mcpbeat

Tavily Research

tavily-ai/tavily-research

| Conduct comprehensive AI-powered research with citations via the Tavily CLI. Use this skill when the user wants deep research, a detailed report, a comparison, market analysis, literature review, or says "research", "investigate", "analyze in depth", "compare X vs Y", "what does the market look like for", or needs multi-source synthesis with explicit citations. Returns a structured report grounded in web sources. Takes 30-120 seconds. For quick fact-finding, use tavily-search instead.

892 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
443
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/tavily-ai/skills --skill tavily-research

What it tells the agent to use

found in the instruction text
Bash runs shell commands — read the instruction before connecting

The instruction itself

9 sections, as written by the author

tavily research

AI-powered deep research that gathers sources, analyzes them, and produces a cited report. Takes 30-120 seconds.

Before running any command

If tvly is not found on PATH, install it first:

curl -fsSL https://cli.tavily.com/install.sh | bash && tvly login

Do not skip this step or fall back to other tools.

See tavily-cli for alternative install methods and auth options.

When to use

  • You need comprehensive, multi-source analysis
  • The user wants a comparison, market report, or literature review
  • Quick searches aren't enough — you need synthesis with citations
  • Step 5 in the workflow: search → extract → map → crawl → research

Quick start

# Basic research (waits for completion)
tvly research "competitive landscape of AI code assistants"

# Pro model for comprehensive analysis
tvly research "electric vehicle market analysis" --model pro

# Stream results in real-time
tvly research "AI agent frameworks comparison" --stream

# Save report to file
tvly research "fintech trends 2025" --model pro -o fintech-report.md

# JSON output for agents
tvly research "quantum computing breakthroughs" --json

Options

| Option | Description |

|--------|-------------|

| --model | mini, pro, or auto (default) |

| --stream | Stream results in real-time |

| --no-wait | Return request_id immediately (async) |

| --output-schema | Path to JSON schema for structured output |

| --citation-format | numbered, mla, apa, chicago |

| --poll-interval | Seconds between checks (default: 10) |

| --timeout | Max wait seconds (default: 600) |

| -o, --output | Save output to file |

| --json | Structured JSON output |

Model selection

| Model | Use for | Speed |

|-------|---------|-------|

| mini | Single-topic, targeted research | ~30s |

| pro | Comprehensive multi-angle analysis | ~60-120s |

| auto | API chooses based on complexity | Varies |

Rule of thumb: "What does X do?" → mini. "X vs Y vs Z" or "best way to..." → pro.

Async workflow

For long-running research, you can start and poll separately:

# Start without waiting
tvly research "topic" --no-wait --json    # returns request_id

# Check status
tvly research status <request_id> --json

# Wait for completion
tvly research poll <request_id> --json -o result.json

Tips

  • Research takes 30-120 seconds — use --stream to see progress in real-time.
  • Use --model pro for complex comparisons or multi-faceted topics.
  • Use --output-schema to get structured JSON output matching a custom schema.
  • For quick facts, use tvly search instead — research is for deep synthesis.
  • Read from stdin: echo "query" | tvly research - --json

See also

  • tavily-search — quick web search for simple lookups
  • tavily-crawl — bulk extract from a site for your own analysis

How to use it

Copy the folder

Take tavily-ai/tavily-research from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.