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Deep Research MCP Server

Self-hostable deep research app with MCP and SSE APIs for generating multi-step research reports using configurable LLM and search providers.

by u14app · submitted by oktofeesh1·added 2026-06-05·
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Open the source and read safety notes before installing.

Citation facts

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Source URLs
https://github.com/u14app/deep-research#model-context-protocol-mcp-server, https://github.com/u14app/deep-research, https://research.u14.app
Brand
Deep Research
Brand domain
research.u14.app
Brand asset source
brandfetch
Safety notes
Deep Research can make repeated model and search-provider calls, so set budgets, rate limits, and provider quotas before exposing it to broad agent workflows., Generated reports can contain stale, incomplete, or misinterpreted sources; require citation review before using output in legal, medical, financial, security, or customer-facing decisions., Bind Docker deployments to localhost unless a trusted reverse proxy or firewall is in front of the service, and set `ACCESS_PASSWORD` or equivalent gateway controls before enabling MCP access., Uploaded documents and local knowledge bases should be reviewed for copyright, sensitive data, and permission to process before research begins.
Privacy notes
Research prompts, uploaded files, generated reports, search queries, citations, model inputs, model outputs, provider API keys, access passwords, and deployment logs can contain sensitive data., Browser-local history and knowledge-base storage are local to the deployed app context, but server-side API mode can route data through the deployment host, model providers, and search providers., Review hosting logs, cache behavior, environment variable handling, and third-party provider retention before using Deep Research with private or regulated material.
Author
u14app
Submitted by
oktofeesh1
Claim status
unclaimed
Last verified
2026-06-05

Decision playbook

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

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Selected

0

Current score

63

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No baseline selected

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Safety and privacy checks

Complete

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  • Trust level risk gateRequired

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Package and install checks

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  • Install payload available

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Compare-driven decision checks

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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
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    No review metadata found; increase manual validation.

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  • Verify install payload

    Install/config payload exists and can be inspected.

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Security checks

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  • Review safety notesRequired

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  • Review privacy notesRequired

    Privacy notes are present.

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  • Verify package integrity metadata

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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.

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  • 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.

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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

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Source/provenance metadata is available.

Done

triage

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Pending

verify

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verify

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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

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

0/4 ready
Account & credentials2Permissions & scopes1Network & hosting125 minutes

Safety & privacy surface

Safety & privacy surface

4 safety and 3 privacy notes across 4 risk areas. Review closely: credentials & tokens, permissions & scopes, third-party handling.

4 areas
  • SafetyThird-party handlingDeep Research can make repeated model and search-provider calls, so set budgets, rate limits, and provider quotas before exposing it to broad agent workflows.
  • SafetyGeneralGenerated reports can contain stale, incomplete, or misinterpreted sources; require citation review before using output in legal, medical, financial, security, or customer-facing decisions.
  • SafetyCredentials & tokensBind Docker deployments to localhost unless a trusted reverse proxy or firewall is in front of the service, and set `ACCESS_PASSWORD` or equivalent gateway controls before enabling MCP access.
  • SafetyPermissions & scopesUploaded documents and local knowledge bases should be reviewed for copyright, sensitive data, and permission to process before research begins.
  • PrivacyCredentials & tokensResearch prompts, uploaded files, generated reports, search queries, citations, model inputs, model outputs, provider API keys, access passwords, and deployment logs can contain sensitive data.
  • PrivacyThird-party handlingBrowser-local history and knowledge-base storage are local to the deployed app context, but server-side API mode can route data through the deployment host, model providers, and search providers.
  • PrivacyThird-party handlingReview hosting logs, cache behavior, environment variable handling, and third-party provider retention before using Deep Research with private or regulated material.

Safety notes

  • Deep Research can make repeated model and search-provider calls, so set budgets, rate limits, and provider quotas before exposing it to broad agent workflows.
  • Generated reports can contain stale, incomplete, or misinterpreted sources; require citation review before using output in legal, medical, financial, security, or customer-facing decisions.
  • Bind Docker deployments to localhost unless a trusted reverse proxy or firewall is in front of the service, and set `ACCESS_PASSWORD` or equivalent gateway controls before enabling MCP access.
  • Uploaded documents and local knowledge bases should be reviewed for copyright, sensitive data, and permission to process before research begins.

