A Model Context Protocol (MCP) server for searching, downloading, and reading arXiv papers — designed as a specialist agent for integration into multi-agent systems like Microsoft Magentic-UI and AutoGen.

The idea: Rather than treating arXiv search as a simple lookup tool, this server is structured as a first-class research agent — one you can plug directly into a Magentic-One-style team as an McpAgent, giving an Orchestrator access to the full scientific literature as a delegatable resource.

Magentic-UI supports custom McpAgent instances via mcp_agent_configs in its config file. This server plugs in directly:

# examples/magentic_ui_config.yaml
client:
  mcp_agent_configs:
    - agent_name: ArxivResearcher
      description: >
        Specialist agent for searching and reading arXiv papers.
        Use when the task requires finding academic papers, understanding
        research literature, or retrieving technical details from published work.
      server_params:
        type: StdioServerParams
        command: python
        args: ["-m", "arxiv_mcp_server"]
        env:
          PYTHONPATH: /path/to/arxiv-deep-research/src

Once registered, the Magentic-UI Orchestrator can delegate research subtasks to this agent through the standard Task Ledger / Progress Ledger pattern — exactly how WebSurfer handles web browsing, but for academic literature.

See examples/autogen_research_team.py for a complete 3-agent team:

Orchestrator (MagenticOneGroupChat)
├── ArxivSurfer  ← this MCP server, wrapped via StdioServerParams + mcp_server_tools
└── Coder        ← synthesizes findings into structured markdown reports

pip install "autogen-agentchat" "autogen-ext[openai]" "mcp>=1.2.0"
export OPENAI_API_KEY=...
python examples/autogen_research_team.py

Supports rich query syntax — quoted phrases, boolean operators, field-specific search (ti:, au:, abs:), and category filtering:

{
  "query": "\"multi-agent\" AND \"orchestration\" ANDNOT survey",
  "max_results": 10,
  "date_from": "2024-01-01",
  "categories": ["cs.AI", "cs.MA"],
  "sort_by": "relevance"
}

At a high level, arxiv-deep-research runs a simple but powerful multi‑stage loop:

1. Plan the research task
   • A coordinator agent (for example the AutoGen MagenticOneGroupChat Orchestrator) takes the user goal and breaks it into sub‑tasks.
2. Discover candidate papers
   • The coordinator calls the MCP search_papers tool to find relevant arXiv papers by topic, category, and date.
3. Download and normalize content
   • For selected IDs, it calls download_paper, which fetches the PDF and converts it into clean markdown for LLMs to read.
4. Deep paper analysis
   • The coordinator (or another agent) uses the deep-paper-analysis prompt to ask for a structured analysis of a given paper ID, optionally across multiple calls as you explore related work.
5. Synthesis and reporting
   • A downstream agent such as Coder (in the AutoGen example) turns these analyses into a final research report: summaries, comparison tables, open problems, and next‑step suggestions.

You can run this pipeline manually by calling the tools and prompts from any MCP‑aware client, or automatically using the sample AutoGen team.

The repo includes a retrieval quality benchmark (eval/benchmark.py) measuring:

• Precision@K — fraction of top-K results that are relevant
• Recall@K — fraction of known relevant papers found in top-K
• MRR — Mean Reciprocal Rank of first relevant result

Ground-truth queries are seeded from landmark papers (AutoGen 2308.08155, Magentic-One 2411.04468, RAG 2005.11401, CoT 2201.11903) and can be extended automatically using the synthetic data pipeline below.

python eval/benchmark.py --k 10 --output results.json

scripts/generate_eval_tasks.py implements a 4-stage pipeline that generates diverse benchmark queries from arXiv abstracts — mirroring the AgentInstruct approach:

Stage 1: Seed collection     → fetch paper abstracts from arXiv by category
Stage 2: Content transform   → extract key concepts and problem statements
Stage 3: Instruction gen     → generate realistic research queries via GPT-4o-mini
Stage 4: Instruction refine  → create harder variants at subtopic intersections

export OPENAI_API_KEY=...
python scripts/generate_eval_tasks.py --seed-category cs.AI --num-seeds 20 --output eval/generated_queries.json

Output includes easy/medium/hard difficulty tiers for stratified evaluation.

Every tool call is instrumented with OpenTelemetry spans (mirrors AutoGen v0.4's built-in OTel support):

# Console output (no infrastructure needed)
export ARXIV_MCP_TRACE_CONSOLE=true
python -m arxiv_mcp_server

# OTLP export to Jaeger / Azure Monitor
docker run -d --name jaeger -p 16686:16686 -p 4317:4317 jaegertracing/all-in-one
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
export OTEL_SERVICE_NAME=arxiv-mcp-server
python -m arxiv_mcp_server
# View traces: http://localhost:16686

Spans recorded: mcp.tool.search_papers, mcp.tool.download_paper, mcp.tool.read_paper — each with query, categories, result count, latency, and error status as attributes.

