llama-index-tools-mcp
llama-index tools mcp integration
Decision gist · record as of 2026-08-14
Yes. If you are building LlamaIndex agents and need to integrate tools from MCP servers, this package eliminates the integration work. It has low install friction, active maintenance, no known vulnerabilities, and a permissive license. Install it when you have an MCP server available and want agents to use its tools.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an MCP server running and accessible at the specified URL or command.
- Low install friction with three straightforward runtime dependencies.
- Active maintenance with a recent release.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal obligations.
last release 2026-08-11 (3 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 78,918 downloads/mo, #14,392 on PyPI
Alternatives
Verify before relying
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
mcp_client = BasicMCPClient("http://127.0.0.1:8000/sse")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = mcp_tool_spec.to_tool_list()- Whether BasicMCPClient supports all MCP transport types mentioned (HTTP, SSE, stdio) equally well in practice
- Performance characteristics when calling tools through multiple MCP servers simultaneously
- Compatibility guarantees with specific MCP server implementations or versions
What it is and what it does
This package bridges LlamaIndex agents and the Model Context Protocol (MCP) ecosystem. It provides a client to connect to MCP servers, extract their exposed tools, and make those tools available to LlamaIndex agents for use in agentic workflows. It also works in reverse: it can wrap a LlamaIndex Workflow as an MCP server so other MCP clients can call it.
The package handles the mechanics of MCP communication—tool listing, resource access, prompt retrieval, and OAuth authentication—so you can focus on integrating external capabilities into your agent. It supports both synchronous and asynchronous operations, multiple transport types (HTTP, Server-Sent Events, stdio), and optional filtering of tools by name.
Use it for
- Equip a LlamaIndex agent with tools from an external MCP server (e.g., weather, database, file system).
- Expose a LlamaIndex workflow as an MCP service for other applications or agents to call.
- Build an agent that queries multiple MCP servers and combines their tools in a single reasoning loop.
- Implement OAuth-protected access to MCP servers requiring authentication.
- Convert a complex LlamaIndex workflow into a reusable tool for other systems via MCP.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
If you are building LlamaIndex agents and need to integrate tools from MCP servers, this package eliminates the integration work. It has low install friction, active maintenance, no known vulnerabilities, and a permissive license. Install it when you have an MCP server available and want agents to use its tools.
Install
llama-index-tools-mcp on PyPI
Before you install
Low install friction with three straightforward runtime dependencies. Active maintenance with a recent release.
Requires an MCP server running and accessible at the specified URL or command.
License in practice
MIT license permits unrestricted use, modification, and distribution with minimal obligations.
Quickstart
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
mcp_client = BasicMCPClient("http://127.0.0.1:8000/sse")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = mcp_tool_spec.to_tool_list()
Verify before relying
- Whether BasicMCPClient supports all MCP transport types mentioned (HTTP, SSE, stdio) equally well in practice
- Performance characteristics when calling tools through multiple MCP servers simultaneously
- Compatibility guarantees with specific MCP server implementations or versions
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesllama-index-coremcppydantic |
| Maintenance | Actively maintained 3 days since the last release |
| First released | |
| Downloads | 78,918 / month, #14,392 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_tools_mcp-0.5.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “llamaindex agent tools”
- llama-index-tools-mcpConnects LlamaIndex agents to MCP (Model Context Protocol) servers to…
- llama-index-agent-openaiIntegrates OpenAI language models with LlamaIndex agents, enabling…
- datarobot-genaiA toolkit for building and deploying AI agents on DataRobot,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also mcpo · mcp · mcp-use · llama-index-workflows · nvidia-nat-mcp · mcpadapt · llama-index-agent-openai · arcade-mcp · mcp-proxy · arcade-mcp-server