How do I use TelemetryFlow Python MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@TelemetryFlow Python MCP Server Analyze the latest telemetry data for anomalies" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

TelemetryFlow Python MCP Server

Official

by telemetryflow

Overview Schema Related Servers Score Discussions

Python

Local

Version License Python Version MCP Protocol LLM Providers TFO SDK Architecture Tests PostgreSQL ClickHouse

Enterprise-Grade Model Context Protocol Server with Multi-Provider LLM Integration

A comprehensive MCP server implementation built using Python and the official MCP SDK (mcp>=1.27.0), following Domain-Driven Design (DDD) patterns, providing seamless integration between the Model Context Protocol and 12 LLM providers with 112 models.

This server works as the AI integration layer for the TelemetryFlow Platform, providing:

Multi-provider LLM conversation capabilities via MCP (12 providers, 112 models)
Tool execution with 17 built-in tools (8 builtin + 4 PostgreSQL + 5 ClickHouse)
Resource management and prompt templates
TelemetryFlow SDK observability integration
TFO-Platform ContextCollector and PromptBuilder integration

TelemetryFlow Ecosystem

graph LR
    subgraph "TelemetryFlow Ecosystem v1.2.0"
        subgraph "Instrumentation"
            SDK_GO[TFO-Go-SDK v1.2.0<br/>OTEL SDK v1.43.0]
            SDK_PY[TFO-Python-SDK v1.2.0<br/>OTEL SDK v1.42.1]
            SDK_OTHER[TFO-AnyStacks-SDK<br/>OTEL AnyStacks SDK]
        end
        subgraph "Collection"
            AGENT[TFO-Agent v1.2.0<br/>OTEL SDK v1.43.0]
        end
        subgraph "Processing"
            COLLECTOR[TFO-Collector v1.2.1<br/>OTEL v0.152.1]
        end
        subgraph "AI Integration"
            MCP_GO[TFO-Go-MCP v1.2.0<br/>LLM + MCP]
            MCP_PY[TFO-Python-MCP v1.2.0<br/>LLM + MCP + Context]
        end
        subgraph "Platform"
            CORE[TFO-Core v1.4.0<br/>NestJS IAM]
        end
    end

    SDK_GO --> AGENT
    SDK_PY --> AGENT
    SDK_OTHER --> AGENT
    AGENT --> COLLECTOR
    COLLECTOR --> CORE
    MCP_GO --> CORE
    MCP_PY --> CORE
    MCP_GO -.-> |AI Capabilities| COLLECTOR
    MCP_PY -.-> |AI Capabilities| COLLECTOR

    style MCP_GO fill:#E1BEE7,stroke:#7B1FA2
    style MCP_PY fill:#FFA1E1,stroke:#C989B4,stroke-width:5px
    style SDK_GO fill:#C8E6C9,stroke:#388E3C
    style SDK_PY fill:#C8E6C9,stroke:#388E3C
    style SDK_OTHER fill:#DFDFDF,stroke:#0F0F0F
    style AGENT fill:#BBDEFB,stroke:#1976D2
    style COLLECTOR fill:#FFE0B2,stroke:#F57C00
    style CORE fill:#B3E5FC,stroke:#0288D1

Component	Version	OTEL Base	Role
TFO-Core	v1.4.0	-	Identity & Access Management
TFO-Agent	v1.2.0	SDK v1.43.0	Telemetry Collection Agent
TFO-Collector	v1.2.1	v0.152.1	Central Telemetry Processing
TFO-Go-SDK	v1.2.0	SDK v1.43.0	Go Instrumentation
TFO-Python-SDK	v1.2.0	SDK v1.42.1	Python Instrumentation
TFO-Go-MCP	v1.2.0	SDK v1.43.0	Go MCP Server + LLM AI
TFO-Python-MCP	v1.2.0	TFO SDK v1.2.0	Python MCP Server + LLM AI

Related MCP server: openai-mcp

Quick Facts

Property	Value
Version	1.2.0
Language	Python 3.11+
MCP Protocol	2024-11-05
MCP SDK	mcp>=1.27.0 (official)
Claude SDK	anthropic>=0.40.0
OTEL SDK	telemetryflow-sdk>=1.2.0
Architecture	DDD/CQRS
Transport	stdio, SSE (planned), WebSocket (planned)
Built-in Tools	17 tools + ContextCollector + PromptBuilder
Context Types	78 context types across 8 categories
Supported Models	112 models across 12 LLM providers
Test Coverage	98% coverage, 1174 tests
Async Runtime	asyncio with async/await

