Google Firestore Collection MCP Server for LlamaIndexGive LlamaIndex instant access to 3 tools to Delete Document, Get Document, Set Document
GDPR Free for SubscribersLlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Google Firestore Collection 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 MCP Server for LlamaIndex
The Google Firestore Collection MCP Server for LlamaIndex is a standout in the Industry Titans category — giving your AI agent 3 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
Geminiimport 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 Google Firestore Collection. "
"You have 3 tools available."
),
)
response = await agent.run(
"What tools are available in Google Firestore Collection?"
)
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 Google Firestore Collection MCP Server
This server strips away dangerous global GCP permissions. It gives your AI agent one surgical superpower: the ability to query, insert, and update documents inside one specific Firestore Collection.
LlamaIndex agents combine Google Firestore Collection tool responses with indexed documents for comprehensive, grounded answers. Connect 3 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.
By strictly scoping access, your AI can safely manage structured data, store chat histories, and process complex NoSQL queries without ever touching your critical cloud databases.
The Superpowers
- Absolute Containment: The agent is locked to a single collection. It cannot query other collections or drop your production data.
- Native Firestore Integration: Direct interactions with Firestore, supporting rich document structures and filters.
- Plug & Play Database: Instantly gives your agent a scalable NoSQL database to store structured memories and application state.
The Google Firestore Collection MCP Server exposes 3 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 3 Google Firestore Collection tools available for LlamaIndex
When LlamaIndex connects to Google Firestore Collection through Vinkius, your AI agent gets direct access to every tool listed below — spanning nosql, document-database, data-persistence, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Delete document on Google Firestore Collection
Delete a document from the Google Firestore collection
Get document on Google Firestore Collection
Read a document from the configured Google Firestore collection
Set document on Google Firestore Collection
If the document exists, fields are updated. Create or update a document in the Google Firestore collection
Connect Google Firestore Collection to LlamaIndex via MCP
Follow these steps to wire Google Firestore Collection into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind 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 Google Firestore Collection MCP Server
LlamaIndex provides unique advantages when paired with Google Firestore Collection through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Google Firestore Collection tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Google Firestore Collection tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Google Firestore Collection, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Google Firestore Collection tools were called, what data was returned, and how it influenced the final answer
Google Firestore Collection + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Google Firestore Collection MCP Server delivers measurable value.
Hybrid search: combine Google Firestore Collection real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Google Firestore Collection 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 Google Firestore Collection for fresh data
Analytical workflows: chain Google Firestore Collection queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Google Firestore Collection in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Google Firestore Collection immediately.
"Get the document with ID 'task-99'."
"Save this workflow result to a new document 'result-123': {"status": "done", "score": 95}."
"Delete the temporary 'draft-01' document."
Troubleshooting Google Firestore Collection MCP Server with LlamaIndex
Common issues when connecting Google Firestore Collection to LlamaIndex through Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGoogle Firestore Collection + LlamaIndex FAQ
Common questions about integrating Google Firestore Collection 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?
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