"Pull requests authored by user '8randonpickart5'" matching MCP servers:

Enables assistants to securely work on a Chest via personal access token: reading tool logs, builds, and databases, editing rows and variables, and installing or linking tools, with every write requiring a dry run and human confirmation.

Enables AI assistants to search, analyze, and debug application API traffic captured by Tusk Drift, including HTTP requests, database queries, distributed traces, latency metrics, and error rates.

Enables MCP clients to read console logs and network requests from a connected mobile H5 page and, with explicit user authorization, execute JavaScript in that page.

MCP server for VibeCheck bug reports. Gives AI assistants direct access to screen recordings, console logs, network requests, user actions, and web vitals — right inside your editor.

Provides MCP tools for querying LinkYun operations metrics, including listing metrics, querying metrics, managing agents, and home recommendations, by forwarding requests to the LinkYun Agent Ops HTTP API.

Enables coding agents to read a replay link or a gn-tracing-*.zip package, surfacing console errors with source-mapped stacks, failed requests, and the user timeline.

Enables coding agents to debug backend requests by providing tools to list, view, wait for, diagnose, and clear request traces.

Enables governed, read-only investigations of Amazon Athena access logs by searching exact user, IP, or correlation indicators and summarizing activity by correlation ID, with parameterized queries, validation, and result limits.

Read-only MCP server for Grafana Loki: lets AI agents (Claude Code, Cursor, Copilot) discover labels, read bounded LogQL pages, count lines by label or time, and export logs — across several Loki stands

MCP server exposing rlg (RustLogs) log files as tools for on-call / SRE agent workflows. Three tools over JSON-RPC stdio — tail_log (last N events, filtered by level/target), filter_log (predicate filtering), summarize_errors (grouped error taxonomy). Pure Rust. MIT OR Apache-2.0.

MCP server for measuring web performance with Google PageSpeed Insights, providing median scores with uncertainty and real-user data. It offers tools for scores, diagnostics, and comparisons to identify regressions.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

Uptime monitoring for websites, APIs, SSL certificates, domain expiry, ping and TCP/UDP ports. 15 tools to list, create, pause and delete monitors, pull incident timelines with error codes, and read hourly or daily uptime and response-time statistics.

Enables log analysis for Tenable Patch Management (SaaS and on-prem) by parsing, de-duplicating, and decoding server/client logs into structured verdicts, ranked issues, and known-issue fixes.

MCP server for the Chrome UX Report, providing real-user Core Web Vitals (LCP, INP, CLS) and historical trends for any origin or URL via natural language queries.

TrustPilot for APIs, built for AI agents. Independent reliability ratings for APIs and MCP servers — look up trust scores, compare providers side by side, and leave reviews from real agent traffic.

Enables security analysts to investigate Microsoft Entra ID security logs through natural language, exposing read-only tools for user context, sign-ins, risky users, risk detections, directory audits, and conditional access policies.

Enables systematic investigation of VMware errors and events by correlating event timelines, ranking root-cause hypotheses, and suggesting next diagnostic steps.

Laravel Telescope-style debugging for Hono apps whose dashboard endpoint is also an MCP server: read recent exceptions with the request that produced them, recent requests filtered by status or duration, one request in full, and the slowest queries. Five read-only tools, zero runtime dependencies.

Shows what is consuming your agent context window before a session starts: always-loaded skill descriptions ranked by token cost, skills installed more than once under different names, CLAUDE.md/AGENTS.md sizes including what their @import lines pull in, and MCP servers configured across clients. Read-only: no child processes, no network.

Enables AI clients to assess infrastructure-as-code deployment risk, generate incident response runbooks, review CI/CD delivery readiness, and calculate SLO error budgets through structured advisory tools. Runs locally over stdio without cloud credentials or external API calls, returning guidance for platform work, pull request review, and production readiness checks.

Enables a discovery subagent that performs docs research, logs triage, and codebase exploration, returning structured findings and specifying raw reads needed by the parent agent.

MCP server that exposes Avangenio's service status by fetching and parsing status.avangenio.com/data.txt into normalized JSON.

Enables agents to autonomously investigate deployment health by inspecting Kubernetes resources, comparing live cluster state against Git, and correlating findings with Dynatrace problems.

Enables Claude to discover and understand infrastructure-as-code drift by listing projects with drift, analyzing Terraform plan outputs, and providing remediation information.

A read-only MCP server for Proxmox VE that provides AI assistants with structured visibility into cluster nodes, guests, storage, and Docker workloads. It is designed to prevent any mutating operations by construction.

Sigma detection rule writing, validation, and pySigma-based multi-backend conversion (Splunk, Elastic, Wazuh, Kibana) via 3 MCP tools and 3 Claude Code skills, backed by a 61-rule production corpus across 11 MITRE ATT\&CK tactic categories.

Lets an AI assistant answer performance questions from Tideways data by reading projects, metrics, issues and traces through the read-only REST API.

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 diagnosing long AI-agent traces by identifying the most likely root-cause steps, so users can quickly find where an agent run went wrong.