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Blender MCP Server

MCP server and Blender add-on that let Claude inspect scenes, create and modify 3D objects, run Blender Python, capture viewport screenshots, and use asset or 3D-generation integrations from inside Blender.

Review first review before installing

Open the source and read safety notes before installing.

Citation facts

Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.

Source URLs
https://github.com/ahujasid/blender-mcp/blob/main/README.md, https://github.com/ahujasid/blender-mcp
Brand
Blender MCP
Brand domain
github.com
Safety notes
The MCP server talks to a Blender add-on over a socket and can create, modify, delete, import, and inspect objects in the active Blender scene., The `execute_blender_code` tool can run arbitrary Python code inside Blender, so save work before use and review generated scripts before allowing execution., Optional Poly Haven, Sketchfab, Hyper3D, and Hunyuan3D workflows can download assets, import generated models, or call external services that may have their own usage limits, licenses, and terms., The README warns to run only one MCP server instance for the same Blender connection to avoid confusing the socket workflow., Do not expose the Blender socket or any remote-host setup beyond trusted local or development networks without reviewing access controls.
Privacy notes
Tool calls can expose scene names, object metadata, materials, generated Python snippets, viewport screenshots, prompt text, and asset queries to the connected MCP client and model provider., Blender MCP includes telemetry code and project terms describing collection of prompts, generated code, scene metadata, screenshots or viewport images when telemetry consent is enabled., The repository documents `DISABLE_TELEMETRY=true`, `BLENDER_MCP_DISABLE_TELEMETRY=true`, and `MCP_DISABLE_TELEMETRY=true` as environment-variable opt-outs for telemetry., The terms state that collected data may be retained, used for AI training or research, and shared in anonymized or aggregated datasets; review the current terms before using it on confidential client work., Third-party asset and generation services may receive prompts, image references, asset searches, downloads, or account identifiers depending on which integrations are enabled.
Author
Siddharth Ahuja
Submitted by
oktofeesh1
Claim status
unclaimed
Last verified
2026-06-06

Decision playbook

Review trust signals before you adopt

Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.

Compare context
Selected

0

Current score

63

Baseline

Delta

No baseline selected

No major trust-signal divergence detected in the current selection.

Source and provenance checks

Needs review

Confirm ownership and provenance before trusting install instructions.

  • Source link availableRequired

    Open the canonical repository and verify ownership.

    Done
  • Source provenance statusRequired

    Marked as source-backed.

    Done
  • Metadata reviewed

    No reviewed flag detected in metadata.

    Pending

Safety and privacy checks

Complete

Validate risk disclosures before installation or API wiring.

  • Safety notes presentRequired

    Review the listed safety guidance before running commands.

    Done
  • Privacy notes presentRequired

    Review data handling notes before connecting accounts or secrets.

    Done
  • Trust level risk gateRequired

    Trust level does not block evaluation.

    Done

Package and install checks

Needs review

Check package metadata and artifact integrity signals.

  • Install payload available

    Install or copy payload is available for review.

    Done
  • Package verification flag

    No package verification flag provided.

    Pending
  • Checksum metadata

    No checksum provided for downloaded artifact.

    Pending

Compare-driven decision checks

Needs review

Use compare context to validate trade-offs before adoption.

  • Compare tray has multiple entries

    Add at least one more entry to compare trust differences.

    Pending
  • Baseline comparison available

    No baseline peer selected yet.

    Pending
  • Diverging trust signals identified

    No major trust-signal divergence found.

    Pending

Setup at a glance

CLI install

Copy-ready — paste the snippet to get started.

15 minutes

Adoption plan

Balanced adoption plan

Current risk score 24/100. Use staged verification before broader rollout.

Risk 24

Pre-adoption checks

Validate source and review signals before any execution.

  • Confirm source provenanceRequired

    Source URL/provenance metadata is present.

    Done
  • Confirm metadata review state

    No review metadata found; increase manual validation.

    Pending
  • Verify install payload

    Install/config payload exists and can be inspected.

    Done

Security checks

Confirm safety, privacy, and package integrity signals.

  • Review safety notesRequired

    Safety notes are present.

    Done
  • Review privacy notesRequired

    Privacy notes are present.