Privacy notes

  • Research prompts, uploaded files, generated reports, search queries, citations, model inputs, model outputs, provider API keys, access passwords, and deployment logs can contain sensitive data.
  • Browser-local history and knowledge-base storage are local to the deployed app context, but server-side API mode can route data through the deployment host, model providers, and search providers.
  • Review hosting logs, cache behavior, environment variable handling, and third-party provider retention before using Deep Research with private or regulated material.

Prerequisites

  • Deployed Deep Research instance on Docker, Vercel, Cloudflare Pages, or another supported host with `ACCESS_PASSWORD` or equivalent access controls configured.
  • LLM provider credentials for the configured thinking and task models.
  • Search provider credentials when using Tavily, Firecrawl, Exa, Bocha, Brave, Searxng, or another non-model search path.
  • MCP client with Streamable HTTP or SSE transport support and timeout settings long enough for research runs.

Schema details

Install type
cli
Troubleshooting
No
Source repository stats
Scope
Source repo
Collection metadata
Estimated setup
25 minutes
Difficulty
advanced
Tool listing metadata
Full copyable content
{
  "mcpServers": {
    "deep-research": {
      "url": "https://YOUR_DEEP_RESEARCH_DEPLOYMENT/api/mcp",
      "transportType": "streamable-http",
      "timeout": 600,
      "headers": {
        "Authorization": "Bearer YOUR_ACCESS_PASSWORD"
      }
    }
  }
}

About this resource

Content

Deep Research MCP Server exposes a self-hostable deep research workflow to MCP clients. It can generate research plans, gather information from configured web search providers or local knowledge sources, and produce structured reports using separate thinking and task model settings.

The upstream project supports both Server-Sent Events and Model Context Protocol access. Its README documents Streamable HTTP at /api/mcp and SSE at /api/mcp/sse, plus MCP-specific environment variables for provider, search, thinking model, and task model configuration.

Source Review

These sources were reviewed on 2026-06-05. Prefer the live README and API docs for current deployment paths, MCP endpoints, environment variables, model provider options, search provider options, and transport support.

Features

  • MCP support through Streamable HTTP and SSE transports.
  • Deep research report generation with configurable thinking and task models.
  • Search provider support for model-native search and external search services.
  • Multi-LLM support across Gemini, OpenAI, Anthropic, DeepSeek, Grok, Mistral, Azure OpenAI, OpenRouter, Ollama, and OpenAI-compatible providers.
  • Local knowledge-base workflows with uploaded text, Office, PDF, and other resource files.
  • Research history, further research, report editing, translation, and knowledge graph features in the web app.
  • Docker, Vercel, Cloudflare Pages, static export, and local development deployment paths.

Installation

Deploy Deep Research first, bind local Docker deployments to localhost, set ACCESS_PASSWORD or equivalent access controls, configure the MCP environment variables for model and search provider behavior, and then point your MCP client at the deployed MCP endpoint:

{
  "mcpServers": {
    "deep-research": {
      "url": "https://YOUR_DEEP_RESEARCH_DEPLOYMENT/api/mcp",
      "transportType": "streamable-http",
      "timeout": 600,
      "headers": {
        "Authorization": "Bearer YOUR_ACCESS_PASSWORD"
      }
    }
  }
}

The upstream README also documents an SSE MCP endpoint. Use a longer timeout than a simple lookup tool because deep research jobs can run for several minutes.

Use Cases

  • Ask Claude to run a structured research workflow from an MCP client.
  • Compare different thinking, task, and search providers for the same topic.
  • Generate first-pass research briefs with citations for later human review.
  • Use uploaded documents as a local knowledge source for research reports.
  • Self-host a research service for teams that want explicit provider and deployment control.

Safety and Privacy

Deep Research can make many web search and model-provider calls from one agent request. Configure provider budgets, access controls, rate limits, and timeout behavior before exposing it to shared or autonomous workflows. Treat generated reports as drafts until citations and source quality are reviewed by a person.

Research prompts, uploaded files, search queries, local knowledge-base content, reports, citations, API keys, access passwords, and deployment logs can contain sensitive data. Review deployment hosting, model-provider, and search-provider retention policies before routing private or regulated information through the service.

Duplicate Check

No u14app/deep-research entry or matching source URL was found in content/mcp. Existing deep-research mentions in the repository are role or workflow references, not this self-hosted MCP research service.

