Hosted Streamable HTTP MCP server for creating, inspecting, testing, publishing, unpublishing, analyzing, and styling Dreamlit notification workflows from AI clients.
Open the source and read safety notes before installing.
Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.
Decision playbook
Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.
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No baseline selected
No major trust-signal divergence detected in the current selection.
Confirm ownership and provenance before trusting install instructions.
Source link availableRequired
Open the canonical repository and verify ownership.
Source provenance statusRequired
Marked as source-backed.
Metadata reviewed
No reviewed flag detected in metadata.
Validate risk disclosures before installation or API wiring.
Safety notes presentRequired
Review the listed safety guidance before running commands.
Privacy notes presentRequired
Review data handling notes before connecting accounts or secrets.
Trust level risk gateRequired
Trust level does not block evaluation.
Check package metadata and artifact integrity signals.
Install payload available
Install or copy payload is available for review.
Package verification flag
No package verification flag provided.
Checksum metadata
No checksum provided for downloaded artifact.
Use compare context to validate trade-offs before adoption.
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Baseline comparison available
No baseline peer selected yet.
Diverging trust signals identified
No major trust-signal divergence found.
Setup at a glance
Copy-ready — paste the snippet to get started.
Adoption plan
Current risk score 24/100. Use staged verification before broader rollout.
Validate source and review signals before any execution.
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Source URL/provenance metadata is present.
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No review metadata found; increase manual validation.
Verify install payload
Install/config payload exists and can be inspected.
Confirm safety, privacy, and package integrity signals.
Review safety notesRequired
Safety notes are present.
Review privacy notesRequired
Privacy notes are present.
Verify package integrity metadata
No package verification/checksum metadata.
Adopt in controlled steps based on the selected plan.
Run in isolated sandbox firstRequired
Use a constrained sandbox and observe behavior across multiple tasks.
Roll out graduallyRequired
Roll out to a small cohort before wider usage.
Set monitoring and fallback
Define rollback path and monitor errors after adoption.
Evidence readiness
Missing required evidence: Metadata review. Risk score 31.
Source repository/provenance is listed.
Required in this preset
Review metadata is missing.
Required in this preset
Safety notes are present.
Required in this preset
Privacy notes are present.
Optional in this preset
Package integrity metadata is missing.
Optional in this preset
Install payload is available.
Required in this preset
Required gaps: Metadata review
Decision timeline
Blocking gaps: Check metadata review status. Risk 28.
triage
Source/provenance metadata is available.
triage
Review metadata is missing.
verify
Safety notes are available.
verify
Privacy notes are available.
verify
Package integrity metadata is missing.
rollout
Install payload is available.
Blockers: Check metadata review status
Prerequisite readiness
4 prerequisites to line up before setup. Have accounts and credentials ready first. Includes a review or approval gate.
Safety & privacy surface
5 safety and 4 privacy notes across 6 risk areas. Review closely: credentials & tokens, permissions & scopes, network access, third-party handling.
{
"mcpServers": {
"dreamlit": {
"url": "https://mcp.dreamlit.ai/mcp",
"transport": "http"
}
}
}Dreamlit MCP Server connects MCP-capable AI clients to Dreamlit's hosted notification workflow platform for Supabase and Postgres applications. It is designed for outcome-oriented workflow authoring: create drafts from prompts, inspect existing workflows, send tests, apply saved brand kits, publish or unpublish workflows, and answer bounded analytics questions about notification performance.
The production server endpoint is:
https://mcp.dreamlit.ai/mcp
Dreamlit documents the transport as Streamable HTTP. Authentication is OAuth, with a personal access token fallback for clients that do not support OAuth.
These sources were reviewed on 2026-06-05. Prefer the live Dreamlit repository and current MCP client documentation over model memory for endpoint behavior, scopes, token handling, and tool coverage.
https://mcp.dreamlit.ai/mcp.prepare_publish followed by
confirm_publish.get_status: read Dreamlit guidance, workspace context, project setup,
schema hints, workflow state, and app URLs.list_projects: find accessible projects in the approved workspace.list_workflows: find workflows by name, description, trigger type, and
pagination.list_brand_kits: inspect saved brand kit IDs and style summaries.get_workflow_and_preview_url: inspect a draft and get a preview or builder
URL before editing or publishing.get_analytics: query bounded recipient engagement and workflow run data.create_or_update_workflow: create a new draft or update an existing draft.send_workflow_test: confirm and send a draft email or Slack test.prepare_publish: validate a workflow and return confirmation fields.confirm_publish: publish or schedule a workflow after explicit user
confirmation.unpublish_workflow: disable live triggers or schedules.codex mcp add dreamlit --url https://mcp.dreamlit.ai/mcp
codex mcp login dreamlit
To request every documented v1 scope explicitly:
codex mcp login dreamlit --scopes workflows:read,workflows:write,workflows:publish,analytics:read
For a narrower read-only setup, request only the scopes needed for the client,
such as workflows:read or analytics:read.