System Architecture

graph TB
    subgraph "Client Applications"
        CC[Claude Code]
        IDE[IDE Extensions]
        CLI[CLI Tools]
        CUSTOM[Custom MCP Clients]
    end

    subgraph "TFO-Python-MCP Server"
        subgraph "Presentation Layer"
            SERVER[MCP Server<br/>Official MCP SDK mcp>=1.27.0]
            TOOLS[Built-in Tools<br/>17 Tools]
            RESOURCES[Resources]
            PROMPTS[Prompts]
        end

        subgraph "Application Layer - CQRS"
            CMD[Commands]
            QRY[Queries]
            HANDLERS[Handlers]
            CONTEXT[ContextCollector<br/>78 Context Types]
            PROMPT_BUILDER[PromptBuilder<br/>60+ Analyst Personas]
        end

        subgraph "Domain Layer - DDD"
            AGG[Aggregates<br/>Session, Conversation]
            ENT[Entities<br/>Message, Tool, Resource]
            VO[Value Objects<br/>IDs, Content, Types]
            EVT[Domain Events]
            SVC[Domain Services]
        end

        subgraph "Infrastructure Layer"
            LLM[LLM API Client<br/>12 Providers]
            CONFIG[Configuration<br/>Pydantic Settings]
            REPO[Repositories]
            LOG[Structured Logging<br/>structlog]
            OTEL[TelemetryFlow SDK v1.2.0]
        end
    end

    subgraph "External Services"
        PROVIDERS[12 LLM Providers<br/>Anthropic, Google, OpenAI, ...]
        TFO[TelemetryFlow Platform]
    end

    CC --> SERVER
    IDE --> SERVER
    CLI --> SERVER
    CUSTOM --> SERVER

    SERVER --> CMD
    SERVER --> QRY
    TOOLS --> HANDLERS
    RESOURCES --> HANDLERS
    PROMPTS --> HANDLERS

    CONTEXT --> PROMPT_BUILDER
    HANDLERS --> AGG
    HANDLERS --> SVC
    AGG --> ENT
    AGG --> VO
    AGG --> EVT

    SVC --> LLM
    HANDLERS --> REPO
    CONFIG --> SERVER
    LOG --> SERVER
    OTEL --> TFO

    LLM --> PROVIDERS

    style SERVER fill:#3776AB,stroke:#FFD43B,stroke-width:2px
    style LLM fill:#FFCDD2,stroke:#C62828
    style PROVIDERS fill:#FFCDD2,stroke:#C62828
    style AGG fill:#C8E6C9,stroke:#388E3C
    style HANDLERS fill:#BBDEFB,stroke:#1976D2
    style OTEL fill:#E1BEE7,stroke:#7B1FA2

Built-in Tools

graph TB
    subgraph "Tool Registry"
        REG[Tool Registry<br/>Manages all tools]
    end

    subgraph "AI Tools"
        T1[claude_conversation<br/>AI-powered chat]
    end

    subgraph "File Tools"
        T2[read_file<br/>Read file contents]
        T3[write_file<br/>Write to files]
        T4[list_directory<br/>List directory]
        T5[search_files<br/>Search by pattern]
    end

    subgraph "System Tools"
        T6[execute_command<br/>Run shell commands]
        T7[system_info<br/>System information]
    end

    subgraph "Utility Tools"
        T8[echo<br/>Testing utility]
    end

    subgraph "PostgreSQL Datasource Tools"
        T9[pg_query<br/>Execute SQL]
        T10[pg_list_tables<br/>List tables]
        T11[pg_describe_table<br/>Describe schema]
        T12[pg_sessions<br/>Session history]
    end

    subgraph "ClickHouse Analytics Tools"
        T13[ch_query<br/>Execute SQL]
        T14[ch_tool_analytics<br/>Tool analytics]
        T15[ch_session_analytics<br/>Session analytics]
        T16[ch_error_analytics<br/>Error analytics]
        T17[ch_api_usage<br/>API usage analytics]
    end