    Done
  • Verify package integrity metadata

    No package verification/checksum metadata.

    Pending

Rollout

Adopt in controlled steps based on the selected plan.

  • Run in isolated sandbox firstRequired

    Use a constrained sandbox and observe behavior across multiple tasks.

    Pending
  • Roll out graduallyRequired

    Roll out to a small cohort before wider usage.

    Pending
  • Set monitoring and fallback

    Define rollback path and monitor errors after adoption.

    Pending

Evidence readiness

Evidence readiness matrix · balanced

Missing required evidence: Metadata review. Risk score 31.

Risk 31

Source provenance

Present

Source repository/provenance is listed.

Required in this preset

Metadata review

Missing

Review metadata is missing.

Required in this preset

Safety notes

Present

Safety notes are present.

Required in this preset

Privacy notes

Present

Privacy notes are present.

Optional in this preset

Package integrity

Missing

Package integrity metadata is missing.

Optional in this preset

Install payload

Present

Install payload is available.

Required in this preset

Required gaps: Metadata review

Decision timeline

Decision timeline · balanced

Blocking gaps: Check metadata review status. Risk 28.

Risk 28

triage

Confirm source provenanceRequired

Source/provenance metadata is available.

Done

triage

Check metadata review statusRequired

Review metadata is missing.

Pending

verify

Review safety notesRequired

Safety notes are available.

Done

verify

Review privacy notes

Privacy notes are available.

Done

verify

Validate package integrity metadata

Package integrity metadata is missing.

Pending

rollout

Verify install payload and commandsRequired

Install payload is available.

Done

Blockers: Check metadata review status

Prerequisite readiness

Prerequisite readiness

5 prerequisites to line up before setup. Have accounts and credentials ready first.

0/5 ready
Account & credentials1Install & runtime1Permissions & scopes1General215 minutes

Safety & privacy surface

Safety & privacy surface

5 safety and 5 privacy notes across 7 risk areas. Review closely: permissions & scopes, network access, third-party handling.

7 areas
  • SafetyGeneralThe MCP server talks to a Blender add-on over a socket and can create, modify, delete, import, and inspect objects in the active Blender scene.
  • SafetyExecution & processesThe `execute_blender_code` tool can run arbitrary Python code inside Blender, so save work before use and review generated scripts before allowing execution.
  • SafetyNetwork accessOptional Poly Haven, Sketchfab, Hyper3D, and Hunyuan3D workflows can download assets, import generated models, or call external services that may have their own usage limits, licenses, and terms.
  • SafetyExecution & processesThe README warns to run only one MCP server instance for the same Blender connection to avoid confusing the socket workflow.
  • SafetyPermissions & scopesDo not expose the Blender socket or any remote-host setup beyond trusted local or development networks without reviewing access controls.
  • PrivacyThird-party handlingTool calls can expose scene names, object metadata, materials, generated Python snippets, viewport screenshots, prompt text, and asset queries to the connected MCP client and model provider.
  • PrivacyTelemetryBlender MCP includes telemetry code and project terms describing collection of prompts, generated code, scene metadata, screenshots or viewport images when telemetry consent is enabled.
  • PrivacyTelemetryThe repository documents `DISABLE_TELEMETRY=true`, `BLENDER_MCP_DISABLE_TELEMETRY=true`, and `MCP_DISABLE_TELEMETRY=true` as environment-variable opt-outs for telemetry.
  • PrivacyData retentionThe terms state that collected data may be retained, used for AI training or research, and shared in anonymized or aggregated datasets; review the current terms before using it on confidential client work.
  • PrivacyNetwork accessThird-party asset and generation services may receive prompts, image references, asset searches, downloads, or account identifiers depending on which integrations are enabled.

Safety notes

  • The MCP server talks to a Blender add-on over a socket and can create, modify, delete, import, and inspect objects in the active Blender scene.
  • The `execute_blender_code` tool can run arbitrary Python code inside Blender, so save work before use and review generated scripts before allowing execution.
  • Optional Poly Haven, Sketchfab, Hyper3D, and Hunyuan3D workflows can download assets, import generated models, or call external services that may have their own usage limits, licenses, and terms.
  • The README warns to run only one MCP server instance for the same Blender connection to avoid confusing the socket workflow.
  • Do not expose the Blender socket or any remote-host setup beyond trusted local or development networks without reviewing access controls.