Source citations

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How it compares

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

Field

Self-hostable deep research app with MCP and SSE APIs for generating multi-step research reports using configurable LLM and search providers.

Open dossier

MCP server for GPT Researcher that gives Claude deep research, quick search, report writing, source retrieval, research context, and research-resource tools backed by web search and LLM providers.

Open dossier

AGPL-licensed MCP-native platform with a private MCP registry, MCP gateway, Kubernetes MCP orchestrator, access control, credential resolution, observability, and deterministic tool guardrails for shared AI deployments.

Open dossier

Open-source AI gateway that can connect to downstream MCP servers and expose their aggregated tools through a single HTTP or SSE MCP endpoint for Claude Desktop, Cursor, and other MCP clients.

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 ✓
BrandDeep Research logoDeep ResearchGPT Researcher MCP Server logoGPT Researcher MCP ServerArchestra logoArchestraBifrost logoBifrost
Categorymcpmcpmcpmcp
SourceSource-backedSource-backedSource-backedSource-backed
Authoru14appAssaf Elovicarchestra-aimaximhq
Added2026-06-052026-06-062026-06-062026-06-06
Platforms
Harness
Source repo
Safety notesDeep Research can make repeated model and search-provider calls, so set budgets, rate limits, and provider quotas before exposing it to broad agent workflows. Generated reports can contain stale, incomplete, or misinterpreted sources; require citation review before using output in legal, medical, financial, security, or customer-facing decisions. Bind Docker deployments to localhost unless a trusted reverse proxy or firewall is in front of the service, and set `ACCESS_PASSWORD` or equivalent gateway controls before enabling MCP access. Uploaded documents and local knowledge bases should be reviewed for copyright, sensitive data, and permission to process before research begins.GPT Researcher MCP Server sends research queries to configured search retrievers and LLM providers, which can create API costs and external data exposure. The server exposes `deep_research`, `quick_search`, `write_report`, source, context, prompt, and resource workflows that can gather and synthesize live web content. Docker mode auto-selects SSE transport on `0.0.0.0:8000`; bind it only on trusted networks and avoid exposing unauthenticated endpoints publicly. Generated reports can contain outdated, biased, incomplete, or hallucinated claims; review sources before acting on medical, legal, financial, or safety-critical output. Protect Claude Desktop or MCP client configuration files because they may contain API keys in the `env` block.Archestra is a platform/control-plane entry, not a single-purpose local MCP helper; admins can expose many MCP servers, agents, tools, and credentials through one gateway. The upstream quickstart mounts the host Docker socket so the platform can run MCP servers; treat that as highly privileged host access and avoid using it on sensitive machines without isolation. Self-hosted MCP servers may run as Kubernetes workloads with injected environment variables, secrets, images, network policies, and restart controls. Tool assignments, gateway visibility, credential resolution, custom headers, and load-tools-on-demand settings should be reviewed per team and environment. Deterministic tool guardrails can reduce some unsafe tool chains, but they depend on correct policies and do not make untrusted MCP servers safe by default.Bifrost can expose all selected downstream MCP tools through one `/mcp` gateway endpoint, so treat the endpoint like an access layer for every connected tool. The docs state that default LLM tool calls are suggestions until an explicit tool execution API call is made, but agent mode can enable configured automatic execution. Gateway-mode auto-approval is controlled by the external MCP host, such as Claude Desktop, Cursor, Cline, or a custom client, not by Bifrost's `tools_to_auto_execute` setting. Stdio connections spawn local commands inside the Bifrost runtime; Docker deployments need images that include the requested executables. Use virtual keys, per-tool allowlists, auth headers, OAuth, per-user credentials, and network controls to limit which clients can reach high-impact tools.
