"A tool for searching local files" matching MCP servers:

这是面向智能运维场景的 MCP 工具服务，让 Agent 能够按需检索日志内容并获取主机与应用的监控指标数据。它把实时的日志与指标证据接入 Plan-Execute-Replan 诊断流程，从而支撑自动故障排查与根因分析。

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

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.

An MCP server for AI-powered log analysis that enables parsing, searching, and debugging across nine log formats directly within Claude. It features automated error extraction, sensitive data scanning, and streaming support for large log files.

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

Provides structured access to Claude Code agent log files, allowing agents to query assistant lines and tool calls without reading raw JSONL data.

MCP server that stream-parses NDJSON log files without loading them into memory — filter by pattern, detect error spikes via Z-score analysis, summarize severity timelines by time window.

Enables LLMs to analyze Linux perf data files using 26 perf analysis commands, including report, script, annotate, and more, through typed tool parameters.

Local-first dashboard + MCP server that parses Claude Code and Codex JSONL files into a SQLite cost / token tracker. Per-MCP and per-tool breakdown, session drill-down, dedup by request_id; never talks to vendor APIs

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

Enables AI agents such as Cursor and Claude Code to inspect local log files through MCP tools for tailing, searching, filtering, statistics, and root-cause diagnosis using local Ollama or a built-in rule engine.

Enables checking whether MCP servers are alive, maintained, and safe to use by scoring metadata health and trust, scanning MCP config files, and optionally wrapping a server to log tool calls and errors locally.

Enables local-first observability for MCP agent workflows by recording tool calls, prompts, latency, and token usage in a local SQLite database, and provides tools for inspecting, comparing, exporting, and generating dashboards from traces.

A secure, local-first MCP server for read-only inspection and troubleshooting of development environments, exposing narrow, typed, auditable capabilities for repository inspection, log summarization, Docker review, and security scanning without granting unrestricted machine access.

Enables observability and governance for local LLMs via Ollama, including auditing model usage, scanning prompts for secrets/PII, and enforcing allow/deny policies.

Safety-first local MCP tool gate with control plane, runtime security, and observability for managing MCP backends.

Enables MCP-aware AI agents to query the local machine's health over stdio, retrieving CPU, memory, disk, and load-average metrics along with boot time and the top processes by CPU usage. It gives coding agents a structured, protocol-standard way to answer questions like whether it is safe to run a build or what is currently consuming resources.

A FastMCP-based tool for interacting with Splunk Enterprise/Cloud through natural language. This tool provides a set of capabilities for searching Splunk data, managing KV stores, and accessing Splunk resources

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 log analysis MCP server that enables tailing, searching, filtering, and summarizing logs from local files and Docker containers.

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.

Enables AI assistants to interact with local Docker containers, Kubernetes clusters, and system logs using the Model Context Protocol.

Ships only the MCP server, not the recorder. Recording works by wrapping a process on the host — a local proxy, a PATH shim, a shadow git index. None of that survives a container boundary, so orca record inside here would watch an empty namespace. What does travel is the read side: the six MCP tools serve runs that already exist on disk, so a client mounts its .orca directory and r

Enables users to log AI tool activity without conversation text and contribute back only user-approved, small-cell-suppressed counts through a local MCP interface.

A flight recorder for Claude Code that captures tool calls and observations into a SQLite database, then classifies them into a distilled timeline for incident troubleshooting.

Enables MCP clients to call Cloudflare Workers-hosted tools while automatically tracking events, checkpoints, and tool calls in Agnost AI analytics.

Enables MCP clients to call a hello tool and access observability features, including metrics and request traces, through a Streamable HTTP transport with SQLite-backed storage.

MITM proxy MCP server that intercepts and verifies AI agent tool calls, detecting fabrication, tracking costs, verifying outcomes, and inferring user satisfaction.

Enables developers to build production-grade MCP services with dependency injection, zero global state, layered imports, and a sample user-notes tool, resource, and prompt. It includes Testcontainers, Prometheus metrics, Kubernetes probes, and structured logging for testable local and production deployments.

Enables local LLMs to perform enterprise-grade code auditing, AST security screening, architectural planning, and real-time Discord observability with zero cloud API costs.