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AI Model Radar

REMOTE · AIMODELRADAR.DEV · SCANNED SEP 20

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

Available components

−46 this week 25 Trust /100

Deprecated

This server is marked deprecated in the MCP registry. The registry records this reason: Renamed: use dev.aicomputeradar/ai-compute-radar (https://aicomputeradar.dev/api/mcp).

Trust breakdown (7 categories)

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. How we score → Why this is hard to score →

Endpoint Security63
Transport & Reachability0
Schema Quality & AI Usability0
  • Schema not yet verified: we couldn't read the endpoint's schema.Unverified
Stability & Change Management0
  • Stability not yet verified: not enough scan history yet (needs a 30-day window).Unverified
Tool Coverage0
  • Tool coverage not yet verified: we couldn't read the endpoint's tools.Unverified
Tool Safety0
  • Tool safety not yet verified: we couldn't read the endpoint's tools.Unverified
Capabilities0
  • Capabilities not yet verified: we couldn't read the endpoint's capabilities.Unverified

Unverified: 5 categories

Categories scored 0 because we could not verify them: authentication we do not have, an unreachable endpoint, or not enough scan history. We only credit what we can confirm.

Install

How do I install the AI Model Radar MCP server?

AI Model Radar is a hosted endpoint at https://aimodelradar.dev/api/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

remote · aimodelradar.dev

# add to Claude Code
claude mcp add --transport http dev-aimodelradar-ai-model-radar 'https://aimodelradar.dev/api/mcp'
// .cursor/mcp.json
{
  "mcpServers": {
    "dev-aimodelradar-ai-model-radar": {
      "url": "https://aimodelradar.dev/api/mcp"
    }
  }
}
// .vscode/mcp.json
{
  "servers": {
    "dev-aimodelradar-ai-model-radar": {
      "type": "http",
      "url": "https://aimodelradar.dev/api/mcp"
    }
  }
}
# ~/.codex/config.toml
[mcp_servers.dev-aimodelradar-ai-model-radar]
url = "https://aimodelradar.dev/api/mcp"
// opencode.json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "dev-aimodelradar-ai-model-radar": {
      "type": "remote",
      "url": "https://aimodelradar.dev/api/mcp",
      "enabled": true
    }
  }
}
# add to OpenClaw
openclaw mcp add dev-aimodelradar-ai-model-radar --url 'https://aimodelradar.dev/api/mcp' --transport streamable-http
# ~/.hermes/config.yaml
mcp_servers:
  dev-aimodelradar-ai-model-radar:
    url: "https://aimodelradar.dev/api/mcp"
// ~/.netclaw/config/netclaw.json
{
  "McpServers": {
    "dev-aimodelradar-ai-model-radar": {
      "Transport": "http",
      "Url": "https://aimodelradar.dev/api/mcp"
    }
  }
}
# add to Vellum
assistant mcp add dev-aimodelradar-ai-model-radar -t streamable-http -u 'https://aimodelradar.dev/api/mcp'
// mcp.json
{
  "mcpServers": {
    "dev-aimodelradar-ai-model-radar": {
      "type": "http",
      "url": "https://aimodelradar.dev/api/mcp"
    }
  }
}

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

Changelog

Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.

  • 18 Sept 26 −48
    • Endpoint reachability: reachable → not serving MCP security
    • Stability: 0.37 → unverified security
    • Tool safety: pass → unverified security
    • Transport: pass → fail security
    • Authorization: Authorisation not fully verified: no authorisation is required to connect, but we couldn't read the tool list to see what that exposes. security
    • Tool “gpu_prices” rewrote its description, which is the text the model reads security
    • Capabilities: pass → unverified functional
    • Tool coverage: 100 → unverified functional
    • First check of Schema quality: unverified functional
  • 17 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.

  • 15 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.

  • 13 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 20 to 23. That category is still filling its 30-day observation window: 6 days of observed history at the previous scan, 7 at this one. The score rises as the window fills, whether or not the server changes.

  • 11 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 13 to 17. That category is still filling its 30-day observation window: 4 days of observed history at the previous scan, 5 at this one. The score rises as the window fills, whether or not the server changes.

  • 9 Sept 26 0
    • Schema quality: 397 → 521 functional
    • Server version: 1.0.0 → 1.1.0 functional
    • New tool “weekly_pick” functional
  • 8 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.

  • 7 Sept 26 0
    • Stability: unverified → 0.03 functional
Diagnostics

Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.

Captured 20 Sept 2026 · Probed https://aimodelradar.dev/api/mcp

TLS valid

Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .

Subject Issuer Valid from Valid until Key Signature Serial
CN=*.aimodelradar.dev CN=YR2,O=Let's Encrypt,C=US 30 Aug 2026 28 Nov 2026 RSA 2048 SHA256-RSA 672f3751d9f4d67203f7111773cfd869164
SANs: *.aimodelradar.dev, aimodelradar.dev
CN=YR2,O=Let's Encrypt,C=US (CA) CN=Root YR,O=ISRG,C=US 3 Sept 2025 2 Sept 2028 RSA 2048 SHA256-RSA 4ebd24947e24d394802d84a52fd5b319
CN=Root YR,O=ISRG,C=US (CA) CN=ISRG Root X1,O=Internet Security Research Group,C=US 13 May 2026 2 Sept 2032 RSA 4096 SHA256-RSA f24b6d17f9d9ad7cb1c9fea78782699f

Background: What to check on a remote MCP endpoint →

DNSSEC insecure

Validation of aimodelradar.dev. Not signed

Zone DS Keys Algorithms Outcome
. trust_anchor 20326, 38696 8, 8 Verified
dev. present 60074 8 Verified
aimodelradar.dev. absent Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation
Authentication No authorisation required

The endpoint answered without asking for a token. Anyone who knows the URL can reach it.

Result No authorisation required
HTTP status 301
Header Value
strict-transport-security max-age=63072000

Background: How OAuth 2.1 works in the 2026 MCP spec →

Transports 2 probes
Transport URL Outcome Status Location
streamable-http https://aimodelradar.dev/api/mcp Redirected 301 https://aicomputeradar.dev/api/mcp
http (plaintext) http://aimodelradar.dev/api/mcp HTTPS enforced 308 https://aimodelradar.dev/api/mcp
MCP tools · 5 exposed · ~488 tokens

The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →

Tool Tokens
find_fit ~149

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.

NameTypeReqDescription
contextintegerContext length in tokens (default 8192).
hardwarestringyesHardware id or page slug, e.g. rtx-4090, mac-studio-m3-ultra-96gb.
kvstringKV-cache quantization (default f16).
modelstringRestrict to one model slug.

No output schema declared.

No examples provided.

gpu_prices ~80

Median verified on-demand rental price per GPU class on Vast.ai (USD per hour), with min/p75 and offer counts, the collection timestamp, and per class the Rent Index: this week's median against last week and against the first week collected, a trend word, and the days excluded as marketplace glitches. The index describes what prices did; it never forecasts.

Input schema present but exposes no named parameters.

No output schema declared.

No examples provided.

list_hardware ~38

Curated GPU and Mac profiles the fit engine knows — ids, memory, usable memory after margins, bandwidth. Use an id with find_fit.

Input schema present but exposes no named parameters.

No output schema declared.

No examples provided.

trending_models ~97

Tracked AI models ranked by Heat Score (0–100, weighted percentiles of measured Hugging Face/OpenRouter signals) with the raw signals, local-run facts (GGUF size, quantization) and links. Models still collecting a week of history have heat=null and rank after scored ones.

NameTypeReqDescription
limitintegerHow many models to return (default 12).
slugstringReturn a single model by slug.

No output schema declared.

No examples provided.

weekly_pick ~124

The current pick of the week: one tracked model chosen by a published rule (largest counted Heat Score rise among models that run comfortably on a consumer card of up to 24 GB), with the numbers frozen at selection time, a device-by-device fit ladder and the written report including its caveats. Pass week (e.g. 2026-w37) for a past issue. issue is null until the first issue is published.

NameTypeReqDescription
weekstringISO week label of a past issue, e.g. 2026-w37 (default: the current issue).

No output schema declared.

No examples provided.

Common questions

What is the AI Model Radar MCP server?

AI Model Radar is an MCP server listed in the public MCP registry as dev.aimodelradar/ai-model-radar. Which open models fit your GPU or Mac, measured. Model momentum, GPU rental prices, weekly pick. This page covers its hosted endpoint (https://aimodelradar.dev/api/mcp).

Is the AI Model Radar MCP server safe to use?

AI Model Radar scores 25 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

What tools does the AI Model Radar MCP server expose?

AI Model Radar exposes 5 tools: trending_models, find_fit, gpu_prices, list_hardware, weekly_pick. Their descriptions and schemas cost roughly 488 tokens of context every time the server is loaded.

Does the AI Model Radar MCP server require authentication?

No. We connected to AI Model Radar without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

Is the AI Model Radar MCP server still maintained?

AI Model Radar is marked deprecated in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.