    REG --> T1
    REG --> T2
    REG --> T3
    REG --> T4
    REG --> T5
    REG --> T6
    REG --> T7
    REG --> T8
    REG --> T9
    REG --> T10
    REG --> T11
    REG --> T12
    REG --> T13
    REG --> T14
    REG --> T15
    REG --> T16
    REG --> T17

    style T1 fill:#E1BEE7,stroke:#7B1FA2,stroke-width:2px
    style REG fill:#FFE0B2,stroke:#F57C00

Tool Reference

Tool	Category	Description	Key Parameters
`claude_conversation`	AI	Send messages to Claude AI	`message`, `model`, `system_prompt`
`read_file`	File	Read file contents	`path`, `encoding`
`write_file`	File	Write content to file	`path`, `content`, `create_dirs`
`list_directory`	File	List directory contents	`path`, `recursive`
`search_files`	File	Search files by pattern	`path`, `pattern`
`execute_command`	System	Execute shell commands	`command`, `working_dir`, `timeout`
`system_info`	System	Get system information	-
`echo`	Utility	Echo input (testing)	`message`

TFO Datasource Tools - PostgreSQL

Tool	Category	Description	Key Parameters
`pg_query`	datasource	Execute SQL against TFO PostgreSQL	`query`, `params`, `max_rows`, `read_only`
`pg_list_tables`	datasource	List tables in TFO PostgreSQL	`schema`
`pg_describe_table`	datasource	Describe table schema	`table`, `schema`
`pg_sessions`	datasource	Query MCP session history	`limit`, `state`

TFO Datasource Tools - ClickHouse Analytics

Tool	Category	Description	Key Parameters
`ch_query`	analytics	Execute SQL against TFO ClickHouse	`query`, `max_rows`, `read_only`
`ch_tool_analytics`	analytics	Tool call analytics	`tool_name`, `hours`, `limit`
`ch_session_analytics`	analytics	Session analytics	`hours`, `limit`
`ch_error_analytics`	analytics	Error analytics	`hours`, `limit`
`ch_api_usage`	analytics	LLM API usage analytics	`hours`, `model`, `limit`

TFO-Platform Integration

ContextCollector Service

Python port of TFO-Platform ContextCollector.service.ts — collects live telemetry context from ClickHouse materialized views and PostgreSQL for AI analysis.

78 Context Types across 8 categories:

Category	Context Types
Observability	metrics, logs, traces, exemplars, correlations, dashboard
Infrastructure	uptime, status-page, audit, infra-overview, infra-cpu/memory/storage/network
Kubernetes	overview, clusters, namespaces, nodes, pods, deployments, pv, api-server, coredns
Hybrid (PG+CH)	agents, service-map, network-map
Platform (PG)	alerts, alert-rules, iam, iam-users/roles/permissions/matrix/assignments, tenancy, tenancy-regions/organizations/workspaces/tenants
Security	data-masking, ai-assistant, system-setup, system-channels
Account	profile, security, sessions, notifications, preferences, organization
AI Intelligence	anomaly-detection, corrective-maintenance, predictive-maintenance, cost-optimization
DB Monitoring	inventory, clickhouse, mariadb, mysql, percona, sqlite3, timescaledb, aurora, mssql, postgresql, mongodb-community/atlas, aws-rds-mysql/aurora, aws-dynamodb, cockroachdb, qan

ClickHouse Materialized Views queried: metrics_5m (AggMT), logs_1h (SumMT), service_latency_percentiles_1h, service_error_rates_1h, exemplars_1h, uptime_checks, audit_logs_1h, signal_correlations_1h, vm_metrics_1h, kubernetes_metrics_1h, service_map_metrics_1h, network_map_traffic_1h, network_map_connection_metrics_1h

PromptBuilder Service

Python port of TFO-Platform PromptBuilder.service.ts — builds context-aware system prompts for LLM interactions.

