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Django MCP Server

Django extension that exposes MCP endpoints and stdio transport for Django apps, with declarative model query tools, custom toolsets, DRF create/list/ update/delete tool publishing, serializer output, and MCP inspection.

by Smart GTS · submitted by oktofeesh1·added 2026-06-06·
Review first review before installing

Open the source and read safety notes before installing.

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Source URLs
https://raw.githubusercontent.com/gts360/django-mcp-server/main/README.md, https://github.com/gts360/django-mcp-server
Brand
Django MCP Server
Brand domain
djangoproject.com
Brand asset source
brandfetch
Safety notes
Django MCP Server can expose Django model querysets, custom Python methods, DRF create/list/update/delete views, serializers, resources, and low-level FastMCP tools to an MCP client., Published DRF create, update, and delete tools can mutate application data if their serializers, views, and authentication rules permit it., The README notes that built-in DRF authentication classes, permission classes, filter backends, and pagination are disabled for published DRF tools in favor of MCP authentication; review this carefully before reusing production views., Query tools can evaluate QuerySets and return database records; restrict queryset scope and fields before exposing sensitive models., Require confirmation and application-level authorization before exposing write tools, email-sending methods, admin-like actions, or tools that touch customer, employee, financial, health, or regulated data.
Privacy notes
Django sessions, request headers, model names, field names, primary keys, QuerySet results, serializer output, DRF request bodies, custom tool arguments, and tool responses can be exposed to the MCP client., Exposed models may contain user accounts, permissions, customer records, orders, messages, files, logs, internal notes, audit trails, or application-specific secrets., Remote streamable HTTP deployments can move application data outside the original Django UI and audit path if MCP auth, OAuth metadata, and retention are not configured correctly., Stdio usage can still expose data through local MCP client logs, transcripts, and tool traces., Keep MCP endpoint access, serializer fields, queryset filters, and tool docstrings intentionally narrow for each app.
Author
Smart GTS
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.

25 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. Includes a review or approval gate.

0/5 ready
Account & credentials1Install & runtime2Configuration1Review & approval125 minutes

Safety & privacy surface

Safety & privacy surface

5 safety and 5 privacy notes across 5 risk areas. Review closely: credentials & tokens, permissions & scopes, network access.

5 areas
  • SafetyGeneralDjango MCP Server can expose Django model querysets, custom Python methods, DRF create/list/update/delete views, serializers, resources, and low-level FastMCP tools to an MCP client.
  • SafetyGeneralPublished DRF create, update, and delete tools can mutate application data if their serializers, views, and authentication rules permit it.
  • SafetyPermissions & scopesThe README notes that built-in DRF authentication classes, permission classes, filter backends, and pagination are disabled for published DRF tools in favor of MCP authentication; review this carefully before reusing production views.
  • SafetyPermissions & scopesQuery tools can evaluate QuerySets and return database records; restrict queryset scope and fields before exposing sensitive models.
  • SafetyPermissions & scopesRequire confirmation and application-level authorization before exposing write tools, email-sending methods, admin-like actions, or tools that touch customer, employee, financial, health, or regulated data.
  • PrivacyCredentials & tokensDjango sessions, request headers, model names, field names, primary keys, QuerySet results, serializer output, DRF request bodies, custom tool arguments, and tool responses can be exposed to the MCP client.
  • PrivacyCredentials & tokensExposed models may contain user accounts, permissions, customer records, orders, messages, files, logs, internal notes, audit trails, or application-specific secrets.
  • PrivacyCredentials & tokensRemote streamable HTTP deployments can move application data outside the original Django UI and audit path if MCP auth, OAuth metadata, and retention are not configured correctly.
  • PrivacyExecution & processesStdio usage can still expose data through local MCP client logs, transcripts, and tool traces.
  • PrivacyNetwork accessKeep MCP endpoint access, serializer fields, queryset filters, and tool docstrings intentionally narrow for each app.

Disclosure: Community-maintained MIT Django extension for exposing MCP tools and endpoints inside Django apps. Users are responsible for app-level authentication, authorization, data retention, and compliance behavior.

