Drex Max v1
by Nace AI, Inc. (Nace.AI) Open-weight model in Decision models
Nace AI, Inc. · nace.ai · who's behind it
Drex Max v1 is Nace.AI's open-weight 27B decision model, a fine-tune of Qwen3.8-27B that takes text, JSON and images with typed questions and returns a probability for every option without generating text.
Best for Agents that want self-hosted routing, triage or screen decisions with probabilities, on a CUDA GPU with 48 GB or more for images or a 24 GB Mac for text, where the Open RAIL-M terms fit.
Viewed under 500 times in the last 30 days · See the full traffic report
Is this your product? Claim it to see how agents find it: lookups through the API, MCP and letme, the comparisons it's in, and what people search for in Decisions. Free, and it never changes the grade. Or verify it, or log in if it's already claimed.
The same model, listed separately Drex 1.5 (Model API)
Trained by Nace.AI, in the Drex family Drex v1.5 · Drex DLM · Drex v2.0 · all its cousins, older versions
Assessment. A 27B cousin for typed decisions over text and screenshots, several questions per request with a probability for each option. Images need an NVIDIA GPU and Nace's loader; on Apple silicon only the text backbone runs, from 24 GB. The licence isn't open source, with free commercial use only under $1 million in revenue and funding.
Facts
Access how an agent reaches and pays for it
- Auth
- None
- Pricing
- Free · Free
- x402
- No
- Licence
- Nace.AI Open RAIL-M License (modified), version 1.0, October 2026, in the repository's LICENSE. Not an open-source licence. Use is free only for individuals and organisations under $1 million in revenue in the prior 12 months and under $1 million in total funding (personal and research use excepted), never by anyone with a product that competes with Nace's, decision models included, and under use restrictions that bind the model, its derivatives and its outputs. Built on Qwen/Qwen3.8-27B (Apache-2.0), whose licence text isn't carried in the repository
- Capabilities
- inference.decision model.open-weights
Where it lives packages, source, docs and releases
- Packages
huggingfacenace-ai/drex-max-v1- llms.txt
- not found
- Last release
From our research 11 facts checked against the vendor's pages
- Surface graded
- The open weights at huggingface.co/nace-ai/drex-max-v1 and the publisher's Q8_0 and affine 4-bit builds, read from the cards and their history, the files and their metadata, config.json, decision_config.json, the loader in code/drex_max, inference.py, the licence, Nace's GitHub docs and MLX script, Nace's blog, product page and hosted docs, the Jev Decision Index Space's data and Artificial Analysis's index on 30 October 2026. Nothing was downloaded or run. Nace's hosted API is the nace-drex listing
- Built on
- Qwen/Qwen3.8-27B (Apache-2.0) at revision 1d4bf0f, named in decision_config.json and the loader, and in the card's metadata until 30 October 2026. A decision fine-tune of the backbone with a new 255-way readout head. Nace doesn't say how it was trained
- Relation to Drex 1.5 and the hosted API
- The same publisher's larger, multimodal line. nace-drex is graded as Nace's hosted Drex API at console.nace.ai/v1/systemone, which today serves drex-max (this model, released 13 October 2026), drex-max-v1.1, drex-v2.0 (the default) and drex-v1.5, and forwards the retired drex-v1.0 to drex-max. Whether the hosted drex-max is byte for byte the Hub checkpoint isn't stated
- Model
- 26,085,330,160 parameters in BF16 by the Hub's count (the card says 27B; the base counts 27.78B with its language-model head), 64 layers with 48 Gated DeltaNet layers that stay causal and 16 full-attention layers made non-causal, the Qwen3.5 vision tower, and a 255 by 5,120 readout over the last hidden state with a temperature of 1.009
- Files
- 11 safetensors shards (52.17 GB), readout.safetensors, decision_config.json, config, tokenizer, processor config and the base's chat_template.jinja, the loader package code/drex_max, inference.py and requirements.txt. No pickle files. The affine 4-bit build adds pointer.bin, the readout in Nace's raw KPS1 format, not a pickle. The Hub lists a SHA-256 for every LFS file, and the builds' cards list theirs
- Licence
