GatherContent MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add GatherContent as an MCP tool provider through the 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 GatherContent. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in GatherContent?"
)
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 GatherContent MCP Server
Connect your GatherContent (by Bynder) account to any AI agent to automate your structured content operations and editorial workflows through the Model Context Protocol (MCP). GatherContent is a content operations platform that helps teams organize and produce structured content at scale. This MCP server enables you to manage your content projects, retrieve item data, and track workflow statuses directly through natural conversation.
LlamaIndex agents combine GatherContent tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
Key Features
- Project Orchestration — List all content projects and fetch detailed configuration metadata for each environment.
- Content Oversight — Access and retrieve structured data from your content items (pages, articles), including field-level metadata.
- Workflow Automation — Monitor and list the workflow statuses (e.g., Draft, Review, Published) configured for your projects.
- Item Management — Programmatically create new content items or update existing ones to keep your production pipeline moving.
- Template Discovery — Access available content templates and fetch field schemas to ensure consistent data entry.
- Folder Navigation — List project folders to understand your content hierarchy and organization.
- User Identity — Fetch profile information for the authenticated API identity to verify access levels.
- Real-time Synchronization — Keep your structured content strategy accessible to your AI assistant without leaving your primary workspace.
The GatherContent MCP Server exposes 12 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 GatherContent to LlamaIndex via MCP
Follow these steps to integrate the GatherContent 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 12 tools from GatherContent
Why Use LlamaIndex with the GatherContent MCP Server
LlamaIndex provides unique advantages when paired with GatherContent through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine GatherContent tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain GatherContent tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query GatherContent, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what GatherContent tools were called, what data was returned, and how it influenced the final answer
GatherContent + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the GatherContent MCP Server delivers measurable value.
Hybrid search: combine GatherContent real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query GatherContent 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 GatherContent for fresh data
Analytical workflows: chain GatherContent queries with LlamaIndex's data connectors to build multi-source analytical reports
GatherContent MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect GatherContent to LlamaIndex via MCP:
create_content_item
Create new item
get_item_content
Get item metadata/content
get_my_identity
Get current user profile
get_project_details
Get project metadata
get_template_schema
Get template fields
list_content_projects
List all projects
list_content_templates
List project templates
list_project_folders
List project folders
list_project_items
List content items
list_workflow_statuses
) for a project. List workflow states
update_content_item
Modify item metadata
verify_api_connection
Check connection
Example Prompts for GatherContent in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with GatherContent immediately.
"List all active content projects in my account."
"Show me the content items in the 'Blog Production' project (ID: 12345)."
"Get the field values for item 'item_98765'."
Troubleshooting GatherContent MCP Server with LlamaIndex
Common issues when connecting GatherContent to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGatherContent + LlamaIndex FAQ
Common questions about integrating GatherContent 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 GatherContent 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 GatherContent to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.