Safety notes

  • Django MCP Server can expose Django model querysets, custom Python methods, DRF create/list/update/delete views, serializers, resources, and low-level FastMCP tools to an MCP client.
  • Published DRF create, update, and delete tools can mutate application data if their serializers, views, and authentication rules permit it.
  • The README notes that built-in DRF authentication classes, permission classes, filter backends, and pagination are disabled for published DRF tools in favor of MCP authentication; review this carefully before reusing production views.
  • Query tools can evaluate QuerySets and return database records; restrict queryset scope and fields before exposing sensitive models.
  • Require confirmation and application-level authorization before exposing write tools, email-sending methods, admin-like actions, or tools that touch customer, employee, financial, health, or regulated data.

Privacy notes

  • Django sessions, request headers, model names, field names, primary keys, QuerySet results, serializer output, DRF request bodies, custom tool arguments, and tool responses can be exposed to the MCP client.
  • Exposed models may contain user accounts, permissions, customer records, orders, messages, files, logs, internal notes, audit trails, or application-specific secrets.
  • Remote streamable HTTP deployments can move application data outside the original Django UI and audit path if MCP auth, OAuth metadata, and retention are not configured correctly.
  • Stdio usage can still expose data through local MCP client logs, transcripts, and tool traces.
  • Keep MCP endpoint access, serializer fields, queryset filters, and tool docstrings intentionally narrow for each app.

Prerequisites

  • Django 4 or 5 application with Python 3.10 or newer.
  • mcp_server added to INSTALLED_APPS and mcp_server.urls included in the Django URL configuration.
  • Review of which Django models, querysets, custom methods, DRF views, serializers, and request context should be exposed to MCP clients.
  • Authentication classes configured through DJANGO_MCP_AUTHENTICATION_CLASSES before exposing non-public data over streamable HTTP.
  • Local or deployment-specific plan for stdio, WSGI, ASGI, OAuth2, sessions, and Dynamic Client Registration when remote clients need access.

Schema details

Install type
cli
Troubleshooting
No
Source repository stats
Scope
Source repo
Collection metadata
Estimated setup
25 minutes
Difficulty
advanced
Tool listing metadata
Disclosure
Community-maintained MIT Django extension for exposing MCP tools and endpoints inside Django apps. Users are responsible for app-level authentication, authorization, data retention, and compliance behavior.
Full copyable content
{
  "mcpServers": {
    "django": {
      "command": "python",
      "args": ["manage.py", "stdio_server"]
    }
  }
}

About this resource

Content

Django MCP Server adds MCP support to Django applications. It can expose model query tools, custom MCPToolset methods, DRF create/list/update/delete views, serializer output, low-level FastMCP tools, streamable HTTP endpoints, and a local stdio management command.

Use it when Claude needs supervised access to a Django application's approved models, business logic, or REST framework operations through MCP.

Source Review

These sources were reviewed on 2026-06-06. Prefer the live repository, README, PyPI project, license file, Python package metadata, core Django MCP implementation, query tool implementation, URL routing, stdio command, and example MCP toolset for current setup and behavior details.

Features

  • Serve an MCP endpoint inside an existing Django app.
  • Run a local stdio MCP server through python manage.py stdio_server.
  • Expose Django models with declarative ModelQueryToolset classes.
  • Publish custom Python methods through MCPToolset.
  • Publish DRF create, list, update, and delete views as MCP tools.
  • Serialize tool output through Django REST Framework serializers.
  • Register low-level FastMCP tools and resources from Django code.
  • Inspect declared tools with python manage.py mcp_inspect.

Installation

Install the package and add it to the Django project:

pip install django-mcp-server

Add mcp_server to INSTALLED_APPS, include mcp_server.urls, then define approved tools in an app-level mcp.py file. For local stdio clients:

{
  "mcpServers": {
    "django": {
      "command": "python",
      "args": ["manage.py", "stdio_server"]
    }
  }
}

Use the inspection command before connecting an agent:

python manage.py mcp_inspect

Use Cases

  • Let Claude query approved Django model records through scoped querysets.
  • Expose safe business operations as custom MCP tools.
  • Wrap selected DRF list, create, update, or delete views for agent workflows.
  • Serialize tool responses through DRF serializers.
  • Test local Django MCP tools through Claude Desktop or other stdio clients.
  • Add OAuth-backed streamable HTTP MCP access for remote agents after an explicit authentication and authorization review.

Safety and Privacy

Django MCP Server inherits the risk profile of the Django app it exposes. Keep toolsets narrow, review every queryset and serializer field, enable MCP authentication for non-public data, and require confirmation before publishing write-capable DRF views or custom methods with side effects.

