"author:Alpha-Park" matching MCP servers:

Enables enterprise agent fleets to register workers, dispatch tasks by capability, monitor continuous heartbeat telemetry, trip automated circuit breakers, and recover from checkpointed state through a zero-dependency MCP server.

Enables statistical forecasting and anomaly detection over numerical time-series data using Holt linear smoothing, multi-step projections, and variance-based confidence bands.

Enables inference engines to match longest common prompt token prefixes via a radix trie, so cached KV-cache blocks are reused instead of re-prefilled. This eliminates redundant prefill computation and lowers time-to-first-token, alongside paged attention allocation, speculative decoding verification, INT8 quantization, and latency telemetry.

Enables autonomous agent runtime diagnostics and self-healing by detecting configuration drift, corrupted execution state, and missing runtime resources, then classifying root causes and executing snapshot rollback.

Enables real-time sliding-window token velocity anomaly detection and adaptive rate-limiting through a native STDIO MCP server built entirely on the Python standard library. Exposes deterministic algorithmic checks that report anomalies and throttle behavior as JSON tool results for MCP clients such as Claude Desktop, Cursor, and Antigravity.

Enables agents to guard external API calls with circuit-breaker health checks, success/failure reporting, and recovery state inspection, including exponential backoff with jitter and fail-fast rejection.

Enables monitoring of distributed autonomous agent nodes through heartbeat ingestion, jitter-aware liveness checks, zombie/stalled worker isolation, and coordinated checkpoint hydration and failover rerouting.

Enables monitoring of running agent loops with token and monetary cost estimation, pruning of verbose thinking chains near limits, and routing sub-tasks between fast/cheap and deep reasoning models. It provides deterministic, MCP-compatible budget optimization for autonomous agents and copilot frameworks.

Enables AI agents to locate and track smart cordless tools, program torque and speed profiles, fetch telematics, and remotely lock or unlock tools based on jobsite geofences.

Enables end-to-end profiling of conversational voice agent latency, tracking VAD, STT, LLM-TTFT, TTS-TTFB, and playout bottlenecks to identify pipeline delays.

Enables monitoring and governing distributed multi-agent fleets through MCP by evaluating token burn velocity, recursive spawning depth, and cyclic deadlocks, then triggering circuit-breaker and kill-switch containment policies with auditable tokens.

Monitors robotic joint actuator thermal and torque loads, triggering preventative throttling for DJI/Robomaster hardware and enabling real-time actuator safety supervision through MCP.