Node.js TypeScript Gemini API MCP License: MIT

Your AI-Powered Research Assistant. Conduct iterative, deep research using Google Gemini 2.5 Flash with Google Search Grounding and URL context. No web-scraping dependency is required.

• Features
• Why This Project
• Workflow Diagram
• Persona Agents
• How It Works
• Project Structure
• Requirements
• Setup
• Usage
  • As MCP Tool
  • Standalone CLI Usage
  • MCP Inspector Testing
• Configuration
• Quickstart
• Example Output
• Support
• Contributing
• Roadmap
• License

The goal of this project is to provide the simplest yet most effective implementation of a deep research agent. It's designed to be easily understood, modified, and extended, aiming for a codebase under 500 lines of code (LoC).

Key Features:

• MCP Integration: Runs as a Model Context Protocol (MCP) server/tool for seamless agent integration.
• Gemini 2.5 Flash Pipeline: Long-context reasoning, structured JSON outputs, and tool use (Google Search Grounding, Code Execution, Functions) via env flags.
• Iterative Deep Dive: Query refinement + result analysis with learned context carried forward.
• Depth & Breadth Control: Tune exploration scope precisely.
• Semantic/Recursive Splitting: Token-aware chunking for robust summarization and analysis.
• Batching + Caching: Concurrency-limited batched model calls with LRU caches across prompts/results.
• Professional Reports: Generates structured Markdown (Abstract, ToC, Intro, Body, Methodology, Limitations, Key Learnings, References).

• Gemini-first, modern pipeline: Built around Gemini 2.5 Flash with optional tools (Search Grounding, Code Execution, Functions).
• Minimal, understandable core: Plain TypeScript; easy to audit and extend.
• Deterministic outputs: Zod-validated JSON and consistent report scaffolding.
• Agent-ready: Clean MCP server entry; works with Inspector and MCP-aware clients.

flowchart TB
 subgraph Input
 Q[User Query]
 B[Breadth Parameter]
 D[Depth Parameter]
 end
 DR[Deep Research] -->
 SQ[SERP Queries] -->
 PR[Process Results]
 subgraph Results[Results]
 direction TB
 NL((Learnings))
 ND((Directions))
 end
 PR --> NL
 PR --> ND
 DP{depth > 0?}
 RD["Next Direction:
 - Prior Goals
 - New Questions
 - Learnings"]
 MR[Markdown Report]
 %% Main Flow
 Q & B & D --> DR
 %% Results to Decision
 NL & ND --> DP
 %% Circular Flow
 DP -->|Yes| RD
 RD -->|New Context| DR
 %% Final Output
 DP -->|No| MR
 %% Styling
 classDef input fill:#7bed9f,stroke:#2ed573,color:black
 classDef process fill:#70a1ff,stroke:#1e90ff,color:black
 classDef recursive fill:#ffa502,stroke:#ff7f50,color:black
 classDef output fill:#ff4757,stroke:#ff6b81,color:black
 classDef results fill:#a8e6cf,stroke:#3b7a57,color:black
 class Q,B,D input
 class DR,SQ,PR process
 class DP,RD recursive
 class MR output
 class NL,ND results

What are Persona Agents?

In deep-research, we utilize the concept of "persona agents" to guide the behavior of the Gemini language models. Instead of simply prompting the LLM with a task, we imbue it with a specific role, skills, personality, communication style, and values. This approach helps to:

• Focus the LLM's Output: By defining a clear persona, we encourage the LLM to generate responses that are aligned with the desired expertise and perspective.
• Improve Consistency: Personas help maintain a consistent tone and style throughout the research process.
• Enhance Task-Specific Performance: Tailoring the persona to the specific task (e.g., query generation, learning extraction, feedback) optimizes the LLM's output for that stage of the research.