Treat Django model data, primary keys, user records, sessions, request metadata, serializer output, tool arguments, tool responses, logs, and MCP transcripts as sensitive application data. Avoid exposing broad querysets, admin-like actions, or production write paths without app-specific authorization controls.

Source citations

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How it compares

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

Field

Django extension that exposes MCP endpoints and stdio transport for Django apps, with declarative model query tools, custom toolsets, DRF create/list/ update/delete tool publishing, serializer output, and MCP inspection.

Open dossier

PortSwigger's Burp Suite MCP Server extension connects Burp Suite to MCP clients through an SSE server or packaged stdio proxy for request, Repeater, Intruder, history, scanner, Collaborator, and configuration workflows.

Open dossier

Connect Claude to a Google Colab runtime. Colab MCP starts a local FastMCP server and websocket proxy, waits for an authorized Colab-origin browser connection, then exposes the session's notebook and runtime tools to your MCP client over stdio.

Open dossier

MCP server example from FunASR that lets Claude transcribe local audio files with local speech recognition, automatic language handling, timestamps, and speaker labels when available.

Open dossier
Next steps
Trust
Review statusNot reviewedNot reviewedNot reviewedNot reviewed
Package trustPackage not verifiedPackage not verifiedPackage not verifiedPackage not verified
Source provenanceSource-backedSource-backedSource-backedSource-backed
Submitteroktofeesh1oktofeesh1oktofeesh1oktofeesh1
Install riskReview firstReview firstReview firstReview first
Notes Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓
BrandDjango MCP Server logoDjango MCP ServerBurp Suite MCP Server logoBurp Suite MCP ServerColab MCP logoColab MCPFunASR logoFunASR
Categorymcpmcpmcpmcp
SourceSource-backedSource-backedSource-backedSource-backed
AuthorSmart GTSPortSwiggerGoogle ColabFunASR
Added2026-06-062026-06-062026-06-062026-06-06
Platforms
Harness
Source repo
Safety notesDjango MCP Server can expose Django model querysets, custom Python methods, DRF create/list/update/delete views, serializers, resources, and low-level FastMCP tools to an MCP client. Published DRF create, update, and delete tools can mutate application data if their serializers, views, and authentication rules permit it. The README notes that built-in DRF authentication classes, permission classes, filter backends, and pagination are disabled for published DRF tools in favor of MCP authentication; review this carefully before reusing production views. Query tools can evaluate QuerySets and return database records; restrict queryset scope and fields before exposing sensitive models. Require confirmation and application-level authorization before exposing write tools, email-sending methods, admin-like actions, or tools that touch customer, employee, financial, health, or regulated data.Burp Suite MCP Server can send HTTP/1.1 and HTTP/2 requests, create Repeater tabs, send requests to Intruder, toggle Proxy Intercept, pause or resume Burp's task execution engine, and update the active message editor. In Burp Suite Professional it can also expose scanner issues and generate or poll Collaborator payloads for out-of-band testing. The extension includes approval flows for outbound HTTP requests and sensitive data access, but users can configure always-allow targets and disable some approval requirements. Configuration editing tools can import project-level or user-level Burp options when enabled in the extension, which can change proxy, scanner, target, and other Burp behavior. Use only on systems and applications where testing is authorized; active requests, Intruder traffic, scanner workflows, and Collaborator payloads can affect third-party services. Keep the MCP server bound to trusted local interfaces and avoid exposing the SSE server to untrusted networks.Colab MCP runs a local websocket proxy and waits for a Google Colab browser session to connect before proxying session tools. The server creates a bearer-style proxy token and accepts a single authorized Colab-origin websocket connection at a time. A connected Colab runtime can execute code and interact with notebooks, outputs, files, variables, packages, and mounted resources depending on the session and tools exposed by Colab. Use disposable notebooks or reviewed development sessions before allowing Claude to run code or inspect outputs. Avoid connecting notebooks that have access to production data, cloud credentials, private datasets, paid accelerators, or long-running jobs unless the workflow has explicit approval.The MCP server exposes a `transcribe_audio` tool that reads the local file path supplied by the agent. Configure clients so Claude can only request audio files from approved directories; do not expose arbitrary private folders or shared drives. First use can download FunASR model weights and dependencies from upstream model hosts; review network policy, cache location, and disk usage before use in restricted environments. Long recordings and GPU transcription can consume significant CPU, GPU, memory, and disk cache resources. Require confirmation before transcribing meetings, calls, interviews, voice notes, customer audio, regulated recordings, or files containing other people.
