"Connecting Alpaca to Brokerages, News Sites, and Spark Cluster for Workflow Rebalancing" matching MCP servers:

A Model Context Protocol server enabling LLMs like Claude to interact with the Alpaca trading API through natural language for stock trading, checking positions, fetching market data, and managing your account.

Enables AI-driven COMSOL batch jobs on the Harvard FASRC cluster, allowing users to run COMSOL models, manage jobs, and fetch results directly through natural language.

A Model Context Protocol server that enables LLMs like Claude to interact with the Alpaca trading API, allowing for trading stocks, checking positions, fetching market data, and managing accounts through natural language.

A standalone runtime and MCP server that executes Claude Code dynamic workflow files through interchangeable agent providers, ensuring compatibility with Claude Code's workflow format.

Enables natural language trading operations through AI assistants using Alpaca's Trading API. Supports stocks, options, crypto trading, portfolio management, and real-time market data access.

Enables bounded, multi-phase coding workflows inside Codex that plan, implement, independently review, repair, and verify repository changes, with an inline dashboard for inspecting runs.

Enables multi-agent code review and iterative improvement loop using different LLM backends, with deterministic scoring and audit trail.

Enables natural language trading operations through Alpaca's API, supporting stocks, options, crypto, portfolio management, and real-time market data with comprehensive order execution and account management capabilities.

A comprehensive MCP server for professional trading operations with Alpaca, providing 31 tools for account management, market data, order execution, custom strategy execution, and advanced portfolio analytics with intelligent position classification.

Exposes Alpaca paper trading as MCP tools for checking quotes, managing a watchlist, checking market hours, and placing simulated trades.

Enables agentic coding clients to enforce portable policy guardrails by evaluating proposed shell, file write, git, and network actions and returning allow, deny, or ask decisions.

Guides agents through structured workflows with predefined steps, allowing for flexible execution via instructions and tool calls.

Delegates autonomous coding tasks to Z.ai's GLM models with a genuine agent loop, file operations, and verification via git and real process results.

Enables AI agents to create, share, discover, and execute reusable multi-step workflow templates.

A production-grade multi-agent workflow orchestrator built on the Model Context Protocol, featuring planner/executor/critic agents, durable run state, and replayable traces.

Enables enterprise multi-agent decision workflows that expose 8+ MCP tools such as SQL query, web search, Python sandbox, RAG, file access, data cleaning, chart generation, and HTTP calls, orchestrated with LangGraph, streaming output, and human-in-the-loop approvals.

Enables deterministic, step-by-step execution of YAML-defined skills and workflows for AI agents, with progressive disclosure of active-step instructions and tools. It manages workflow state, conditional DAG transitions, loops, and persistent sessions via start_workflow, next_step, and end_workflow.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.