"Laravel 12 PHP Framework" matching MCP servers:

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.

MCP server that auto-emits tamper-evident receipts for every tool call, enabling EU AI Act Article 12 compliance with signed, chain-linked receipts.

Enables coding agents to locally investigate and manage error tracking issues, including Laravel error summaries, paginated stack traces, triage, comments, and deletion.

Enables debugging any Laravel application remotely via the Clockwork HTTP API, from any MCP client, with tools for query analysis, slow query detection, N+1 detection, exception inspection, and full request profiling.

A Claude Code MCP server that connects to Laravel Clockwork to provide insights into application performance, database queries, errors, and request flow.

Web-only MCP server for php-spx reports that calls embedded SPX web endpoints, parses full reports, and returns LLM-sized JSON summaries instead of raw event streams.

A framework for building MCP servers with built-in enterprise capabilities such as authentication, authorization, telemetry, audit logging, and data classification, allowing domain developers to focus on business logic while maintaining governance and compliance.

A minimal guardrails framework for AI agents, acting as an MCP proxy to enforce policies, approvals, and audit logging on tool calls.

Enables AI assistants to perform read-only PostgreSQL DBA analysis and monitoring through natural language, covering performance, bloat, locks/deadlocks, autovacuum, and schema inspection across PostgreSQL 12-18 without required extensions.

Enables an AI assistant to diagnose a local WordPress install by listing plugins, checking PHP/WP environment, tailing debug logs, running guarded read-only SQL queries, and inspecting Gravity Forms error and add-on logs.

Enables AI assistants to perform PostgreSQL database operations and monitoring through natural language, including performance analysis, bloat detection, lock and deadlock monitoring, autovacuum and schema inspection across PostgreSQL 12-18.

Enables AI assistants to perform PostgreSQL database operations, monitoring, and management through natural language, including performance analysis, bloat detection, lock/deadlock monitoring, autovacuum and schema inspection, and maintenance recommendations across PostgreSQL 12-18.

MCP-native agent evaluation and observability server. Log traces, evaluate output quality with 12 built-in rules (PII detection, prompt injection, cost thresholds), and track agent costs. Real-time dashboard, OTel-compatible spans. Self-hosted, MIT licensed.

A read-only MCP server that enables AI assistants to search, aggregate, and explore OpenSearch log data through 12 tools for connectivity, index discovery, search, and aggregations.

Open-source .NET framework and server for Model Context Protocol (MCP) with enterprise OAuth 2.0/OIDC auth, per-tool MFA, OpenTelemetry, and health checks.

Prediction stability engine for AI agents. Evaluate model stability, detect ghosts, probe any LLM for instability, monitor fleet drift. 20 tools + 1 resource. Works with Claude, Cursor, VS Code.

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.

A read-only MCP server for AppDynamics that exposes 12 task-oriented tools to query applications, topology, metrics, snapshots, health rules, anomalies, events, alerting config, analytics events, and dependency maps via OAuth API-client credentials.

Enables AI assistants to access Scout Monitoring performance and error data through Scout's API. Provides traces, errors, metrics, and insights for Rails, Django, FastAPI, Laravel and other applications to help identify and fix performance issues like N+1 queries, slow endpoints, and memory bloat.

Zero-config MCP server that gives AI coding assistants a real-time diagnostic snapshot of your local dev environment. Detects framework, running services, recent errors, git state, and provides a health diagnosis in one call.

An MCP server that gives Bob direct access to your Grafana instance — dashboards, datasources, queries, alerting rules, and more.

A specialized diagnostic framework that enables AI models to self-diagnose and fix MCP-related issues by analyzing logs, validating configurations, testing connections, and implementing solutions.

Universal verifiable recovery for long-running AI agents with semantic checkpoints, idempotent action ledger and hash chained log as a deny by default MCP server. Framework agnostic with adapters for LangGraph, LangChain and OpenAI plus gateway and OTel.

Monitors and analyzes Precision Time Protocol (PTP) systems in OpenShift clusters, enabling configuration analysis, real-time log monitoring, and health checks.

Enables read-only access to operations and telemetry data across 12 domains through 45 agent tools, including asset inventory, metrics, outages, incidents, and platform settings, with support for AWS SigV4-signed live access and a fixture-based mock mode that runs without a backend.

A gateway MCP server that lets agents reach any upstream MCP server through one endpoint while enforcing policy rules, holding risky tool calls for human approval, containing prompt-injected sessions, and logging every call.

Enables AI assistants to list OpenObserve log streams, inspect their schemas, and run read-only SELECT queries with enforced safety caps.

Enables AI assistants and coding agents to audit, compare, and optimize websites for performance, SEO, AEO, GEO, and AI crawler accessibility directly from chat or development workflows.

A modular Retrieval-Augmented Generation (RAG) framework that provides hybrid search and knowledge retrieval capabilities via the Model Context Protocol. It enables users to integrate document-based knowledge into LLM workflows with support for dense/sparse retrieval, reranking, and observability.

This server provides real-time system performance metrics via Performance Co-Pilot (PCP), enabling users to monitor CPU, memory, disk, network, processes and more through natural language queries.