AI Compute Radar
REMOTE · AICOMPUTERADAR.DEV · 2 COMPONENTS
Which open models fit your GPU or Mac, measured. Model momentum, GPU rental prices, weekly pick.
Available components
Last scanned (13 hours ago)
Scores higher than 81% of remote servers
Reachable. Last reached 5 Nov 2026.
Category AI and Machine Learning
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
- The endpoint's TLS certificate is valid, in date, and uses a strong key. View diagnostics → Pass
- Authorisation not fully verified: no authorisation is required to call this server, and 6 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe. See how to fix → View diagnostics → Unverified
- HTTPS is enforced; there's no plaintext access path. View diagnostics → Pass
- The HSTS (Strict-Transport-Security) header is present. View diagnostics → Pass
- DNSSEC check failed: this domain isn't protected by DNSSEC. See how to fix → View diagnostics → Fail
Transport & Reachability100
- Verified streamable-http transport via a live MCP handshake. View diagnostics → Pass
Schema Quality & AI Usability70
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 1012 tokens (~168/item across 6 items; 6 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management100
- No destabilizing schema changes in the last 30 days.Pass
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 6 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 7 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Checks we ran (12)
- TLS certificate
- HTTPS
- HSTS header
- DNSSEC
- Transport
- Schema quality
- Stability
- Tool coverage
- Injection markers
- Destructive annotations
- Judged manipulation
- MCP protocol
Checks we could not complete (1)
These are gaps on our side, not findings about this hosted endpoint. We only credit what we can confirm, so a category made up entirely of them scores 0.
- Authorization in Endpoint SecurityAuthorisation not fully verified: no authorisation is required to call this server, and 6 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe.
Does not apply to this hosted endpoint (2)
- Supply Chain SecurityScored on package channels only. This is a hosted endpoint, so there is nothing to install or inspect.
- Provenance & TransparencyScored on package channels only. This is a hosted endpoint, so there is nothing to install or inspect.
The score does not measure everything. What the score does not measure →
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.
- 30 Oct 26 0
- “price_history” reworded the description of “days” cosmetic
1 cosmetic change on this day. Switch on “Show cosmetic changes” to see it.
- 27 Oct 26 0
- The server rewrote its instructions, which are the text every model session reads security
- Schema quality: 789 → 1002 ▼ functional
- Server version: 1.2.2 → 1.2.3 functional
- New tool “price_history” functional
- 24 Oct 26 −1
- The server rewrote its instructions, which are the text every model session reads security
- Server version: 1.2.1 → 1.2.2 functional
- 22 Oct 26 0
- The server rewrote its instructions, which are the text every model session reads security
- Schema quality: 129 → 147 ▼ functional
- Server version: 1.2.0 → 1.2.1 functional
- 18 Oct 26 +1
- Stability: 0.97 → pass security
- 16 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.
- 13 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 80 to 83. That category is still filling its 30-day observation window: 24 days of observed history at the previous scan, 25 at this one. The score rises as the window fills, whether or not the server changes.
- 11 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 73 to 77. That category is still filling its 30-day observation window: 22 days of observed history at the previous scan, 23 at this one. The score rises as the window fills, whether or not the server changes.
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 5 Nov 2026 · Probed https://aicomputeradar.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=*.aicomputeradar.dev | CN=YR2,O=Let's Encrypt,C=US | 25 Oct 2026 | 23 Jan 2027 | RSA 2048 | SHA256-RSA | 64b89c79a9607d298bdb1f477584169503f |
| SANs: *.aicomputeradar.dev, aicomputeradar.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 aicomputeradar.dev. — Not signed
| Zone | DS | Keys | Algorithms | Outcome |
|---|---|---|---|---|
| . | Trust anchor | 20326, 38696 | 8, 8 | Verified |
| dev. | DS published | 60074 | 8 | Verified |
| aicomputeradar.dev. | No DS | 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 | 200 |
| 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://aicomputeradar.dev/api/mcp | Verified | 200 | |
| http (plaintext) | http://aicomputeradar.dev/api/mcp | HTTPS enforced | 308 | https://aicomputeradar.dev/api/mcp |
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 →
find_fit Find models that fit a machine ~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.
