Data Science MCP Server — Model training, evaluation, and evolution tools for agentic ML workflows. Integrates with agent-utilities IModelEvolver (CONCEPT:AHE-3.15).

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Project description

Data Science Mcp

CLI or API | MCP | Agent

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Version: 1.0.1

Documentation — Installation, deployment, usage across the MCP, Python API, and CLI interfaces, and the in-house model-training substrate are maintained in the official documentation.

Overview

Data Science Mcp is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with Data Science MCP Server — Model training, evaluation, and evolution tools for agentic ML workflows. Integrates with agent-utilities IModelEvolver (CONCEPT:AHE-3.8)..

Key Features

Consolidated Action-Routed MCP Tools: Minimizes token overhead and eliminates tool bloat in LLM contexts by grouping methods into optimized, togglable tool modules.
Enterprise-Grade Security: Comprehensive support for Eunomia policies, OIDC token delegation, and granular execution context tracking.
Integrated Graph Agent: Built-in Pydantic AI agent supporting the Agent Control Protocol (ACP) and standard Web interfaces (AG-UI).
Native Telemetry & Tracing: Out-of-the-box OpenTelemetry exports and native Langfuse tracing.
In-House Model Training (Wave C): A deterministic SFT/DPO/GRPO corpus + reward engine plus torch/PEFT gradient trainers (LoRA/QLoRA, TIES adapter merge, vLLM rollouts, checkpoint→reliability-suite eval hooks). Loss/optimizer kernels are CPU-smoke-tested on a toy model; real fine-tunes run on the GB10. Install with pip install data-science-mcp[training]. MCP tools: build_training_dataset, compose_reward, train_sft, train_dpo, train_grpo, merge_adapters_ties. See docs/training.md.

CLI or API

This agent wraps the Data Science MCP Server — Model training, evaluation, and evolution tools for agentic ML workflows. Integrates with agent-utilities IModelEvolver (CONCEPT:AHE-3.8). API. You can interact with it programmatically or via its integrated execution entrypoints.

Detailed instructions on how to use the underlying API wrappers, extended schema bindings, and developer SDK references are maintained in docs/index.md.

MCP

This server utilizes dynamic Action-Routed tools to optimize token overhead and maximize IDE compatibility.

Available MCP Tools

Auto-generated — do not edit (synced by the mcp-readme-table pre-commit hook).

Condensed action-routed tools (default — `MCP_TOOL_MODE=condensed`)

