How to Use the Wiki.js MCP in LlamaIndex

Q: How does LlamaIndex use the searchpages tool?

LlamaIndex takes the output of searchpages and indexes it into a vector store. This allows you to query historical data or configurations based on meaning, not just keywords.

Q: What if I need to verify page existence using LlamaIndex and Wiki.js?

You call getpage or listpages. The resulting data confirms the path structure, which is crucial before trying to build a new index segment.

Q: How can I use the deletepage tool within LlamaIndex?

While you call deletepage, LlamaIndex's primary function is reading. The deletion simply removes data that would otherwise contribute to your index.

Build searchable knowledge graphs with LlamaIndex and Wiki.js.

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Connect Wiki.js MCP to LlamaIndex

Create your Vinkius account to connect Wiki.js 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 Wiki.js with LlamaIndex

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Index search results from Wiki.js

When you run `search_pages`, the tool output becomes part of your indexed knowledge base, not just a temporary result. You build RAG apps where live API data is permanently searchable. This means if you query past configurations, LlamaIndex can ground answers in actual Wiki.js documentation found via this MCP Server.

Retrieve specific page context

Need a deep dive on one topic? Use `get_page` to pull the full content. This rich data is perfect for augmenting your index, ensuring that detailed procedural steps are available when needed. It gives LlamaIndex enough raw text to build accurate semantic embeddings.

Maintain documentation structure

The `create_page` and `update_page` tools ensure that the source of truth remains current. By indexing these changes, your knowledge base stays reliable. This process allows you to query not just what *was* written, but what's currently documented in Wiki.js.

Setup guide

Set up Wiki.js 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 Wiki.js 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 Wiki.js tools.",
)
response = await agent.run("List recent Wiki.js data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Wiki.js. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Wiki.js MCP in LlamaIndex

LlamaIndex takes the output of `search_pages` and indexes it into a vector store. This allows you to query historical data or configurations based on meaning, not just keywords.

Yes. Since the output becomes part of a unified index, you can combine live API data from this MCP Server with documents from other sources into one queryable pool.

You call `get_page` or `list_pages`. The resulting data confirms the path structure, which is crucial before trying to build a new index segment.

The server works with Wiki.js page content and paths (string/markdown). You should assume all retrieved text is sensitive documentation material.

While you call `delete_page`, LlamaIndex's primary function is reading. The deletion simply removes data that would otherwise contribute to your index.

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