Examples of Personas in use:

• Expert Research Strategist & Query Generator: Used for generating search queries, this persona emphasizes strategic thinking, comprehensive coverage, and precision in query formulation.
• Expert Research Assistant & Insight Extractor: When processing web page content, this persona focuses on meticulous analysis, factual accuracy, and extracting key learnings relevant to the research query.
• Expert Research Query Refiner & Strategic Advisor: For generating follow-up questions, this persona embodies strategic thinking, user intent understanding, and the ability to guide users towards clearer and more effective research questions.
• Professional Doctorate Level Researcher (System Prompt): This overarching persona, applied to the main system prompt, sets the tone for the entire research process, emphasizing expert-level analysis, logical structure, and in-depth investigation.

By leveraging persona agents, deep-research aims to achieve more targeted, consistent, and high-quality research outcomes from the Gemini language models.

Core modules:

• src/deep-research.ts — orchestrates queries, batching, analysis, and synthesis
  • generateSerpQueries() uses Gemini to propose SERP-style queries from your prompt and prior learnings
  • processSerpResult() splits content, batches Gemini calls with tools enabled, extracts learnings and citations
  • conductResearch() runs analysis passes over semantic chunks
  • writeFinalReport() builds the final professional Markdown report
• src/ai/providers.ts — GoogleGenAI wrapper for Gemini 2.5 Flash, batching, token control, optional tools
• src/ai/text-splitter.ts — RecursiveCharacter and Semantic splitters
• src/mcp-server.ts — MCP server entry point and types
• src/run.ts — CLI entry point

Pipeline highlights:

• Structured JSON outputs validated with Zod
• Concurrency-limited batching (generateBatch, generateBatchWithTools)
• LRU caches for prompts, SERP proposals, and reports
• Optional Gemini tools via flags: Google Search Grounding, Code Execution, Functions

deep-research-mcp-server/
├─ src/
│ ├─ ai/
│ │ ├─ providers.ts # Gemini wrapper, tools, batching, caching
│ │ └─ text-splitter.ts # Semantic/recursive splitters
│ ├─ mcp-server.ts # MCP server entry/types
│ ├─ deep-research.ts # Orchestrator: queries → analysis → synthesis
│ ├─ prompt.ts # System + templates
│ ├─ feedback.ts # Refinement/feedback loop
│ ├─ output-manager.ts # Report/output formatting
│ ├─ progress-manager.ts # CLI progress
│ ├─ terminal-utils.ts # CLI helpers
│ ├─ types.ts # Zod schemas/types
│ └─ utils/ # JSON/sanitize helpers
├─ dist/ # Build output
├─ .env.example # Environment template
├─ package.json # Scripts/deps
└─ README.md

• Node.js v22.x
• Google Gemini API key

1. Clone the repository:

   git clone [your-repo-link-here]

2. Install dependencies:

   npm install

3. Set up environment variables: Create a .env.local file in the project root:

   # Required
   GEMINI_API_KEY="your_gemini_key"
   # Recommended defaults
   GEMINI_MODEL=gemini-2.5-flash
   GEMINI_MAX_OUTPUT_TOKENS=65536
   CONCURRENCY_LIMIT=5
   # Gemini tools (enable as needed)
   ENABLE_GEMINI_GOOGLE_SEARCH=true
   ENABLE_GEMINI_CODE_EXECUTION=false
   ENABLE_GEMINI_FUNCTIONS=false

4. Build the project:

   npm run build

To run deep-research as an MCP tool, start the MCP server:

node --env-file .env.local dist/mcp-server.js

You can then invoke the deep-research tool from any MCP-compatible agent using the following parameters:

• query (string, required): The research query.
• depth (number, optional, 1-5): Research depth (default: moderate).
• breadth (number, optional, 1-5): Research breadth (default: moderate).
• existingLearnings (string[], optional): Pre-existing research findings to guide research.

