# Msty Admin Mcp

> AI-powered administration for Msty Studio Desktop. 24 MCP tools for database insights, config sync, local model orchestration, and Claude handoff workflows.

- **Type:** MCP server
- **Install:** `agentstack add mcp-m-pineapple-msty-admin-mcp`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [M-Pineapple](https://agentstack.voostack.com/s/m-pineapple)
- **Installs:** 0
- **Category:** [Databases](https://agentstack.voostack.com/c/databases)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [M-Pineapple](https://github.com/M-Pineapple)
- **Source:** https://github.com/M-Pineapple/msty-admin-mcp

## Install

```sh
agentstack add mcp-m-pineapple-msty-admin-mcp
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Msty Admin MCP — v5.0.0

Comprehensive MCP (Model Context Protocol) server for administering Msty Studio Desktop with 36 tools across 6 phases, Bloom behavioral evaluation, and support for four service backends (Ollama, MLX, LLaMA.cpp, Vibe CLI Proxy).

**Requirements**: Python 3.10+, MCP SDK v1.0.0+

**Latest**: v5.0.0 (2024) — Msty 2.4.0+ architecture, port-based service discovery, Bloom integration, Streamable HTTP transport

> **New to Bloom?** Jump to the [Bloom Behavioral Evaluation](#bloom-behavioral-evaluation) section or read the [full Bloom guide](docs/BLOOM_GUIDE.md).

---

## Installation

### Quick Start
```bash
pip install msty-admin-mcp
msty-admin-mcp  # Runs on stdio (default MCP transport)
```

### With HTTP Transport
```bash
pip install msty-admin-mcp[http]
msty-admin-mcp --transport streamable-http  # Runs on http://localhost:8000
```

### From Source
```bash
git clone https://github.com/M-Pineapple/msty-admin-mcp
cd msty-admin-mcp
pip install -e .
```

---

## Configuration

Environment variables (all optional, sensible defaults):

```bash
# Msty installation host
MSTY_HOST=127.0.0.1

# Service backend ports
MSTY_AI_PORT=11964           # Local AI (Ollama)
MSTY_MLX_PORT=11973          # MLX service
MSTY_LLAMACPP_PORT=11454     # LLaMA.cpp service
MSTY_VIBE_PORT=8317          # Vibe CLI Proxy

# Service timeout
MSTY_TIMEOUT=10              # Seconds

# Bloom integration (required for Phase 6 tools)
ANTHROPIC_API_KEY=sk-...     # Required for Bloom judge model
```

---

## Architecture

### Service Discovery (Msty 2.4.0+)

Msty 2.4.0+ exposes services via ports. The MCP server auto-detects available services:

```
Msty Studio Desktop
├── Local AI (Ollama) → port 11964
├── MLX → port 11973
├── LLaMA.cpp → port 11454
└── Vibe CLI Proxy → port 8317

         ↓ (port-based discovery)

MCP Server (stdio / HTTP)
├── Phase 1: Foundational (6 tools)
├── Phase 2: Configuration (4 tools)
├── Phase 3: Service Integration (11 tools)
├── Phase 4: Intelligence (5 tools)
├── Phase 5: Calibration (4 tools)
└── Phase 6: Bloom Evaluation (6 tools)
```

### Data Storage

Metrics and calibration results stored in SQLite:
- **Location**: `~/.msty-admin/msty_admin_metrics.db`
- **Tables**: `model_metrics`, `calibration_tests`, `handoff_triggers`, `conversation_analytics`
- **Auto-init**: Database created on first tool run

---

## Use Cases

### 1. Database Inspection

Query Msty's internal SQLite database directly:

```python
# Get all configured tools
read_msty_database(
    query="SELECT name, version FROM tools"
)
```

### 2. Health Monitoring

Check system health across all components:

```python
analyse_msty_health()
# Returns: CPU, memory, database size, service connectivity, recent errors
```

### 3. Configuration Sync

Export/import Msty configurations:

```python
# Export current configuration
export_tool_config(tool_name="research_assistant")

# Import configuration
import_tool_config(tool_data={...})
```

### 4. Multi-Backend Orchestration

Chat with different model backends transparently:

```python
# Chat with Ollama
chat_with_local_model(model="llama3.2:7b", messages=[...])

# Chat with MLX
chat_with_mlx_model(model="mistral", messages=[...])

# Chat with LLaMA.cpp
chat_with_llamacpp_model(model="dolphin", messages=[...])
```

### 5. Performance Analytics

Analyze model performance over time:

```python
get_model_performance_metrics(
    model_id="llama3.2:7b",
    timeframe="7d"
)
# Returns: latency, throughput, quality scores, error rates
```

### 6. Model Calibration

Test and calibrate local models:

```python
run_calibration_test(
    model_id="llama3.2:7b",
    category="reasoning",
    passing_threshold=0.6
)
# Returns: quality scores, pass rate, recommendations
```

### 7. Tiered AI Workflow

Evaluate when to hand off tasks to Claude:

```python
identify_handoff_triggers(
    analyse_recent=True
)
# Returns: patterns where local models underperform

run_calibration_test(model_id="llama3.2:3b", category="analysis")
evaluate_response_quality(prompt="...", response="...", category="analysis")
```

### 8. Behavioral Evaluation (Bloom)

Evaluate problematic behaviors using Anthropic's Bloom framework:

```python
bloom_evaluate_model(
    model="llama3.2:7b",
    behavior="sycophancy",
    task_category="advisory_tasks",
    total_evals=3
)
# Returns: evaluation results with quality scores

bloom_check_handoff(
    model="llama3.2:3b",
    task_category="research_analysis"
)
# Returns: handoff recommendation with confidence
```

---

## Tools Summary (36 Total)

### Phase 1: Foundational (6 tools)
- `detect_msty_installation`: Find Msty installation and paths
- `read_msty_database`: Query Msty SQLite database
- `list_configured_tools`: List all configured tools
- `get_model_providers`: List available model providers
- `analyse_msty_health`: Comprehensive system health
- `get_server_status`: MCP server status

### Phase 2: Configuration (4 tools)
- `export_tool_config`: Export tool configurations
- `sync_claude_preferences`: Sync Claude preferences
- `generate_persona`: Create AI personas
- `import_tool_config`: Import configurations

### Phase 3: Service Integration (11 tools)
- `get_service_status`: Status of all services
- `list_available_models`: List models across services
- `query_local_ai_service`: Query Local AI/Ollama
- `chat_with_local_model`: Chat with Local AI models
- `recommend_model`: Get model recommendations
- `list_mlx_models`: List MLX models
- `chat_with_mlx_model`: Chat with MLX models
- `list_llamacpp_models`: List LLaMA.cpp models
- `chat_with_llamacpp_model`: Chat with LLaMA.cpp models
- `get_vibe_proxy_status`: Check Vibe proxy
- `query_vibe_proxy`: Query Vibe proxy

### Phase 4: Intelligence (5 tools)
- `get_model_performance_metrics`: Model performance analytics
- `analyse_conversation_patterns`: Conversation analysis
- `compare_model_responses`: Compare model outputs
- `optimise_knowledge_stacks`: Stack optimization
- `suggest_persona_improvements`: Persona suggestions

### Phase 5: Calibration (4 tools)
- `run_calibration_test`: Test model quality
- `evaluate_response_quality`: Score responses (0.0-1.0)
- `identify_handoff_triggers`: Find escalation patterns
- `get_calibration_history`: Historical results

### Phase 6: Bloom Evaluation (6 tools)
- `bloom_evaluate_model`: Run Bloom evaluation
- `bloom_check_handoff`: Check handoff recommendation
- `bloom_get_history`: Get past evaluations
- `bloom_list_behaviors`: List evaluable behaviors
- `bloom_get_thresholds`: Get quality thresholds
- `bloom_validate_model`: Validate model suitability

---

## Bloom Behavioral Evaluation

Phase 6 introduces behavioral evaluation powered by [Anthropic's Bloom framework]([https://github.com/anthropics/safety-research/tree/main/bloom](https://www.anthropic.com/research/bloom)). Rather than testing what a model knows, Bloom tests how it behaves — detecting failure modes like sycophancy, hallucination, and overconfidence that standard benchmarks miss.

### How it works

Bloom sends your local model a series of prompts designed to trigger specific failure modes. An external judge model (Claude, via ANTHROPIC_API_KEY) then scores the responses. The results tell you whether a model is safe to use for a given task category, or whether it should hand off to Claude instead.

### Quick example

```python
# 1. Check the model is suitable
bloom_validate_model(model="llama3.2:7b")

# 2. Evaluate a specific behavior
bloom_evaluate_model(
    model="llama3.2:7b",
    behavior="sycophancy",
    task_category="advisory_tasks",
    total_evals=3,
    max_turns=2
)

# 3. Should this model handle advisory work, or hand off to Claude?
bloom_check_handoff(
    model="llama3.2:7b",
    task_category="advisory_tasks"
)
```

### What Bloom evaluates

Eight behaviors are tested out of the box: sycophancy, hallucination, overconfidence, scope creep, task quality degradation, certainty calibration, context window degradation, and instruction following. Each maps to one of four task categories (research analysis, data processing, advisory tasks, general tasks) with defined quality thresholds and three-tier handoff triggers.

### Learn more

For the full walkthrough — including all tool parameters, behavior descriptions, threshold tables, practical workflows, customisation, and troubleshooting — see the **[Bloom Knowledge Base Guide](docs/BLOOM_GUIDE.md)**.

---

## Performance Expectations

### Apple Silicon (M1/M2/M3)

| Task | Model | Latency | Throughput |
|------|-------|---------|------------|
| Simple chat | llama3.2:3b | 200-300ms | 15-20 tok/s |
| Complex reasoning | llama3.2:7b | 500-800ms | 8-12 tok/s |
| Calibration test | llama3.2:7b | 5-10s | -- |
| Bloom evaluation | llama3.2:7b | 30-60s | -- |

### Hardware Recommendations

- **Minimal**: 8GB RAM, M1 (for 3b models only)
- **Standard**: 16GB RAM, M1/M2 (for up to 7b models)
- **Optimal**: 32GB+ RAM, M2/M3 (for 13b+ models)

---

## FAQ

### Q: How does service discovery work?
**A**: Msty 2.4.0+ exposes services on specific ports. The MCP server checks each port to detect available services. Fully automatic — no configuration needed.

### Q: Can I use this with Msty 

Your support helps me:

* Maintain and improve this project with new features
* Keep the project open-source and free for everyone
* Dedicate more time to addressing user requests and bug fixes
* Explore new terminal integrations and command intelligence

Thank you for considering supporting my work! 🙏

## Support

For issues, questions, or feature requests, visit: https://github.com/M-Pineapple/msty-admin-mcp
[](https://mseep.ai/app/m-pineapple-msty-admin-mcp)

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [M-Pineapple](https://github.com/M-Pineapple)
- **Source:** [M-Pineapple/msty-admin-mcp](https://github.com/M-Pineapple/msty-admin-mcp)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-m-pineapple-msty-admin-mcp
- Seller: https://agentstack.voostack.com/s/m-pineapple
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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