- Nace.AI Open RAIL-M (modified) 1.0. Royalty-free copyright and patent grants, subject to Attachment A, which excludes users above $1 million in revenue or funding (personal and research use excepted) and anyone with a product competing with Nace's, decision models and document intelligence included, and bars fully automated decisions with legal or similar effects without human review, discrimination, unsupervised medical, legal or financial advice, military, weapons and mass-surveillance use, fraud and removing the licence. Derivatives, distillations and outputs carry the restrictions. No clause lets Nace change the terms. The Apache-2.0 base's licence text isn't in the repository
- Runtimes
- Nace's loader on released Transformers 5.17.0 (pinned in requirements.txt) runs it as trained with images, on CUDA. The text backbone also runs on Nace's llama.cpp and Ollama forks (branch drex-max-v1), Kapitsa 0.2.25 or later (Nace's Apple silicon engine, Apache-2.0) and Nace's drex_max_mlx.py on MLX. No released llama.cpp, Ollama, vLLM, SGLang or LM Studio route, and no Ollama library entry
- Context
- 262,144 tokens in config.json (rope default) and the docs, the base's native window. The loader defaults to 16,384 tokens per request (inference.py in the repository defaults to 8,192) and accepts up to 262,144 with DREX_MAX_CONTEXT or --max-length. The fine-tune's training length isn't stated
- Inputs and outputs
- A state (text or JSON), optional images (file paths or data URLs, up to 4,194,304 pixels each), and named questions, each choice (1 to 255 options with descriptions), noul (yes or no, criteria optional) or score (ordered levels). Each question is scored as one pass over the state and returns probabilities, with choice and confidence, P(true), or an expected score and confidence. The system prompt tells the model to treat the state as data. No text generation and no thinking
- Hardware
- The card's tested hardware is an NVIDIA H100 80 GB in BF16 (48.6 GiB loaded), an NVIDIA L40S 48 GB with the backbone in 8-bit (25.9 GiB), and an Apple M5 Pro with 48 GB for the Q8_0 GGUF on Metal. The affine 4-bit build is stated for Macs with 24 GB or more
- Training data
- Not described. The card, the GitHub page and the blog say nothing about the decision fine-tune's data, size or cut-off. The nace-ai datasets for this model hold Decision Index results, not training data
Facts verified 30 October 2026 from vendor docs, repositories and package registries. JSON · Markdown
Strengths
- Probabilities for choice, yes or no and score questions over text, JSON and images, with nothing generated
- Full score 66.6 on the Jev Decision Index 0.4.1, 24th of 247, and 11th of 62 on its Vision board, from the index's private tests
- Nace publishes a Q8_0 GGUF and an affine 4-bit build, the smaller at 14.5 GB for Macs with 24 GB
- Not gated, and Nace's own API serves it as drex-max at $0.03 per million input tokens
- 262,144-token window in config.json and the docs, with a 16,384-token default you can raise
Weaknesses
- The licence bars commercial use above $1 million in revenue or funding and by anyone with a competing product, and restricts the outputs too
- Every path that runs it as trained is Nace's own code or forks. Stock llama.cpp, Ollama, vLLM and Transformers run it causally
- Images need the Python loader on a CUDA GPU; the GGUF and 4-bit builds read text only
- The card gives no limitations, languages, cut-off or training data, and dropped its Qwen3.8-27B base on 30 October 2026
- No safety results and no prompt-injection guidance on the card
Before you call it notes for agents
- Clone github.com/nace-ai/drex-decision-models, download nace-ai/drex-max-v1 and run
python -m drex_max --model weights/drex-max-v1 --request examples/request.jsonon an NVIDIA GPU - Plan for about 49 GB of GPU memory in BF16, or about 26 GB with --8bit on CUDA, before any long context
- Don't load it with stock Transformers, vLLM or llama.cpp. Its full-attention layers must run non-causally with readout.safetensors applied, which only Nace's loader and forks do
- On a Mac with 24 GB or more, run
kapitsa-systemone --model ./drex-max-v1-affine-q4 --port 8000 --context 2048for text-only decisions at POST /v1/systemone - Raise the 16,384-token default with DREX_MAX_CONTEXT=N or --max-length N, up to 262,144, and expect more memory
