"An analysis of financial or statistical charts" matching MCP servers:

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

Collects page performance metrics using Playwright, CDP, and Lighthouse, and returns a normalized PerformanceReport.

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

Provides MCP tools to create and query SigNoz trace funnels, enabling agents to measure their own step conversion rates from within the agent loop.

Enables AI assistants to search Elasticsearch logs, retrieve log details, analyze service health, scan local codebases for APIs, and create Kibana dashboards.

MCP server wrapping the Akamai mPulse Query API v2 to fetch RUM aggregate data (summaries, histograms, per-minute time series) with loss-free numbers for downstream statistical analysis.

Provides a set of read-only Kubernetes functions via an MCP server, enabling interaction with Kubernetes clusters through agents or coding assistants like GitHub Copilot.

Enables AI agents to investigate Splunk exports or live queries using deterministic detectors and an iterative analysis loop, all running locally without data leaving the machine.

An AI-powered server that provides rapid debugging of server logs with actionable fixes in under 30 seconds, featuring real-time monitoring and root cause analysis through Google Gemini integration.

Connects AI assistants to cloud and SaaS logs via Tailpipe, enabling natural language querying and analysis of log data.

Enables cost-parity benchmarking of agent loops by starting, extending, and monitoring paired trials, and retrieving receipts with statistical verdicts.

Query Prometheus from CLI or any MCP client, with retries, caching, and an optional metrics exporter.

Provides financial audit reporting and ledger query tools for AI agents, enabling generation of compliance, risk, and transaction reports and querying of transaction data, balances, and summaries from an in-memory SQLite database via MCP.

An MCP server that uses SWI-Prolog for logical deduction to find root causes of errors in service dependencies, enabling root cause analysis, impact analysis, and cycle detection.

An MCP server that gives AI agents observability over their own tool calls, enabling auditing, cost tracking, latency analysis, and alerting.

Provides cost analysis of AI agent usage by previewing scrubbed metadata before upload and analyzing spend, cache-hit rates, and waste recovery through a local or remote server.

Enables natural language analysis of network packet captures, including protocol detection, flow analysis, and security threat identification, integrated with AI assistants.

Enables comprehensive analysis of Apache Spark event logs from S3, HTTP, or local sources, providing performance metrics, resource monitoring, shuffle analysis, and automated optimization recommendations with interactive HTML reports.

Enables simulation of production incident alerts and resolution by exposing an active alerts resource and a tool to restart services with simulated success or failure outcomes.

Read-only MCP server for querying AI usage metrics (exact tokens, cost, latency, errors, productivity) from a local SQLite database, enabling charts and analysis in Claude Code and Cursor.