Quickstart

Set up DeepSieve in your coding agent with one prompt

Connect DeepSieve and your agent gets a cited, structured dataset it can query, re-verified while monitoring is on — instead of re-researching the same subjects on every task.

Don't configure it by hand — let your coding agent do it. Paste this into Claude Code, Cursor, Copilot, Codex, or any assistant that can read a URL:

Copy this into your coding assistant

Set up DeepSieve by following the instructions here:
https://deepsieve.ai/setup.md

Your agent will then:

Detect which agent it is and whether it can open a browser.
Connect the DeepSieve MCP server (deepsieve) — browser login, or an API key if it's running headless.
Install the DeepSieve Agent Skill, and append a ## DeepSieve section to your agent's rules file (CLAUDE.md, AGENTS.md, or .cursor/rules/).
Offer to install the CLI if you work in a terminal.
Verify the connection and report your workspace, scopes, and run costs.

What it will not do, stated as constraints the payload itself carries:

Never start a research run. Runs cost money — $10 standard, $20 at max depth. Setup spends nothing, explicitly including "to check it works".
Never ask you for an API key in chat while browser login is available — transcripts get logged and shared.
Never install a global runtime or touch your shell config, and nothing outside this project and your agent's own config.

Read it yourself before you run it — it's plain markdown, and the constraints above are in its own words: https://deepsieve.ai/setup.md.

Prefer to wire it yourself?

There's a full REST API underneath all of this — see The API for the whole surface, one link per component. The options below are the common starting points.

The server is at https://deepsieve.ai/mcp and uses browser login — no API key.

bash

claude mcp add --transport http deepsieve https://deepsieve.ai/mcp

Then run /mcp, pick deepsieve, and choose Authenticate.

For config-file agents (Cursor .cursor/mcp.json, VS Code .vscode/mcp.json), register the same URL — full per-agent snippets are in MCP server.

Create a key at Settings → API keys with the Agent preset (runs research and reads results; can't change your schema or spend settings). Put it in the environment as DEEPSIEVE_API_KEY — never in a commit or a chat message.

bash

claude mcp add deepsieve -- uvx deepsieve-mcp

Or call the REST API directly:

bash

curl -s https://deepsieve.ai/v1/me -H "Authorization: Bearer $DEEPSIEVE_API_KEY"

Response· json

{"object": "identity", "org_id": "…", "role": "admin",
 "workspace_schema": "…", "auth_kind": "api_key",
 "scopes": ["data:read", "export:read", "runs:read", "runs:write", "…"]}

The skill teaches any assistant to drive the REST API — auth, running research, reading cited data:

bash

npx skills add https://deepsieve.ai

Agent Skills is an open standard, so the same package works in Claude Code, Cursor, GitHub Copilot, Codex, Gemini CLI and ~70 other clients. Target one agent with -a claude-code, or install globally with -g.

No Node? It's plain markdown at stable URLs — fetch and drop into your agent's skills directory, preserving the relative paths:

bash

curl -s https://deepsieve.ai/.well-known/skills/index.json          # name + every file
curl -sO https://deepsieve.ai/.well-known/skills/deepsieve/SKILL.md

Two free paths, and they answer different questions

Your first real research run is free — one per account, before you subscribe. It's a genuine run on your own question, and its report is readable (capped at 10 rows). If it fails, it isn't consumed. That's the one that tells you whether the research is any good, because you get real cells about a market you know, each with its sources and a verdict — including contradicted and unverifiable when we couldn't stand a claim up. See reading a citation honestly.

dry_run is free and unlimited — it tells you whether your integration works. Same shape, same statuses, ~15 seconds, nothing billed or persisted, but the rows are samples. Build your poll loop here; judge the product on the free real run.

Every integration should be built against dry_run first. It behaves exactly like a real run — 202 + Location, poll to completion — but finishes in ~15 seconds, costs nothing, and persists nothing:

bash

curl -s -X POST https://deepsieve.ai/v1/research/runs \
  -H "Authorization: Bearer $DEEPSIEVE_API_KEY" -H "Content-Type: application/json" \
  -d '{"query": "anything", "dry_run": true}'

It returns sample cited rows in your Blueprint's real shape ("test": true). Build your whole create → poll → parse loop here, then drop dry_run.

Ask your agent to do this for you — "start a dry run and show me the rows" — once setup finishes.

Where next

See the whole API → The API
Run research from my app → Your first research run
All the MCP tools → MCP server
Work from a terminal → CLI
Which integration should I use? → Choosing an integration
Sync results into my systems → Dataset sync + Webhooks
Ask about a value, or get it re-checked → Chat with the research agent

Machine-readable: /llms.txt · /llms-full.txt · /openapi.json — and any page here is markdown if you append .md.