Use the client's remote server flow and enter:
https://mcp.dreamlit.ai/mcp
The client should open a Dreamlit authorization page. Choose the workspace the AI client may access, then approve only the requested scopes that match the workflow.
Dreamlit OAuth access is scoped to the workspace selected during authorization. Personal access tokens are project scoped and can be created from Dreamlit project settings.
Documented scopes:
workflows:readworkflows:writeworkflows:publishanalytics:readUse the smallest scope set that fits the job. A review-only assistant generally
needs workflows:read; analytics-only tasks should request analytics:read;
authoring and publishing should be separated when possible.
get_status first to understand workspace context and project setup.list_projects and list_workflows when the user gives names instead
of IDs.list_brand_kits before applying a saved style to generated email
drafts.get_workflow_and_preview_url before editing an existing workflow.get_analytics only for bounded questions, with filters and cursor
pagination.create_or_update_workflow.prepare_publish followed
by confirm_publish.Dreamlit MCP can access workflow metadata, saved brand kit summaries, connected project context, preview URLs, and bounded analytics needed for the requested action. The public docs state that clients do not need to send database credentials through MCP.
Avoid putting secrets, raw credentials, private tokens, or integration credentials in prompts. Use Dreamlit's app settings for integrations and credential management.
Confirm the client supports remote Streamable HTTP MCP servers and OAuth. If it does not, use Dreamlit's documented personal access token fallback.
Check that the authorized workspace and project are the ones the user expects.
Then verify that the granted token includes workflows:read.
That is expected. Dreamlit's documented flow keeps authoring and publishing separate. Run tests, open the preview or builder URL, then publish only after a human confirms the workflow.
Use narrower filters and follow returned cursors for larger result sets. The Dreamlit docs describe analytics access as structured and paginated rather than bulk CSV export.
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[](https://heyclau.de/entry/mcp/dreamlit-mcp-server)Dreamlit MCP Server side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
| Field | Hosted Streamable HTTP MCP server for creating, inspecting, testing, publishing, unpublishing, analyzing, and styling Dreamlit notification workflows from AI clients. Open dossier | Apache-2.0 agentic proxy that can expose stdio, HTTP, SSE, and Streamable HTTP MCP servers through a managed gateway with federation, OAuth/JWT authentication, RBAC/CEL policy, CORS, TLS, observability, and Kubernetes Gateway API support. Open dossier | Built-in Streamable HTTP MCP server for Dagu that lets AI agents read workflow state, inspect DAG specs and logs, preview or apply workflow changes, and start, enqueue, retry, or stop DAG runs. Open dossier | MCP server for querying, searching, analyzing, syncing, reading, and sending alerts from a self-hosted TrendRadar news, RSS, and public-opinion monitoring workspace. Open dossier |
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| Next steps | ||||
| Trust | ||||
| Review status | Not reviewed | Not reviewed | Not reviewed | Not reviewed |
| Package trust | Package not verified | Package not verified | Package not verified | Package not verified |
| Source provenance | Source-backed | Source-backed | Source-backed | Source-backed |
| Submitter | oktofeesh1 | oktofeesh1 | oktofeesh1 | oktofeesh1 |
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Brand | — | |||
| Category | mcp | mcp | mcp | mcp |
| Source | Source-backed | Source-backed | Source-backed | Source-backed |
| Author | Dreamlit | agentgateway | dagucloud | TrendRadar |
| Added | 2026-06-05 | 2026-06-06 | 2026-06-06 | 2026-06-06 |
| Platforms | ||||
| Harness | ||||
| Source repo | — | — | — | — |
| Safety notes | ✓Dreamlit MCP is a hosted remote server at `https://mcp.dreamlit.ai/mcp`; the public repository contains setup docs and registry metadata, not the production backend source. Write scopes can create or update workflow drafts and send workflow tests; publish scopes can publish, schedule, or unpublish live notification workflows. Publishing is a separate explicit step in Dreamlit's recommended flow; keep a human confirmation gate before `confirm_publish` or `unpublish_workflow`. Use least-privilege OAuth scopes. Read-only review clients should not receive `workflows:write` or `workflows:publish`. Test important email or Slack notification drafts before publishing, especially when prompts include personalization rules, timing, timezone, or unsubscribe behavior. | ✓Agentgateway can aggregate and expose many downstream MCP targets through one endpoint; every target's permissions become part of the gateway surface. Stdio targets are spawned by the gateway process, so commands such as package runners or containers inherit the gateway host's trust boundary. Demo configs use permissive CORS and local authorization servers; lock down