How to Use the Glama MCP in LlamaIndex

Q: Can LlamaIndex query Glama model specs?

It can. Your agent uses glamagetgatewaymodeldetails to fetch parameters, ensuring your RAG queries stay within model limits.

Q: Does Glama help LlamaIndex search across servers?

It does. You use glamalistmcpservers to find and index tools across the directory, which your LlamaIndex agent then treats as searchable data.

Index live API data from the Glama registry directly into your LlamaIndex knowledge base.

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Connect Glama MCP to LlamaIndex

Create your Vinkius account to connect Glama to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Glama with LlamaIndex

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Indexing Glama tools with LlamaIndex

Turn external API responses into searchable knowledge. Your LlamaIndex agent invokes `glama_list_mcp_servers` to find data sources, then indexes the output. This makes your RAG pipeline grounded in live data. You get answers based on current API results rather than static documents.

Querying Glama attributes in LlamaIndex

Inspect the parameters and context windows of your models. Use `glama_get_mcp_attributes` to filter which servers your LlamaIndex application accesses. It allows you to build a refined knowledge base that only queries high-performance servers. You control the scope of your agent's data retrieval.

Unified model details for LlamaIndex

Retrieve exact model configurations before you query. Your agent uses `glama_get_gateway_model_details` to check price and context limits. LlamaIndex agents use this to make smart decisions about which models to use for specific RAG tasks. It prevents errors by validating specs beforehand.

Setup guide

Set up Glama 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 Glama 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 Glama tools.",
)
response = await agent.run("List recent Glama data")

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Common questions about Glama MCP in LlamaIndex

You install the MCP adapters and point your client at the Glama endpoint. Then you use `glama_list_mcp_servers` to populate your tool list.

It can. Your agent uses `glama_get_gateway_model_details` to fetch parameters, ensuring your RAG queries stay within model limits.

It does. You use `glama_list_mcp_servers` to find and index tools across the directory, which your LlamaIndex agent then treats as searchable data.

The output goes into your vector store. You can then query it alongside your other documents for grounded AI responses.

All connections are authenticated via a single endpoint token. The server only processes the specific tool outputs you choose to index into your local vector store.

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