"Redash: Open-source Data Visualization and Query Tool" matching MCP servers:

Routes every Claude Code/Hermes prompt through a local LLM planner and Open-Jev probe, injecting calibrated probability context while failing open if local services are unavailable.

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

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.

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

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.

Enables Claude to delegate web search, page reading, text drafting, and parallel multi-model image generation to cheaper helper models on any OpenAI-compatible backend. Claude stays in charge of planning and final decisions while consuming fewer of its own tokens.

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.

Connects Claude Code and Codex to Jev-compatible System One services for context compaction, completion verification, and decision tasks like screening, classification, and gating.

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.

Enables AI agents and IDEs to discover, install, and execute skills over Model Context Protocol via stdio or SSE, with semantic search and framework bridges.

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 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.

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.