"Using Cline as a Knowledge Graph for Coding" matching MCP servers:

A local MCP server that gives LLMs long-term memory by indexing code, infrastructure, logs, and docs into a queryable graph. It enables semantic and structural search, evidence-backed reasoning, and tracked plans that persist across sessions and teams.

Enables AI agents to query Backstage catalog data as a typed graph over MCP, answering blast-radius, ownership and escalation, dependency, path, change-risk, and catalog-drift questions through bounded read-only tools.

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.

Exposes a CognOS agent system as a machine-readable JSON graph, enabling Claude to get a full system snapshot and inspect node status, edges, and blocked nodes.

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.

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

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.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

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.

Read-only MCP server for TrueNAS Scale debugging, providing k3s tools for kubectl operations and whitelisted midclt calls for system info, apps, and pools.

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.

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 AI agents to investigate incidents by listing services and dependencies, searching redacted logs, summarizing metrics, checking recent deploys, and producing ranked timelines with scoped tokens, validation, rate limiting, and audit logging.

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.

A secure MCP proxy server with JWT authentication, SQL guardrails, rate limiting, and Kafka audit logging, enabling enterprise-grade access control for MCP clients like Cursor and Claude Desktop.

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.

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.

Exposes a materialized service dependency graph as an MCP toolset for persistent, queryable root-cause analysis via Cypher queries.

Enables searching Sumo Logic logs using the search_logs tool, with support for query parameters such as time range and maximum results.

A comprehensive Linux system performance profiler with MCP remote invocation support, featuring advanced process profiling and flame graph generation.

Enables agents to interact with a ServiceNow instance through thirteen consolidated tools covering records, schema, CMDB graph traversal, catalog, attachments, code, operations, knowledge, and administration, with a local store for notes, cache, saved queries, history, and revertible writes.

Enables coding agents to diagnose production incidents by traversing a FalkorDB graph of services, deploys, feature flags, owners and past incidents to find the true root cause rather than the alerting service. It also lets agents act on that reasoning — rolling back, restarting or flipping flags with dry-run-first safety — then verify recovery, notify owners, and store the incident and its fix as memory for faster future resolution.

Connects AI coding assistants to Shipbook for investigating issues using real application logs with error classification, log search, and session tracing.

Maple is a unified MCP server for agent observability, safety control, and behavior evolution, acting as a monitoring and auditing layer for high-agency agents. It enables developers to capture session timelines, replay risky branches, detect anomalies using ML, and enforce action guardrails.

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

Gives AI coding agents read-only access to Grafana's Loki logs, Prometheus metrics, Tempo traces and active alerts from within a project, using per-project context declared in the repo and one-command switching between test and prod environments. Credentials are discovered by parsing shell rc files directly, so it works even when spawned as a non-interactive MCP client process.

Enables agents to traverse a FalkorDB service graph by reading topology and open incidents, running graph algorithms, and executing multi-hop root-cause investigations whose LOCATE, SUGGEST and PROPOSE stages persist as resumable traces. It also surfaces episodic memories and searches runbooks and ownership records, while keeping raw Cypher, tenant switching, approvals and simulation out of reach.

Read-only MCP server for observing Linux hosts (systemd, ZFS, Docker, VMs, network, hardware, NixOS) as a graph of objects with provenance. Provides seven tools to list hosts, get status, collections, objects, evidence, lookup, and changes.

Runtime feedback MCP server for AI coding agents. It watches dev server logs, parses errors, and exposes them as MCP tools so AI agents can instantly verify code changes.

Enables MCP clients and coding agents to discover and call Arize AX tools through a hosted gateway using one API key, with actual schemas, policy controls, and governed execution.

A modular Retrieval-Augmented Generation (RAG) framework that provides hybrid search and knowledge retrieval capabilities via the Model Context Protocol. It enables users to integrate document-based knowledge into LLM workflows with support for dense/sparse retrieval, reranking, and observability.

Embeds a runtime MCP inspector in a NestJS app so coding agents can inspect the module and provider graph, routes and their guards/interceptors/pipes/filters, logs, request traces, event listeners, and scheduled jobs through an HTTP endpoint.

A command-line tool for monitoring and analyzing New Relic application metrics using MCP.

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