"A service for error tracking and performance monitoring" matching MCP servers:

Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.

Automates error tracking workflow by finding code owners in Bitbucket, retrieving related pull requests, and creating assigned JIRA tickets for code errors.

Explains known developer errors offline and deterministically (CORS, ESM/CommonJS, npm ERESOLVE, Next.js builds, Postgres connection and DATABASE_URL errors). One tool, explain_error; no LLM, no network.

Enables supervised creation of tracking stack resources—WhatConverts accounts/profiles, GA4 properties, and GTM containers—with dry-run-by-default writes, duplicate-name refusal, rate limiting, and an append-only action log.

Enables natural language time tracking through Claude with markdown file storage. Supports multi-company tracking, flexible time parsing, auto-calculated summaries, and commitment warnings all through conversational input.

Connects AI agents to live observability stacks including Sentry, GitHub, Vercel, Better Stack, and Cloudflare, enabling end-to-end incident investigation, deployment correlation, root cause analysis, and regression triage.

Enables web performance analysis using Google PageSpeed Insights and Chrome UX Report APIs, providing compact metrics and diagnostics without local browser automation.

An MCP server that enables Claude to learn from user corrections by recording them as persistent rules in CLAUDE.md files. It manages project-specific or global guidelines to ensure the assistant avoids repeating the same mistakes in future sessions.

Provides tools for logging, confirming, and triaging issues with the semfora-engine, enabling AI agents to report bugs discovered during workflows.

Provides intelligent error detection and debugging capabilities across multiple programming languages with real-time monitoring of build, lint, runtime, console, and test errors. Offers AI-enhanced error analysis with automated resolution suggestions and context-aware debugging.

A high-performance, modular MCP server with safe-by-default profiles, read-only workspace access, search, diagnostics, and benchmarking tools, built with TypeScript and the MCP v2 SDK.

Enables AI agents to perform read-only static analysis of Node.js backend projects, detecting database query anti-patterns, async bottlenecks, connection pooling mistakes, and dependency hygiene issues while returning structured evidence-backed findings.

A production-ready MCP server for Google Tag Manager, GA4 Measurement Protocol, DataLayer schema validation, Meta CAPI auditing, and tracking code snippet generation.

A minimal Model Context Protocol (MCP) service for integration with external platforms. Provides CRUD operations, reference data fetching, and authentication capabilities.

A Model Context Protocol (MCP) service for integration with external platforms, providing CRUD operations for 23 entity types through a standardized MCP interface.

A production-ready generic FastMCP server template with SQLAlchemy async CRUD, enabling rapid bootstrapping of MCP data-management services. It provides registry-driven CRUD, FK resolution, conflict versioning, and multiple transports.

An MCP server that enables Claude to execute SQL queries on MySQL databases with automatic connection management and error handling.

A reusable MCP HTTP server with OAuth 2.1 that turns any JSON-RPC handler into a production-ready endpoint with dynamic client registration, PKCE, refresh tokens, and OpenAPI docs.

A unified HTTP/HTTPS service that exposes Model Context Protocol (MCP) functionality over HTTP endpoints, allowing clients to list available tools and call them through a standardized API.

Provides GPU-accelerated gradient boosting model training and inference through a cloud service. Enables AI agents to train models on NVIDIA A10G GPUs and get fast cached predictions with portable model artifacts.