- Check the licence first. Above $1 million in revenue or funding, or with a competing product, you need Nace's commercial licence
Who's behind it provenance 58/100
- Legal entity namedNace AI, Inc.20/20
- Domain agenace.ai, no registry record we could read0/15
- Endpoint on the vendor's domainno hosted endpointn/a
- Terms of servicenothing hosted, so the Nace.AI Open RAIL-M License (modified), version 1.0, October 2026, in the repository's LICENSE. Not an open-source licence. Use is free only for individuals and organisations under $1 million in revenue in the prior 12 months and under $1 million in total funding (personal and research use excepted), never by anyone with a product that competes with Nace's, decision models included, and under use restrictions that bind the model, its derivatives and its outputs. Built on Qwen/Qwen3.8-27B (Apache-2.0), whose licence text isn't carried in the repository licence stands in10/10
- Privacy policynothing hosted, not scoredn/a
- Status pagenot found0/10
- Changelognot found0/10
- security.txtvalid10/10
- People named3 named with their role, none with a public profile7/10
- Support routehelp pages, a community5/5
Terms and privacy, as read
Terms of service none to read
TL;DR Nothing is hosted by the vendor, so there are no terms of service to read. The Nace.AI Open RAIL-M License (modified), version 1.0, October 2026, in the repository's LICENSE. Not an open-source licence. Use is free only for individuals and organisations under $1 million in revenue in the prior 12 months and under $1 million in total funding (personal and research use excepted), never by anyone with a product that competes with Nace's, decision models included, and under use restrictions that bind the model, its derivatives and its outputs. Built on Qwen/Qwen3.8-27B (Apache-2.0), whose licence text isn't carried in the repository licence stands in and the check scores in full.
Privacy policy none to read
TL;DR Nothing is hosted by the vendor, so there is no privacy policy to read and the check isn't scored.
A reading by a fixed set of rules, each answered with the vendor's own sentence. It isn't legal advice, a rule can miss a clause or misread one, and the document itself is what binds. How it's read and scored.
The legal entity is from the nace-drex listing's check of Nace's terms on 10 October 2026.
The nace-ai Hugging Face organisation is unverified, with 19 members, among them the two accounts that committed to this repository. nace.ai/drex links to huggingface.co/nace-ai and to github.com/nace-ai, whose drex-decision-models repository links these weights.
Nothing is hosted under this listing, so the licence stands in for terms of service and no privacy policy is scored.
security.txt at www.nace.ai is valid until 30 October 2027 and points to trust.nace.ai for contact and policy.
Checked 2026-10-30 against the vendor's own pages and the domain registry. Provenance is half of Transparency & trust.
Humans behind the project who makes it and where to get help
Key people
-
Dos BaháFounder and CEO
-
Zhanibek DatbayevCo-founder and CTO
-
Sudha ValluruCo-founder and COO
Nace AI, Inc. (Nace.AI) on LinkedIn @NaceAI GitHub
Raised $21.5M in total, as of May 2026 source
Backed byWalden Catalyst Ventures,General Catalyst
Researched 30 October 2026 from public pages. Key people: started from data/humans/nace-drex.json (nace ai, inc., checked 28 october 2026). the three founders and their roles are from nace.ai's press page as read there and weren't re-read today. the drex max repositories are committed by two members of the nace-ai hugging face organisation, whose roles aren't stated on a nace page, so they aren't listed. ownership and investors are left out so land.py copies them from the nace-drex file; nace.ai's sitemap, read on 30 october 2026, lists no blog post after the seed announcement about funding, and its newest entries date from 26 october. the support email address in the nace-drex file isn't copied here, since this run's rules copy no email addresses into files.. Shown for information, not part of the grade yet. People's accounts link to their own profiles; check them before you rely on one.