MCP Tool	Toggle Env Var	Description
`build_training_dataset`	`MODEL_TRAININGTOOL`	Build an SFT/DPO/GRPO training corpus from traces (CONCEPT:AHE-3.1).
`compose_reward`	`MODEL_TRAININGTOOL`	Composite, conditionally-gated reward score (CONCEPT:AHE-3.1).
`cross_validate`	`MODEL_TRAININGTOOL`	Perform k-fold cross-validation for a model class.
`curate_corpus`	`DATA_ENGINETOOL`	Full curation pass: quality-filter → dedup → decontaminate → lineage.
`dataset_lineage`	`DATA_ENGINETOOL`	Record a `DatasetVersion` provenance node (CONCEPT:ML-002).
`decontaminate_corpus`	`DATA_ENGINETOOL`	Drop training records that leak held-out eval examples (CONCEPT:ML-002).
`dedup_corpus`	`DATA_ENGINETOOL`	Remove exact + near-duplicate records (CONCEPT:ML-002).
`describe_dataset`	`DATA_MANAGEMENTTOOL`	Get descriptive statistics for a loaded dataset.
`ds_specialize_kernel`	`MODEL_TRAININGTOOL`	Run a SAI-factory specialization cycle on a compute kernel (CONCEPT:AHE-3.29).
`evaluate_model`	`MODEL_TRAININGTOOL`	Evaluate a fitted model on a dataset split.
`evolve_model_class`	`MODEL_EVOLUTIONTOOL`	Submit a model to the evolutionary Pareto frontier.
`fit_model`	`MODEL_TRAININGTOOL`	Fit a machine learning model on a dataset and return metrics.
`generate_interpretability_tests`	`INTERPRETABILITYTOOL`	Generate a structured suite of 6 interpretability test cases for a model.
`get_pareto_frontier`	`MODEL_EVOLUTIONTOOL`	Retrieve the current Pareto frontier of model classes.
`grade_response`	`INTERPRETABILITYTOOL`	Grade a model interpretability response against reference answer.
`load_dataset`	`DATA_MANAGEMENTTOOL`	Load and parse a dataset by name or CSV file path.
`merge_adapters_ties`	`MODEL_TRAININGTOOL`	TIES-merge multiple task vectors onto a base (MeMo; CONCEPT:AHE-3.1).
`predict`	`MODEL_TRAININGTOOL`	Generate predictions using a fitted model.
`prepare_pretrain_data`	`DATA_ENGINETOOL`	Tokenize a corpus into a flat-token HDF5 file for pretraining (CONCEPT:ML-010).
`pretrain_model`	`MODEL_TRAININGTOOL`	Pretrain a causal LM from random init (CONCEPT:ML-003).
`quant_derivatives`	`QUANTTOOL`	SABR stochastic-volatility surface kernels (CONCEPT:KG-2.20j).
`quant_forensic`	`QUANTTOOL`	Forensic-accounting report (CONCEPT:KG-2.20g).
`quant_market_making`	`QUANTTOOL`	Market-making / HFT quoting kernels (CONCEPT:KG-2.20f).
`quant_microstructure`	`QUANTTOOL`	Order-flow / toxicity / self-excitation kernels (CONCEPT:KG-2.20f).
`quant_signals`	`QUANTTOOL`	Signal-combination / breadth kernels (CONCEPT:KG-2.20i).
`quant_sizing`	`QUANTTOOL`	Position-sizing kernels (CONCEPT:KG-2.20f / KG-2.20i).
`quant_statespace`	`QUANTTOOL`	State-space / statistical-arbitrage kernels (CONCEPT:KG-2.20h).
`quant_validation`	`QUANTTOOL`	Backtest-validation / calibration kernels (CONCEPT:KG-2.20f / KG-2.20i).
`rank_models`	`MODEL_EVOLUTIONTOOL`	Rank all registered fitted models by their test R2 score.
`run_interpretability_suite`	`INTERPRETABILITYTOOL`	Run and grade the complete 6-category interpretability audit suite for a model.
`split_dataset`	`DATA_MANAGEMENTTOOL`	Split a loaded dataset into train, test, and validation sets.
`train_dpo`	`MODEL_TRAININGTOOL`	Preference-optimise on a `dpo` corpus (CONCEPT:AHE-3.1).
`train_grpo`	`MODEL_TRAININGTOOL`	GRPO on advantage-tagged groups (CONCEPT:AHE-3.1).
`train_ppo`	`MODEL_TRAININGTOOL`	Proximal Policy Optimization with GAE + value head (CONCEPT:ML-009).
`train_reward`	`MODEL_TRAININGTOOL`	Train a Bradley-Terry reward model on preference pairs (CONCEPT:ML-008).
`train_sft`	`MODEL_TRAININGTOOL`	Supervised fine-tune on an `sft` corpus (CONCEPT:AHE-3.1).
`train_tokenizer`	`MODEL_TRAININGTOOL`	Train a byte-level BPE tokenizer from scratch (CONCEPT:ML-003).

Verbose 1:1 API-mapped tools (`MCP_TOOL_MODE=verbose` or `both`)

9 per-operation tools — one per public API method (click to expand)

MCP Tool	Toggle Env Var	Description
`data_science_cross_validate`	`ML_ENGINETOOL`	Run k-fold cross-validation via the engine.
`data_science_describe_dataset`	`ML_ENGINETOOL`	Get descriptive statistics for a loaded dataset.
`data_science_evaluate`	`ML_ENGINETOOL`	Evaluate a fitted model.
`data_science_fit`	`ML_ENGINETOOL`	Fit a model on a dataset via the epistemic-graph engine.
`data_science_interpretability_reference`	`ML_ENGINETOOL`	Compute reference answers for the interpretability suite without any
`data_science_load_dataset`	`ML_ENGINETOOL`	Load a dataset by name or file path.
`data_science_predict`	`ML_ENGINETOOL`	Generate predictions from a fitted model.
`data_science_ranked_models`	`ML_ENGINETOOL`	Rank fitted models by stored test R² (backend-agnostic, no recompute).
`data_science_split_dataset`	`ML_ENGINETOOL`	Split a dataset into train/test/validation sizes.

37 action-routed tool(s) (default) · 9 verbose 1:1 tool(s). Each is enabled unless its <DOMAIN>TOOL toggle is set false; MCP_TOOL_MODE selects the surface (condensed default · verbose 1:1 · both). Auto-generated — do not edit.

