"Information on Working as an Actuary" matching MCP servers:

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

Enables any MCP client to watch a running DSH agent and steer it mid-flight, streaming thinking, tool and file notifications while sharing intent and exposing a floor lock that denies the agent's writes while held. It also lets clients interrupt the current turn with an injected alert and delegate reviews through an on-disk inbox.

Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.

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.

MCP server for On-Call Health burnout analysis. Connects AI assistants to your on-call data for workload insights.

Enables Claude to discover and understand infrastructure-as-code drift by listing projects with drift, analyzing Terraform plan outputs, and providing remediation information.

Semantic log clustering for incident triage, exposed as an MCP server.

Enables AI clients to access real-time system information such as memory and swap usage from a Linux machine via MCP tools.

Predictive context-rot scoring, Ed25519-signed agent-write journal, and working/episodic/semantic tiering for LLM agents on Markdown vaults.

A minimal guardrails framework for AI agents, acting as an MCP proxy to enforce policies, approvals, and audit logging on tool calls.

Real-time conversation health monitor for AI agents that tracks structural dynamics like semantic drift and information gain using deterministic arithmetic, without calling an LLM.

Provides read-only access to Tactical RMM, enabling agent searches, detailed agent information, audit log queries, and execution of information-gathering PowerShell scripts on agents, with Entra ID authentication and a strict read-only guard.

Enables MCP-compatible clients to inspect the local machine's CPU, memory, and disk information through tools, a structured resource, and an upgrade-assessment prompt.

A governed MCP server enforcing RBAC outside the model, with OpenTelemetry traces on every run and an LLM-as-judge eval suite proving the agent stays inside policy.

An MCP server that connects to Sentry.io or self-hosted Sentry instances to retrieve and analyze error reports, stack traces, and debugging information.

Enables natural-language PostgreSQL operations and monitoring across PG 12-18 without requiring extensions, covering performance analysis, slow-query identification, bloat detection, lock and connection monitoring, autovacuum inspection, replication/WAL status, and schema relationship discovery. All operations are read-only and production-safe, working with regular user permissions on RDS/Aurora as well as self-hosted instances.

An MCP server for querying Grafana Loki directly with a discovery-first workflow — labels, values, series, and LogQL queries without requiring Grafana.

An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.

Enables classification of GPU workloads as training, inference, or idle from telemetry data, with tools for one-shot classification, benchmarking, and audit log verification.

MCP server exposing rlg (RustLogs) log files as tools for on-call / SRE agent workflows. Three tools over JSON-RPC stdio — tail_log (last N events, filtered by level/target), filter_log (predicate filtering), summarize_errors (grouped error taxonomy). Pure Rust. MIT OR Apache-2.0.

Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.

Tells an agent what went wrong with its own Ollama calls, in sentences with the fix: lost prompt cache and where the prompt stopped matching, context overflow, a model running partly on CPU, tool schemas crowding the prompt. Also the last turn in numbers and which local models fit in video memory. Local, read-only, nothing leaves the machine.

A read-only MCP server that exposes local coding-agent session logs as three tools for introspection of recent work, debugging tool failures, and tracking token usage and estimated cost without parsing log files.

Compares AI coding tool spend before and after a migration -- Cursor, Claude Code, GitHub Copilot, OpenCode, Codex CLI -- via an MCP server, real numbers from each vendor's own API or local logs.

Enables log analysis for Tenable Patch Management (SaaS and on-prem) by parsing, de-duplicating, and decoding server/client logs into structured verdicts, ranked issues, and known-issue fixes.

An MCP server that connects language models to a Wazuh SIEM cluster, enabling read-only plain-language queries, schema inspection, field coverage analysis, and decoder testing.

An MCP server that automatically instruments Python AI agents with the ioa-observe-sdk, adding OpenTelemetry-based tracing, metrics, and logs with zero manual effort.

Enables an LLM agent to investigate a Kong Gateway through fourteen read-only, schema-validated tools and a deterministic diagnostic engine, so it can trace routes, services, plugins and upstream health and explain faults such as why authentication is failing on a path. Every fact comes from the gateway itself, and the model only selects tools and writes the evidence-backed explanation.

An MCP server that exposes a dbt project's run state as tools, enabling AI assistants to answer questions like 'is the warehouse healthy?' or 'what broke and why?' via plain English, using read-only access to dbt artifacts and warehouse data.

Model Context Protocol server for Verlon AI that exposes gates, logs, recommendations, and experiments as MCP tools, enabling coding agents to inspect and manage AI infrastructure natively.