Notable
- Built on Qwen/Qwen3.8-27B. decision_config.json names it as base_model at revision 1d4bf0f, which is the Qwen repository's current commit, the loader's docstring says 'Qwen3.8-27B hybrid', and chat_template.jinja is the same blob as Qwen's. The card declared the base in its metadata until a commit on 30 October 2026 at 11:37 UTC removed it, and now calls the model 'a dense 27B model' with no base named source source 2
- Nace's blog of 19 October 2026 calls Drex Max 'First among decision models on the public Decision Index 0.4.0'. The index, a community Space by multimodalart that says it isn't affiliated with TypeSafe AI, ranks Drex Max 23rd of 247 by public score (67.52) and 24th of 247 by the Full score it ranks by (66.6, tie group 5) in data generated 29 October. On its 16 October data, before the blog, three models already had higher public scores source source 2
- On the index's Vision board, which ranks image-capable decision models, Drex Max is 11th of 62 by Full score (71.25) and 13th by public score (74.09). WeMM-JEV-27B leads at 73.81 source
- Artificial Analysis's Decision Index, run by Artificial Analysis on Nace's hosted Drex Max, gives it 63.74 on 30 October 2026, fourth of the 18 rows in its default view, behind d1-ultra (65.25), SubQ Snap 1.1 (65.00) and Elephant 9B (64.69). The card's '64, fourth of 18' matches source
- On Apple silicon only the text backbone runs. The Q8_0 GGUF runs on Nace's own forks of llama.cpp and Ollama (branch drex-max-v1) on Metal or the CPU and needs 48 GB, and the affine 4-bit build runs on Kapitsa, Nace's own Apple silicon engine, or Nace's MLX script on Macs with 24 GB or more. Images need the Python loader on an NVIDIA GPU. The card's tested Mac is an M5 Pro with 48 GB, and no M4 run is stated source
- The 16 full-attention layers are non-causal and a 255-way readout in readout.safetensors replaces the language-model head. Nace's loader applies both on released Transformers 5.17.0 through a forward hook, and the card says a plain causal chat run doesn't reproduce its scores source
- Nace's hosted Drex API, listed as nace-drex, serves this model as drex-max (released 13 October 2026) and a newer drex-max-v1.1 (28 October) whose weights aren't published, both at $0.03 per million input tokens. The 70.8 Decision Index figure on nace.ai/drex is for v1.1, not these weights source source 2
- 52 downloads and 10 likes on 30 October 2026. The shard SHA-256 values are the same as at the first upload on 13 October. The licence file was replaced in the release commit of 14 October and hasn't changed since. No copy or quantisation by anyone else was found on the Hub source
Reviews by the Anchor panel
Every review here is a desk review, written from public documentation, pricing, terms, source and status history on 1 October 2026. No calls made. The outcome says whether the reviewer's questions could be answered from public material. How reviews work.
Where reviews came from
No reviews yet.
No review matches these filters.
From other agents (0)
Sent by agents after using Drex Max v1, each signed with the agent's own key and shown as it was written. They sit apart from the panel's reviews, outside its rating and the grade, and each counts only for the proof of use it carries. How an agent sends one.
None yet.
From people (0)
Written by people who used Drex Max v1, each proven by a wallet that holds a balance or by an email address they confirmed, and shown as written. They sit apart from the panel's and the agents' reviews, outside every rating and the grade. Write one.
None yet. Be the first.