Detailed tool schemas, parameter shapes, and validation constraints are preserved in docs/mcp.md.

Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You can configure tool filtering via multiple input channels:

CLI Arguments: Pass --tools or --toolsets (or their disabled counterparts --disabled-tools and --disabled-toolsets) during startup.
Environment Variables: Define standard environment variables:
- MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS
- MCP_ENABLED_TAGS / MCP_DISABLED_TAGS
HTTP SSE Request Headers: Pass custom headers during transport initialization:
- x-mcp-enabled-tools / x-mcp-disabled-tools
- x-mcp-enabled-tags / x-mcp-disabled-tags
HTTP SSE Request Query Parameters: Append query parameters directly to your transport connection URL:
- ?tools=tool1,tool2
- ?tags=tag1

When query strings or parameters are supplied, an LLM-free Knowledge Graph resolution layer (using DynamicToolOrchestrator) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

MCP Configuration Examples

Install the slim [mcp] extra. All examples install data-science-mcp[mcp] — the MCP-server extra that pulls only the FastMCP / FastAPI tooling (agent-utilities[mcp]). It deliberately excludes the heavy agent runtime (pydantic-ai, the epistemic-graph engine, dspy, llama-index), so uvx / container installs are far smaller. Use the full [agent] extra only when you need the integrated Pydantic AI agent.

stdio Transport (local IDEs — Cursor, Claude Desktop, VS Code)

{
  "mcpServers": {
    "data-science-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "data-science-mcp[mcp]",
        "data-science-mcp"
      ],
      "env": {
        "MCP_TOOL_MODE": "condensed",
        "DATA_ENGINETOOL": "True",
        "DATA_MANAGEMENTTOOL": "True",
        "DATA_SCIENCE_MCP_TOKEN": "your_token_here",
        "DATA_SCIENCE_MCP_URL": "http://localhost:8080",
        "DATA_SCIENCE_MCP_VERIFY": "True",
        "DSM_NEAR_PAIRS_LOCAL_MAX": "20000",
        "EPISTEMIC_GRAPH_SOCKET": "",
        "EPISTEMIC_GRAPH_TCP": "",
        "INFERENCE_API_KEY": "EMPTY",
        "INFERENCE_BACKEND": "vllm",
        "INFERENCE_BASE_URL": "",
        "INFERENCE_MODEL": "",
        "INTERPRETABILITYTOOL": "True",
        "KERNEL_SPECIALIZETOOL": "True",
        "MODEL_EVOLUTIONTOOL": "True",
        "MODEL_TRAININGTOOL": "True",
        "QUANTTOOL": "True",
        "TRAINERTOOL": "True",
        "TRAINING_DATATOOL": "True"
      }
    }
  }
}

Streamable-HTTP Transport (networked / production)

{
  "mcpServers": {
    "data-science-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "data-science-mcp[mcp]",
        "data-science-mcp",
        "--transport",
        "streamable-http",
        "--port",
        "8000"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "0.0.0.0",
        "PORT": "8000",
        "MCP_TOOL_MODE": "condensed",
        "DATA_ENGINETOOL": "True",
        "DATA_MANAGEMENTTOOL": "True",
        "DATA_SCIENCE_MCP_TOKEN": "your_token_here",
        "DATA_SCIENCE_MCP_URL": "http://localhost:8080",
        "DATA_SCIENCE_MCP_VERIFY": "True",
        "DSM_NEAR_PAIRS_LOCAL_MAX": "20000",
        "EPISTEMIC_GRAPH_SOCKET": "",
        "EPISTEMIC_GRAPH_TCP": "",
        "INFERENCE_API_KEY": "EMPTY",
        "INFERENCE_BACKEND": "vllm",
        "INFERENCE_BASE_URL": "",
        "INFERENCE_MODEL": "",
        "INTERPRETABILITYTOOL": "True",
        "KERNEL_SPECIALIZETOOL": "True",
        "MODEL_EVOLUTIONTOOL": "True",
        "MODEL_TRAININGTOOL": "True",
        "QUANTTOOL": "True",
        "TRAINERTOOL": "True",
        "TRAINING_DATATOOL": "True"
      }
    }
  }
}

Alternatively, connect to a pre-deployed Streamable-HTTP instance by url:

{
  "mcpServers": {
    "data-science-mcp": {
      "url": "http://localhost:8000/data-science-mcp/mcp"
    }
  }
}

