"Firecrawl MCP (Model Context Protocol) Integration" matching MCP servers:

Conformance test harness for Model Context Protocol servers, validating JSON-RPC, transport, capability, schema, and more across multiple spec versions.

Enables AI assistants to generate and statically verify IEC 61131-3 Structured Text logic, mutate .csp/.cpj OLE2 project containers, trigger in-GUI Error Check compilation, manage HMI screens, and run deterministic OCS scan-cycle simulations and Modbus telemetry scaling against Cscape 10.2. All physical hardware and firmware-download paths are intercepted fail-closed, so commissioning must be performed manually by authorized field personnel.

Enables inspecting ONNX models, probing the deployment environment, and benchmarking inference latency and FPS either locally or on remote SSH targets such as Linux workstations and Jetson devices. Benchmark results are validated against FPS, P95 latency and model-size constraints to produce PASS/FAIL deployment gates.

Enables running the same prompt(s) across roughly 300 models from Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek, Qwen and others, then comparing per-model outputs, latency, token usage, error rates and billed cost side by side. Supports free cost estimates before launching, asynchronous run creation with status polling and cancellation, prepaid x402 credit top-ups, and an optional AI-written comparison summary of the results.

A local MCP server that lets Claude Code and Codex delegate repository exploration and test proposals to a remote LM Studio model, while enforcing security boundaries by keeping all repository access read-only and never applying patches or running commands remotely.

Enables MCP-compatible clients to discover API endpoints from OpenAPI/Swagger specs, send HTTP requests, validate responses, and analyze status codes for automated API testing.

Enables running deterministic multi-model prompt regression and golden dataset benchmark suites, exposing structured results and telemetry through the Model Context Protocol for integration with MCP-compliant clients.

ModelSafetyMCP is an MCP server for scanning machine learning model artifacts for unsafe serialization, malicious model patterns, risky packaging, URL-based artifact scanning, and directory-level triage.

A server implementation of the Model Context Protocol (MCP) for managing development workflow with features like project management, task tracking, and QA review support.

Enables AI agents to fetch daily deterministic constraint challenges and submit JSON answers for immediate, stateless evaluation via MCP.

Validates governance evidence for Codex development tasks, enforcing requirements alignment, single-active-subject candidates, executable specifications, and independent gates for traceable review packets.

Enables any MCP client to debug programs through the Debug Adapter Protocol, supporting launch/attach, breakpoints, stepping, variable inspection, evaluation, and memory operations via a single debug tool.

Enables intelligent analysis of regression test failures and automatic discovery of solutions in JIRA. Analyzes test logs using AI-driven algorithms and matches errors with relevant JIRA issues through natural language interactions.

A Model Context Protocol (MCP) server for interacting with SonarQube code quality platform.

Performs vulnerability scans using Trivy to generate Software Bill of Materials (SBOM) in CycloneDX format. It enables automated security auditing and dependency tracking through the Model Context Protocol.

A Model Context Protocol (MCP) service that enables integration with Mantis Bug Tracker, allowing users to query and analyze bug tracking data through natural language commands.

A simple server implementing the Model Context Protocol (MCP) that echoes messages back, designed for testing MCP clients.

Integration between Model Context Protocol (MCP) and Facebook's iOS Development Bridge (idb), enabling automated iOS device management and test execution through natural language.

Enables learning and integration testing of the Model Context Protocol by evolving from a minimal server toward production-grade tools, resources, prompts, and agent interoperability.

A Model Context Protocol (MCP) server for automated crash analysis using GDB.