x402 LLM proxy + data-enriched analysis (17 sources) + TimesFM predictive IoT intelligence.
Free account. Installing gives you the manifest plus copy-paste snippets.
See reviewsImported from the official MCP registry - entry
io.github.FoundryNet/foundrynet-inferencev1.0.1. Ratings and installs below are from this marketplace.
x402 LLM proxy + data-enriched analysis (17 sources) + TimesFM predictive IoT intelligence.
streamable-http https://foundrynet-inference-production.up.railway.app/mcpSource: https://github.com/FoundryNet/foundrynet-inference
Registry entry: io.github.FoundryNet/foundrynet-inference
Copy one of these into your project. Installing also returns the manifest and these snippets.
# after Install: the listing is in your workspace's routing pool - nothing else to configure
curl -s -X POST https://api.opensmartroute.ai/api/v1/route -H 'Authorization: Bearer $OSR_API_KEY' -H 'Content-Type: application/json' -d '{"text": "...", "plan": true}'
# or pin it on the OpenAI-compatible endpoint: {"model": "mcp-io-github-foundrynet-foundrynet-inference", ...}An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.
{
"ocm": "1",
"id": "mcp-io-github-foundrynet-foundrynet-inference",
"kind": "tool",
"name": "foundrynet-inference",
"description": "x402 LLM proxy + data-enriched analysis (17 sources) + TimesFM predictive IoT intelligence.",
"publisher": "FoundryNet",
"version": "1.0.1",
"capabilities": {
"domains": [
"general"
],
"tags": [
"mcp",
"mcp-server",
"mcp-registry"
],
"languages": [
"en"
],
"supports_tools": true
},
"quality_prior": 0.6,
"examples": [
"x402 LLM proxy + data-enriched analysis (17 sources) + TimesFM predictive IoT intelligence."
],
"primary": false,
"metadata": {
"source": {
"provider": "mcp-registry",
"repository": "https://github.com/FoundryNet/foundrynet-inference",
"path": "server.json",
"ref": "1.0.1",
"url": "https://github.com/FoundryNet/foundrynet-inference",
"key": "io.github.FoundryNet/foundrynet-inference",
"registry": "https://registry.modelcontextprotocol.io"
}
},
"endpoints": [
{
"protocol": "mcp",
"url": "https://foundrynet-inference-production.up.railway.app/mcp"
}
]
}Fetch it by URL: GET /api/v1/registry/mcp-io-github-foundrynet-foundrynet-inference/manifest?version=1.0.1
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