Overview

inquisitor makes AI agents solve problems the way a chess engine plays chess: it cannot explore every branch, so it estimates complexity first, prunes paths that add no information, and spends its search budget only where the problem actually is.

It ships as two coordinated layers:

• MCP server (inquisitor-mcp) — the engine. Web search, project analysis, code tracing, project scaffolding, and a persistent investigation state machine. Works with any MCP-compatible agent: OpenCode, Claude Code, Claude Desktop, Cursor.
• Agent skill (skills/inquisitor/SKILL.md) — the behavioral layer. Injects the probe-and-gate loop, the pruning rules, and the full methodology into the agent's reasoning.

The method is not invented here — it is assembled from primary sources: Newton's Analysis→Synthesis skeleton, hardened with the scientific method's own defenses against wrong assumptions (falsification, strong inference, competing-hypotheses analysis, anti-fixation reframes, pre-mortems) and constrained by engineering discipline (NASA/JPL's Power of Ten, surgical-change guidelines, the minimalism ladder). Every source is cited in Foundations.

Web search, codebase scans, and code tracing are tools invoked when local evidence is insufficient — never mandatory rituals.

How it decides

Depth is an output, not a label. An LLM's up-front difficulty guess is its least reliable signal — poorly calibrated, and biased to under-rate exactly the hard problems that matter. So inquisitor never classifies a problem before understanding it: it runs a cheap probe (the single cheapest action that could confirm or kill the best current hypothesis), and the probe's result — never the prediction — sets the depth:

flowchart TD
    P([Problem]) --> F["FRAME<br/>done = ? · must not break = ?"]
    F --> D{"delegate?<br/>a specialist skill<br/>owns this"}
    D -->|yes| SK["hand off — /tdd,<br/>/code-review, ..."]
    D -->|no| PR["PROBE<br/>cheapest action that could confirm<br/>or kill the best hypothesis"]
    PR -->|"obvious, local"| SH["<b>Shallow</b><br/>fix → verify<br/><i>no ceremony</i>"]
    PR -->|"clear single-<br/>component cause"| ST["<b>Standard</b><br/>frame → minimal evidence<br/>→ fix → verify"]
    PR -->|"root cause unknown,<br/>multi-component"| DE["<b>Deep</b> — Newton 7-phase:<br/>DEFINE → AXIOMS → ANALYSIS →<br/>EXPERIMENT → SYNTHESIS →<br/>VALIDATE → QUERY<br/><i>+ session tracking as memory</i>"]

    SH -.->|"gate trigger /<br/>failed fix / low confidence"| ST
    ST -.->|"gate only raises depth —<br/>never lowers"| DE

Escalation is enforced, not just allowed. The probe is only a starting point: objective gate triggers (touching infra/deploy/routing/config, auth/security, data migrations, multi-file fixes, prod-only symptoms) force a minimum depth regardless of how "clear" the problem feels, and a 3-question confidence check (read the runtime path? can name the runtime signal? verified the platform assumption?) bumps the depth up per unanswered question. Downgrades need cited evidence, never a feeling. Inflated ceremony is not allowed either — a 7-phase investigation of a typo is as wrong as a blind guess at a race condition.

Retry is never blind. "Loop until it passes" agents (the Ralph-loop pattern) have persistence but no memory: on failure they revert, flush, and re-roll — the same wrong idea, retried with fresh confidence. Inquisitor keeps a failure ledger instead: every dead hypothesis is recorded with the evidence that killed it, every retry must name what is different and why that changes the outcome, and two dead hypotheses from the same family force a reframe — re-audit an assumption, invert the question, widen the system boundary — never a third attempt at the same idea. Persistence with memory, creativity on evidence.

Foundations

Each rule in the method traces to a primary source. The left column is the citation; the right column is the exact mechanism inquisitor takes from it.

The scientific method

Engineering discipline

Installation

Requires uv and Python 3.12+.

Claude Code — plugin install (recommended)

inquisitor ships as a Claude Code plugin that installs both the skill and the MCP server — no cloning, no editing absolute paths, no manual symlink.

From within Claude Code, first add the marketplace:

/plugin marketplace add iamalisson/inquisitor

Then install the plugin:

/plugin install inquisitor@inquisitor

That's it. The plugin bundles the inquisitor-mcp server (registered automatically via ${CLAUDE_PLUGIN_ROOT}) and the inquisitor skill. uv syncs the server's dependencies on first launch. Update later with /plugin marketplace update inquisitor.

Prefer to point at a local checkout instead of GitHub? /plugin marketplace add /path/to/inquisitor works too.

For OpenCode and Claude Desktop (which don't use Claude Code plugins), or for a manual Claude Code setup, use the steps below.

Step 1 — Get the server

Option A — no clone (recommended). Once published to PyPI, uvx fetches and runs it on demand — no clone, no absolute paths:

uvx inquisitor-mcp   # prints a ready message and waits for a client — Ctrl+C to exit

You'll reference uvx inquisitor-mcp directly in the config below.

Option B — from a checkout (for local development, or before the PyPI release):

git clone https://github.com/iamalisson/inquisitor.git ~/tools/inquisitor
cd ~/tools/inquisitor
uv sync

The clone path is up to you — just use the same absolute path in the config below. ~ does not expand inside JSON config files, so write the full path (e.g. /home/you/tools/inquisitor).

You do not run the server manually. It's a stdio MCP server: your agent spawns and manages it automatically. (If you run it by hand it prints a ready message on stderr and waits silently — that's normal.)

