How to Use the Markdown Task Extractor MCP in LlamaIndex

Q: Can I filter folders scanned by Markdown Task Extractor in LlamaIndex?

Yes, you control the absolute directory path passed to the extractmarkdowntodos tool. LlamaIndex agents will only scan the specific directory you configure, keeping the rest of your file system private.

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Connect Markdown Task Extractor MCP to LlamaIndex

Create your Vinkius account to connect Markdown Task Extractor to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Markdown Task Extractor with LlamaIndex

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Turn markdown tasks into searchable vector indexes

The `extract_markdown_todos` tool extracts all open and completed checkboxes from your local directory so you can index them. LlamaIndex takes this raw text output and embeds it into your vector store, turning scattered notes into structured, searchable data. This prevents tasks from getting lost in old files. Your RAG application queries the index to find what you worked on last week, matching your queries semantically instead of relying on exact keyword matches.

Ground agent responses in actual markdown files

The `extract_markdown_todos` tool provides the raw data needed to ground your agent's responses in your actual daily logs. By feeding the extracted tasks into a LlamaIndex query engine, you ensure the agent answers questions based on real files rather than fabricating progress. The agent checks the status of your tasks before drafting status updates. It avoids hallucinations because it works directly with the parsed checkbox strings retrieved from your local directory.

Filter and control MCP Server access in LlamaIndex

The `extract_markdown_todos` tool can be restricted or allowed dynamically within your LlamaIndex setup using the allowed_tools filter. This gives you precise control over when the agent is permitted to read your local directories. You define exactly which scripts can trigger a local file scan. It protects your file system from accidental reads while still giving your knowledge agent access to your todo lists when required.

Setup guide

Set up Markdown Task Extractor MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Markdown Task Extractor MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Markdown Task Extractor tools.",
)
response = await agent.run("List recent Markdown Task Extractor data")

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Common questions about Markdown Task Extractor MCP in LlamaIndex

Use the McpToolSpec to load the tools from the MCP server, then call the `extract_markdown_todos` tool to fetch your tasks. You can parse the resulting text into LlamaIndex Document objects and build your vector index from there.

The MCP Server itself extracts the raw tasks, and then LlamaIndex handles the embedding and semantic search. Once extracted, you can search across your open and completed tasks based on meaning rather than exact keywords.

Yes, you control the absolute directory path passed to the `extract_markdown_todos` tool. LlamaIndex agents will only scan the specific directory you configure, keeping the rest of your file system private.

Install llama-index-tools-mcp and initialize the BasicMCPClient with your server endpoint. Convert the client to a tool spec and pass the asynchronous tool list directly to your FunctionAgent.

We process your markdown files entirely in memory within an ephemeral V8 sandbox. Your private files are never stored or uploaded anywhere, and the parsed checkboxes are only exposed to your local LlamaIndex pipeline.

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