60+ specialized analyst personas — one per context type (e.g., metrics analyst, log analyst, trace analyst, Kubernetes admin, DB administrator, etc.)
5 insight types: chronology, prediction, recommendation, root-cause, pattern
Context-aware prompt generation with live data injection (10K char JSON truncation)

Built-in Resources

Resource	Description
`config://server`	Server configuration
`status://health`	Health status
`file:///{path}`	File access (template)

Built-in Prompts

Prompt	Description
`code_review`	Get thorough code review
`explain_code`	Get code explanation
`debug_help`	Get debugging assistance

Installation

Prerequisites

Python 3.11 or later
Anthropic API key (or other supported LLM provider API key)

From Source

# Clone the repository
git clone https://github.com/telemetryflow/telemetryflow-python-mcp.git
cd telemetryflow-python-mcp

# Install package
pip install -e .

# Or with all optional dependencies
pip install -e ".[all]"

# Or with telemetry support only
pip install -e ".[telemetry]"

Using pip

pip install tfo-mcp

# Install with PostgreSQL datasource support
pip install tfo-mcp[postgres]

# Install with ClickHouse analytics support
pip install tfo-mcp[clickhouse]

Docker

# Build image
docker build -t telemetryflow-python-mcp:1.2.0 .

# Run container
docker run --rm -it \
  -e ANTHROPIC_API_KEY="your-api-key" \
  telemetryflow-python-mcp:1.2.0

Configuration

Configuration File

Create tfo-mcp.yaml or run tfo-mcp init-config:

# =============================================================================
# TelemetryFlow Python MCP Server Configuration
# Version: 1.2.0
# =============================================================================

server:
  name: "TelemetryFlow-MCP"
  version: "1.2.0"
  transport: "stdio" # stdio, sse, websocket
  debug: false

claude:
  # api_key: Set via ANTHROPIC_API_KEY env var
  default_model: "claude-sonnet-4-20250514"
  max_tokens: 4096
  temperature: 1.0
  timeout: 120.0
  max_retries: 3

mcp:
  protocol_version: "2024-11-05"
  enable_tools: true
  enable_resources: true
  enable_prompts: true
  enable_logging: true
  tool_timeout: 30.0

logging:
  level: "info" # debug, info, warn, error
  format: "json" # json, text
  output: "stderr"

telemetry:
  enabled: false
  api_key_id: "" # or TELEMETRYFLOW_API_KEY_ID env var
  api_key_secret: "" # or TELEMETRYFLOW_API_KEY_SECRET env var
  endpoint: "api.telemetryflow.id:4317"
  service_name: "telemetryflow-python-mcp"
  environment: "production"

Environment Variables

Variable	Description	Default
`ANTHROPIC_API_KEY`	Claude API key (required)	-
`TELEMETRYFLOW_MCP_SERVER_DEBUG`	Debug mode	`false`
`TELEMETRYFLOW_MCP_LOG_LEVEL`	Log level	`info`
`TELEMETRYFLOW_MCP_CLAUDE_DEFAULT_MODEL`	Default Claude model	`claude-sonnet-4-20250514`
`TELEMETRYFLOW_ENABLED`	Enable telemetry	`false`
`TELEMETRYFLOW_API_KEY_ID`	TelemetryFlow API key ID	-
`TELEMETRYFLOW_API_KEY_SECRET`	TelemetryFlow API secret	-
`TELEMETRYFLOW_ENDPOINT`	OTLP endpoint	`api.telemetryflow.id:4317`

Usage

Running the Server

# Run with default config
tfo-mcp serve

# Run with custom config
tfo-mcp serve --config /path/to/config.yaml

# Run in debug mode
tfo-mcp serve --debug

# Show version
tfo-mcp --version

# Validate configuration
tfo-mcp validate

# Show server info
tfo-mcp info

# Generate default config
tfo-mcp init-config

Integration with Claude Desktop

Add to your Claude Desktop configuration (claude_desktop_config.json):

{
  "mcpServers": {
    "telemetryflow": {
      "command": "tfo-mcp",
      "args": ["serve"],
      "env": {
        "ANTHROPIC_API_KEY": "your-api-key"
      }
    }
  }
}

TelemetryFlow SDK Integration

The MCP server integrates with the TelemetryFlow Python SDK (telemetryflow-sdk>=1.2.0) to provide comprehensive observability:

Enable Telemetry

# Install with telemetry support
pip install -e ".[telemetry]"

# Configure via environment variables
export TELEMETRYFLOW_ENABLED=true
export TELEMETRYFLOW_API_KEY_ID=tfk_your-key-id
export TELEMETRYFLOW_API_KEY_SECRET=tfs_your-secret-key
export TELEMETRYFLOW_ENDPOINT=api.telemetryflow.id:4317

TFO-Platform Integration

The MCP server integrates with TFO-Platform components for context-aware AI analysis via Python port services:

ContextCollector — Python port of ContextCollector.service.ts. Collects live telemetry context from ClickHouse materialized views and PostgreSQL, providing rich operational data for AI analysis across 78 context types in 8 categories
PromptBuilder — Python port of PromptBuilder.service.ts. Builds context-aware system prompts per context type, leveraging 60+ specialized analyst personas and 5 insight types for targeted telemetry analysis

Collected Telemetry

Signal	Metric/Span	Description
Metrics	`mcp.tools.calls`	Tool call count by tool name
Metrics	`mcp.tools.duration`	Tool execution duration
Metrics	`mcp.tools.errors`	Tool error count
Metrics	`mcp.resources.reads`	Resource read count
Metrics	`mcp.prompts.gets`	Prompt get count
Metrics	`mcp.sessions.events`	Session lifecycle events
Traces	`mcp.tools.execute.*`	Tool execution spans
Logs	Various	Structured logs for debugging

Project Structure

telemetryflow-python-mcp/
├── src/tfo_mcp/
│   ├── domain/                    # Domain Layer (DDD)
│   │   ├── aggregates/            # Session, Conversation aggregates
│   │   ├── entities/              # Message, Tool, Resource, Prompt
│   │   ├── valueobjects/          # Immutable value objects
│   │   ├── events/                # Domain events
│   │   ├── repositories/          # Repository interfaces
│   │   └── services/              # Domain service interfaces
│   ├── application/               # Application Layer (CQRS)
│   │   ├── commands/              # Write operations
│   │   ├── queries/               # Read operations
│   │   ├── handlers/              # Command/Query handlers
│   │   └── services/              # Application services (ContextCollector, PromptBuilder)
│   ├── infrastructure/            # Infrastructure Layer
│   │   ├── claude/                # LLM API client (12 providers)
│   │   ├── config/                # Pydantic configuration
│   │   ├── logging/               # Structured logging
│   │   ├── persistence/           # Repository implementations
│   │   └── telemetry/             # TelemetryFlow SDK integration
│   ├── presentation/              # Presentation Layer
│   │   ├── server/                # MCP server implementation (mcp>=1.27.0)
│   │   ├── tools/                 # Built-in tools (17 total)
│   │   ├── resources/             # Built-in resources
│   │   └── prompts/               # Built-in prompts
│   └── main.py                    # CLI entry point
├── configs/                       # Configuration files
├── tests/                         # Test suites (1174 tests, 98% coverage)
│   ├── unit/                      # Unit tests
│   │   ├── domain/                # Domain layer tests
│   │   ├── application/           # Application layer tests
│   │   ├── infrastructure/        # Infrastructure layer tests
│   │   └── presentation/          # Presentation layer tests
│   ├── integration/               # Integration tests
│   │   ├── datasource/            # PostgreSQL & ClickHouse tests
│   │   ├── handlers/              # Handler integration tests
│   │   └── server/                # Server integration tests
│   └── e2e/                       # End-to-end tests
│       ├── protocol/              # MCP protocol tests
│       └── flow/                  # Full flow tests
├── docs/                          # Documentation
├── .kiro/                         # Specifications and steering
├── Makefile                       # Build automation
├── Dockerfile                     # Container build
├── docker-compose.yaml            # Development stack
├── pyproject.toml                 # Python package config
└── .env.example                   # Environment template

Development

Make Commands

# Development
make deps               # Install dependencies
make dev                # Install with dev dependencies
make setup              # Full development setup

# Code Quality
make fmt                # Format code (black + ruff)
make lint               # Run linters
make typecheck          # Run mypy type checking

# Testing
make test               # Run all tests (1174 tests)
make test-unit          # Run unit tests
make test-integration   # Run integration tests
make test-cov           # Tests with coverage (98%)

# CI/CD
make ci-test            # Full CI test pipeline
make ci-lint            # CI lint pipeline
make ci-security        # Security scanning

# Docker
make docker-build       # Build Docker image
make docker-run         # Run Docker container

Testing

# Run all tests
make test

# Run with coverage
make test-cov

# Run specific test file
pytest tests/unit/domain/test_aggregates.py -v

# Run CI test pipeline
make ci-test

MCP Capabilities Matrix

Capabilities are handled internally by the official MCP SDK (mcp>=1.27.0).