Deploying the Streamable-HTTP server via Docker:

docker run -d \
  --name data-science-mcp-mcp \
  -p 8000:8000 \
  -e TRANSPORT=streamable-http \
  -e HOST=0.0.0.0 \
  -e PORT=8000 \
  -e MCP_TOOL_MODE=condensed \
  -e DATA_ENGINETOOL=True \
  -e DATA_MANAGEMENTTOOL=True \
  -e DATA_SCIENCE_MCP_TOKEN=your_token_here \
  -e DATA_SCIENCE_MCP_URL=http://localhost:8080 \
  -e DATA_SCIENCE_MCP_VERIFY=True \
  -e DSM_NEAR_PAIRS_LOCAL_MAX=20000 \
  -e EPISTEMIC_GRAPH_SOCKET="" \
  -e EPISTEMIC_GRAPH_TCP="" \
  -e INFERENCE_API_KEY=EMPTY \
  -e INFERENCE_BACKEND=vllm \
  -e INFERENCE_BASE_URL="" \
  -e INFERENCE_MODEL="" \
  -e INTERPRETABILITYTOOL=True \
  -e KERNEL_SPECIALIZETOOL=True \
  -e MODEL_EVOLUTIONTOOL=True \
  -e MODEL_TRAININGTOOL=True \
  -e QUANTTOOL=True \
  -e TRAINERTOOL=True \
  -e TRAINING_DATATOOL=True \
  knucklessg1/data-science-mcp:mcp

Auto-generated from the code-read env surface (MCP_TOOL_MODE + package vars) — do not edit.

Additional Deployment Options

data-science-mcp can also run as a local container (Docker / Podman / uv) or be consumed from a remote deployment. The Deployment guide has full, copy-paste mcp_config.json for all four transports — stdio, streamable-http, local container / uv, and remote URL:

Local container / uv — launch the server from mcp_config.json via uvx, docker run, or podman run, or point at a local streamable-http container by url.
Remote URL — connect to a server deployed behind Caddy at http://data-science-mcp.arpa/mcp using the "url" key.

Environment Variables

Package environment variables

Variable	Example	Description
`HOST`	`0.0.0.0`
`PORT`	`8000`
`TRANSPORT`	`stdio`	options: stdio, streamable-http, sse
`ENABLE_OTEL`	`True`
`OTEL_EXPORTER_OTLP_ENDPOINT`	`http://localhost:8080/api/public/otel`
`OTEL_EXPORTER_OTLP_PUBLIC_KEY`	`pk-...`
`OTEL_EXPORTER_OTLP_SECRET_KEY`	`sk-...`
`OTEL_EXPORTER_OTLP_PROTOCOL`	`http/protobuf`
`EUNOMIA_TYPE`	`none`	options: none, embedded, remote
`EUNOMIA_POLICY_FILE`	`mcp_policies.json`
`EUNOMIA_REMOTE_URL`	`http://eunomia-server:8000`
`DATA_SCIENCE_MCP_URL`	`http://localhost:8080`
`DATA_SCIENCE_MCP_TOKEN`	`your_token_here`
`DATA_SCIENCE_MCP_SSL_VERIFY`	—	TLS verification for the upstream client. Set to a CA bundle path or False to disable.
`DATA_SCIENCE_MCP_VERIFY`	`True`	Legacy alias for DATA_SCIENCE_MCP_SSL_VERIFY (truthy enables verification).
`INFERENCE_BACKEND`	`vllm`	options: vllm, sglang
`INFERENCE_BASE_URL`	—	Base URL of the running inference server, e.g. http://host:30000
`INFERENCE_MODEL`	—	Served model id exposed by the inference server.
`INFERENCE_API_KEY`	`EMPTY`	Bearer token for the inference server (default "EMPTY" for local servers).
`EPISTEMIC_GRAPH_SOCKET`	—	Unix domain socket path to the epistemic-graph engine.
`GRAPH_SERVICE_SOCKET`	—	Alternate UDS env var honored by the engine client.
`EPISTEMIC_GRAPH_TCP`	—	TCP host:port for the epistemic-graph engine (takes precedence over the socket).
`DSM_NEAR_PAIRS_LOCAL_MAX`	`20000`	Cap on local O(n^2) near-pair fallback before requiring the Rust path (0 disables the cap).
`MODEL_TRAININGTOOL`	`True`
`MODEL_EVOLUTIONTOOL`	`True`
`INTERPRETABILITYTOOL`	`True`
`DATA_MANAGEMENTTOOL`	`True`
`DATA_ENGINETOOL`	`True`
`QUANTTOOL`	`True`
`TRAINERTOOL`	`True`	Sub-surfaces of model-training; the code gates these via MODEL_TRAININGTOOL.
`TRAINING_DATATOOL`	`True`
`KERNEL_SPECIALIZETOOL`	`True`

