cercano-deep-research

name	cercano-deep-research
description	Use when the user needs thorough, multi-source research with ranked findings, citations, and synthesis. Use this for literature reviews, competitive analysis, technical deep-dives, or any research that needs more than a quick answer. Prefer this over cercano-research when depth and comprehensiveness matter.
compatibility	Requires Cercano server running, connected to an Ollama instance, and Python venv with ddgs package (run cercano setup).

Cercano Deep Research

Multi-source research tool that takes a topic and intent, identifies authoritative sources, systematically searches each one, and compiles a ranked, annotated encyclopedia of findings.

MCP Tool

Tool name: cercano_deep_research

Parameters

Parameter	Type	Required	Description
topic	string	Yes	The research topic to investigate.
intent	string	Yes	What you need this research for — drives relevance scoring and source selection.
depth	string	No	"survey" (quick scan, ~2 min), "standard" (balanced, ~5-8 min), or "deep" (exhaustive, ~15+ min). Default: "standard".
date_range	string	No	Filter results by date (e.g. "2024-2026", "last 2 years").
sources	string[]	No	Override auto-detected sources. If omitted, sources are chosen based on topic domain.
output_dir	string	No	Write report to this directory as multiple files. Recommended for standard/deep research and required for incremental deepening.
project_dir	string	No	Project root directory.
phase	string	No	Run a specific phase: "plan", "search", "analyze", "synthesize". Omit to run all phases.
use_model	string	No	Override the default model for this research run.

Depth Tiers

	Survey	Standard (default)	Deep
Sources	2-3	3-4	4-5
Results/query	3	4	6
Reference chasing	None	1-hop, max 15	1-hop, max 50
Analysis	3-pass (facts, relevance, quality gate)	3-pass	3-pass
Target time	~2 min	~5-8 min	~15+ min

How It Works

1. Source Planning — Local model analyzes topic + intent and identifies relevant sources from 25+ options across academic, industry, news, reference, and regulatory categories
2. Systematic Search — Searches each source using tailored queries (free APIs for PubMed, arXiv; site-scoped DuckDuckGo for others)
3. Content Extraction — Fetches and extracts readable content from top results
4. Analysis & Annotation — 3-pass pipeline: fact extraction, relevance scoring (1-5), quality gate with retry
5. Reference Chasing (standard/deep only) — Identifies cited works relevant to intent, searches and analyzes them
6. Synthesis — Executive summary, narrative synthesis, contradiction detection, gap analysis, reading order, follow-up suggestions

Incremental Deepening

Run a survey first to get a quick landscape, then deepen to standard or deep without re-doing prior work:

1. Run survey with an output_dir
2. Review the results
3. Run again with the same output_dir and a deeper depth — existing findings are preserved, new sources are added, and middle-scored findings (2-4) are re-evaluated with richer context

The tool stores state in research_state.json inside the output directory. Each deepening pass expands the plan with complementary sources and enriches the analysis.

Output

Structured markdown report with:

• Executive Summary (TL;DR)
• Source Plan (which sources were searched and why)
• Ranked Findings (sorted by relevance, with annotations)
• Discovered References (works found via citation chasing)
• Synthesis (narrative connecting the findings)
• Contradictions & Open Debates
• Gap Analysis (what the research didn't find)
• Recommended Reading Order
• Suggested Follow-Up Research
• Next Steps (suggested deeper research command)

Examples

Quick survey:

{"topic": "quantum computing error correction", "intent": "preparing a conference talk", "depth": "survey", "output_dir": "/tmp/qec-research"}

Standard research (default):

{"topic": "CRISPR gene therapy for sickle cell disease", "intent": "writing a grant proposal for a novel delivery mechanism", "output_dir": "/tmp/crispr-research"}

Deep research:

{"topic": "transformer architecture improvements", "intent": "literature review for PhD thesis", "depth": "deep", "date_range": "2024-2026", "output_dir": "/tmp/transformer-research"}

Incremental deepening (survey → standard):

{"topic": "quantum computing error correction", "intent": "preparing a conference talk", "depth": "standard", "output_dir": "/tmp/qec-research"}