Privacy notes

  • Tool calls can expose scene names, object metadata, materials, generated Python snippets, viewport screenshots, prompt text, and asset queries to the connected MCP client and model provider.
  • Blender MCP includes telemetry code and project terms describing collection of prompts, generated code, scene metadata, screenshots or viewport images when telemetry consent is enabled.
  • The repository documents `DISABLE_TELEMETRY=true`, `BLENDER_MCP_DISABLE_TELEMETRY=true`, and `MCP_DISABLE_TELEMETRY=true` as environment-variable opt-outs for telemetry.
  • The terms state that collected data may be retained, used for AI training or research, and shared in anonymized or aggregated datasets; review the current terms before using it on confidential client work.
  • Third-party asset and generation services may receive prompts, image references, asset searches, downloads, or account identifiers depending on which integrations are enabled.

Prerequisites

  • Blender installed locally with permission to install and enable the repository's `addon.py` file.
  • uv or uvx available to run the packaged `blender-mcp` server.
  • Blender MCP add-on running in Blender and connected through the local socket server, usually on `localhost:9876`.
  • Optional API keys or account setup for third-party integrations such as Hyper3D, Sketchfab, Hunyuan3D, or asset-download workflows.
  • Clear project policy for whether Claude may run generated Blender Python, download assets, upload screenshots through telemetry, or use scene prompts for model-training telemetry.

Schema details

Install type
cli
Troubleshooting
No
Source repository stats
Scope
Source repo
Collection metadata
Estimated setup
15 minutes
Difficulty
intermediate
Full copyable content
{
  "mcpServers": {
    "blender": {
      "command": "uvx",
      "args": ["blender-mcp"]
    }
  }
}

About this resource

Content

Blender MCP Server connects Claude to Blender through a local MCP server and a Blender add-on. The add-on opens a socket inside Blender, while the Python MCP server exposes tools Claude can use to inspect the current scene, manipulate objects and materials, run Blender Python, capture viewport screenshots, and coordinate optional asset or 3D-generation workflows.

The project is popular because it turns Blender into an interactive creative workspace for agent-assisted 3D work. Claude can ask for scene information, create or modify objects, update materials, position cameras, download Poly Haven or Sketchfab assets, and work with Hyper3D or Hunyuan3D generation when those integrations are enabled.

Source Review

These sources were reviewed on 2026-06-06. Prefer the live repository, README, package metadata, server implementation, add-on source, telemetry code, terms, and PyPI page for current setup steps, tool behavior, telemetry controls, and third-party integration details.

Features

  • Local Blender add-on plus Python MCP server architecture.
  • Claude Code, Claude Desktop, Cursor, VS Code, and OpenCode configuration examples.
  • Scene inspection for objects, materials, cameras, and Blender state.
  • Object creation, editing, deletion, material assignment, and texture workflows.
  • Viewport screenshot capture so Claude can reason about the visible scene.
  • Arbitrary Blender Python execution through execute_blender_code.
  • Optional Poly Haven asset search and download support.
  • Optional Sketchfab model search, preview, and download support.
  • Optional Hyper3D Rodin and Hunyuan3D generation/import workflows.
  • Environment variables for Blender socket host and port configuration.
  • Telemetry opt-out controls through add-on preferences and environment variables.

Installation

Install uv first if it is not already available, then add the MCP server to Claude Code:

claude mcp add blender uvx blender-mcp

For MCP clients that use JSON configuration:

{
  "mcpServers": {
    "blender": {
      "command": "uvx",
      "args": ["blender-mcp"]
    }
  }
}

To disable telemetry from the MCP server command, include the environment variable documented by the project:

{
  "mcpServers": {
    "blender": {
      "command": "uvx",
      "args": ["blender-mcp"],
      "env": {
        "DISABLE_TELEMETRY": "true"
      }
    }
  }
}

Download addon.py from the repository, install it in Blender through Preferences > Add-ons, enable the Blender MCP add-on, and click the add-on's connect button from Blender's 3D View sidebar before using the MCP tools.

