Glama MCP Server for Pydantic AI 8 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Glama through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Glama "
"(8 tools)."
),
)
result = await agent.run(
"What tools are available in Glama?"
)
print(result.data)
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.
Pydantic AI validates every Glama tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the Glama MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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 with type-safe schemas
Why Use Pydantic AI with the Glama MCP Server
Pydantic AI provides unique advantages when paired with Glama through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Glama integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Glama connection logic from agent behavior for testable, maintainable code
Glama + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Glama MCP Server delivers measurable value.
Type-safe data pipelines: query Glama with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Glama tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Glama and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Glama responses and write comprehensive agent tests
Glama MCP Tools for Pydantic AI (8)
These 8 tools become available when you connect Glama to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting Glama to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiGlama + Pydantic AI FAQ
Common questions about integrating Glama MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.