"Information about guardrails" matching MCP servers:

Enables coding agents to write, run, and validate SAS 9.4 code locally via SASPy, with log triage, schema discovery, and safety guardrails.

MCP server for Python build123d to help AIs develop and reason about 3D models and CAD

A comprehensive learning project demonstrating MCP server implementation with tools for calculator operations, file management, and system information retrieval, including Google ADK integration patterns and best practices.

Enables any MCP-compatible AI agent to directly operate PixInsight through about 80 tools for opening and editing images, running processes, PixelMath, astrometry, and channel operations.

A local Ethereum contract analysis tool that retrieves on-chain information, integrates Etherscan and RPC data, and automatically executes view functions to provide comprehensive contract insights.

Enables pre-execution security checks and sandboxed execution for AI agent workflows, including prompt-injection detection, MCP rugpull and sampling abuse protection, untrusted tool-output sanitization, loop circuit breaking, SQL AST validation, PII redaction, and ephemeral Python execution.

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK. Includes example tools for calculations and greetings, plus system information resources.

A simple MCP server that provides a tool to fetch current weather information for cities using the Open-Meteo API, communicating through stdin/stdout.

A Model Context Protocol server that retrieves current weather information for any city using the OpenWeatherMap API, designed for integration with Claude Code.

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK. Includes example tools like calculator and greeting functions, plus system information resources.

Provides comprehensive documentation about AWS AgentCore framework to GenAI tools, enabling users to build production-ready AI agents with enterprise-grade security, observability, and scalability. Offers guidance on identity management, API integration, monitoring, code execution, memory storage, and tool integration for AI agents.

A simple demonstration server for the MCP Python SDK that provides weather alerts for locations, allowing users to query weather information through Claude Desktop or Cursor.

Provides AI-driven development tools including file system operations, multi-language code analysis with tree-sitter, Git operations, code execution, and system information retrieval.

An MCP server that exposes Language Server Protocol features to external clients, allowing access to hover information, definitions, completions, references, and rename functionalities.

Enables large language models to access user geolocation information through EdgeOne Pages Functions using the Model Context Protocol.

MCP server to provide Sketch layout information to AI coding agents like Cursor

A starter template for building Model Context Protocol (MCP) servers using TypeScript and Zod for schema validation. It provides a foundational structure and examples for implementing custom tools and resources, such as calculators and system information endpoints.

Implements a Model Context Protocol server that connects with Zerodha Kite API, allowing users to buy/sell stocks and retrieve holdings and positions information.

A Model Context Protocol server that enables Claude AI to interact with Paybyrd's payment processing API, allowing for creating payment links, processing refunds, and retrieving order information.

A starter template for quickly developing Model Context Protocol (MCP) servers using the TypeScript SDK and Zod for schema validation. It includes example implementations for custom tools and resources like calculators, weather services, and system information.

A boilerplate project for quickly developing Model Context Protocol servers using TypeScript SDK, with example calculator and greeting tools, and server information resources.

Enables agents to run SQL queries against PostgreSQL databases through MCP with connection pooling, tenant isolation, and read-only guardrails. Supports introspection of schemas, tables, and columns.

Enables AI assistants to control Houdini by executing Python code, querying scene information, and creating nodes via the Model Context Protocol.

A Model Context Protocol server that enables natural language querying of the Censys Search API for domain, IP, and FQDN reconnaissance, providing information about hosts, DNS, certificates, and services in real-time.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

Enables coding agents to compile supplied files, run optional focused tests, and get static findings across ten programming languages, providing a deterministic feedback loop on the code they are about to return.

Enables interaction with Groq models, including compound models, through tools for asking questions with real-time information and code execution.

Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.

Enables an AI agent to drive Roblox Studio, including running Luau code, syncing Rojo projects, starting solo or multiplayer play tests, and retrieving logs, screenshots, and GUI information.

Enables compiling Lean 4 proof snippets on the local machine against a Mathlib project and returns structured compiler feedback — status, one-line hints, and the compiler's real error, warning, and information messages — so a model can iterate on proofs instead of guessing. Compilation is serialized, timeouts kill stuck tactics, and rejected code is reported as a normal result rather than a tool failure.