Quantum Suitability Validator
AI triage for quantum computing POC proposals. Screens before budget is allocated.
Open source Repository Open in the app JSON README (API)
About
AI triage for quantum computing POC proposals. Screens before budget is allocated.
Details
- Kind
- MCP servers
- Topic
- No topic detected
- Publisher
- ojaskord
- Origin
- official
- Category
- ferramentas
- Transport
- http
- Version
- 1.0.20
- Last push
- 2026-08-19T03:49:18Z
- Repository state
- ativo
- Language
- TypeScript
- License
- MIT
- Added
- 2026-08-29 03:02:08
- Updated
- 2026-08-29 03:02:08
- Origin id
io.github.OjasKord/quantum-suitability-validator-mcp-server
README
[](https://smithery.ai/servers/OjasKord/quantum-suitability-validator-mcp-server)
# Quantum Suitability Validator MCP
[](https://toolrank.dev/ranking)
MCP server that screens quantum computing POC proposals against expert decision rules -- before your agent escalates any initiative to a committee, allocates budget, or routes to a specialist.
## What it does
Enterprise innovation agents and R&D workflow agents process backlogs of proposed technology initiatives tagged as potential quantum computing candidates. Before escalating any candidate to a human committee, allocating POC budget, or routing to a quantum specialist, the agent calls `quantum_assess_problem` to produce an auditable triage verdict.
This server is **refusal-first by design**. It downgrades or refuses more often than it approves. Every verdict is auditable and machine-readable.
## Tools
### `quantum_assess_problem` (Free: 5/month, no key required)
Screens a quantum computing proposal using an expert-validated four-dimensional scoring framework. Returns:
- `verdict`: SCIENTIFICALLY_RECOMMENDED_NOW | COMMERCIALLY_RECOMMENDED_NOW | INVESTIGATE_FURTHER | PREMATURE | NOT_QUANTUM_AMENABLE
- `four_scores`: scientific_fit (40% weight), hardware_feasibility (25%), advantage_potential (25%), commercial_relevance (10%), composite -- four independent 0.0-1.0 scores so a scientifically valid investigation is never confused with proven commercial advantage
- `advantage_claim_level`: NONE | HYPOTHESISED | EXPERIMENTAL_SIGNAL | BENCHMARK_SUPPORTED | PRODUCTION_VALIDATED
- `suitability_score`: 0.0-1.0 (equal to four_scores.composite)
- `confidence_score`: 0.0-1.0
- `problem_class`: combinatorial_optimisation | portfolio_optimisation | molecular_simulation | ml_kernel | cryptography_pqc | sampling_monte_carlo | other
- `dominant_blockers`: specific reasons why the problem fails screening
- `hype_flags`: detected hype language patterns
- `baseline_question`: always "What is your classical baseline today, and what metric must improve for this to matter?"
- `next_best_action`: specific actionable recommendation
- `agent_action`: ESCALATE_TO_POC | ROUTE_TO_SIMULATOR | DEFINE_BASELINE_FIRST | REJECT | REQUEST_MORE_INFORMATION
### `quantum_readiness_report` (Pro only)
Full auditable Quantum Readiness Report, weighted by audience profile (RESEARCH, ENTERPRISE, or INVESTOR -- the same problem legitimately scores differently by profile). Everything from `quantum_assess_problem` plus:
- `recommended_workflow`: CLASSICAL_ONLY | HYBRID | SIMULATOR_ONLY | ANNEALING_PATH | GATE_MODEL_VARIATIONAL | INSUFFICIENT_INFORMATION
- `formulation_guidance`: QUBO/Ising/variational suitability, estimated binary variables, penalty dominance risk
- `hardware_recommendations`: hardware family fit scores with access routes (D-Wave Leap, IBM Cloud, IonQ Cloud)
- `error_budget_assessment`: viability against current noise floors
- `classical_baseline_assessment`: baseline strength and minimum benchmark requirement
- `validation_plan`: ordered steps for technical review board submission
- `refusal_reason`: populated when the report declines to recommend a path forward
- `commercial_reality_statement`: populated for ENTERPRISE and INVESTOR profiles -- states plainly that production advantage over classical has not yet been broadly demonstrated
## Connect
### HTTP (Railway -- no install)
```json
{"type": "http", "url": "https://quantum-suitability-validator-mcp-production.up.railway.app"}
```
### stdio (npm -- requires ANTHROPIC_API_KEY)
```bash
npx quantum-suitability-validator-mcp
```
## Harness Integration
Note: this server exposes tools at `/mcp` not the root URL.
### Claude Code / Claude Desktop (.mcp.json)
```json
{
"mcpServers": {
"quantum-suitability-validator": {
"type": "http",
"url": "https://quantum-suitability-validator-mcp-production.up.railway.app/mcp"
}
}
}
```
### LangChain (Python)
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient({
"quantum-suitability-validator": {
"url": "https://quantum-suitability-validator-mcp-production.up.railway.app/mcp",
"transport": "http"
}
})
tools = await client.get_tools()
```
### OpenAI Agents SDK (Python)
```python
from agents import Agent, HostedMCPTool
agent = Agent(
name="Assistant",
tools=[HostedMCPTool(tool_config={
"type": "mcp",
"server_label": "quantum-suitability-validator",
"server_url": "https://quantum-suitability-validator-mcp-production.up.railway.app/mcp",
"require_approval": "never"
})]
)
```
### LangGraph
Same as LangChain above — langchain-mcp-adapters works with LangGraph natively.
## Pricing
- **Free**: 5 `quantum_assess_problem` calls/month per IP -- no API key required
- **Pro**: $199/month -- unlimited `quantum_assess_problem` + full `quantum_readiness_report`
- **Enterprise**: $499/month -- volume + SLA
Upgrade: [kordagencies.com](https://kordagencies.com)
## Legal
AI-assisted triage -- NOT a substitute for experimental physicist review. Results are for informational and planning purposes only and do not constitute expert quantum computing advice. Full terms: kordagencies.com/terms.html
Kord Agencies Pte Ltd, Singapore