"Using Jira MCP API in Python" matching MCP servers:

Just to be clear about the final JSON: the MCP server is named herdr-inbox, and it enables Claude Code agents in herdr to ask questions asynchronously without stalling, with all open questions answered from one popup and answers routed back to the asking agent.

Enables secure execution of Python code in a sandboxed WebAssembly environment using Pyodide and Deno. Automatically handles package management and captures complete execution results including stdout, stderr, and return values.

MCP server for AI security scanning. Detects prompt injections, jailbreaks, data exfiltration, and social engineering attacks in real-time using 42 regex patterns via the ClawGuard Shield API. Sub-10ms response times.

Simple sequential thinking MCP in python

Connect AI agents to Jev AI (jev-ai.pro) for classification, scoring, yes/no checks, advisory action assessment, batched typed decisions and saved judges. Returns structured answers and probabilities using your Jev AI API key.

Multi-agent swarm orchestrator for AI workflows. Exposes blackboard read/write, agent dispatch, permission gating (AuthGuardian), token budget enforcement (FederatedBudget), and audit log tools over MCP. Supports 14 AI framework adapters including LangChain, AutoGen, CrewAI, Codex, and A2A — mix any frameworks in one swarm with race-condition-safe shared state.(Traffic light for agents)

Industry 4.0 / IIoT AI agents for manufacturing: explain OEE losses per shift, triage machine alarms, find downtime root causes, prepare predictive-maintenance jobs and write shift reports from Unified Namespace (UNS), MQTT or historian data. Plus an industrial AI-readiness diagnostic and a vendor-neutral digital-transformation advisor. Works in Claude Code, Codex and any MCP client.

Transforms HTML into Figma designs using only the user’s design system, ensuring correct component usage or safe fallback to primitives, while continuously learning from each build to improve mapping accuracy and consistency.

Enables probabilistic judgment on typed questions using ten tools, converting validated answers into policy decisions (auto, review, or escalate).

It gives AI agents one tool to access the Omega-API judge, which evaluates conversational turns and returns a decision and gate action (release, clarify, suppress, or none) to guide the agent's response.

A Python code execution sandbox based on IPython and Docker. Stateful code execution, file transfer between host and container, configurable network access.

Enables AI agents to participate in meetings by pulling pending requests, reading context, and submitting answers through a Supabase-based MCP server.

Zero-knowledge proof generation MCP server. AI agents can prove identity claims (Coinbase KYC, Country, Google OIDC, Google Workspace, Microsoft 365) without revealing personal data. Runs in AWS Nitro Enclave TEE with x402 USDC payments.

Enables agents to dynamically create, edit, call, and persist Python tools across sessions.

Enables Claude Code to perform programmatic tool calling by executing Python scripts that interact with multiple MCP servers in a single round-trip. This reduces latency and token consumption by keeping intermediate tool results within the local Python runtime instead of the conversation context.

A Model Context Protocol (MCP) server that provides intelligent search capabilities for discovering relevant Claude Agent Skills using vector embeddings and semantic similarity. This server implements the same progressive disclosure architecture that Anthropic describes in their Agent Skills enginee

Enables a main agent to delegate tasks to reusable Pi agents with persistent sessions, question handoffs, bounded waiting, and in-run correction, while continuing other work. It supports named agents, context retention across MCP restarts, and result retrieval through MCP without repeated polling.

Stdio MCP Server wrapping custom Python runtime (LocalPythonExecutor) from Hugging Faces' smolagents framework. The runtime combines the ease of setup (compared to docker, VM, cloud runtimes) while providing safeguards and limiting operations/imports that are allowed inside the runtime.

Capsule MCP server gives your AI agent the ability to safely run untrusted Python and JavaScript code in a secure WebAssembly sandbox.

Enables AI agents to delegate complex coding tasks, refactorings, and automated code reviews to OpenAI Codex and Google Antigravity CLIs, running them autonomously in YOLO mode with process supervision.