Capability	Status	Description
`tools`	✅	Tool listing and execution (SDK)
`tools.listChanged`	✅	Dynamic tool registration (SDK)
`resources`	✅	Resource listing and reading (SDK)
`resources.subscribe`	✅	Resource change subscriptions (SDK)
`resources.listChanged`	✅	Dynamic resource registration (SDK)
`prompts`	✅	Prompt templates (SDK)
`prompts.listChanged`	✅	Dynamic prompt registration (SDK)
`logging`	✅	Log level management (SDK)
`sampling`	🔜	LLM sampling (planned)

LLM AI Integration

Supported Providers (12 Providers, 112 Models)

Provider	Example Models	API Key Env Variable
Anthropic	Claude 4 Opus, Claude 4 Sonnet, Claude 3.5	`ANTHROPIC_API_KEY`
Google	Gemini 2.5 Pro, Gemini 2.5 Flash	`GOOGLE_API_KEY`
OpenAI	GPT-4o, GPT-4o-mini, o3, o4-mini	`OPENAI_API_KEY`
DeepSeek	DeepSeek-V3, DeepSeek-R1	`DEEPSEEK_API_KEY`
Qwen	Qwen3-235B, Qwen3-32B	`QWEN_API_KEY`
Ollama	llama3, mistral, codellama (local)	`OLLAMA_HOST`
Mistral	Mistral Large, Mistral Medium, Codestral	`MISTRAL_API_KEY`
Grok	grok-3, grok-3-mini	`XAI_API_KEY`
Kimi	moonshot-v1-128k, moonshot-v1-32k	`MOONSHOT_API_KEY`
Zhipu	GLM-4, GLM-4-Plus, GLM-4-Flash	`ZHIPU_API_KEY`
MiMo	MiMo-7B	`MIMO_API_KEY`
Custom	Any OpenAI-compatible endpoint	`CUSTOM_LLM_API_KEY`

Default Model

The default model is claude-sonnet-4-20250514 (Anthropic Claude 4 Sonnet), configurable via the claude.default_model setting or TELEMETRYFLOW_MCP_CLAUDE_DEFAULT_MODEL environment variable.

Security Considerations

Aspect	Implementation
API Key Storage	Environment variables only
Command Execution	Configurable timeout, path validation
File Access	Path validation, no traversal
Rate Limiting	Configurable per-minute limits
Input Validation	Pydantic validation for all inputs

Documentation Index

Document	Description
README.md	Project overview and quick start
docs/ARCHITECTURE.md	Detailed architecture documentation
docs/CONFIGURATION.md	Configuration reference
docs/COMMANDS.md	CLI commands reference
CONTRIBUTING.md	Contribution guidelines
SECURITY.md	Security policy
CHANGELOG.md	Version history

Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Development Guidelines

Follow Python best practices and PEP 8
Use DDD patterns for domain logic
Write unit tests for all handlers
Document public APIs
Keep commits atomic and well-described

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

TelemetryFlow Go MCP - Go implementation
TelemetryFlow Python SDK - Python observability SDK
TelemetryFlow Go SDK - Go observability SDK
TelemetryFlow Platform - Main platform

Support

Documentation: TelemetryFlow Docs
Issues: GitHub Issues
Discussions: GitHub Discussions

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

5moRelease cycle

2Releases (12mo)

Commit activity

Resources

GitHub Repository

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TelemetryFlow Ecosystem

Quick Facts

System Architecture

Built-in Tools

Tool Reference

TFO Datasource Tools - PostgreSQL

TFO Datasource Tools - ClickHouse Analytics

TFO-Platform Integration

ContextCollector Service

PromptBuilder Service

Built-in Resources

Built-in Prompts

Installation

Prerequisites

From Source

Using pip

Docker

Configuration

Configuration File

Environment Variables

Usage

Running the Server

Integration with Claude Desktop

TelemetryFlow SDK Integration

Enable Telemetry

TFO-Platform Integration

Collected Telemetry

Project Structure

Development

Make Commands

Testing

MCP Capabilities Matrix

LLM AI Integration

Supported Providers (12 Providers, 112 Models)

Default Model

Security Considerations

Documentation Index

Contributing

Development Guidelines

License

Related Projects

Support

Maintenance

Resources

Looking for Admin?

Latest Blog Posts

MCP directory API