Which integrations are available for this server?

Enables local AI analysis of images and multimodal data processing through Ollama integration. Provides an interactive REPL for executing Python code, performing data exploration, and introspecting functions within a persistent Pixeltable session. Supports running YOLOX object detection on images and photos managed within the Pixeltable infrastructure.

How do I use Pixeltable MCP Server (Developer Edition)?

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., "@Pixeltable MCP Server (Developer Edition) Create a table for my images and run object detection on them" 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.

Pixeltable MCP Server (Developer Edition)

⚠️ Experimental — do not use with important data.

Multimodal AI data infrastructure as an MCP server. 32 tools · 13 resources · 6 prompts for table management, AI/ML pipelines, dependency management, an interactive REPL, and more.

Uses sync endpoints + uvloop for best performance with Pixeltable ≥ 0.5.19.

Quick Start

Requires uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

Claude Code (easiest) — just say:

"Install https://github.com/pixeltable/mcp-server-pixeltable-developer as a uv tool and add it to your MCPs"

Manual install:

uv tool install --from git+https://github.com/pixeltable/mcp-server-pixeltable-developer.git mcp-server-pixeltable-developer
claude mcp add pixeltable mcp-server-pixeltable-developer   # Claude Code

From source:

git clone https://github.com/pixeltable/mcp-server-pixeltable-developer && cd mcp-server-pixeltable-developer
uv sync

Client Configuration

{
  "mcpServers": {
    "pixeltable": {
      "command": "mcp-server-pixeltable-developer",
      "env": {
        "PIXELTABLE_HOME": "/Users/{you}/.pixeltable",
        "PIXELTABLE_FILE_CACHE_SIZE_G": "10"
      }
    }
  }
}

From source — use "command": "uv" with "args": ["run", "--directory", "{repo}", "python", "-m", "mcp_server_pixeltable_stio"].

.cursor/mcp.json:

{
  "mcpServers": {
    "pixeltable-developer": {
      "command": "mcp-server-pixeltable-developer"
    }
  }
}

Restart your client after configuration changes.

Tools (32)

Category	Tools
Tables	`create_table` · `drop_table` · `create_view` · `create_snapshot`
Data	`create_replica` · `query_table` · `insert_data` · `query` · `add_computed_column`
Directories	`create_dir` · `drop_dir` · `move`
Config	`configure_logging` · `set_datastore`
AI/ML	`create_udf` · `create_array` · `create_tools` · `connect_mcp`
Deps	`check_dependencies` · `install_dependency`
Types	`create_type` (Image, Video, Audio, Array[Float], …)
Docs	`search_docs`
REPL	`execute_python` · `introspect_function` · `list_available_functions` · `install_package`
Logging	`log_bug` · `log_missing_feature` · `log_success` · `generate_bug_report` · `get_session_summary`
Display	`display_in_browser`

All tools are prefixed pixeltable_ (except REPL/logging helpers). Full docstrings available via introspect_function.

Resources (13)

URI	What it returns
`pixeltable://tables`	All tables with count
`pixeltable://tables/{path}`	Info about a table / view / snapshot
`pixeltable://tables/{path}/schema`	Column schema
`pixeltable://directories`	All directories
`pixeltable://ls` / `pixeltable://ls/{path}`	Directory listing
`pixeltable://version`	Pixeltable version
`pixeltable://config/datastore`	Datastore config
`pixeltable://types`	Available data types
`pixeltable://functions`	Registered Pixeltable functions
`pixeltable://tools`	MCP tool list
`pixeltable://help`	Workflow guidance
`pixeltable://diagnostics`	System & dependency diagnostics

Prompts (6)

pixeltable_usage_guide · getting_started · computer_vision_pipeline · rag_pipeline · video_analysis_pipeline · audio_processing_pipeline

Examples

Create a table called movies with title, year, and rating columns → insert sample data → query ratings above 8.5

Add a computed column that runs YOLOX object detection on every image

Check what deps I need for openai.vision(...) → install them

execute_python("print(pxt.list_tables())")

Architecture

src/mcp_server_pixeltable_stio/
  server.py            FastMCP server, tool/resource/prompt registration, uvloop activation
  core/
    tables.py          Table CRUD, views, snapshots, replicas, queries, computed columns
    directories.py     Directory CRUD, listing, moving
    dependencies.py    Dependency checking, unified installer, diagnostics
    udf.py             UDF creation, type system, LLM tool wrappers, MCP connections
    helpers.py         Config, version, docs search, shared utilities
    resources.py       Read-only MCP resource handlers
  prompt.py            Prompt templates for common workflows
  repl_functions.py    Persistent Python REPL, introspection, package management
  canvas_server.py     Browser canvas for rich content display