"Scheduler that invokes the agent based on a predefined schedule" matching MCP servers:

Enables scheduling of one-time and recurring reminders, periodic metric collection, and summary generation, with persistent storage in SQLite and job management via

MCP server that diagnoses ML model regressions by correlating drift reports, eval runs, and deploy logs, providing evidence-cited incident reports through a set of investigation tools.

Registers a set of inert, tempting decoy tools with any MCP-capable client so that any call from a hijacked or prompt-injected agent instantly raises an alert with full attack context and a per-call trace token. Also plants worthless canary tokens in honeypot files that trigger zero-false-positive leak alerts when they surface in agent output, files, or outbound requests.

MCP server exposing AI agent observability tools: list, inspect, and deterministically replay recorded agent execution traces for debugging failures.

Exposes rate-limit and usage data for AI coding agent CLIs as MCP tools, enabling querying of usage snapshots and reset times.

Enables generating on-chain attestations of agent service performance across Ethereum, Polygon, Arbitrum, Optimism, and Base, plus audit trail retrieval, attestation verification, and price lock lookup.

Enables AI agents to explore a synthetic product-event dataset and answer questions about signups, trends, funnels, and property breakdowns using a small set of composable, self-describing tools that return precomputed totals and actionable error hints.

MCP-based production monitoring agent for manufacturing OEE, providing real-time monitoring, downtime root-cause analysis, and automated alerts through integration with SQL Server, MES APIs, and incident management systems.

Enables AI agents to self-report every tool call, token usage, decision, and session grade into a local SQLite store, so users can trace execution, audit reasoning, and see per-session costs in a local dashboard. Also supports proxy mode to intercept and trace MCP tool invocations for agents that cannot self-report.

An MCP server that provides append-only, tamper-evident local receipts for AI agent actions, capturing command executions, outputs, and handoff evidence.

Local-first observability for AI agent stacks. Provides MCP tools for agents to check health, shadow configs, and burnout metrics proactively.

An MCP-powered AI platform for satellite mission control, enabling space weather analysis, orbital collision detection, satellite health monitoring, and secure command validation through natural language.

Local-first MCP server for credit risk analysis. It enables explainable rule discovery, statistical validation, PSI drift monitoring, and root-cause diagnosis on user-approved Parquet data while preserving privacy.

Enables AI apps to inspect and manage a NextDNS account in plain language — profiles, analytics, query logs, blocklists, security and parental control settings — with every change saved and reversible via undo. It runs self-hosted on your own computer or server, keeps per-profile read/write/hidden permissions, and requires double confirmation for deletions or overwrites.

An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.

Provides cost analysis of AI agent usage by previewing scrubbed metadata before upload and analyzing spend, cache-hit rates, and waste recovery through a local or remote server.

MCP server for AI agent observability, providing trace and span logging, search, latency/tokens/cost metrics, and anomaly detection using an in-memory buffer.

Enables AI agents to manage prompts and sessions via the ai-agent-logs API, with tools for health checks, login, listing/creating prompts, and retrieving sessions.

Tracks AI agent token usage and spending in real time, with budget alerts, per-task cost breakdown, and a visual dashboard.

Enables MCP clients to query and diagnose machine-learning training runs through read-only tools for listing experiments, reading configurations and final metrics, comparing runs, sampling metric curves, and detecting training failures.