Bleez MCP Server for LlamaIndexGive LlamaIndex instant access to 8 tools to Create Accounting Entry, Create Invoice, Get Account Info, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Bleez 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 Bleez app connector for LlamaIndex is a standout in the Document Management category — giving your AI agent 8 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 Bleez. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Bleez?"
)
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 Bleez MCP Server
Connect your Bleez account to any AI agent and take full control of your French accounting workflows and digital document management through natural conversation.
LlamaIndex agents combine Bleez tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Ledger Orchestration — List and manage accounting journal entries programmatically, retrieving detailed historical ledger data in real-time
- Invoice Lifecycle Management — Create and track sales and purchase invoices programmatically to maintain a perfectly coordinated billing pipeline
- Document Ingestion — Programmatically upload digital documents (PDF/Images) to the 'Factures à traiter' module for high-fidelity automated processing
- Contact Discovery — Access complete directories of customer and supplier profiles to coordinate your organizational relationship ecosystem
- Folder Visibility — Retrieve organization-level metadata and folder settings directly through your agent for instant financial reporting
The Bleez MCP Server exposes 8 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 8 Bleez tools available for LlamaIndex
When LlamaIndex connects to Bleez through Vinkius, your AI agent gets direct access to every tool listed below — spanning ledger-management, invoicing, accounting-automation, 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.
Provide date, label, and account lines. Create a new accounting entry
Provide contact ID and amount. Create a new invoice
Get current account settings
List all accounting journal entries
List all customers
List all invoices
List all suppliers
Upload a digital document for processing
Connect Bleez to LlamaIndex via MCP
Follow these steps to wire Bleez 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 Bleez MCP Server
LlamaIndex provides unique advantages when paired with Bleez through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Bleez tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Bleez tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Bleez, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Bleez tools were called, what data was returned, and how it influenced the final answer
Bleez + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Bleez MCP Server delivers measurable value.
Hybrid search: combine Bleez real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Bleez 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 Bleez for fresh data
Analytical workflows: chain Bleez queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Bleez in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Bleez immediately.
"List all active customers in my Bleez folder."
"Upload the file 'invoice_123.pdf' to Bleez for processing."
"Show the last 5 accounting entries recorded this month."
Troubleshooting Bleez MCP Server with LlamaIndex
Common issues when connecting Bleez to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpBleez + LlamaIndex FAQ
Common questions about integrating Bleez MCP Server with LlamaIndex.