Tracing is a zero-cost no-op when opentelemetry-sdk is not installed.

Requires Python 3.11+

git clone https://github.com/freyzo/arxiv-deep-research
cd arxiv-deep-research
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

# Optional: OTel tracing
pip install -e ".[tracing]"

{
  "mcpServers": {
    "arxiv": {
      "command": "/path/to/.venv/bin/python",
      "args": ["-m", "arxiv_mcp_server", "--storage-path", "/path/to/papers"]
    }
  }
}

{
  "mcpServers": {
    "arxiv": {
      "command": "python",
      "args": ["-m", "arxiv_mcp_server"],
      "env": { "PYTHONPATH": "/path/to/arxiv-deep-research/src" }
    }
  }
}

Comprehensive analysis workflow covering executive summary, methodology, results, implications, and future directions:

{ "paper_id": "2401.12345" }

There are two main ways to run research sessions today.

This uses OpenAI models to coordinate a full research workflow.

cd arxiv-deep-research
python3 -m venv .venv
source .venv/bin/activate
pip install -e .
pip install "autogen-agentchat" "autogen-ext[openai]" "mcp>=1.2.0"

export OPENAI_API_KEY=your_openai_key
python examples/autogen_research_team.py

This starts an interactive console UI where:

• the Orchestrator plans the work,
• ArxivSurfer searches and downloads papers via MCP, and
• Coder writes the final markdown report.

To resume a session, you can:

• run the script again and paste the previous summary as part of a new task, or
• keep the same console session open and give the team a follow‑up instruction (for example, “Now focus on safety trade‑offs”).

You can also talk to the MCP server directly and build your own loop:

cd arxiv-deep-research
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

export ARXIV_MCP_TRACE_CONSOLE=true   # optional
python -m arxiv_mcp_server

While this server runs, any MCP‑aware client can:

• call search_papers and download_paper,
• use read_paper to pull content into the chat, and
• call the deep-paper-analysis prompt multiple times.

The prompt handler keeps a simple global research context, so repeated calls in the same process will mention previously analyzed paper IDs and encourage the model to connect them. In practice, “resuming” a research session means:

• keeping the same MCP server process alive, and
• issuing new deep-paper-analysis calls for new paper IDs from the same client or workspace.

arxiv-deep-research/
├── src/arxiv_mcp_server/
│   ├── server.py          # MCP server + OTel init
│   ├── tracing.py         # @trace_tool decorator, OTLP + console exporters
│   ├── config.py
│   ├── tools/             # search, download, read, list
│   └── prompts/           # deep research analysis prompt
├── examples/
│   ├── autogen_research_team.py   # Magentic-One-style 3-agent team
│   └── magentic_ui_config.yaml    # McpAgent config for Magentic-UI
├── eval/
│   └── benchmark.py       # Precision@K / Recall@K / MRR harness
├── scripts/
│   └── generate_eval_tasks.py     # AgentInstruct-style query generator
└── pyproject.toml

If you use the optional eval data generator, you also need:

• Model support is OpenAI‑only today.
  • The AutoGen research team and the synthetic eval generator both call OpenAI models (gpt-4o / gpt-4o-mini) via the OpenAI Python SDK.
  • There is no first‑class google-genai / Gemini or Gemma integration yet, even though the design would support it.
• No MCP Resources yet.
  • Papers are exposed only via tools (read_paper) rather than as MCP Resources with stable arxiv:// URIs. MCP clients that prefer Resources cannot list papers yet.
• Limited testing.
  • The core retrieval and eval logic has very light automated testing; metric functions and tool handlers should gain unit tests over time.

Planned improvements (subject to change):

• Gemini / Gemma support via google-genai
  • Add an optional google-genai dependency and a small runner that can call Gemini/Gemma models using GEMINI_API_KEY.
  • Expose this as an alternative backend for the research team demo and the eval generator.
• MCP Resources for downloaded papers
  • Implement list_resources / read_resource so downloaded PDFs appear as arxiv://paper_id resources in MCP clients.
• Stronger testing and evals
  • Add unit tests for metrics, search helpers, and prompt handlers.
  • Automate running eval/benchmark.py and track regression over time.
• Richer research sessions
  • Replace the simple global research context with explicit session IDs and persisted state, so “resume session X” becomes a first‑class feature across restarts.