"Parsing and Unparsing CSV Files for AI Context Use" matching MCP servers:

Runtime access for AI coding agents to observe and control Android and iOS apps.

MCP server providing deep PostgreSQL context to AI assistants, including schema DDL, index health, foreign key associations, query execution plans, and performance statistics via read-only tools.

Enables tracing mcp-use tool callbacks and capturing tool errors and exceptions in Sentry via MCP middleware, without SDK changes or additional services.

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.

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.

MCP server providing lossless file reads with deduplication, per-repo token metering, and HMAC-signed context receipts. Enables auditable, vendor-neutral measurement of what an AI agent saw.

Enables assistants to read-only explore and analyze Grafana dashboards and data sources, execute queries against Prometheus and Azure Log Analytics, and run guided investigation workflows.

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

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

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.

Perimeter security for your AI Infrastructure.

Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.

Connects AI-powered tools to the Confident AI platform for evaluating, observing, and iterating on AI quality, enabling cloud evaluations, dataset management, prompt versioning, production tracing, and human annotations directly from editors like Cursor and Claude Code.

Enables AI agents to access real-time Windows PC context including active window, system performance, screen time, productivity analytics, and historical usage through MCP tools.

Windows tray process that exposes local ambient context (presence, foreground app, battery, etc.) as MCP tools with privacy classification and opt-in controls.

Enables offline, read-only forensic analysis of Claude Code session transcripts, providing exact token usage, cost estimates, context growth, and compaction suggestions.

Enables AI assistants to query system health, metrics, service reconnaissance, run multi-node patrols, and send incident alerts across distributed infrastructure through MCP tools.

Compresses log files into templates and statistics using Drain3, and exposes them to AI assistants via an MCP server for efficient log monitoring and anomaly detection.

Provides a read-only interface to Kubernetes clusters, enabling LLMs to list pods, get pod status and logs, fetch deployment manifests, and perform pod health analysis with resource trend tracking.

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