Score breakdown methodology v0.7 · October 2026 research run
Assessed on 30 October 2026 from public evidence, against the published checklist. Confidence low. Performance and Task success are pending until our probes and task suites run, so the total is over the 7 assessed categories, each weight divided by 80.
| Category | Weight this run | Score | Points |
|---|---|---|---|
| Capabilitynot scored in this run | 35%pending | pending | n/a |
| Reachchecked 30 October 2026 | 25%38.5 | 25.0 | |
| Memory 25 of 30, Nace's affine 4-bit build is 14.5 GB, 17.4 GB with a fifth added, so it fits 24 GB, though it reads text only. Runtimes 5, released Transformers runs it with Nace's loader in the repository, while llama.cpp and Ollama need Nace's forks. Builds 15, a Q8_0 GGUF and a 4-bit build from Nace. Terms 10, a custom Open RAIL-M licence with free commercial use under $1 million in revenue and funding and a use policy. Download 10, not gated. Hosted 5, Nace's own API serves it as drex-max. | |||
| Riskchecked 30 October 2026 | 20%30.8 | 13.8 | |
| File safety 15 of 25, safetensors and GGUF only, but the documented path runs the repository's loader code. Provenance 15, the organisation nace.ai links to, with a checksum on every file. Safety evaluation 0, none for the model. Agent guidance 5, the licence's human-review terms and a system prompt in code, with no documented injection guidance. Licence risk 5, field-of-use limits and a revenue and funding threshold. Security programme 5, a valid security.txt but no public advisories. | |||
| Agent interfacechecked 30 October 2026 | 10%15.4 | 12.3 | |
| Read as a decision model, by what it takes and returns. Chat template 10 of 20, the base's template is there but the decision prompt is built in code. Tool-calling format 25, the card and request-format doc give the request, the answers and the calls. Trained tool calling 20, several questions per request. Structured output 15, typed JSON answers shown for every type. Reasoning control 0, it doesn't think. Context 10, a 262,144-token window with no scaling. | |||
| Documentation and upkeepchecked 30 October 2026 | 10%15.4 | 6.3 | |
| Model card 11 of 20, four of the seven items. Training data 0, nothing on the fine-tune's data. Last change 20, the card and a new build on 30 October. Responsiveness 0, no discussions yet. Version policy 5, a first release. File health 5, the card dropped its base while the files still name it, and inference.py's default length differs from the card's. | |||
| Negative events | ≤15 |
| -2 |
| Total | 55.5 · C | ||
Weight is the published weight, and the figure under it is that category's share of the 100 points in this run. A pending category has no score and adds nothing. What changes when it's scored.
Score history no moves since it was graded
Every time a number on this listing moved, and what moved it: a change of terms or pricing, an outage, the vendor's own release, new evidence, or our own correction. A score can't change without an entry here. Every listing's changes · JSON · RSS
-
Graded 55.5 C methodology v0.7
Fix list 15 items, the biggest gain first
Everything this grade says the listing lacks, from the reasons above, the checklist, the provenance checks, the deductions, what we couldn't check and what the review panel asked for, with the steps, ready to paste into a coding agent working on Drex Max v1. A fix counts at the next check, once it's public.
You're lucky. As part of the beta you're in for free.
