AI Model Catalog MCP Server — NVIDIA NIM + Multi-Provider

An MCP (Model Context Protocol) server that gives any MCP client (Claude Desktop / Claude Code, Cursor, IDEs) a live, audited catalog of AI models:

• NVIDIA NIM — canonical source https://build.nvidia.com/models; fetched without an API key, deprecated models excluded, OpenAPI/templates resolved, cURL + Node.js + Python examples generated, tiered verification evidence stored. The hosted NIM catalog is 100% free to call with an NVIDIA API key.

• OpenRouter — https://openrouter.ai/api/v1/models (official API)

• Nous Portal — https://inference-api.nousresearch.com/v1/models (official API)

• opencode Zen — https://models.dev/api.json (opencode's official catalog; see https://opencode.ai/docs/zen)

• Blackbox AI — official docs pricing table (docs.blackbox.ai); authenticated https://api.blackbox.ai/v1/models when BLACKBOX_API_KEY is set

• Vercel AI Gateway — https://ai-gateway.vercel.sh/v1/models (official API)

FREE_ONLY mode

.env (tracked, no secrets) ships with:

FREE_ONLY=yes

With FREE_ONLY=yes, every provider listing — MCP tools, MCP resources, and HTTP routes — only surfaces free models. Paid models cannot be listed or looked up directly (server-side enforcement, not client-side filtering). NVIDIA NIM is exempt because its hosted catalog is 100% free to call. Free detection per provider uses each official source: :free/-free id suffixes and zero prompt+completion pricing (OpenRouter/Nous/Vercel), zero/absent cost (opencode Zen), and "Free" pricing rows (Blackbox docs).

API keys never go into .env — put them in .env.local (gitignored) or real environment variables. See .env.example.

Related MCP server: Models PLUS

Quick start (install.sh)

./install.sh --install    # venv + deps + tests + build catalog + start services
./install.sh --status     # service state, ports, catalog counts
./install.sh --restart    # restart both services
./install.sh --update     # refresh catalog data, then --restart to serve it
./install.sh --logs       # tail service logs
./install.sh --systemd    # systemd units (auto-start on boot, auto-restart)
./install.sh --stop / --uninstall

Ports live in .env (tracked): MCP_PORT=9100 (MCP + dashboard), PORT=8787 (JSON API), HOST=127.0.0.1. Real env vars always win; put secrets only in .env.local.

MCP server (primary interface)

make install          # deps
make catalog          # build the NIM + provider catalog (key-less)
make mcp              # stdio (Claude Desktop / Claude Code)
make mcp-http         # Streamable HTTP + dashboard on 127.0.0.1:9100
#   http://127.0.0.1:9100/mcp        MCP protocol endpoint (Streamable HTTP)
#   http://127.0.0.1:9100/dashboard  live HTML dashboard (also served at /)
#   http://127.0.0.1:9100/health     liveness probe
#   http://127.0.0.1:9100/ready      readiness probe

Claude Desktop / Code config (.mcp.json is included in the repo):

{
  "mcpServers": {
    "nim-catalog": {
      "command": "python",
      "args": ["mcp_server.py", "--database", "data/active_nvidia_nim.sqlite3"]
    }
  }
}

Tools: search_models, get_model, get_usage_example, get_endpoints, recommend_models, get_stats, list_tiers, list_publishers (NVIDIA NIM) + list_providers, search_provider_models, get_provider_model (multi-provider).

Production deployment

Version 2.0.0 — enterprise-grade serving, stdlib-only hardening:

docker compose up -d                 # API :8787 + MCP HTTP :9100 (hardened containers)
docker compose run --rm refresher    # scheduled catalog rebuild

Operations guide: docs/OPERATIONS.md · Security policy: SECURITY.md · Release notes: CHANGELOG.md

Dashboard

make dashboard generates exports/dashboard.html — a self-contained (no-CDN) HTML dashboard with KPIs, NIM verification tiers/modalities/ publishers/transports, per-provider totals vs free with fetch health, a searchable free-model explorer, a searchable NIM model explorer, and a data-quality panel mirroring validate_project.py. The HTTP service serves it live at GET /dashboard (also /):

make service   # then open http://127.0.0.1:8787/dashboard

Resources: nim://stats, nim://models, nim://tiers, nim://models/{publisher}/{slug}[/{language}], providers://catalog, providers://{provider}/models.

Current audited result

Counts drift as providers add/remove models; the numbers below are from the last audited run (2026-07-28). Re-run make catalog for fresh numbers — the /stats endpoint and exports/tiered-audit.json always reflect your local DB.

