matrix-compute-mcp: Official MCP Server & High-Level Verifiable AI Primitives

matrix-compute-mcp is an independent extension package built on top of matrix-compute-sdk that provides:

1. High-Level Verifiable AI Primitives (matrix_compute_mcp.ai): Drop-in PyTorch layers (DistributedLinear, DistributedLoRALinear), massive exact cosine similarity search (distributed_cosine_similarity), and distributed spectral PCA/SVD (distributed_pca_svd) — all verified in $O(n^2)$ via Freivalds' algorithm.
2. Official Model Context Protocol Server (matrix-compute-mcp): Exposes decentralized GPU TFLOPS to autonomous AI agents (Claude Desktop, Cursor, Antigravity, LangChain, OpenClaw) over stdio/JSON-RPC 2.0 with L402 Macaroon or trial API key authentication.

Quick Start

1. MCP Client Configuration (Claude Desktop / Cursor / Antigravity)

{
  "mcpServers": {
    "matrix-compute": {
      "command": "uvx",
      "args": ["matrix-compute-mcp"],
      "env": {
        "MATRIX_NOSTR_RELAYS": "wss://relay.matrix-compute.net",
        "MATRIX_SETTLEMENT_API_URL": "https://settlement.matrix-compute.net",
        "MATRIX_L402_MACAROON": "<your-l402-macaroon-or-leave-unset-for-trial>"
      }
    }
  }
}

2. Exposed MCP Tools

3. High-Level Verifiable AI Primitives (matrix_compute_mcp.ai)

import numpy as np
from matrix_compute_mcp.ai import (
    DistributedLinear,
    DistributedLoRALinear,
    distributed_cosine_similarity,
    distributed_pca_svd,
)

# 1. Exact K-NN Cosine Similarity with OUTPUT_GRID_2D zero download redundancy
scores, indices = distributed_cosine_similarity(queries, corpus_embeddings, top_k=10)

# 2. Drop-in PyTorch Linear & LoRA Linear layers with verified forward & backward passes
layer = DistributedLinear(in_features=4096, out_features=4096)
lora_layer = DistributedLoRALinear(in_features=4096, out_features=4096, r=16, lora_alpha=32.0)

# 3. Distributed Randomized SVD / Stateful Power-Iteration PCA
U, S, Vt = distributed_pca_svd(embeddings, k=32, method="randomized")