Bring Bpm
to LlamaIndex
Learn how to connect Zeev to LlamaIndex and start using 11 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Zeev MCP Server?
What you can do
- List and manage your pending tasks in real-time.
- Start new process requests with custom form data.
- Complete tasks and make decisions directly from your AI agent.
- Delegate tasks to other team members and track process history.
How it works
- Get your Zeev domain and API Key from your profile settings.
- Enter your credentials in Vinkius platform.
- Start chatting with your Zeev agent to manage your workflows.
Who is it for?
- Process managers looking for automated workflow control.
- Operations teams needing quick task execution.
- Developers integrating BPM into their AI-driven applications.
Built-in capabilities (11)
Cancel an active process request
Start a new process request in Zeev
Delegate a task to another user
Finish/Complete a Zeev task
Get current user information
Get details of a process definition
Get details of a specific process request
Get details of a specific Zeev task
List available process definitions
List process requests (instances) in Zeev
List pending tasks in Zeev
Why LlamaIndex?
LlamaIndex agents combine Zeev tool responses with indexed documents for comprehensive, grounded answers. Connect 11 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.
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Data-first architecture: LlamaIndex agents combine Zeev tool responses with indexed documents for comprehensive, grounded answers
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Query pipeline framework lets you chain Zeev tool calls with transformations, filters, and re-rankers in a typed pipeline
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Multi-source reasoning: agents can query Zeev, a vector store, and a SQL database in a single turn and synthesize results
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Observability integrations show exactly what Zeev tools were called, what data was returned, and how it influenced the final answer
Zeev in LlamaIndex
Zeev and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Zeev to LlamaIndex through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Zeev in LlamaIndex
The Zeev 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. All 11 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in LlamaIndex only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Zeev for LlamaIndex
Every tool call from LlamaIndex to the Zeev MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I start any process using the AI agent?
Yes, as long as you have the process ID and the required initial form data.
Is it possible to delegate tasks to specific users?
Yes, you can use the delegate_task tool by providing the Task ID and the target User ID.
Can I see the history of a request?
Yes, the get_request tool provides details including the current status and history of the instance.
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 Zeev 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.
BasicMCPClient not found
Install: pip install llama-index-tools-mcp
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