness"></a>
reStructuredText 
.. image:: https://glimind.com/v1/readiness/badge/mcp-registry%2Fdev.aicomputeradar%2Fai-compute-radar.svg
   :target: https://glimind.com/server/mcp-registry%2Fdev.aicomputeradar%2Fai-compute-radar?ref=badge
   :alt: MCP agent-readiness

Tool-definition log

Glimind records every version of this server's tool definitions in a signed, append-only transparency log. Current manifest sha256:23adc1fdfe60e1d3…, first seen 2026-10-17, included at leaf 16842 of 49,121. Version history · verify what your client received.

Tools (5)

Compare: most reliable ml servers · most reliable data servers

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Add to Cursor Add to VS Code

Claude Code

claude mcp add --transport http glimind https://glimind.com/mcp

Claude Desktop · Windsurf · Cline (mcpServers JSON)

{
  "mcpServers": {
    "glimind": {
      "type": "http",
      "url": "https://glimind.com/mcp"
    }
  }
}

FAQ

Is the Aicomputeradar Ai Compute Radar MCP server down right now?

As of 2026-10-23 00:56 UTC, the Aicomputeradar Ai Compute Radar MCP server is up with a Glimind reliability score of 97/100 (grade A). Over the last 30 days it answered 100% of Glimind's liveness checks.

How many tools does Aicomputeradar Ai Compute Radar MCP server provide?

Aicomputeradar Ai Compute Radar MCP server advertises 5 tools, including find_fit, gpu_prices, list_hardware, trending_models, weekly_pick.

How do I install Aicomputeradar Ai Compute Radar MCP server?

Point your MCP client at this streamable-http endpoint: https://aicomputeradar.dev/api/mcp

Is the Aicomputeradar Ai Compute Radar MCP server safe to connect to an AI agent?

Glimind scanned all 5 tool definitions for tool-poisoning patterns — hidden or invisible characters, instructions that override the model, concealment from the user, requests to read credential or MCP config files, conversation exfiltration and cross-tool shadowing — and found none. This checks what the server tells the model, not what its code does; review the source before granting access to sensitive data.

Operator of Aicomputeradar Ai Compute Radar MCP server?

Status page: 90-day uptime, every outage and how long it lasted.

Claim this listing to get the Verified operator marker, a remediation checklist and owner analytics, or embed the live badge from any of the tool pages above. Maintainers: alerts, deploy attestation and monthly reports.

Status is an automated estimate from Glimind's liveness checks (handshake and tool listing only; no tool is ever called) and may lag or differ from your network. Glimind is not affiliated with this server or its operator. Operator? Request a correction or opt out at https://glimind.com/opt-out.

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