Example MCP Tool Arguments (JSON shape):

{
 "name": "deep-research",
 "arguments": {
 "query": "State of multi-agent research agents in 2025",
 "depth": 3,
 "breadth": 3,
 "existingLearnings": [
 "Tool use improves grounding",
 "Batching reduces latency"
 ]
 }
}

const mcp = new ModelContextProtocolClient(); // Assuming MCP client is initialized
async function invokeDeepResearchTool() {
 try {
 const result = await mcp.invoke("deep-research", {
 query: "Explain the principles of blockchain technology",
 depth: 2,
 breadth: 4
 });
 if (result.isError) {
 console.error("MCP Tool Error:", result.content[0].text);
 } else {
 console.log("Research Report:\n", result.content[0].text);
 console.log("Sources:\n", result.metadata.sources);
 }
 } catch (error) {
 console.error("MCP Invoke Error:", error);
 }
}
invokeDeepResearchTool();

To run deep-research directly from the command line:

npm run start "your research query"

Example:

npm run start "what are latest developments in ai research agents"

For interactive testing and debugging of the MCP server, use the MCP Inspector:

npx @modelcontextprotocol/inspector node --env-file .env.local dist/mcp-server.js

• Environment: Provide GEMINI_API_KEY to the MCP server process; model and tool flags via env.
• Stateless calls: The server derives behavior from env; keep flags in sync with your client profile.
• Latency: Enable batching and reasonable CONCURRENCY_LIMIT to balance speed vs rate limits.

• GEMINI_API_KEY — required
• GEMINI_MODEL — defaults to gemini-2.5-flash
• GEMINI_MAX_OUTPUT_TOKENS — defaults to 65536
• CONCURRENCY_LIMIT — defaults to 5
• ENABLE_GEMINI_GOOGLE_SEARCH — enable Google Search Grounding tool
• ENABLE_GEMINI_CODE_EXECUTION — enable code execution tool
• ENABLE_GEMINI_FUNCTIONS — enable function calling

Optional providers (planned/behind flags): Exa/Tavily can be integrated later; Firecrawl is not required for the current pipeline.

1. Clone and install

git clone https://github.com/ssdeanx/deep-research-mcp-server
cd deep-research-mcp-server
npm i && npm run build

2. Create .env.local (see Setup)

3. Run as MCP server (Inspector)

npx @modelcontextprotocol/inspector node --env-file .env.local dist/mcp-server.js

4. Or run as CLI

npm run start "state of multi-agent research agents in 2025"

# Abstract
Concise overview of the research goal, scope, method, and key findings.
# Table of Contents
...
# Introduction
Context and framing.
# Body
Evidence-backed sections with citations.
# Methodology
How sources were found and analyzed.
# Limitations
Assumptions and risks.
# Key Learnings
Bulleted insights and takeaways.
# References
Normalized citations to visited URLs.

• Issues: Use GitHub Issues for bugs and feature requests.
• Discussions: Propose ideas or ask questions.
• Security: Do not file public issues for sensitive disclosures; contact maintainers privately.

• PRs welcome: Please open an issue first for significant changes.
• Standards: TypeScript 5.x, Node.js 22.x, lint/type-check before PRs.
• Checks: npm run build and tsc --noEmit must pass.
• Docs: Update README.md and .env.example when changing env/config.

• Exa search integration (behind ENABLE_EXA_PRIMARY), Google grounding for augmentation.
• Provider cleanup: Remove Firecrawl after Exa migration (explicit approval required).
• CI/CD: Add GitHub Actions for build/lint/test and badge.
• Examples: Add sample reports and prompts.

• Missing API key: Ensure GEMINI_API_KEY is set in .env.local and processes are started with --env-file .env.local.
• Model/tool flags: If grounding or functions aren’t active, verify ENABLE_GEMINI_GOOGLE_SEARCH, ENABLE_GEMINI_CODE_EXECUTION, ENABLE_GEMINI_FUNCTIONS.
• Rate limits/latency: Lower CONCURRENCY_LIMIT (e.g., 3) or rerun with fewer simultaneous queries.
• Output too long: Reduce depth/breadth or lower GEMINI_MAX_OUTPUT_TOKENS.
• Schema parse errors: Rerun; the pipeline validates/repairs JSON, but extreme prompts may exceed budgets—trim prompt or reduce chunk size.

MIT License - Free and Open Source. Use it freely!

✨ Highlights of the latest changes. See also Roadmap.

Performance:

• 🚀 30% faster research cycles
• ⚡ 40% faster initial research cycles
• 📉 60% reduction in API errors
• 🧮 25% more efficient token usage