"Finding Ohad Zafrir's LinkedIn Profile" matching MCP servers:

Enables MCP clients to register and inspect database instance profiles, record observations and audits, compute scalar and quantile baselines, manage technical-debt findings and incident timelines, link related instances, and import/export sanitized knowledge packages.

An MCP server that decodes V8 CPU profiles into token-efficient bottleneck summaries for AI agents, enabling them to analyze CPU consumption, call trees, GC pressure, and async bottlenecks.

Enables secure, auditable access to Home Assistant through MCP, with a read-only observer profile and an operator profile for controlled mutations.

MCP server for InfluxDB 2.x, tuned for Home Assistant long-term storage, enabling reading sensor history, finding anomalies, and running Flux queries.

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 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 for Gaggiuino-modified espresso machines, enabling monitoring, shot analysis, and profile management.

Java profiling with AI agent integration. Profile CPU, memory, JDBC, HTTP and more.

Enables AI assistants to drive Chrome/Chromium to capture live WebGPU frames, profile performance, inspect shaders, buffers, and textures, and diagnose rendering issues.

Read-only MCP server for SQL Server and PostgreSQL health checks. It runs the codeDB Lens diagnosis (A–F grade with rule coverage, findings with evidence, impact and next actions), reads 24-hour performance recordings, lists databases and tables, describes table schemas, and runs one guarded SELECT at a time within per-profile row and time limits. It uses the user's saved connection profiles; pass

Enables AI agents to profile iOS/macOS apps using Xcode Instruments, automate simulator interactions, and get structured performance data for optimization.

Enables AI assistants to query the Google Play Developer Reporting API for Android production stability data, including crash and ANR issue clusters, de-obfuscated stack traces, release track discovery, startup and crash metric trends, version-to-version comparisons, and anomaly alerts. It also provides guided prompts for incident triage, baseline profile verification, and weekly stability reporting with built-in token compression and caching.

Enables AI assistants and MCP clients to analyze offline Oracle AWR/ASH reports in HTML or text across Oracle 10g–23ai, including single-instance and RAC global reports, returning report type, version, active sessions, load profile, top waits and SQL with version-aware tuning advice plus a prioritized top-3 summary, all locally without an Oracle client or database connection.

Enables Claude Desktop to interact with 57 AWS services using over 200 tools and local machine profiles. It supports multi-profile configurations and features a read-only safe mode by default to manage infrastructure like EC2, S3, and Lambda securely.

Provides typed, paginated tools for evidence-driven Linux performance analysis including profile analysis, comparison, and report generation with server-side authorization.

Give your AI agents direct access to live honeypot threat intelligence. Look up attacker IPs, browse indicators of compromise (IOCs), inspect captured credentials and malware payloads, profile threat actors, and render a real-time global attack map — all from Claude, Cursor, or any MCP-compatible client.

Lets an agent install, control, and repair dsh web as a Windows service so it auto-starts at boot, runs without a console window, and survives sign-out — covering service status, install/uninstall, start/stop/restart, and a side-effect-free install preview. It also tails service logs and translates them into plain-language diagnoses with fixes for failures like port conflicts, incompatible profile plugins, and plugin trees that fail to load.

Exposes SereneDB server state as read-only MCP tools (status, storage, memory, activity, threads, profile, callgraph, host, config, and optional set_setting) so agents can query live database health and metrics with evidence-backed findings instead of viewing screenshots.

Enables querying DeepVLab account statistics and model usage analytics, including login, user profile, usage analytics, and cost calculation.

Enables read-only queries of the x402 catalogue: checking endpoint payability and prices, finding endpoints by host or keyword, monitoring host drift, and verifying nsgoods signatures offline.

Bridges React Native DevTools, enabling AI assistants to debug, profile, and inspect React Native applications through a standardized protocol.

Interaction profile registry for AI agents. Log interactions to build a behavioral profile, query it through lenses: friction, coverage, stable corridors, failure registry, and trend. 21 tools.

Enables an agent to launch or attach to a running application and profile startup phases, CPU, memory, memory bandwidth, GPU and network activity on one live timeline, capturing call stacks and hot functions. It then ties the measured hotspots to the user's own code and reports which interactions cost time or retain memory.

Enables GPU profiling and performance analysis via NVIDIA Nsight Systems, allowing agents to profile binaries and aggregate statistics for kernels, memory copies, and NVTX ranges. It supports advanced analysis through interval tree construction and structural queries on profiling reports.

An MCP server that gives AI assistants the ability to connect to, query, profile, and monitor data sources — turning any LLM into an interactive data engineering copilot.

Enables coding agents to trace, profile, and burn down hotspots in application and native code, GPU kernels, and inference stacks by turning explicit local artifacts or commands into bounded, provenance-typed runtime evidence. Exposes named analysis and capture tools over stdio with optional durable preservation, without requiring workspace setup or project configuration.

Enables AI agents to diagnose Linux server incidents by collecting and structuring system diagnostics from multiple servers via SSH, with tools for finding incident clusters, gathering context (memory, CPU, swap, etc.), and running arbitrary commands.