AnnealBridge MCP server

AnnealBridge

<!-- mcp-name: io.github.TheTsungYing/annealbridge -->

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Combinatorial optimization middleware for AI agents. An agent describes what to optimize as structured JSON; AnnealBridge decides how to encode and solve it, checks every answer against the original problem, and returns ranked, verified solutions over MCP, a CLI, or plain Python.

flowchart LR
    U[Natural language] --> A[AI agent]
    A -->|OptimizationProblem JSON<br/>variables · objective · constraints| B
    subgraph B[AnnealBridge]
        direction LR
        V[validate] --> C[compile<br/>BQM / CQM] --> S[solve<br/>local or remote] --> R[re-validate against<br/>the original problem] --> K[rank]
    end
    B -->|SolveResult<br/>ranked, verified solutions| A
    A --> N[Natural-language answer]

Quick start

pip install annealbridge

A 0/1 knapsack: four items, capacity 10, maximize value. No file needed.

from annealbridge.models import OptimizationProblem
from annealbridge.orchestration import OptimizationService

problem = OptimizationProblem.model_validate({
    "version": "1.0",
    "name": "knapsack",
    "variables": [{"name": n, "type": "binary"} for n in ["a", "b", "c", "d"]],
    "objective": {"direction": "maximize", "linear_terms": [
        {"variable": "a", "coefficient": 10}, {"variable": "b", "coefficient": 8},
        {"variable": "c", "coefficient": 7}, {"variable": "d", "coefficient": 6}]},
    "constraints": [{"id": "capacity", "type": "hard", "operator": "<=", "rhs": 10, "terms": [
        {"variable": "a", "coefficient": 6}, {"variable": "b", "coefficient": 5},
        {"variable": "c", "coefficient": 4}, {"variable": "d", "coefficient": 3}]}],
})

result = OptimizationService().solve(problem)
print(result.status)                        # success
print(result.solutions[0].variables)        # {'a': 1, 'b': 0, 'c': 1, 'd': 0}
print(result.solutions[0].objective_value)  # 17.0

Domain failures come back as results, never as exceptions: result.status is one of success, infeasible, invalidproblem, resourcelimitexceeded, backendunavailable, configurationerror or solvererror. See docs/output-format.md.

Optional extras:

pip install "annealbridge[mcp]"       # + MCP server (annealbridge-mcp)
pip install "annealbridge[dwave]"     # + D-Wave cloud backends
pip install "annealbridge[all]"       # everything
pip install "annealbridge[gpu]"       # + PyTorch, for simulated_bifurcation on CUDA

[gpu] is deliberately not part of [all]: PyTorch is a large download, and on Windows the wheel PyPI serves is the CPU-only build, so a CUDA run needs torch installed from PyTorch's own index first — see docs/backends.md.

Use it from an AI agent (MCP)

With uv installed, add the server to claudedesktopconfig.json (or your host's equivalent) and restart the host. The first run fetches the package into uvx's own cached environment; that download — numpy, dimod, dwave-samplers and the rest — can take tens of seconds, long enough for a host's start-up timeout to show the server as disconnected. Warm the cache once in a terminal first:

Shortened. The full README is on GitHub.