Use Cases

  • Ask Claude to inspect a scene and summarize objects, materials, and camera state.
  • Prototype simple 3D scenes, props, layouts, and material changes from text prompts.
  • Generate and review Blender Python before allowing it to run in the active scene.
  • Capture a viewport screenshot so Claude can adjust composition or object placement.
  • Search and import public assets or HDRIs when Poly Haven or Sketchfab workflows are enabled.
  • Generate rough 3D assets through Hyper3D or Hunyuan3D integrations for early concept work.
  • Use Claude as a scene assistant while keeping Blender as the local creative environment.

Safety and Privacy

Blender MCP is powerful because it gives Claude direct control over a local creative application. Treat it as an execution-capable local tool, not a passive documentation server. Review generated Python, keep backups of important projects, and avoid running it against sensitive production assets without a clear review step.

Telemetry deserves special attention. The README and telemetry source document ways to disable collection, while the project terms describe possible collection and use of prompts, generated code, scene metadata, and screenshots or viewport images when telemetry is enabled. Disable telemetry and review the current terms before using Blender MCP on confidential client scenes, unreleased models, or work that cannot be used for training or shared research datasets.

Duplicate Check

No ahujasid/blender-mcp, blender-mcp, Blender socket add-on MCP, Poly Haven Blender MCP, Sketchfab Blender MCP, Hyper3D Blender MCP, or Hunyuan3D Blender MCP entry was found in content/mcp, content/tools, content/guides, content/agents, or content/skills.

Source citations

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How it compares

Blender MCP Server side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

3 trust signals differ across this comparison (Package trust, Source provenance, Submitter).

Field

MCP server and Blender add-on that let Claude inspect scenes, create and modify 3D objects, run Blender Python, capture viewport screenshots, and use asset or 3D-generation integrations from inside Blender.

Open dossier

Official MCP server for agent-device, Callstack's device automation CLI for inspecting, controlling, debugging, recording, and collecting evidence from iOS, Android, TV, macOS, Linux, React Native, Expo, Flutter, and native apps.

