Glama MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Glama 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
Vinkius supports streamable HTTP and SSE.
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 Glama. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Glama?"
)
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 Glama MCP Server
Empower your local Vinkius terminal intelligence with the Glama.ai infrastructure bridge. Rather than navigating generic web interfaces to find compatible model contexts, let your core logic intuitively search, index, and introspect external MCP servers on the fly. In addition, harness the power to query multiple standard LLM networks via the Glama API Gateway, consolidating all programmatic text completion requirements cleanly.
LlamaIndex agents combine Glama tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- MCP Registry Scuba — Seamlessly query
list_mcp_serversandget_mcp_server_infoto find context protocols needed dynamically without interrupting deep-work focus states. - Gateway Proxies — List active LLM models navigating
list_gateway_modelsand push semantic prompts viarun_gateway_chatexecuting parallel logic chains outside local memory. - Matrix Attributes — Uncover standard classification strings with
get_mcp_attributesassessing global MCP logic matrices. - Hosted Telemetry — Scan local instances routing
get_hosted_instancesand actively parse behavior metrics pushing logs throughsend_telemetry.
The Glama MCP Server exposes 8 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 Glama to LlamaIndex via MCP
Follow these steps to integrate the Glama 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 8 tools from Glama
Why Use LlamaIndex with the Glama MCP Server
LlamaIndex provides unique advantages when paired with Glama through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Glama tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Glama tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Glama, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Glama tools were called, what data was returned, and how it influenced the final answer
Glama + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Glama MCP Server delivers measurable value.
Hybrid search: combine Glama real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Glama 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 Glama for fresh data
Analytical workflows: chain Glama queries with LlamaIndex's data connectors to build multi-source analytical reports
Glama MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Glama to LlamaIndex via MCP:
glama_get_gateway_model_details
g. "anthropic/claude-3-5-sonnet") to fetch the specific configurations exposed by the Glama unified API proxy. Investigate granular attributes (prices, context window, parameters) of a specific proxied Gateway Model
glama_get_gateway_models
Audit the complete list of AI models supported natively by the Glama OpenAI-compatible gateway
glama_get_hosted_instances
Cannot access public instances natively from here. Fetch all Private Hosted MCP instances assigned to your specific Glama account
glama_get_mcp_attributes
List filtering attributes and semantic categorizations mapped within the Glama MCP Registry
glama_get_mcp_server_info
Requires its namespace and slug. Extract detailed parameters and installation instructions for a specific Glama MCP server
glama_list_mcp_servers
Capable of loose text matching to discover new agentic capabilities. Search and list MCP servers directly from the global Glama directory
glama_run_gateway_chat
Bifurcate an isolated conversational prompt using a specific model through the Glama proxy network
glama_send_telemetry
Can be triggered after your AI uses a specific external server. Report semantic usage execution metrics back to the Glama Telemetry backend
Example Prompts for Glama in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Glama immediately.
"Find all MCP servers relating to CRM logic inside the registry, then let me know their basic descriptions."
"Are there smaller LLMs available on the Glama API gateway we can proxy text to quickly?"
"Report a successful telemetry execution map event back to Glama for the GitHub repo tool."
Troubleshooting Glama MCP Server with LlamaIndex
Common issues when connecting Glama to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGlama + LlamaIndex FAQ
Common questions about integrating Glama 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?
Connect Glama with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Glama to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.