How do I find my Metatext API Key?

Log in to Metatext and navigate to your account settings to find and copy your API Key.

Can I run inference on any model type?

Yes, as long as the model is fully trained and deployed, you can use the `run_model_inference` tool.

Is my AI data secure?

Absolutely. Your token is encrypted at rest and injected securely at runtime.

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

Name: Metatext
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 Metatext 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 Metatext. "
            "You have 10 tools available."
        ),
    )

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

asyncio.run(main())

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About Metatext MCP Server

Connect your Metatext account to any AI agent and take full control of your NLP models and data pipelines through natural conversation.

LlamaIndex agents combine Metatext 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

Model Orchestration — List all trained NLP models and fetch detailed metadata and training statuses
Real-time Inference — Programmatically run predictions, classifications, and extractions using your deployed models
Dataset Management — Enumerate datasets and create new records for model training or evaluation
Deployment Monitoring — List active model deployments and retrieve account usage information
Search & Discovery — Search for specific NLP models by name to quickly access their capabilities

The Metatext 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 Metatext to LlamaIndex via MCP

Follow these steps to integrate the Metatext 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 Metatext

Why Use LlamaIndex with the Metatext MCP Server

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

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

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

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

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

Metatext + LlamaIndex Use Cases

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

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

Data enrichment: query Metatext 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 Metatext for fresh data

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

Metatext MCP Tools for LlamaIndex (10)

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

create_dataset_record

Create a new record in a dataset

get_account_info

Get account information

get_dataset_details

Get details for a specific dataset

get_model_details

Get details for a specific model

list_dataset_records

List records in a dataset

list_model_deployments

List active model deployments

list_nlp_datasets

List all datasets

list_nlp_models

List all trained NLP models

run_model_inference

Run prediction on a model

search_nlp_models

Search models by name

Example Prompts for Metatext in LlamaIndex

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

"List all my trained NLP models in Metatext."

"Analyze this text with model ID 'mod_123': 'I love this product!'"

"Add a new record to dataset 'ds_987' with text 'Refund requested' and label 'Support'."

Troubleshooting Metatext MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Metatext + LlamaIndex FAQ

Common questions about integrating Metatext 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.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Metatext tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.