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dep-scout

dep-scout

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@unievolver1RustMITUpdated 2mo ago

Help AI agents find and evaluate mature packages across 6 ecosystems instead of reinventing them.

dep-scout

先查再造 (research before you build) — an MCP server that stops AI coding agents from reinventing the wheel.

Before an agent writes a feature from scratch, dep-scout searches the right registry — crates.io (Rust), npm (JS/TS/frontend), PyPI (Python), pkg.go.dev (Go), Maven Central (Java/Kotlin) or NuGet (.NET) — for a mature package that already solves the problem, and scores each candidate on reuse quality (popularity, maintenance recency, version stability, metadata) — and flags known security advisories (OSV) and license-compliance risks. The agent then reuses a proven, safe solution instead of hand-rolling buggy code. It can also search the official MCP registry so the agent reuses an existing MCP server instead of building one.

Works with Cursor (and any MCP client) over stdio. No API keys — every data source is a keyless public API.

Why

AI now writes a lot of code — and its worst habit is reimplementing solved problems, picking abandoned libraries, or hallucinating packages. dep-scout turns the best practice "don't build it if a maintained package already does it" into a tool the agent calls automatically, across languages.

Tools

ToolWhat it does
find_packagesGiven a feature description + ecosystem (rust | npm | python | go | maven | nuget), returns ranked candidates with a 0–100 reuse score, signals, and warnings.
inspect_packageDeep-dives one package by exact name + ecosystem: license (+ compliance flag), known OSV vulnerabilities, deprecation/yank status, runtime/version requirements, downloads or GitHub stars, last-update recency.
find_mcp_serversSearches the official MCP registry for existing MCP servers (name, install info, repo, status) — so you don't rebuild one.

ecosystem accepts aliases: js/ts/javascript/typescript/node/frontendnpm, py/pippython, golanggo, java/kotlin/gradle/jvmmaven, dotnet/csharp/netnuget, rs/cargorust.

Reuse score (0–100)

  • Popularity (≤40) — downloads normalised to a ~90-day window; for Go/Maven (no public download stats) GitHub stars are used as a proxy.
  • Maintenance (≤30) — how recently the package was updated.
  • Stability (≤15) — stable 1.0+ vs 0.x; penalty for pre-releases.
  • Metadata (≤15) — repository, docs/homepage, license present.
  • Security penalty — known OSV advisories on the resolved version sink the score and de-rank the candidate.
  • License flag — copyleft / commercially-restricted licenses (GPL, AGPL, LGPL, MPL, SSPL, BUSL…) are surfaced as compliance warnings.
  • Penalty — latest version yanked (crates/PyPI) or deprecated (npm).

Verdicts: ≥78 ✅ strongly recommend · 58–77 🟡 usable · 38–57 🟠 cautious · <38 🔴 find an alternative.

Ranking blends textual relevance (query-term overlap, exact-name match) with the reuse score and the registry's own ordering, so results are both on-topic and high quality.

Data sources (all keyless)

EcosystemSearchDetailsPopularity
Rustcrates.io API (relevance + downloads merge)crates.io APIcrates.io downloads (90-day)
npmregistry.npmjs.org/-/v1/searchregistry.npmjs.org/{pkg}/latestapi.npmjs.org (last-month)
Pythondeps.dev name search (+ token fallback)pypi.org/pypi/{pkg}/jsonpypistats (last-month)
Godeps.dev name searchdeps.dev v3 (GetPackage/GetVersion/GetProject)GitHub stars
Java/KotlinMaven Central solr searchdeps.dev v3GitHub stars
.NETNuGet search APINuGet search APINuGet total downloads
MCP serversofficial registry.modelcontextprotocol.io
SecurityOSV.dev query (detail) + querybatch (flag) across all ecosystems

Python note: PyPI has no public full-text search API (the legacy XML-RPC search is gone and the web search is bot-blocked), so Python search is name-oriented via deps.dev. It excels when the query is/contains the library name; for pure capability phrases, prefer inspect_package with a known name.

Build

Requires a recent Rust toolchain (edition 2024).

cargo build --release

The binary is produced at target/release/dep-scout (.exe on Windows).

Use in Cursor

  1. Register the MCP server. A portable project config lives at .cursor/mcp.json — it uses cargo run --release so no machine-specific paths are required. For global use, add an entry to ~/.cursor/mcp.json:

    {
      "mcpServers": {
        "dep-scout": {
          "command": "/absolute/path/to/dep-scout/target/release/dep-scout"
        }
      }
    }
    

    On Windows use ...\\target\\release\\dep-scout.exe. Build the release binary first (cargo build --release above) when using a direct binary path.

  2. Restart Cursor; you should see dep-scout with its tools enabled.

  3. The .cursor/rules/research-before-build.mdc rule makes the agent search before building. A portable, client-agnostic version lives at skill/SKILL.md.

How it talks

Standard MCP over stdio (JSON-RPC 2.0), built on the official rmcp SDK. Logs go to stderr so they never corrupt the protocol stream on stdout.

Roadmap

  • More ecosystems: Ruby (RubyGems), PHP (Packagist), Dart/Flutter (pub.dev).
  • Per-ecosystem popularity calibration (npm/PyPI volumes dwarf crates.io).
  • Last-update recency for NuGet; richer Maven search ranking.
  • Deeper repo health signals (open issues, release cadence, maintainer count).
  • A curated trust/quality dataset — the real moat over a plain registry mirror.

Project layout

src/
  main.rs      # MCP server + tools (find_packages / inspect_package / find_mcp_servers)
  model.rs     # Ecosystem enum, normalised Package, reuse scoring, relevance
  sources.rs   # keyless registry clients (crates.io, npm, PyPI, deps.dev, MCP registry)
.cursor/
  rules/research-before-build.mdc   # always-on Cursor rule
  mcp.json                          # local server registration
skill/SKILL.md                      # portable skill for any MCP client

MCP Registry

Published to the official MCP Registry as io.github.unievolver/dep-scout. Download the .mcpb bundle from GitHub Releases or build from source with cargo build --release.

  • MCP Registry name: mcp-name: io.github.unievolver/dep-scout

License

MIT