origins, headers, issuers, audiences, JWKS, and resource metadata before production use. CEL/RBAC policies can inspect MCP tool names, arguments, results, prompts, resources, request bodies, JWT claims, API keys, and backend metadata; test policies before relying on them. Remote MCP proxying, OAuth provider adaptation, Kubernetes routing, TLS termination, and guardrails are infrastructure changes that should go through normal security review. | ✓Dagu is a workflow engine; MCP access can expose shell commands, Docker containers, Kubernetes Jobs, SSH commands, SQL queries, HTTP calls, agent harnesses, and other workflow steps. The MCP server registers `dagu_change` and `dagu_execute` as destructive-capable tools, so require preview/review before applying DAG changes or starting/stopping production runs. API keys can be scoped to the MCP surface; avoid reusing broad admin credentials for agent access. Workflow edits may change schedules, parameters, retries, secrets usage, queues, resource limits, notifications, and downstream infrastructure actions. Keep the Dagu server and MCP endpoint behind trusted network boundaries, TLS, and authentication for shared or remote deployments. | ✓TrendRadar can crawl public hotlists and RSS feeds, read article URLs, query local news databases, and trigger manual crawls through MCP tools. The MCP server can send messages to configured notification channels including Feishu, DingTalk, WeCom, Telegram, email, ntfy, Bark, Slack, and generic webhooks. The `sync_from_remote` tool can download stored news databases from configured S3-compatible storage such as Cloudflare R2 into the local output directory. Article-reading tools fetch URLs through Jina Reader and may fail on paywalled, login-gated, rate-limited, or restricted sites. Keep crawl frequency bounded and respect source-site terms, robots guidance, API limits, and the project's own warning about controlling Docker crawl frequency. |
| Privacy notes | ✓Dreamlit MCP can expose workflow metadata, saved brand kit summaries, connected project context, preview or builder URLs, and bounded notification analytics. Analytics access can reveal recipient engagement and workflow run data; filter narrowly and paginate instead of requesting broad reporting dumps. Do not paste database credentials, raw secrets, private tokens, or integration credentials into prompts or shared MCP configs. Review Dreamlit, MCP client, AI-provider, and downstream-tool retention policies before using real customer, recipient, or workspace data. | ✓MCP requests, tool arguments, tool results, prompt names, resource names, session IDs, JWT/API-key claims, raw request/response bodies, logs, traces, and telemetry may pass through the gateway. CEL policy and observability features may buffer or inspect request and response bodies depending on configuration. OAuth/JWT metadata, bearer tokens, API keys, DCR secrets, Keycloak/Auth0/Okta settings, and upstream MCP credentials must be protected as secrets. Any data returned by downstream MCP tools can still be sent onward by the MCP client to the configured model provider. | ✓DAG specs, run parameters, logs, documents, audit records, secrets references, API keys, environment variables, and workflow outputs may be exposed to the MCP client. Workflow logs can contain credentials, customer data, internal hostnames, database query results, command output, file paths, or incident context. Dagu stores state locally by default and can also run distributed workers; review where DAG files, logs, audit entries, and secrets are persisted. Any workflow state returned through the MCP client may be sent onward to the configured model provider. | ✓MCP outputs can reveal watched keywords, RSS subscriptions, platform lists, notification channels, local storage paths, remote bucket names, article URLs, and trend-analysis interests. Notification tools require webhook URLs, bot tokens, chat IDs, SMTP credentials, ntfy topics or tokens, Bark URLs, Slack webhooks, or generic webhook URLs in config or environment variables. Remote storage sync requires S3-compatible endpoint, bucket, access key ID, and secret access key configuration; keep these out of prompts, repository files, logs, and screenshots. AI analysis and translation settings can send stored news, RSS content, prompts, and summaries to the configured LiteLLM provider. Article-reading requests send target article URLs to Jina Reader unless the deployment is modified to use another reader. |
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Put agentgateway in front of MCP tools to aggregate multiple targets, proxy remote servers, spawn stdio servers, serve Streamable HTTP and SSE endpoints, and add auth, policy, telemetry, guardrails, and Kubernetes-native routing.
Connect Claude, Codex, Gemini CLI, and other MCP clients to a running Dagu server so agents can manage local-first YAML workflows through `dagu_read`, `dagu_change`, and `dagu_execute` with Dagu's audit, API key, and secrets controls.
Source-backed guides for putting this to work.
Review remote MCP OAuth and token-audience handling before connecting clients.
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