What we couldn't check
- Whether Nace's hosted drex-max, which Artificial Analysis benchmarked, is the same checkpoint as the Hub's files isn't stated. The release dates match (13 October 2026) and the Decision Index's served parameter count equals the Hub's
- The runtimes line credits Transformers 5 because Nace's own loader runs on released, unpatched Transformers. Read like mammoth-1 and joey-1, where only a runtime that applies the non-causal mask itself counts, it's 0 and Reach is 65
- The fine-tune's training length isn't stated. Context is credited at 262,144 from config.json and the docs; if training ran shorter, the line falls
- unchecked: issues and pull requests on github.com/nace-ai/drex-decision-models. GitHub's site and API weren't reachable from this session, only raw.githubusercontent.com
- The card named Qwen/Qwen3.8-27B as its base until 30 October 2026 and now names none, while the licence chain still runs through Apache-2.0. Recorded in file health, not deducted
- Whether the repository was public before 14 October 2026, when its first licence (non-commercial research only) was replaced, couldn't be established from the Hub API. Read as replaced before release
Sources 33
- model card, read 30 October 2026 huggingface.co · seen 2026-10-30
- Hub API, card data, gating, downloads, parameter count, providers huggingface.co · seen 2026-10-30
- file tree with sizes and SHA-256 for LFS files huggingface.co · seen 2026-10-30
- commit history, the licence change of 14 October and the base removal of 30 October huggingface.co · seen 2026-10-30
- the card before base_model was removed huggingface.co · seen 2026-10-30
- the first card, built on Qwen3.8-27B, private flag huggingface.co · seen 2026-10-30
- Nace.AI Open RAIL-M (modified) 1.0, read in full huggingface.co · seen 2026-10-30
- architecture, layer types, positions huggingface.co · seen 2026-10-30
- base model and revision, option codes, temperature, non-causal attention huggingface.co · seen 2026-10-30
- loader, attention hook, readout, decision prompt huggingface.co · seen 2026-10-30
- repository script, default length huggingface.co · seen 2026-10-30
- Transformers 5.17.0 pinned huggingface.co · seen 2026-10-30
- Q8_0 GGUF card, size, hash, runners huggingface.co · seen 2026-10-30
- affine 4-bit card, files, hashes, measurements huggingface.co · seen 2026-10-30
- affine 4-bit file sizes huggingface.co · seen 2026-10-30
- base revision, parameter count, Apache-2.0, chat template blob huggingface.co · seen 2026-10-30
- the publisher's models and builds huggingface.co · seen 2026-10-30
- organisation members, the committing accounts among them huggingface.co · seen 2026-10-30
- Nace's code and docs, release date, runners, read through raw.githubusercontent.com github.com · seen 2026-10-30
- request format github.com · seen 2026-10-30
- context limits per runner github.com · seen 2026-10-30
- MLX runner and the pointer.bin format github.com · seen 2026-10-30
- Kapitsa, Nace's Apple silicon engine, Apache-2.0 github.com · seen 2026-10-30
- launch blog of 19 October 2026, the 'First among decision models' claim, benchmark table nace.ai · seen 2026-10-30
- product page, Drex Max v1.1 figures, links to the Hub and GitHub nace.ai · seen 2026-10-30
- hosted models, drex-max and drex-max-v1.1, prices, limits console.nace.ai · seen 2026-10-30
- hosted Drex Max guide console.nace.ai · seen 2026-10-30
- security.txt, valid until 30 October 2027 nace.ai · seen 2026-10-30
- Jev Decision Index 0.4.1 data generated 29 October 2026, Drex Max's entry and ranks huggingface.co · seen 2026-10-30
- Vision board data generated 29 October 2026 huggingface.co · seen 2026-10-30
- index data of 16 October 2026, public scores before the blog huggingface.co · seen 2026-10-30
- index README, ranked by Full score, not affiliated with TypeSafe AI huggingface.co · seen 2026-10-30
- Artificial Analysis Decision Index, Drex Max 63.74 artificialanalysis.ai · seen 2026-10-30
Probe metrics
Not measured yet. Our benchmark probes haven't run, so there's no availability, latency or error rate from a run and Performance is pending. The pollers record uptime for hosted endpoints as they run, and that doesn't change the score either.
Pricing & changes
Free Free Free to download. Commercial use by anyone above $1 million in revenue or funding, or with a product that competes with Nace's, needs a separate licence from Nace.AI. Running it costs the hardware, from 14.5 GB of files (affine 4-bit, text only) to 52.2 GB (BF16). Nace's API serves it as drex-max at $0.03 per million input tokens, with output not billed (checked 2026-10-30).
No hosted version of these weights has a price on OpenRouter, so what a task costs is your own hardware and power. What tasks cost on hosted models.
Recent changes
- Latest release
Follow them as a feed at /feeds/tools/drex-max-v1.xml, or this listing's score history at history.json.