Privacy notesDjango sessions, request headers, model names, field names, primary keys, QuerySet results, serializer output, DRF request bodies, custom tool arguments, and tool responses can be exposed to the MCP client. Exposed models may contain user accounts, permissions, customer records, orders, messages, files, logs, internal notes, audit trails, or application-specific secrets. Remote streamable HTTP deployments can move application data outside the original Django UI and audit path if MCP auth, OAuth metadata, and retention are not configured correctly. Stdio usage can still expose data through local MCP client logs, transcripts, and tool traces. Keep MCP endpoint access, serializer fields, queryset filters, and tool docstrings intentionally narrow for each app.Proxy HTTP history, WebSocket history, Organizer items, scanner issues, request and response bodies, headers, cookies, tokens, session identifiers, and Collaborator interaction data may be returned to the MCP client. The extension can read project-level and user-level Burp configuration; upstream code filters some configuration credentials when configured, but users should still treat exported options as sensitive. MCP prompts, responses, Burp logs, and client transcripts can retain target URLs, credentials, payloads, vulnerability details, and proprietary application behavior. The stdio proxy and SSE server bridge Burp traffic into the MCP client process; keep client configs, proxy paths, and Burp project files protected.Notebook code, prompts, outputs, logs, variables, datasets, file paths, runtime metadata, package lists, browser session details, and generated websocket tokens can be visible to the MCP client and model provider. Colab notebooks may contain API keys, OAuth tokens, mounted Drive paths, secrets in environment variables, private model weights, customer data, or unpublished research. Local log files are created under a temporary Colab MCP log directory by default unless a log directory is specified. Review Google Colab, MCP client, model provider, and organization retention policies before sending notebook or runtime context to an assistant.Audio recordings can contain voices, names, accents, speaker identity, background speech, locations, health details, financial details, customer data, credentials spoken aloud, or other sensitive personal information. The upstream MCP example performs local inference and does not require an API key, but MCP clients, model providers, logs, terminal output, transcripts, screenshots, and shared chats can still retain audio paths and transcription text. Generated transcripts, timestamps, and speaker labels may identify individuals or reveal confidential conversations. Model downloads and package installation can contact PyPI, ModelScope, Hugging Face, or other dependency hosts depending on the environment and model configuration.
Prerequisites
  • Django 4 or 5 application with Python 3.10 or newer.
  • mcp_server added to INSTALLED_APPS and mcp_server.urls included in the Django URL configuration.
  • Review of which Django models, querysets, custom methods, DRF views, serializers, and request context should be exposed to MCP clients.
  • Authentication classes configured through DJANGO_MCP_AUTHENTICATION_CLASSES before exposing non-public data over streamable HTTP.
  • Burp Suite Community or Professional with Java extension support.
  • Java and the `jar` command available for building and loading the extension.
  • Gradle wrapper execution allowed for building `build/libs/burp-mcp-all.jar` from source.
  • An MCP client that can connect to the Burp SSE server or run the packaged stdio proxy.
  • uv or uvx available to run the server from the GitHub repository.
  • Local MCP client that supports `notifications/tools/list_changed`.
  • Google Colab account and browser session you are authorized to use.
  • Local machine access, because upstream documents that the MCP client must run locally.
  • Python environment with FunASR installed from PyPI or a reviewed source checkout.
  • Local checkout or copy of `examples/mcp_server/funasr_mcp.py` from the FunASR repository.
  • Audio files in an approved location and format such as WAV, MP3, FLAC, M4A, or OGG.
  • Optional GPU, Apple silicon, or CPU device selection through `FUNASR_DEVICE`.
Install
pip install django-mcp-server
./gradlew embedProxyJar
uvx git+https://github.com/googlecolab/colab-mcp
pip install funasr
Config
{
  "mcpServers": {
    "django": {
      "command": "python",
      "args": [
        "manage.py",
        "stdio_server"
      ],
      "type": "stdio"
    }
  }
}
Manual-only setup:
./gradlew embedProxyJar
{
  "mcpServers": {
    "colab-mcp": {
      "command": "uvx",
      "args": ["git+https://github.com/googlecolab/colab-mcp"],
      "timeout": 30000
    }
  }
}
Manual-only setup:
pip install funasr
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