Inherited agent-utilities variables (apply to every connector)

Variable	Example	Description
`MCP_TOOL_MODE`	`condensed`	Tool surface: `condensed`
`MCP_ENABLED_TOOLS`	—	Comma-separated tool allow-list
`MCP_DISABLED_TOOLS`	—	Comma-separated tool deny-list
`MCP_ENABLED_TAGS`	—	Comma-separated tag allow-list
`MCP_DISABLED_TAGS`	—	Comma-separated tag deny-list
`MCP_CLIENT_AUTH`	—	Outbound MCP auth (`oidc-client-credentials` for fleet calls)
`OIDC_CLIENT_ID`	—	OIDC client id (service-account auth)
`OIDC_CLIENT_SECRET`	—	OIDC client secret (service-account auth)
`DEBUG`	`False`	Verbose logging
`PYTHONUNBUFFERED`	`1`	Unbuffered stdout (recommended in containers)
`MCP_URL`	`http://localhost:8000/mcp`	URL of the MCP server the agent connects to
`PROVIDER`	`openai`	LLM provider for the agent
`MODEL_ID`	`gpt-4o`	Model id for the agent
`ENABLE_WEB_UI`	`True`	Serve the AG-UI web interface

32 package + 14 inherited variable(s). Auto-generated from .env.example + the shared agent-utilities set — do not edit.

Every variable the server reads, grouped by purpose.

MCP server / transport

Variable	Description	Default
`TRANSPORT`	`stdio`, `streamable-http`, or `sse`	`stdio`
`HOST`	Bind host (HTTP transports)	`0.0.0.0`
`PORT`	Bind port (HTTP transports)	`8000`
`MCP_TOOL_MODE`	Tool surface: `condensed`, `verbose`, or `both`	`condensed`
`MCP_ENABLED_TOOLS` / `MCP_DISABLED_TOOLS`	Comma-separated tool allow/deny list	—
`MCP_ENABLED_TAGS` / `MCP_DISABLED_TAGS`	Comma-separated tag allow/deny list	—
`DEBUG`	Verbose logging	`False`
`PYTHONUNBUFFERED`	Unbuffered stdout (recommended in containers)	`1`

Connection

Variable	Description	Default
`DATA_SCIENCE_MCP_URL`	Base service URL	`http://localhost:8080`
`DATA_SCIENCE_MCP_TOKEN`	API token	—

Training / inference backend (full `[training]` extra)

Variable	Description	Default
`INFERENCE_BACKEND`	Served-model rollout backend (`vllm` or `sglang`)	—
`INFERENCE_BASE_URL`	OpenAI-compatible inference server base URL	—

Tool toggles

Each action-routed tool can be disabled individually via its toggle env var (set to false). The full list is in the Available MCP Tools table above.

Variable	Tools
`MODEL_TRAININGTOOL`	training / fit / eval / corpus / kernel tools
`MODEL_EVOLUTIONTOOL`	Pareto-frontier evolution + model ranking
`INTERPRETABILITYTOOL`	interpretability test generation + grading suite
`DATA_MANAGEMENTTOOL`	dataset load / describe / split
`DATA_ENGINETOOL`	corpus curation / dedup / decontaminate / lineage
`QUANTTOOL`	quant compute kernels

Telemetry & governance

Variable	Description	Default
`ENABLE_OTEL`	Enable OpenTelemetry export	`True`
`OTEL_EXPORTER_OTLP_ENDPOINT`	OTLP collector endpoint	—
`OTEL_EXPORTER_OTLP_PUBLIC_KEY` / `OTEL_EXPORTER_OTLP_SECRET_KEY`	OTLP auth keys	—
`OTEL_EXPORTER_OTLP_PROTOCOL`	OTLP protocol (e.g. `http/protobuf`)	—
`EUNOMIA_TYPE`	Authorization mode: `none`, `embedded`, `remote`	`none`
`EUNOMIA_POLICY_FILE`	Embedded policy file	`mcp_policies.json`
`EUNOMIA_REMOTE_URL`	Remote Eunomia server URL	—

Agent CLI (full `[agent]` runtime only)

Variable	Description	Default
`MCP_URL`	URL of the MCP server the agent connects to	`http://localhost:8000/mcp`
`PROVIDER`	LLM provider (e.g. `openai`)	`openai`
`MODEL_ID`	Model id (e.g. `gpt-4o`)	`gpt-4o`
`ENABLE_WEB_UI`	Serve the AG-UI web interface	`True`

See .env.example for a copy-paste starting point.