• NVIDIA NIM: Build index 139 → active non-deprecated 124 (57 free-hosted · 96 downloadable · 36 partner)

• OpenRouter: 341 models (18 free) · Nous Portal: 289 (5 free) · opencode Zen: 85 (24 free) · Blackbox AI: 47 (2 free) · Vercel AI Gateway: 307 (3 free)

• Live inference verification requires NVIDIA_API_KEY (make verify); gateway reconciliation requires the same key (python ingest_gateway.py).

data/active_nvidia_nim.sqlite3 contains all active official models, the multi-provider catalog (provider_models), and audit evidence. exports/verified-working.sqlite3 contains only latest live-verified hosted models.

Gateway /v1/models reconciliation

export NVIDIA_API_KEY='nvapi-...'
python ingest_gateway.py --database data/active_nvidia_nim.sqlite3 --output exports
python export_unified.py --database data/active_nvidia_nim.sqlite3 --output unified-output

The gateway response currently exposes only four fields: id, object, created, and owned_by. The database preserves all of them in gateway_models, maps aliases to current Build entries, keeps gateway-only IDs, and enriches mapped records with Build OpenAPI/templates/docs. Query the all_model_inventory view for the full union.

Build without API key

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python audit_catalog.py --database data/active_nvidia_nim.sqlite3 build
python providers.py --database data/active_nvidia_nim.sqlite3 --output exports
python audit_catalog.py --database data/active_nvidia_nim.sqlite3 export --output exports
python finalize_audit.py --database data/active_nvidia_nim.sqlite3 --output exports

Optional live verification

export NVIDIA_API_KEY='nvapi-...'
python audit_catalog.py --database data/active_nvidia_nim.sqlite3 verify --rpm 20 --timeout 120

The live-test loop runs under the RPM cap, so a full pass can take a while. To resume an interrupted run (skip models already tested, no re-spent credit):

python audit_catalog.py --database data/active_nvidia_nim.sqlite3 verify --resume --rpm 20 --timeout 120

One Build catalog ID:

python audit_catalog.py --database data/active_nvidia_nim.sqlite3 verify \
  --model z-ai/glm-5.2 --rpm 20 --timeout 120

Automated update

chmod +x bin/update-catalog.sh
NIM_VERIFY=0 bin/update-catalog.sh

Set NIM_VERIFY=1 only when live tests and credit use are intended.

The same pipeline is available as Make targets (make help for the full list):

make install      # pip install -r requirements.txt -r requirements-dev.txt -r requirements-mcp.txt
make test         # pytest unit suite (no network/API key needed)
make lint         # ruff lint gate (same as CI)
make catalog      # full key-less pipeline: build -> providers -> upstreams -> finalize -> export -> unified -> validate
make providers    # refresh only the multi-provider catalogs
make dashboard    # regenerate exports/dashboard.html
make service      # read-only catalog HTTP API on 127.0.0.1:8787
make mcp          # MCP server over stdio (Claude Desktop / Claude Code)
make mcp-http     # MCP server + dashboard over Streamable HTTP on 127.0.0.1:9100
make image        # build the catalog_service Docker image
make ci-local     # reproduce the CI key-less pipeline locally

Testing

tests/ is a fast, deterministic pytest suite (no network, no API key) that pins the data-correctness invariants: model-ID validation/de-dup, page classification, served-id selection, smoke-payload construction, upstream joining, and deprecation date semantics. Run with make test.

Continuous integration

.github/workflows/ci.yml runs the unit tests, then the key-less pipeline (build → upstreams → finalize → export → unified → validate) on every push/PR and daily, so the build.nvidia.com contract and the pipeline runtime stay healthy. No API key is required for CI; live verification stays opt-in via NIM_VERIFY=1 locally or bin/build-strict.sh with an NVIDIA_API_KEY.

Deployment

catalog_service.py ships as a read-only HTTP API. The included Dockerfile serves a pre-built catalog over the API (non-root, healthchecked):

make catalog                                   # build data/active_nvidia_nim.sqlite3 first
docker build -t nvidia-nim-catalog .
docker run -p 8787:8787 -v "$PWD/data:/app/data:ro" nvidia-nim-catalog

MCP server

mcp_server.py exposes the catalog as a Model Context Protocol server, so MCP clients (Claude Desktop, Claude Code, Cursor, IDEs) can discover models, read served IDs/endpoints, and get runnable cURL/Node.js examples on demand — no copy-pasting from build.nvidia.com. It is a read-only adapter over the same shared query layer (catalog_queries.py) as the HTTP API, so both transports behave identically. Build the catalog first (make catalog), then run the server.