Step 2 — Register the MCP server with your agent

OpenCode — add to ~/.config/opencode/opencode.json (global) or ./opencode.json (per-project):

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "inquisitor": {
      "type": "local",
      "command": ["uvx", "inquisitor-mcp"],
      "enabled": true
    }
  }
}

Claude Code — add to .mcp.json in your project, or ~/.claude.json for all projects:

{
  "mcpServers": {
    "inquisitor": {
      "command": "uvx",
      "args": ["inquisitor-mcp"]
    }
  }
}

Claude Desktop — same mcpServers block in claude_desktop_config.json (Settings → Developer → Edit Config).

Using a checkout instead of uvx? Swap the command for uv with args ["run", "--directory", "/home/you/tools/inquisitor", "inquisitor-mcp"].

Step 3 — Install the skill (the behavioral layer)

Symlink it so it stays up to date with the repo:

# OpenCode
mkdir -p ~/.config/opencode/skills
ln -s /home/you/tools/inquisitor/skills/inquisitor ~/.config/opencode/skills/inquisitor

# Claude Code
mkdir -p ~/.claude/skills
ln -s /home/you/tools/inquisitor/skills/inquisitor ~/.claude/skills/inquisitor

(Copying the folder works too — you'll just need to re-copy after updates.)

Or, no clone — install just the skill across agents with the skills CLI:

npx skills add iamalisson/inquisitor

(This installs the skill only. For the inquisitor_* tools you still need the MCP server from Steps 1-2 — or use the Claude Code plugin above, which bundles both.)

Step 4 — Restart your agent

Config is loaded at startup. Quit and reopen OpenCode / Claude Code, then verify: the inquisitor_* tools appear in the tool list, and the inquisitor skill is available.

Tools

Example: inquisitor_search

inquisitor_search(
    query="httpx ConnectTimeout retry pattern",
    max_results=8,
    time_range="year",              # day | week | month | year
    include_domains=["github.com"], # optional site: filter
    fetch_content=True,             # full page text, not just snippets
)

Example: phase tracking (Deep path)

inquisitor_phase_set(
    target_phase="experiment",
    findings="500 only occurs when session token > 4KB",
    evidence="repro script output; nginx.conf:34 large_client_header_buffers",
    open_questions="why did token size grow after v2.3 deploy?",
)

Project Structure

inquisitor/
├── src/inquisitor/
│   ├── server.py                # MCP entry point (FastMCP, 6 tools)
│   ├── config.py                # env configuration
│   ├── tools/                   # thin MCP adapters
│   │   └── search / analyze / trace / scaffold / phase / verify
│   └── backend/                 # pure Python, zero MCP dependency
│       ├── search.py            # DDG / Brave / SearXNG + re-ranking
│       ├── extract.py           # trafilatura → readability fallback, SSRF guard
│       ├── analyzer.py          # project structure scan
│       ├── tracer.py            # callers / callees mapping
│       └── phase_tracker.py     # Newton state machine (SQLite)
├── skills/inquisitor/SKILL.md   # behavioral layer for the agent
├── .claude-plugin/
│   ├── plugin.json              # Claude Code plugin manifest
│   └── marketplace.json         # self-hosted marketplace (source ".")
├── .mcp.json                    # bundled MCP server (${CLAUDE_PLUGIN_ROOT})
└── tests/                       # 36 tests

The backend/ package is importable standalone — no MCP required:

from inquisitor.backend.search import search
results = search("python asyncio best practices", max_results=5)

Environment Variables

No API key is required — DuckDuckGo works out of the box.

PDF extraction

Search results and fetched URLs that are PDFs (RFCs, specs, papers, datasheets) are extracted, not skipped. The default policy is auto, which uses pypdf (pure-Python, fast, no models, handles text PDFs) unless docling is installed and a GPU is present. For complex tables or scanned/OCR PDFs, install the optional docling backend:

uv pip install "inquisitor-mcp[docling]"   # or: pip install "inquisitor-mcp[docling]"

docling is heavy (pulls in torch + ML models) and slow on CPU, so auto only uses it when it's installed and a GPU is present; otherwise it falls back to pypdf. Force it with INQUISITOR_PDF_BACKEND=docling (works on CPU, just slower), or pin pypdf with INQUISITOR_PDF_BACKEND=pypdf. Any docling failure falls back to pypdf, so a PDF read never hard-fails.

Security

• SSRF guard: content fetching refuses non-http(s) schemes and loopback / private / link-local / metadata targets (localhost, 127.0.0.1, 10.x, 192.168.x, 169.254.169.254, …).
• Path traversal guard: session names are sanitized before touching the filesystem.
• No shell execution: subprocess calls use argument lists, never shell=True.
• Parameterized SQL throughout the session store.
• The server runs locally over stdio with your user's privileges — it does not listen on the network.

Companion skills

inquisitor is a router as much as an investigator — it delegates to purpose-built skills (/tdd, /code-review, …) when one fits, but it never installs them for you. See docs/companion-skills.md for the curated set worth installing alongside it (mattpocock/skills, spec-kit, last30days, ponytail, gstack) and design references.

Development

uv sync                  # install deps
uv run pytest tests/ -v  # run tests
uv run ruff check .      # lint

Tech

• uv — package manager
• FastMCP — MCP server framework
• ddgs / httpx — search + HTTP
• trafilatura + readability-lxml — content extraction (two-tier fallback)
• SQLite — investigation state
• pytest / ruff — tests and lint

Acknowledgments

Research sources are cited in Foundations. The implementation additionally builds on:

• andrej-karpathy-skills (forrestchang) — behavioral guidelines derived from Andrej Karpathy's observations on LLM coding pitfalls.
• ponytail (Dietrich Gebert) — the decision ladder and the lazy-senior-dev discipline.
• last30days-skill (mvanhorn) — inspiration for multi-source research design and the SKILL.md-as-contract pattern.

Licensed under MIT.