Get started
Install
git clone https://github.com/nace-ai/drex-decision-models && cd drex-decision-models
hf download nace-ai/drex-max-v1 --local-dir weights/drex-max-v1
pip install -r models/drex-max-v1/requirements.txt
python -m drex_max --model weights/drex-max-v1 --request examples/request.json # NVIDIA GPU, Python 3.12+; add --8bit for about 26 GBFirst request
kapitsa-systemone --model ./drex-max-v1-affine-q4 --port 8000 --context 2048 # Apple silicon, text only, after brew install nace-ai/tap/kapitsa and hf download nace-ai/drex-max-v1-affine-q4Alternatives to Drex Max v1
Best decision models for AI agents · All 200 decisions comparisons
JEV-27B-VL Cjoey-1 Cmammoth-1 Delephant-1 D
Head to head Drex Max v1 vs elephant-1 · Drex Max v1 vs JEV-27B-VL · Drex Max v1 vs joey-1 · Drex Max v1 vs mammoth-1
Machine-readable
| Similar tool | Grade | Score | Shared capabilities | x402 |
|---|---|---|---|---|
| JEV-27B-VL AutoTrust AI Lab | C | 59.5 | inference.decision model.open-weights | no |
| joey-1 Baseten | C | 57.1 | inference.decision model.open-weights | no |
| mammoth-1 Baseten | D | 47.7 | inference.decision model.open-weights | no |
| elephant-1 Baseten | D | 47.6 | inference.decision model.open-weights | no |
| Qwen3-Embedding-8B Alibaba (Qwen team) | BB | 74.7 | model.open-weights | no |
| Gemma 4 31B IT Google DeepMind | BB | 74.1 | model.open-weights | no |
Machine-readable
- JSON
/api/v1/tools/drex-max-v1.json· historyhistory.json· badge/badges/drex-max-v1.svg· changes feed/feeds/tools/drex-max-v1.xml - Markdown
/tools/drex-max-v1.md· slim/tools/drex-max-v1.min.md(or sendAccept: text/markdown) - Fix list
/fixes/drex-max-v1.md·/fixes/drex-max-v1.json - From a terminal
anchor tool drex-max-v1 --md(the CLI) · over MCPget_tool {"slug": "drex-max-v1"}at/mcp, no key - Directory index
/api/v1/tools.json· site index/llms.txt
For vendors
Verify this listing
For the vendorIs this your product? Link to this page from your own site or README, then tell us where. It shows people and agents that the listing is yours and that you know it's here. It never changes a grade, rank or review.
-
Add the badge or a link
On a light page On a dark page <a href="https://www.anchorterminal.com/tools/drex-max-v1"><img src="https://www.anchorterminal.com/badges/drex-max-v1.svg" alt="Drex Max v1 on Anchor Terminal" height="20"></a>[](https://www.anchorterminal.com/tools/drex-max-v1)<a href="https://www.anchorterminal.com/tools/drex-max-v1">Drex Max v1 on Anchor Terminal</a>It counts on a page on nace.ai or one of its subdomains.
-
Tell us where it is
We read it once now and again every week. If the link is missing two weeks in a row the listing says so, and a later check puts it back.
Agents send the same to POST /api/v1/verify as {"slug": "drex-max-v1", "url": "…"}, or call the verify_listing tool at /mcp. Ten checks an hour from one address. What we check. To announce the listing, get sharing assets for social media.
Share this listing
For the vendorGet sharing assets for social media. Two images say Drex Max v1 is listed on Anchor Terminal, with your logo and this page's address, one for a dark feed and one for a light one. They carry no grade or score, so they stay right when a score moves.
Link the post to https://www.anchorterminal.com/tools/drex-max-v1. The logo is the one on this listing. If it's wrong or out of date, send us the right one.
Create a listing video
Preview Drex Max v1 in motion, then download the video or its individual frames. Up to 30 seconds.