Install the MCP dependency (extra):

pip install -r requirements-mcp.txt   # adds the `mcp` SDK

Run (the server reads data/active_nvidia_nim.sqlite3 read-only):

make mcp           # stdio transport (default; for Claude Desktop / Claude Code)
make mcp-http      # Streamable HTTP transport on http://127.0.0.1:8788
# or explicitly:
python mcp_server.py --database data/active_nvidia_nim.sqlite3                      # stdio
python mcp_server.py --database data/active_nvidia_nim.sqlite3 --transport http --port 8788

Connect from Claude Desktop (stdio)

Add an entry to claude_desktop_config.json (Settings → Developer → Edit Config). Use the absolute path to the repo and (if needed) the absolute path to your Python interpreter:

{
  "mcpServers": {
    "nim-catalog": {
      "command": "python",
      "args": ["/absolute/path/to/nvidia-nim_model_fetcher/mcp_server.py",
               "--database", "/absolute/path/to/nvidia-nim_model_fetcher/data/active_nvidia_nim.sqlite3"]
    }
  }
}

Connect from Claude Code (.mcp.json in the project root)

{
  "mcpServers": {
    "nim-catalog": {
      "command": "python",
      "args": ["mcp_server.py", "--database", "data/active_nvidia_nim.sqlite3"]
    }
  }
}

Inspect interactively

mcp dev mcp_server.py        # opens the MCP Inspector UI

What the server exposes

Tools (the assistant calls these):

Resources (URI-addressable): nim://stats, nim://models, nim://tiers, nim://models/{publisher}/{slug} (full model), and nim://models/{publisher}/{slug}/{language} (a code example). The catalog id is publisher/slug; because it contains a slash, the template splits it into two segments — e.g. nim://models/meta/llama-3.1-8b-instruct/curl.

Prompt: use_nim_model(catalog_id, task) returns a ready-to-run integration plan.

Notes

• The server is read-only; it never writes and never runs the build pipeline. Refresh the catalog by rebuilding the DB on a schedule (make catalog / CI). get_stats reports the last build time so freshness is transparent.

• recommend_models defaults to production_only=true, returning only models that actually responded over HTTP — set it false to also see official downloadable/specialized models, clearly tagged.

• Partial-build safety: if the DB was produced by audit_catalog build alone (the tiered audit from finalize_audit.py not yet run), get_stats/list_tiers report audit_populated: false with a clear warning, and recommend_models falls back to free-endpoint models instead of silently returning an empty list. Run make catalog (which includes finalize) for full tiered data.

• A missing database returns a clear startup error rather than failing silently.

For a complete strict rebuild, including the gateway and gRPC stages:

chmod +x bin/build-strict.sh
NIM_RPM=20 bin/build-strict.sh

The strict pipeline now runs Build discovery, authenticated gateway ingestion and gateway-only tests, Build free-endpoint inference tests, gRPC/NVCF health audit, upstream audit, final audit, unified export, and strict export. Gateway-only records remain visible in the unified inventory but enter strict output only after a real inference pass.

Verification policy

• live_inference_verified: received HTTP 2xx and eligible for working DB.

• official_contract_downloadable: active official NIM, but requires local NVIDIA GPU/container deployment.

• official_contract_specialized_transport: official Build/API contract verified; live testing needs gRPC, media, private access, or special infrastructure.

• live_test_failed: timeout/404/5xx; retained in audit but excluded from working.

Every model has cURL and Node.js records. For gRPC/private/custom transports, records explicitly state the official alternative instead of fabricating an HTTP request.

Upstream endpoint audit

python audit_upstreams.py --database data/active_nvidia_nim.sqlite3 --output exports

The audit cross-checks stored endpoints against each model's Build OpenAPI servers, resolved code templates, Build guides, and official NVIDIA docs. Supported upstream families include:

• integrate.api.nvidia.com — hosted LLM/chat/embedding and selected APIs;

• ai.api.nvidia.com — legacy/specialized VLM, retrieval, Cosmos, and AV APIs;

• health.api.nvidia.com — BioNeMo/biology APIs;

• optimize.api.nvidia.com — optimization APIs such as cuOpt;

• climate.api.nvidia.com — climate/weather APIs;

• grpc.nvcf.nvidia.com:443 — hosted NVCF gRPC requiring API key and function ID;

• localhost — self-hosted NIM management/inference endpoints.

exports/upstream-audit.* records the result. A Build URL with documentation-only means NVIDIA publishes no fixed public upstream (for example private/early-access or internally brokered playgrounds).