ReadMe MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ReadMe as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to ReadMe. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in ReadMe?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About ReadMe MCP Server
Connect your ReadMe documentation hub directly to your AI agent. Enabling this integration turns your AI into an expert technical writer and reader, capable of instantly scanning your entire developer documentation, changelogs, and custom pages without context switching.
LlamaIndex agents combine ReadMe tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Documentation Search — Perform full-text searches across all your published guides and API references.
- Content Retrieval — Fetch the exact Markdown content of any specific documentation page, changelog, or category.
- Project Analysis — Understand how your documentation is categorized and structure new content accordingly.
- Changelog Tracking — Pull recent product updates and announcements formally published to your users.
The ReadMe MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect ReadMe to LlamaIndex via MCP
Follow these steps to integrate the ReadMe MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from ReadMe
Why Use LlamaIndex with the ReadMe MCP Server
LlamaIndex provides unique advantages when paired with ReadMe through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ReadMe tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ReadMe tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ReadMe, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ReadMe tools were called, what data was returned, and how it influenced the final answer
ReadMe + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ReadMe MCP Server delivers measurable value.
Hybrid search: combine ReadMe real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ReadMe to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying ReadMe for fresh data
Analytical workflows: chain ReadMe queries with LlamaIndex's data connectors to build multi-source analytical reports
ReadMe MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect ReadMe to LlamaIndex via MCP:
get_category
Retrieves details for a specific documentation category
get_category_docs
Lists all documentation pages under a specific category
get_changelog
Retrieves the full content of a specific changelog post
get_custom_page
Retrieves the full content of a custom page
get_doc
Retrieves the full content of a documentation page
get_project
Retrieves details about the ReadMe project
list_categories
Lists all documentation categories on ReadMe
list_changelogs
Lists all changelog posts
list_custom_pages
Lists all custom standalone pages
search_docs
Performs a full-text search across all documentation pages
Example Prompts for ReadMe in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ReadMe immediately.
"Search the documentation for instructions on configuring webhooks."
"Get the contents of the changelog titled 'v2-api-release'."
"List all main documentation categories."
Troubleshooting ReadMe MCP Server with LlamaIndex
Common issues when connecting ReadMe to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpReadMe + LlamaIndex FAQ
Common questions about integrating ReadMe MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect ReadMe with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect ReadMe to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.