Scribe MCP Server for LlamaIndexGive LlamaIndex instant access to 10 tools to Check Scribe Status, Get Documentation Stats, Get Recent Documents, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Scribe 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 App Connector for LlamaIndex
The Scribe app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 10 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Scribe. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Scribe?"
)
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 Scribe MCP Server
Connect your Scribe organization to any AI agent and access your process documentation library through natural conversation.
LlamaIndex agents combine Scribe 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
- Full Search — Search across all Scribes and Knowledge Pages simultaneously.
- Filtered Search — Search only guides or only pages for targeted results.
- Team Scoping — Find documents within a specific team for departmental queries.
- Date Filtering — Search by creation date range or get recent documents.
- Teams — List all teams and their documents for content auditing.
- Statistics — Get an overview of your documentation footprint.
The Scribe 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.
All 10 Scribe tools available for LlamaIndex
When LlamaIndex connects to Scribe through Vinkius, your AI agent gets direct access to every tool listed below — spanning scribe, process-documentation, knowledge-base, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Verify Scribe API connectivity
Get documentation statistics
Get documents created in the last 30 days
Useful for auditing team documentation. Get all documents for a specific team
List all teams in your Scribe organization
Dates should be in YYYY-MM-DD format. Search documents by creation date range
Search documents within a specific team
Search across all Scribes and Pages
Search only Knowledge Pages
Search only Scribe guides
Connect Scribe to LlamaIndex via MCP
Follow these steps to wire Scribe into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Scribe MCP Server
LlamaIndex provides unique advantages when paired with Scribe through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Scribe tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Scribe tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Scribe, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Scribe tools were called, what data was returned, and how it influenced the final answer
Scribe + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Scribe MCP Server delivers measurable value.
Hybrid search: combine Scribe real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Scribe 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 Scribe for fresh data
Analytical workflows: chain Scribe queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Scribe in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Scribe immediately.
"Search for all documentation about 'onboarding'."
"List all teams in our Scribe organization."
"What documents were created in the last 30 days?"
Troubleshooting Scribe MCP Server with LlamaIndex
Common issues when connecting Scribe to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpScribe + LlamaIndex FAQ
Common questions about integrating Scribe MCP Server with LlamaIndex.