Open dossier

Read and write records, manage bases and tables in Airtable directly from Claude

Open dossier

Interact with Asana workspaces to manage projects and tasks

Open dossier
Next steps
Trust
Review statusNot reviewedNot reviewedNot reviewedNot reviewed
Package trustDiffersPackage not verifiedPackage not verifiedPackage verifiedPackage verified
Source provenanceDiffersSource-backedSource-backedNo submission linkNo submission link
SubmitterDiffersoktofeesh1oktofeesh1
Install riskReview firstReview firstReview firstReview first
Notes Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓
Brandagent-device logoagent-deviceAirtable logoAirtableAsana logoAsana
Categorymcpmcpmcpmcp
SourceSource-backedSource-backedFirst-partyFirst-party
AuthorSiddharth AhujaCallstackdomdomeggAsana
Added2026-06-062026-06-062025-09-182025-09-18
Platforms
Harness
Source repo
Safety notesThe MCP server talks to a Blender add-on over a socket and can create, modify, delete, import, and inspect objects in the active Blender scene. The `execute_blender_code` tool can run arbitrary Python code inside Blender, so save work before use and review generated scripts before allowing execution. Optional Poly Haven, Sketchfab, Hyper3D, and Hunyuan3D workflows can download assets, import generated models, or call external services that may have their own usage limits, licenses, and terms. The README warns to run only one MCP server instance for the same Blender connection to avoid confusing the socket workflow. Do not expose the Blender socket or any remote-host setup beyond trusted local or development networks without reviewing access controls.Agent Device MCP exposes structured tools backed by `AgentDeviceClient`; the docs state it does not expose generic shell execution over MCP. Tools and CLI workflows can open apps, inspect UI, tap, type, scroll, perform gestures, wait, assert state, handle alerts, and close sessions. Evidence workflows can capture screenshots, recordings, logs, traces, network traffic, performance samples, crash context, React profiles, and replay files. Mutating commands should run serially against one session, and separate sessions or devices should be used for parallel work. Prefer dedicated test devices or simulators, and require approval before entering credentials, submitting forms, changing settings, installing apps, sending messages, or touching production accounts.Restrict the Airtable API key to the bases and write scopes Claude is allowed to use before enabling record or schema changes.Use a least-privilege Asana token and test task or project writes in a non-critical workspace before connecting production workflows.
Privacy notesTool calls can expose scene names, object metadata, materials, generated Python snippets, viewport screenshots, prompt text, and asset queries to the connected MCP client and model provider. Blender MCP includes telemetry code and project terms describing collection of prompts, generated code, scene metadata, screenshots or viewport images when telemetry consent is enabled. The repository documents `DISABLE_TELEMETRY=true`, `BLENDER_MCP_DISABLE_TELEMETRY=true`, and `MCP_DISABLE_TELEMETRY=true` as environment-variable opt-outs for telemetry. The terms state that collected data may be retained, used for AI training or research, and shared in anonymized or aggregated datasets; review the current terms before using it on confidential client work. Third-party asset and generation services may receive prompts, image references, asset searches, downloads, or account identifiers depending on which integrations are enabled.Screenshots, recordings, traces, logs, network dumps, replay files, reports, UI snapshots, typed input, and React profiles can contain private UI state, tokens, request data, customer information, or credentials. macOS, iOS, Android, and TV automation can expose local app state, notifications, device names, package identifiers, app content, system dialogs, and permission prompts. Network inspection artifacts may include headers, payloads, session identifiers, URLs, and API data; review before sharing or committing. Interactive CLI runs may check npm for newer package versions unless `AGENT_DEVICE_NO_UPDATE_NOTIFIER=1` is set.Airtable records, field values, attachments, and base metadata may be sent through the MCP client and model context.Asana task text, project metadata, comments, assignees, due dates, and workspace details may be sent through model context.
Prerequisites
  • Blender installed locally with permission to install and enable the repository's `addon.py` file.
  • uv or uvx available to run the packaged `blender-mcp` server.
  • Blender MCP add-on running in Blender and connected through the local socket server, usually on `localhost:9876`.
  • Optional API keys or account setup for third-party integrations such as Hyper3D, Sketchfab, Hunyuan3D, or asset-download workflows.
  • Node.js 22 or newer and `agent-device` installed globally or project-locally.
  • Xcode tooling for iOS, tvOS, or macOS targets, or Android SDK and ADB for Android targets.
  • Device, simulator, emulator, TV, macOS, or Linux desktop target that the agent is allowed to automate.
  • Required local permissions such as Android device trust, iOS Developer Mode, macOS Accessibility, and Screen Recording where applicable.
  • Node.js 18+ installed for running npx commands and MCP server packages
  • Airtable Personal Access Token (PAT) from https://airtable.com/account
  • Required API scopes: data.records:read (minimum), data.records:write (for updates), schema.bases:read (for schema operations)
  • Base ID for the Airtable base you want to access (starts with 'app' prefix)
  • OAuth 2.0 authentication (required for MCP server via SSE transport)
  • Asana account with access to at least one workspace
  • Workspace membership with Member role or higher (Guest role insufficient for task creation)
  • Internet connection for accessing Asana API (https://app.asana.com)
Install
claude mcp add blender uvx blender-mcp
claude mcp add --transport stdio --scope user agent-device -- agent-device mcp
claude mcp add airtable --env AIRTABLE_API_KEY=YOUR_KEY -- npx -y airtable-mcp-server && claude mcp list
claude mcp list && claude mcp status asana
Config
{
  "mcpServers": {
    "blender": {
      "command": "uvx",
      "args": ["blender-mcp"],
      "env": {
        "DISABLE_TELEMETRY": "true"
      }
    }
  }
}
{
  "mcpServers": {
    "agent-device": {
      "command": "agent-device",
      "args": ["mcp"]
    }
  }
}
{
  "mcpServers": {
    "airtable": {
      "env": {
        "AIRTABLE_API_KEY": "${AIRTABLE_API_KEY}"
      },
      "args": [
        "-y",
        "airtable-mcp-server"
      ],
      "command": "npx",
      "type": "stdio"
    }
  }
}
{
  "mcpServers": {
    "asana": {
      "url": "https://mcp.asana.com/sse",
      "type": "sse"
    }
  }
}
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