"Information about Ansible - IT automation tool" matching MCP servers:

Enables MCP clients to run complete browser workflows through a single run_workflow tool, with anti-detection backends, intelligent element location, automatic retries, and session persistence.

MCP router that fronts your other MCP servers behind a few small generic tools, so their tool definitions stay out of the context window and are discovered on demand.

Enables centralized proxy and orchestration of MCP servers, providing unified tool discovery, authentication, and secure request routing. It adds a human-in-the-loop approval gate for high-risk actions and audit logging.

MCP server that routes natural language requests to structured tool calls using a LoRA-tuned small language model, with built-in validation, retry, and fallback recovery.

Enables agents to send only the handful of tool definitions relevant to each question, ranking a large catalog by keywords and optional embeddings to cut context cost and wrong-tool picks. It can also run in front of any mcpServers config as a proxy that exposes just search_tools and call_tool instead of the whole catalog.

Enables ChatGPT to inspect Jobber data and manage controlled automations with scheduling, approval workflows, and audit trails.

A framework for building hot-loadable MCP tools that feature conversational negotiation, graduated permissions, and zero-downtime updates. It enables persistent state continuity across server restarts and provides a hypothetical evaluation system for tool actions.

An MCP proxy that embeds tool definitions and returns only the top-k semantically relevant tools per query, reducing context usage and routing calls to upstream MCP servers.

Central registry for AI tools with vector search and knowledge graph, enabling intelligent tool discovery, chaining, and execution via MCP.

Graph-based tool retrieval for LLM agents. Builds a tool graph from OpenAPI/MCP specs and retrieves multi-step workflows via hybrid search (BM25 + graph traversal + embedding), recovering accuracy from 12% to 82% with 79% fewer tokens. Also works as an MCP Proxy to aggregate multiple servers behind 3 meta-tools.

A single MCP server that provides dynamic access to thousands of APIs through discovery and proxy, eliminating the need to install and configure multiple separate MCP servers.

Orchestrates Fireflies, Asana, and Notion MCP servers to automate end-to-end meeting workflows. Enables users to search meetings, extract action items, create tasks, and generate meeting documentation through natural language commands.

Enables declaring a catalog of tools for LLMs, filtering them by role, validating arguments, guarding sensitive calls with server-side confirmations, and recording an audit log.

Routes external MCP tools behind a small on-demand discovery interface for Codex agents, using Jev to select relevant tools and providing fallback to the full inventory when confidence is low.

Enables locally configured MCP servers to be called over stdio and mixed with xAI server-side tools, without relying on hosted xAI MCP.

Enables agents to search across the pooled tools of many connected MCP servers and invoke them through a single endpoint, returning only the handful of tools relevant to each request. Filters by the caller's permissions before ranking, so agents never see or plan around tools their user is not allowed to use.

Enables AI agents to securely discover, invoke, and manage tools through a hardened MCP endpoint with protections like injection detection, circuit breakers, retry backoff, response caching, context-window limiting, and state snapshots.

Lets a human review what an AI agent is about to post to a chat tool (Slack first) before it is sent. Renders the draft, destination and warnings (@here/@channel, unresolved mentions, format issues) in a neutral local HTML preview; never sends anything.

Analyzes multi-step AI agent tool chains to compute success probability, identify bottlenecks, and suggest better execution orders, enabling more reliable agents via local pure-math computation.

A generic MCP proxy that filters which tools are exposed from a remote MCP server, reducing context window token usage by only loading the tools you actually need.

A configurable MCP gateway that runs multiple Streamable HTTP MCP servers and exposes all their tools through a single endpoint, enabling tool aggregation and routing for MCP clients.

Reference implementation of a Model Context Protocol server for integrating external APIs as agent tools. Includes built-in tools for weather, database queries, web search, and calculations, with authentication, rate limiting, and logging.

Lets AI agents drive a headless Chromium browser to open URLs, click, type, select options, read text/HTML, evaluate JavaScript, wait for elements, and capture real PNG screenshots. A persistent single session keeps cookies and SSO state across tool calls, and an optional live view mirrors pages in VS Code's integrated browser tab.

Enables AI agents to discover and execute tools via a secure MCP server with JWT authentication, RBAC, rate limiting, and audit logging.

Enables AI agents to open URLs in a persistent headless browser and interact with pages by clicking, typing, selecting, reading content, evaluating JavaScript, waiting for elements, and capturing screenshots.

Integrates Claude Desktop with Automation Anywhere Community Edition via FastMCP, enabling AI-driven listing and execution of RPA bots through natural language commands.

Enables PraisonAI agents to solve authorized CAPTCHA challenges with a bounded one-attempt budget, structured result validation, and a human-review stop for denied, unsupported, exhausted, or malformed requests.

This MCP server wraps agent tool calls with fault-tolerant retry logic, including adaptive exponential backoff, full jitter, 429 rate-limit backpressure handling, and circuit breaker patterns. It enables autonomous agents to execute tools reliably without frontier LLM round-trips, using a zero-dependency Python stdio JSON-RPC interface.

Enables agents and MCP-compliant clients to resolve tool execution graphs as topological dependency DAGs, batching independent calls in parallel and speculatively prefetching arguments to cut latency and token burn. Runs deterministically as a zero-dependency JSON-RPC 2.0 stdio server for frameworks like LangGraph, CrewAI, AutoGen, and IDEs such as Cursor, Windsurf, and Claude Desktop.

Enables Langroid agents to solve CAPTCHAs through CapSolver in authorized workflows, with structured validation, single-attempt limits, and human review for unsafe or incomplete requests.