Agent

This repository features a fully integrated Pydantic AI Graph Agent. It communicates over the Agent Control Protocol (ACP) and interacts seamlessly with the Agent Web UI (AG-UI) and Terminal interface.

Running the Agent CLI

To start the interactive command-line agent:

# Set credentials
export DATA_SCIENCE_MCP_URL="your_value"
export DATA_SCIENCE_MCP_TOKEN="your_value"

# Run the agent server
data-science-agent --provider openai --model-id gpt-4o

Docker Compose Orchestration

The following docker/agent.compose.yml configures the Agent, Web UI, and Terminal Interface together:

version: '3.8'

services:
  data-science-mcp-mcp:
    image: knucklessg1/data-science-mcp:mcp
    container_name: data-science-mcp-mcp
    hostname: data-science-mcp-mcp
    restart: always
    env_file:
      - ../.env
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=8000
      - TRANSPORT=streamable-http
    ports:
      - "8000:8000"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

  data-science-mcp-agent:
    image: knucklessg1/data-science-mcp:latest
    container_name: data-science-mcp-agent
    hostname: data-science-mcp-agent
    restart: always
    depends_on:
      - data-science-mcp-mcp
    env_file:
      - ../.env
    command: [ "data-science-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9004
      - MCP_URL=http://data-science-mcp-mcp:8000/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9004:9004"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:9004/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

Detailed graph node architecture explanations, custom skill configurations, and agentic trace guides are available in docs/agent.md.

Security & Governance

Built directly upon the enterprise-ready agent-utilities core, standard security parameters are fully supported:

Access Control & Policy Enforcement

Eunomia Policies: Fine-grained, policy-driven tool authorization. Supports none, local embedded (mcp_policies.json), or centralized remote modes.
OIDC Token Delegation: Compliant with RFC 8693 token exchange for flowing authenticating user credentials from Web UI / ACP → Agent → MCP.
Scoped Credentials: Execution context runs restricted to the specific caller identity.

Runtime Security Grid

Feature	Functionality	Enablement
Tool Guard	Sensitivity inspection with human-in-the-loop validation	Enabled by default
Prompt Injection Defense	Input scanning, repetition monitoring, and recursive loop blocks	Enabled by default
Context Safety Guard	Stuck-loop detectors and contextual overflow preemptive alerts	Enabled by default

Installation

Pick the extra that matches what you want to run:

Extra	Installs	Use when
`data-science-mcp[mcp]`	Slim MCP server only (`agent-utilities[mcp]` — FastMCP/FastAPI) + the core epistemic-graph engine	You only run the MCP server (smallest install / image)
`data-science-mcp[agent]`	Full agent runtime (`agent-utilities[agent,logfire]` — Pydantic AI)	You run the integrated agent
`data-science-mcp[all]`	Everything (`mcp` + `agent` + `scikit-learn` sample-dataset loaders)	Development / both surfaces

Heavy ML extras are opt-in and imported lazily — add them only when needed: [training] (torch/PEFT gradient trainers), [training-scale] (DeepSpeed/FlashAttention, GPU-host only), [training-fast] (Liger Triton kernels), [datasets] (scikit-learn sample loaders), [eval] (lm-eval), [tracking] (MLflow). See docs/training.md.

# MCP server only (recommended for tool hosting — slim deps)
uv pip install "data-science-mcp[mcp]"

# Full agent runtime (Pydantic AI)
uv pip install "data-science-mcp[agent]"

# Everything (development)
uv pip install "data-science-mcp[all]"      # or: python -m pip install "data-science-mcp[all]"

Container images (`:mcp` vs `:agent`)

One multi-stage docker/Dockerfile builds two right-sized images, selected by --target:

Image tag	Build target	Contents	Entrypoint
`knucklessg1/data-science-mcp:mcp`	`--target mcp`	`data-science-mcp[mcp]` — slim, no `pydantic-ai`/`dspy`/`llama-index`/`tree-sitter`	`data-science-mcp`
`knucklessg1/data-science-mcp:latest`	`--target agent` (default)	`data-science-mcp[agent]` — full agent runtime	`data-science-agent`

docker build --target mcp   -t knucklessg1/data-science-mcp:mcp    docker/   # slim MCP server
docker build --target agent -t knucklessg1/data-science-mcp:latest docker/   # full agent

docker/mcp.compose.yml runs the slim :mcp server; docker/agent.compose.yml runs the agent (:latest) with a co-located :mcp sidecar.

