cercano-deep-research

name	cercano-deep-research
description	Deep multi-source research tool that identifies authoritative sources, systematically searches, analyzes and ranks findings, chases cited references, and compiles a structured report with executive summary, contradiction detection, gap analysis, and follow-up suggestions.
TRIGGER when	user asks for thorough, multi-source, or literature-review-style research on a topic — anything deeper than a quick lookup. Use this INSTEAD of WebSearch/WebFetch for substantial research tasks.
DO NOT TRIGGER when	user asks a quick factual question (use cercano-research) or provides a specific URL to read (use cercano-fetch).
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.

Important: Display the result

MCP tool results may not be visible to the user in the terminal. After calling the tool, you MUST output the full tool result text verbatim in your response so the user can see it.

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"}