General Deep Research

Deep Research

Goal

To perform an in-depth, iterative, and comprehensive literature and web research campaign to answer complex scientific questions (e.g., "What are the synthesis methods and solid-state electrolyte performance of LiInCl3?"). This skill produces a high-quality, synthesized research report with citations, significantly exceeding the depth of a single simple literature query.

Instructions

When the user requests deep research on a topic, the agent MUST follow this multi-step iterative protocol. Do not implement this as a python script, but rather execute these steps logically using your own tool-calling capabilities.

Step 1: Query Formulation & Planning

Break down the user's broad research topic into 3-5 specific sub-queries. CRITICAL: You must try different permutations and synonyms for the material or topic. For example, if the topic is LiInCl3, your queries must include variations like LiInCl3, Li-In-Cl, Lithium Indium Chloride, Li3InCl6, etc., to ensure no literature is missed.

Create a rough outline for the final research report in your task plan.

Step 2: Iterative Literature Search

For each sub-query, use the mcp_base_search_literature tool to search the OpenAlex database. Always set download=True to attempt downloading the full text of discovered papers.

mcp_base_search_literature(
    query="Lithium Indium Chloride ionic conductivity",
    limit=50,
    download=True
)

CRITICAL: You must NOT rely solely on the literature search tool. You must ALSO perform a general web search using the search_web tool for all your queries. This captures recent publications, patents, reviews, and data that OpenAlex might miss.

search_web(
    query="Li-In-Cl solid state electrolyte review"
)

Step 3: Information Extraction & Synthesis

Do not just list papers. You must read the content (or the provided summaries/full texts from the MCP tool). Extract specific numbers, methodologies, and limitations (e.g., "Conductivity is 1.2 mS/cm at RT", "Synthesized via mechanochemical milling followed by annealing at 250C").

If gaps in knowledge remain (e.g., you found the conductivity but not the stability window), perform another round of searching with refined queries targeting the missing information.

Step 4: Report Generation

Draft a comprehensive, academic-style markdown report named deep_research_report.md inside the active research_dir (which should be created via mcp_base_create_research_dir).

The report must include:

1. Executive Summary: A high-level overview of the findings.
2. Detailed Findings: Categorized by sub-topics (e.g., Structure, Performance, Synthesis). Include specific data points and conflicting reports if any exist. CRITICAL: When summarizing each point, you MUST include the DOI reference or URL of the source where the info is coming from inline.
3. Methodologies: Common computational or experimental methods used in the literature.
4. Knowledge Gaps: What remains unknown or disputed in the current literature.
5. References: A cited list of the papers and URLs you drew information from, mapping to your inline citations.

Step 5: User Review

Once the report is generated, present it to the user.

notify_user(
    PathsToReview=["/absolute/path/to/research_dir/deep_research_report.md"],
    BlockedOnUser=True,
    Message="Deep research is complete. Please review the comprehensive report."
)

Constraints

• Depth Over Speed: Take the time to run multiple tool calls to search and read. Do not stop after one search query.
• Data Specificity: Extract quantitative data (values, temperatures, error margins) wherever possible rather than qualitative statements.
• Resource Utilization: Use both mcp_base_search_literature and search_web.

Examples

To initiate deep research:

# Agent internally executes Step 1 to Step 5.
# (No specific conda environment required since it's an agentic skill relying on MCP tools).

Author: Agent Contact: GitHub @username