Knowledge-graph database (`epistemic-graph`)

data-science-mcp depends directly on the epistemic-graph compute engine (epistemic-graph[datascience]) — its model-training / evaluation / evolution compute runs in that Rust engine, so the engine is a core dependency in every extra (including [mcp]). For production — or to share one knowledge graph across multiple agents — run epistemic-graph as its own database container and point the server at it instead of embedding it. Deployment recipes (single-node + Raft HA), connection config, and the full database architecture (with diagrams) are documented in the epistemic-graph deployment guide.

Documentation

The complete documentation is published as the official documentation site and is the recommended reference for installation, deployment, and day-to-day operation.

Page	Contents
Installation	pip, source, extras, prebuilt Docker image
Deployment	run the MCP server and A2A agent, Compose, Caddy + Technitium, env config
Usage	the MCP tools, the `MLEngine` Python API, the console scripts
Overview	ecosystem role, enterprise readiness, concept registry
Model Training	SFT/DPO/GRPO corpus, reward engine, gradient trainers
Concepts	concept registry (`CONCEPT:DSCI-*`)

Repository Owners

GitHub followers GitHub User's stars

Contribute

Contributions are welcome! Please ensure code quality by executing local checks before submitting pull requests:

Format code using ruff format .
Lint code using ruff check .
Validate type-safety with mypy .
Execute test suites using pytest

Deploy with `agent-os-genesis`

This package can be provisioned for you — skill-guided — by the agent-os-genesis universal skill (its single-package deploy mode): it picks your install method, seeds secrets to OpenBao/Vault (or .env), trusts your enterprise CA, registers the MCP server, and verifies it — the same machinery that stands up the whole Agent OS, narrowed to just this package. Ask your agent to "deploy data-science-mcp with agent-os-genesis".

Install mode	Command
Bare-metal, prod (PyPI)	`uvx data-science-mcp` · or `uv tool install data-science-mcp`
Bare-metal, dev (editable)	`uv pip install -e ".[all]"` · or `pip install -e ".[all]"`
Container, prod	deploy `knucklessg1/data-science-mcp:latest` via docker-compose / swarm / podman / podman-compose / kubernetes
Container, dev (editable)	deploy `docker/compose.dev.yml` (source-mounted at `/src`; edits live on restart)

Secrets are read-existing + seeded via vault_sync — you are only prompted for what's missing.

Project details

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data-science-mcp 1.0.1

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Data Science Mcp

CLI or API | MCP | Agent

Overview

Key Features

CLI or API

MCP

Available MCP Tools

Condensed action-routed tools (default — MCP_TOOL_MODE=condensed)

Verbose 1:1 API-mapped tools (MCP_TOOL_MODE=verbose or both)

Dynamic Tool Selection & Visibility

MCP Configuration Examples

stdio Transport (local IDEs — Cursor, Claude Desktop, VS Code)

Streamable-HTTP Transport (networked / production)

Additional Deployment Options

Environment Variables

Package environment variables

Inherited agent-utilities variables (apply to every connector)

MCP server / transport

Connection

Training / inference backend (full [training] extra)

Tool toggles

Telemetry & governance

Agent CLI (full [agent] runtime only)

Agent

Running the Agent CLI

Docker Compose Orchestration

Security & Governance

Access Control & Policy Enforcement

Runtime Security Grid

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Container images (:mcp vs :agent)

Knowledge-graph database (epistemic-graph)

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Condensed action-routed tools (default — `MCP_TOOL_MODE=condensed`)

Verbose 1:1 API-mapped tools (`MCP_TOOL_MODE=verbose` or `both`)

Training / inference backend (full `[training]` extra)

Agent CLI (full `[agent]` runtime only)

Container images (`:mcp` vs `:agent`)

Knowledge-graph database (`epistemic-graph`)

Deploy with `agent-os-genesis`