Wiki.js MCP Server for LlamaIndexGive LlamaIndex instant access to 6 tools to Create Page, Delete Page, Get Page, and more

Name: Wiki.js
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Wiki.js as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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The Wiki.js MCP Server for LlamaIndex is a standout in the Productivity category — giving your AI agent 6 tools to work with, ready to go from day one.

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python

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 Wiki.js. "
            "You have 6 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Wiki.js?"
    )
    print(response)

asyncio.run(main())

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* 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 Wiki.js MCP Server

Connect your Wiki.js instance to any AI agent and take full control of your internal knowledge base through natural conversation. This server allows you to interact with your documentation without leaving your chat interface.

LlamaIndex agents combine Wiki.js tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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

Page Management — Create new pages with full metadata, update existing content by ID, or delete outdated documentation.
Search & Discovery — Search across your entire wiki for specific keywords or list all available pages to understand your knowledge structure.
Content Retrieval — Fetch the raw content and metadata of any page using its URL path and locale.
Flexible Editing — Support for different editor types and publishing statuses (published/private) directly via the API.

The Wiki.js MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 6 Wiki.js tools available for LlamaIndex

When LlamaIndex connects to Wiki.js through Vinkius, your AI agent gets direct access to every tool listed below — spanning documentation, wiki, content-creation, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

create

Create page on Wiki.js

Create a new Wiki.js page

delete

Delete page on Wiki.js

Delete a Wiki.js page

get

Get page on Wiki.js

Fetch a Wiki.js page by path

list

List pages on Wiki.js

List all Wiki.js pages

Search pages on Wiki.js

Search for content in Wiki.js

update

Update page on Wiki.js

Update an existing Wiki.js page

Connect Wiki.js to LlamaIndex via MCP

Follow these steps to wire Wiki.js into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

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 6 tools from Wiki.js

Why Use LlamaIndex with the Wiki.js MCP Server

LlamaIndex provides unique advantages when paired with Wiki.js through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Wiki.js tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Wiki.js tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Wiki.js, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Wiki.js tools were called, what data was returned, and how it influenced the final answer

Wiki.js + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Wiki.js MCP Server delivers measurable value.

Hybrid search: combine Wiki.js real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Wiki.js 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 Wiki.js for fresh data

Analytical workflows: chain Wiki.js queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Wiki.js in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Wiki.js immediately.

"Search for 'deployment guide' in our Wiki.js."

"Create a new markdown page at 'docs/api-v2' titled 'API v2 Reference'."

"List all the pages available in our wiki."

Troubleshooting Wiki.js MCP Server with LlamaIndex

Common issues when connecting Wiki.js to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Wiki.js + LlamaIndex FAQ

Common questions about integrating Wiki.js MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Wiki.js tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

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