| Name | Type | Req | Description |
|---|---|---|---|
| context | integer | – | Context length in tokens (default 8192). |
| hardware | string | yes | Hardware id or page slug, e.g. rtx-4090, mac-studio-m3-ultra-96gb. |
| kv | string | – | KV-cache quantization (default f16). |
| model | string | – | Restrict to one model slug. |
No output schema declared.
No examples provided.
gpu_prices GPU rental prices ~166
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, plus RunPod's lowest posted on-demand price per class (a list price, not a median), Clore.ai's median for the same class (a second marketplace, never blended), and the AWS, Azure and Oracle Cloud pay-as-you-go list prices per GPU-hour, each with the instance type, VM size or bare-metal shape the price sits in (list prices, read four times a day, no statement about capacity). 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 List hardware profiles ~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.
price_history GPU rental price history ~207
Daily price history per GPU class and source, up to 90 days: the rows the Rent Index and the 30-day range are computed from. Marketplace days (Vast.ai, Clore.ai) are medians over the day's counted hourly medians with min, p75 and offer count; list-price days (RunPod, AWS, Azure, Oracle Cloud, Lambda, Nebius, CoreWeave, Hyperstack) carry the posted price. Each row says which kind it is; never blend the two. Describes what prices did; it never forecasts.
| Name | Type | Req | Description |
|---|---|---|---|
| days | integer | – | Days back from today (default 30, max 365; the series starts 26 August 2026). |
| gpu | string | – | One class by slug or name, e.g. rtx-4090, h100. Every class without it. |
| source | string | – | One source key, e.g. vast, runpod, aws. Every published source without it. |
No output schema declared.
No examples provided.
trending_models 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.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | How many models to return (default 12). |
| slug | string | – | Return a single model by slug. |
No output schema declared.
No examples provided.
weekly_pick Pick of the week ~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.
| Name | Type | Req | Description |
|---|---|---|---|
| week | string | – | ISO week label of a past issue, e.g. 2026-w37 (default: the current issue). |
No output schema declared.
No examples provided.
How do I install the AI Compute Radar MCP server?
AI Compute Radar is a hosted endpoint at https://aicomputeradar.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 · aicomputeradar.dev
claude mcp add --transport http dev-aicomputeradar-ai-compute-radar 'https://aicomputeradar.dev/api/mcp'
What is the AI Compute Radar MCP server?
AI Compute Radar is an MCP server listed in the public MCP registry as dev.aicomputeradar/ai-compute-radar. Which open models fit your GPU or Mac, measured. Model momentum, GPU rental prices, weekly pick. This page covers its hosted endpoint (https://aicomputeradar.dev/api/mcp).
Is the AI Compute Radar MCP server safe to use?
AI Compute Radar scores 80 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 Compute Radar MCP server expose?
AI Compute Radar exposes 6 tools: trending_models, find_fit, gpu_prices, price_history, list_hardware, weekly_pick. Their descriptions and schemas cost roughly 781 tokens of context every time the server is loaded.
Does the AI Compute Radar MCP server require authentication?
No. We connected to AI Compute Radar without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.
Is the AI Compute Radar MCP server still maintained?
AI Compute Radar is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What did VerifyMCP test on the AI Compute Radar MCP server?
VerifyMCP ran 12 checks on AI Compute Radar, covering TLS certificate, HTTPS, HSTS header, DNSSEC and 8 more. One item could not be completed, a gap on our side rather than a finding about AI Compute Radar: Authorization. Supply Chain Security and Provenance & Transparency do not apply to a hosted endpoint, so they were not tested. The score does not measure everything, and the scoring documentation lists what it leaves out.