"MCP servers for reducing hallucinations while coding" matching MCP servers:

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

Enables AI agents to interact seamlessly with Splunk environments through 20+ tools for search, analytics, data discovery, administration, and health monitoring. Features AI-powered troubleshooting workflows and supports multiple Splunk instances with production-ready security.

Enables AI-driven SOC investigations by providing automated Splunk querying, threat intelligence enrichment, and response actions through natural language. Includes tools for IP pivoting, lateral movement detection, and label harvesting.

A Model Context Protocol server that provides seamless integration with Coroot observability platform, enabling monitoring of applications, analysis of performance metrics, examination of logs and traces, and management of infrastructure through Coroot's comprehensive API.

An MCP server implementation that integrates AI assistants with Langfuse workspaces, allowing models to query LLM metrics by time range.

Provides a standardized way for MCP clients to interact with Apache Airflow's REST API, supporting operations like DAG management and monitoring Airflow system health.

MCP server that integrates with VictoriaMetrics vmanomaly API to enable AI-assisted anomaly detection, model management, and configuration generation.

Enables AI clients like Claude to triage, investigate, and operate Icinga installations through natural language, integrating with Icinga's REST APIs and providing deep awareness of monitoring plugins and historical performance data.

A safety layer and monitoring copilot for AI agents using Splunk MCP, recording tool calls, enforcing policies, and blocking risky actions.

Provides tools to search Splunk logs, inject test data, and send triage emails, enabling AI-driven incident investigation.

Provides Model Context Protocol access to the Dynatrace platform API, letting AI agents run live queries against observability, security, and platform data and manage a Dynatrace environment. It is hosted remotely by Dynatrace, so MCP hosts can add it by name from the official registry without installing anything.

A remote, Dynatrace-hosted MCP server that gives AI agents live API access to your Dynatrace environment. It enables querying and analyzing observability, security, and platform data — such as DQL queries, logs, traces, problems, and dashboards — so agents can act on production context instead of static knowledge.

Integrates Monti APM with the Model Context Protocol to provide AI assistants with access to Meteor application performance monitoring data. It enables users to monitor system metrics, analyze method execution traces, and track application errors through natural language.

Enables coding agents to read performance findings from sixty, get detailed evidence for each finding, and close or resolve them with descriptions, while also checking service telemetry and providing installation instructions.

Enables AI coding agents to access live operational context like processes, containers, and git state, while providing a tamper-evident audit log and policy-as-code compliance hooks locally.

A debugging gateway for distributed systems that provides 17 tools for interacting with AWS CloudWatch, Step Functions, LangSmith, and Jira. It enables efficient log analysis, workflow tracing, and ticket management while significantly reducing token usage through a single-interface discovery pattern.

Enables coding agents to ask TypeSafe Jev for structured decisions while recording every call to a local JSONL trace, and provides tools to read back and export decision histories for debugging.

A lightweight MCP server that enables AI assistants to deploy, inspect, and operate Kubernetes workloads through high-level workflow tools, reducing token usage with structured outputs.

An observable autonomy harness for AI coding agents that enables long task execution without drift or cheating, while recording all actions for cost and quality steering.

Enables autonomous context window management, prompt caching, token optimization, and real-time observability for AI coding agents across multiple IDEs, reducing token costs and improving performance.