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muse-glimmer-agent

by saram-io
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Python
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Muse Glimmer + MCP + Langfuse (local)

Минимальный агент на Pydantic AI, который:

  • запускает muse-glimmer (агентную модель Meta на 30 млрд параметров) через локальный экземпляр Ollama;

  • подключает MCP: локальный сервер FastMCP (mcp_server.py) работает как stdio-подпроцесс и предоставляет get_weather, get_current_time и list_ollama_models в качестве инструментов агента;

  • отправляет полные трейсы (запросы моделей, вызовы инструментов, результаты) в локальный экземпляр Langfuse для наблюдаемости.

Требования

  • Ollama запущен с загруженной моделью:

    ollama pull muse-glimmer

  • Локальный экземпляр Langfuse (например, через docker compose из репозитория Langfuse), доступный по адресу http://localhost:3000.

  • uv (или используйте python3 -m venv + pip).

Related MCP server: MCP Ollama Consult Server

Настройка

cp .env.example .env   # then fill in your Langfuse keys
uv sync                # installs pydantic-ai, fastmcp, langfuse, ...

.env:

LANGFUSE_PUBLIC_KEY=pk-lf-...
LANGFUSE_SECRET_KEY=sk-lf-...
LANGFUSE_BASE_URL=http://localhost:3000
LANGFUSE_HOST=http://localhost:3000

OLLAMA_BASE_URL=http://localhost:11434/v1
OLLAMA_MODEL=muse-glimmer

Запуск

uv run python agent.py

После запуска агент отвечает на три демонстрационных запроса, вызывая MCP-инструменты по мере необходимости:

🧑  User: What is the weather in Paris today?
🤖  Agent: Weather in Paris: clear skies, 22°C, humidity 51%.

🧑  User: What time is it in Tokyo right now?
🤖  Agent: The current time in Asia/Tokyo is 2026-08-25 21:04:33 JST.

🧑  User: Which Ollama models are available locally?
🤖  Agent: NAME  ID  SIZE  MODIFIED ...

Наблюдаемость (Langfuse)

Откройте http://localhost:3000 → Traces. Каждый запуск создаёт трейсы с именем muse-glimmer-mcp-agent, в которых видны спаны для запросов модели (входные/выходные токены) и каждого вызова MCP-инструмента. Можно посмотреть входные данные, результаты, задержки и стоимость.

Предоставление агента как MCP-сервера

agent_mcp_server.py переворачивает архитектуру: агент сам становится MCP-сервером и предоставляет один инструмент — ask_agent(prompt) — любому MCP-клиенту (Claude Desktop, VS Code, Cursor, другому агенту на Pydantic AI, ...). Каждый вызов запускает muse-glimmer через Ollama, сохраняя внутренние MCP-инструменты под капотом, и записывает трейсы в Langfuse.

Запуск сервера

uv run python agent_mcp_server.py

Настройка клиента

Укажите любому MCP-клиенту на этот сервер, используя Python из вашего uv-окружения (uv run --project <repo> python тоже подойдёт).

Claude Desktop — claude_desktop_config.json:

{
  "mcpServers": {
    "muse-glimmer-agent": {
      "command": "/home/d3lee/.local/bin/uv",
      "args": ["run", "--project", "/home/d3lee/my-repos/pydantic-ai-mcp-server-sample", "python", "agent_mcp_server.py"]
    }
  }
}

VS Code — .vscode/mcp.json:

{
  "servers": {
    "muse-glimmer-agent": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "--project", "/home/d3lee/my-repos/pydantic-ai-mcp-server-sample", "python", "agent_mcp_server.py"]
    }
  }
}

Cursor — .cursor/mcp.json:

{
  "mcpServers": {
    "muse-glimmer-agent": {
      "command": "uv",
      "args": ["run", "--project", "/home/d3lee/my-repos/pydantic-ai-mcp-server-sample", "python", "agent_mcp_server.py"]
    }
  }
}

Состав проекта

Файл

Назначение

agent.py

Агент Pydantic AI: модель Ollama + MCP + Langfuse

mcp_server.py

FastMCP server (stdio), exposing a local tool

agent_mcp_server.py

Предоставляет агент as a MCP server (tool ask_agent)

.env

Установка Langfuse + Ollama

pydantic-ai-mcp-server-sample

Available Tools

1 tool
ask_agentC

Ask the Muse Glimmer AI agent a question.

NameRequiredDescriptionDefault
promptYes

Output Schema

NameRequiredDescription
resultYes

TDQS

C2.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavior. It states 'ask a question,' which implies a query or read operation, but it does not disclose any side effects, response format, permissions, or limitations. This is a significant gap for a tool with no annotation support.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, short sentence with no wasted words. The verb is front-loaded and the resource is clear. It is appropriately concise for a simple tool, though this brevity comes at the cost of detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has an output schema (not shown), so return values may be covered, but the description lacks any context about the nature of questions, expected inputs, or edge cases. An agent has no information about what kinds of prompts are valid or what the response will look like, making it incomplete for a one-parameter tool with no annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, and the description does not mention the 'prompt' parameter at all. It adds no information about the expected format, content, or purpose of the prompt beyond what the parameter name itself implies. The description fails to compensate for the schema coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (ask) and the resource (Muse Glimmer AI agent). It is specific enough for a tool with no siblings, though it is generic and could be more detailed about the scope of questions. The verb+resource is clear and not tautological.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives, and there are no sibling tools. The description does not mention any prerequisites, context, or conditions for use, leaving the agent to infer that it should be used when a question needs answering.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev0.1.0
    • First observedask_agent
Deploy Server
Context Every Agent Can ShareAgents share what they learn, hand off work, and keep it when you switch models. Start with 5 GB free.fulcradynamics.comAd

TDQS

B3.3/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or misselection. The tool 'ask_agent' has a clearly distinct purpose as the sole entry point for questioning the agent.

Naming Consistency5/5

The single tool name 'ask_agent' follows a consistent verb_noun pattern, aligned with its singular function. Naming is clear and predictable.

Tool Count3/5

The server has only one tool, which feels thin but is appropriate for its narrow purpose of asking a question. It is not an extreme mismatch, but it borders on insufficient for broader agent workflows.

Completeness5/5

The domain is defined as asking the Muse Glimmer agent a question, and the single tool fully covers this operation. There are no obvious gaps within the stated scope.

Maintenance

ActivityMaintained
ResponsivenessSyncing

Resources

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