How do I find the Object Key for a specific table?

Use the `list_objects` tool. It will return all data objects in your application along with their names and unique keys (e.g., `object_1`). Use these keys for all subsequent record operations.

Can I search records using multiple conditions?

Yes. Use the `search_records` tool and provide a JSON array of filter objects. For example: `[{"field":"field_1", "operator":"is", "value":"active"}, {"field":"field_2", "operator":"contains", "value":"premium"}]`.

How do I know which fields to use when creating a record?

Use the `list_object_fields` tool with the target `object_key`. It will list all fields, their types, and their keys (e.g., `field_1`), which you must use as property names in your data JSON.

Knack MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Knack
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Knack as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 Knack. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Knack?"
    )
    print(response)

asyncio.run(main())

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* 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 Knack MCP Server

Connect your Knack application to any AI agent and take full control of your no-code database through natural conversation.

LlamaIndex agents combine Knack 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

Database Schema Discovery — List all objects and fields to understand your data structure without leaving the chat
Record Management — Create, retrieve, update, and delete records in any object securely
Advanced Querying — Search for specific records using complex filters based on any field criteria
Data Auditing — Get detailed summaries of specific records to verify information or track changes
Bulk Operations — Effortlessly manage multiple records by providing structured data to your agent

The Knack 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 Knack to LlamaIndex via MCP

Follow these steps to integrate the Knack 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 Knack

Why Use LlamaIndex with the Knack MCP Server

LlamaIndex provides unique advantages when paired with Knack through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Knack tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Knack tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Knack, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Knack tools were called, what data was returned, and how it influenced the final answer

Knack + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Knack MCP Server delivers measurable value.

Hybrid search: combine Knack real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Knack 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 Knack for fresh data

Analytical workflows: chain Knack queries with LlamaIndex's data connectors to build multi-source analytical reports

Knack MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Knack to LlamaIndex via MCP:

create_record

You must provide the data as a JSON string where keys are the field keys (e.g., field_1). Ensure you have checked the object schema first to know which fields are required. Create a new record in a Knack object

delete_record

Use with caution as this action cannot be undone. Delete a record from a Knack object

get_object_schema

Returns metadata including the object name, key, and high-level structure. Use this to verify you are working with the correct database table. Get the schema of a specific Knack object

get_record

Requires both the object_key and the record_id. Use this for detailed auditing of a specific entry. Get a specific record by ID

list_account_applications

Use this to verify access or discover application IDs. List all applications in the account

list_object_fields

This is crucial for understanding the data types and identifying the field keys (field_1, field_2, etc.) needed for creating or updating records. List all fields for a specific Knack object

list_objects

This is the first step to understand the database structure and find the "Object Key" needed for record operations. List all objects in the Knack application

list_records

You must provide the object_key. Use this to browse the actual data stored in your database. List records for a specific Knack object

search_records

The filters must be provided as a JSON string following the Knack Filter format (e.g., "[{\"field\":\"field_1\", \"operator\":\"is\", \"value\":\"test\"}]"). Search for records using filters

update_record

Provide only the fields you wish to change in the JSON string data. This is a partial update. Update an existing record in a Knack object

Example Prompts for Knack in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Knack immediately.

"List all database objects in my Knack app"

"Find all premium customers in 'object_1'"

"Create a new customer in 'object_1' with name 'Sarah' and email 'sarah@example.com'"

Troubleshooting Knack MCP Server with LlamaIndex

Common issues when connecting Knack to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Knack + LlamaIndex FAQ

Common questions about integrating Knack MCP Server with LlamaIndex.

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.