Flock MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Flock 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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Vinkius supports streamable HTTP and SSE.
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 Flock. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Flock?"
)
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 Flock MCP Server
Connect your Flock bot to any AI agent and take full control of your team communication, private groups, and organizational roster through natural conversation.
LlamaIndex agents combine Flock 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
- Rich Messaging Orchestration — Provision massively fast payloads strictly into Flock chats, utilizing `
to render rich enterprise attachments and formatted layouts natively - Public Channel Discovery — Enumerate explicitly attached public channels and execute bulk iterations to capture global namespaces and routing configurations synchronously
- Private Group Management — Identify bounded private groups and retrieve precise physical definitions detailing exactly how hidden groups operate within your enterprise
- Organizational Roster Auditing — Discovers global identity blocks mapping direct @` aliases to absolute string UUIDs to solve accurate routing for the entire company
- Identity Metadata Retrieval — Perform structural extraction of profile metadata linked to Flock users, resolving time zones and LDAP/SSO properties securely
- Chat Log Ingestion — Pull chronological asynchronous logs from any room, extracting raw JSON objects mapping historical strings natively from chat fetchers
- Membership Oversight — Audit IAM boundaries and identify explicit active UUIDs directly attached to channels or groups to verify intended audiences flawlessly
The Flock 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.
How to Connect Flock to LlamaIndex via MCP
Follow these steps to integrate the Flock MCP Server with LlamaIndex.
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 10 tools from Flock
Why Use LlamaIndex with the Flock MCP Server
LlamaIndex provides unique advantages when paired with Flock through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Flock tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Flock tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Flock, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Flock tools were called, what data was returned, and how it influenced the final answer
Flock + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Flock MCP Server delivers measurable value.
Hybrid search: combine Flock real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Flock 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 Flock for fresh data
Analytical workflows: chain Flock queries with LlamaIndex's data connectors to build multi-source analytical reports
Flock MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Flock to LlamaIndex via MCP:
channels_get_info
Retrieve explicit Channel descriptions and banner logic mappings
channels_list_members
Identify explicit Active UUIDs directly attached evaluating Channel ingress
channels_list_public
Enumerate explicitly attached `public` channels active within Flock
chat_fetch_messages
Extracts raw JSON objects mapping historical strings natively returned by `chat.fetchMessages`. Read recent structural Chat payloads targeting a Flock Room
chat_send_message
Detects if formatted `<flockml>` definitions are passed and converts the payload dynamically bypassing standard Markdown limits rendering rich enterprise attachments. Provision a massively fast payload strictly into an established Flock Chat
groups_get_info
Inspect deep internal credentials identifying a precise Private Group
groups_list_members
Crucial for verifying sensitive message targets. Audit IAM boundaries explicitly granting read permissions to a Group
groups_list_private
Returns arrays necessary to retrieve correct routing UUIDs. Identify bounded Private Groups tracking strict IAM boundaries
roster_list_directory
Returns explicit array definitions mapping direct `@` aliases to absolute string UUIDs solving accurate routing natively. Identify precise active Human constraints navigating the entire Flock company
users_get_metadata
Perform structural extraction of metadata linked to a Flock Identity
Example Prompts for Flock in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Flock immediately.
"Send a message to group 'g:123': 'Project update is live!'"
"List all public channels in my Flock workspace"
"Get the metadata for user '@john_doe'"
Troubleshooting Flock MCP Server with LlamaIndex
Common issues when connecting Flock to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFlock + LlamaIndex FAQ
Common questions about integrating Flock MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Flock with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Flock to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.