Privacy notesResearch prompts, uploaded files, generated reports, search queries, citations, model inputs, model outputs, provider API keys, access passwords, and deployment logs can contain sensitive data. Browser-local history and knowledge-base storage are local to the deployed app context, but server-side API mode can route data through the deployment host, model providers, and search providers. Review hosting logs, cache behavior, environment variable handling, and third-party provider retention before using Deep Research with private or regulated material.Research queries, prompts, source URLs, fetched snippets, research context, generated reports, and cost metadata can enter the MCP client context. Provider APIs and search retrievers may receive sensitive research topics, entity names, customer details, or internal strategy questions. The server keeps in-process research IDs, context, source lists, and source URLs for later report/source/context calls during the session. Docker, n8n, SSE, or Streamable HTTP deployments can expose research sessions and messages to other systems on the network if not isolated. Local logs and troubleshooting output may include queries, errors, endpoint names, provider configuration issues, or session identifiers.MCP server definitions, tool schemas, gateway tokens, upstream credentials, OAuth tokens, API keys, custom headers, logs, traces, and tool results may be stored or processed by the platform. Built-in observability, LLM proxy, chat, agents, and policy features can reveal prompts, tool arguments, tool outputs, token usage, user identities, team membership, and trace metadata. Registry entries and installations can use personal, team-scoped, or shared credentials; choose the narrowest scope that matches the use case. When external MCP clients call an Archestra gateway, downstream tool results can still be sent by the MCP client to the configured model provider.Bifrost may process provider prompts, model responses, MCP tool names, tool arguments, tool results, headers, virtual keys, OAuth tokens, per-user credentials, logs, traces, metrics, and downstream server metadata. Per-user auth stores credentials against a signed-in user, virtual key, or session identity; review credential lifecycle, revocation, and orphaned-session behavior. Logs, config stores, provider settings, MCP sessions, and gateway analytics can contain sensitive operational or user data. Keep real provider keys, virtual keys, OAuth secrets, MCP endpoint URLs, and upstream service credentials in environment variables or secret stores, not committed config.
Prerequisites
  • Deployed Deep Research instance on Docker, Vercel, Cloudflare Pages, or another supported host with `ACCESS_PASSWORD` or equivalent access controls configured.
  • LLM provider credentials for the configured thinking and task models.
  • Search provider credentials when using Tavily, Firecrawl, Exa, Bocha, Brave, Searxng, or another non-model search path.
  • MCP client with Streamable HTTP or SSE transport support and timeout settings long enough for research runs.
  • Python 3.11 or newer.
  • OpenAI API key, or another GPT Researcher-compatible LLM provider configuration.
  • Tavily API key or another GPT Researcher-compatible search retriever.
  • A cloned `assafelovic/gptr-mcp` repository with dependencies installed from `requirements.txt`.
  • Docker for local evaluation, or Kubernetes and Helm/Terraform-style deployment planning for production use.
  • Organization policy for which MCP servers, credentials, teams, environments, and external network destinations may be exposed.
  • LLM provider keys or local model configuration if using Archestra's built-in chat, agents, or LLM proxy features.
  • Admin review of the quickstart container command before mounting the Docker socket.
  • Node.js with `npx`, Docker, or another supported Bifrost Gateway deployment path.
  • Bifrost Gateway version `v1.4.0-prerelease1` or newer for MCP gateway mode.
  • Downstream MCP server commands or HTTP/SSE endpoint URLs prepared before registering clients.
  • Provider API keys, virtual keys, gateway auth settings, and governance policies reviewed before sharing the endpoint.
Install
docker run -d --name deep-research -p 127.0.0.1:3333:3000 -e ACCESS_PASSWORD=YOUR_ACCESS_PASSWORD xiangfa/deep-research
git clone https://github.com/assafelovic/gptr-mcp.git && cd gptr-mcp && pip install -r requirements.txt
docker pull archestra/platform:latest
npx -y @maximhq/bifrost
Config
{
  "mcpServers": {
    "deep-research": {
      "url": "https://YOUR_DEEP_RESEARCH_DEPLOYMENT/api/mcp",
      "transportType": "streamable-http",
      "timeout": 600,
      "headers": {
        "Authorization": "Bearer YOUR_ACCESS_PASSWORD"
      }
    }
  }
}
Manual-only setup:
python server.py
{
  "mcpServers": {
    "archestra": {
      "url": "LOCAL_ARCHESTRA_MCP_GATEWAY_URL",
      "headers": {
        "Authorization": "Bearer ARCHESTRA_GATEWAY_TOKEN"
      }
    }
  }
}
{
  "mcpServers": {
    "bifrost": {
      "url": "BIFROST_MCP_URL",
      "headers": {
        "Authorization": "Bearer ${BIFROST_VIRTUAL_KEY}"
      },
      "type": "http"
    }
  }
}
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