How to Use the TextGrid MCP in LlamaIndex

Q: How does TextGrid help LlamaIndex search through message history?

You run listsmsmessages, and the returned content is indexed. Later, you can query your knowledge base to find specific messages—for instance, finding all interactions related to a certain customer ID.

Q: Can I use LlamaIndex to ground answers about TextGrid's account status?

Yes. By indexing the output of getaccountdetails, your agent can answer questions like 'What is our current balance?' or 'When did we last update our profile?' directly from the stored API data.

Q: Does TextGrid allow LlamaIndex to understand voice call history?

You can index records from listvoicecalls. This allows your RAG application to answer questions about past calls—like 'How many calls did we make last month?' or 'What were the peak calling times?' based on recorded metadata.

Q: If I use LlamaIndex, can it help manage my API keys?

You can index results from listapikeys. This turns a simple list into searchable knowledge. You can ask, 'What key was created on January 1st?' and get an answer grounded in the data.

Q: For data privacy, what kind of information does TextGrid touch when used with LlamaIndex?

The server primarily handles metadata: usage records (from getusagestatistics), message content details, and account configuration settings. This structured data is what gets indexed.

Index TextGrid data with LlamaIndex for knowledge-augmented AI.

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Setup TextGrid with LlamaIndex

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Creating Searchable Communication Logs

LlamaIndex makes your API output searchable. When you run `list_sms_messages`, the resulting list of messages gets indexed into a vector store. You can then query past communications and get answers grounded in the actual message content. Forget hallucinations. This lets your RAG application pull specific details from historical logs, giving context that raw data dumps never could.

Knowledge-Augmented Account Audits

Don't just read usage reports; index them. By running `get_usage_statistics`, you capture cost records and detailed API interactions. This knowledge base lets your agent answer questions like, 'What was our spending pattern last quarter?' based on the indexed data. It turns transient API calls into permanent, queryable knowledge for your application.

Contextual Number Discovery

The system can index results from `search_available_numbers`. This means you build a searchable guide of available numbers and their compliance details. You aren't just listing them; you're allowing your agent to find the *best* number for a specific regulatory region. This makes complex, multi-criteria decisions possible within a unified index.

Setup guide

Set up TextGrid MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all TextGrid MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to TextGrid tools.",
)
response = await agent.run("List recent TextGrid data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by TextGrid. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about TextGrid MCP in LlamaIndex

You run `list_sms_messages`, and the returned content is indexed. Later, you can query your knowledge base to find specific messages—for instance, finding all interactions related to a certain customer ID.

Yes. By indexing the output of `get_account_details`, your agent can answer questions like 'What is our current balance?' or 'When did we last update our profile?' directly from the stored API data.

You can index records from `list_voice_calls`. This allows your RAG application to answer questions about past calls—like 'How many calls did we make last month?' or 'What were the peak calling times?' based on recorded metadata.

You can index results from `list_api_keys`. This turns a simple list into searchable knowledge. You can ask, 'What key was created on January 1st?' and get an answer grounded in the data.

The server primarily handles metadata: usage records (from `get_usage_statistics`), message content details, and account configuration settings. This structured data is what gets indexed.

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