MCP + UiPath with RF Quantum SCYTHE so ops, bots, and agents all talk to the same “GPU broker / denoise oracle / geolocation brain” safely.

What MCP buys you (and what to watch)

Decouple tools from frameworks. MCP exposes SCYTHE as a set of tools on an MCP server; any MCP client (LLM agent, desktop host, or even a thin shim inside UiPath) can discover and invoke them via reflection, over stdio or SSE/HTTPS. That’s the whole point of MCP’s client–server model.
Runtime tool discovery (“reflection”). Clients enumerate capabilities (name, schema, params) at run-time—no hard binding to LangChain/AutoGen/etc.
Health of the ecosystem is decent but young. Median 5.5 commits/week and ~42% CI adoption; still early, but trending positively.
Security realities: ~7.2% of servers show general vulns; credential exposure (3.6%) is the top class; MCP-specific “tool poisoning” ~5.5% has been observed. Design with audit, auth, and least privilege from day one.

Target Architecture (thin, shippable)

A. SCYTHE Core (already have)

FastAPI service exposing:
- /gpu/hints, /denoise/hints, /gpu/stats
- search & index endpoints (MultiSubspaceFaissIndex)
- voice guard (XLS-R) checks
- geolocation (AoA/TDoA/soft-triangulator) scoring

B. MCP Server for SCYTHE (new)

Wrap the same FastAPI ops as MCP tools:
- gpu.suggest_batch(qos, task) -> {batch} (reads rolling p95)
- denoise.suggest_strengths(qos, bands[]) -> {strengths:[…]} (your new per-band vector)
- signals.search(query_embedding, goal_task, topk) -> [{id,score}]
- geoloc.tdoa_residual(pairs, xy) -> {loss, residuals}
- voice.guard(score_only:bool) -> {prob, verdict}
Transport: SSE/HTTPS for remote; stdio for on-box agents.

C. UiPath “RPA glue” (you already started)

Option 1 (simplest now): Robots call FastAPI directly with HTTP Request activities; use your /gpu/hints + /denoise/hints for closed-loop batch+clean decisions.
Option 2 (MCP-native later): A tiny UiPath → MCP client shim activity that:
1. Performs MCP capability discovery on startup; 2) calls gpu.suggest_batch and denoise.suggest_strengths; 3) posts metrics back (latency/QPS) for the rolling windows.

Minimal MCP tool definitions (server side)

{
  "tools": [
    {
      "name": "gpu.suggest_batch",
      "description": "Suggest SLA-seeking batch size for a QoS tier",
      "input_schema": {
        "type": "object",
        "properties": {
          "qos": {"type":"string","enum":["high","default","low"]},
          "task": {"type":"string"},
          "observed_latency_ms": {"type":"number"},
          "observed_qps": {"type":"number"}
        },
        "required": ["qos","task"]
      }
    },
    {
      "name": "denoise.suggest_strengths",
      "description": "Return per-band denoise strengths for the policy agent",
      "input_schema": {
        "type": "object",
        "properties": {
          "qos": {"type":"string","enum":["high","default","low"]},
          "bands": {"type":"array","items":{"type":"integer"}},
          "hints": { "type":"object",
            "properties":{
              "avg_entropy": {"type":"number"},
              "tdoa_residual": {"type":"number"}
            }
          }
        },
        "required": ["qos","bands"]
      }
    }
  ]
}

Your MCP server just proxies to the FastAPI logic you already wrote, then returns structured JSON; thanks to reflection, the client (agent or UiPath shim) can discover these schemas at runtime.

Secure-by-default checklist (do this before exposing)

Kill credential exposure: no secrets in code or YAML; use env-only + sealed vault; add secret scanners in CI. Community servers leak keys surprisingly often.
AuthN/AuthZ: mTLS or OIDC on SSE endpoints; per-tool RBAC (e.g., robots can call gpu.*/denoise.*, not geoloc.*).
Human-in-the-loop policy: require explicit approval for destructive or expensive tools; disable auto-approval. (Auto-approve was flagged in real servers studied.)
Tool-poisoning guard: allow-list tool names + parameter schemas server-side; lint prompts; log & rate-limit. Tool poisoning showed up in 5.5% of scanned servers.
Transport: enforce TLS, no insecure cipher suites; hard fail on cert errors. Transport weaknesses were observed in “pure MCP” repos.
Auditing: ship SBOM + CI scans; the ecosystem’s CI adoption is ~42% and build success high—match or beat it.

UiPath wiring pattern

Robot loop (HTTP or MCP):

GET /gpu/hints?qos=default&task=bank_x → batch
POST /denoise/hints {qos, bands:[0..N-1], hints:{entropy,tdoa_residual}} → strengths[band]
Process batch with those strengths; push /gpu/stats with observed p95 + QPS.
Export denoise_strength{tenant,band} via your Prometheus exporter for Grafana.

This makes batch and per-band cleaning self-tuning in lockstep.

Migration guide (today → MCP-ready)

Stabilize JSON contracts of the FastAPI endpoints (you’ve done most of it).
Wrap them as an MCP server (stdio + SSE). Expose the two “hints” tools first.
Drop in UiPath HTTP bots now; pilot an MCP client shim activity later.
Wire CI with SonarQube + secret scanners; run mcp-scan in CI to catch MCP-specific issues. (Ecosystem tools are young, but still catch real problems.)
Ship Grafana row (batch curves + per-band strengths) and Prometheus recording rules + Alertmanager routing you already drafted.

Where MCP and SCYTHE especially shine for you

Scheduling intelligence as a shared tool: your /gpu/hints + /denoise/hints become a standard capability any agent or RPA flow can call—zero forked SDKs.
Multi-tenant fairness: the same hints serve UiPath queues, Blue Prism processes, and LLM agents—consistent QoS knobs.
Compliance posture: a single hardened MCP surface (authz, audit, rate-limit) is easier to defend than many bespoke ad-hoc endpoints.

MCP + UiPath with RF Quantum SCYTHE