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Why

When developers and AI coding agents ask LLMs for open-source libraries, general-purpose models routinely hallucinate non-existent package names or recommend abandoned six-year-old repositories. GitHub's native search relies on rigid keyword matching that fails on conceptual queries.

jev-scout solves this by decoupling discovery from decision:

• Grounding first: Queries real package registries (GitHub REST API, crates.io) to fetch actual live metadata.
• System One scoring: Uses TypeSafe AI's jev-latest in a single speculative fan-out call (~1s API floor) to score architectural fit, license suitability, and maintenance freshness.
• Zero hallucinated packages: You only get verified, installable repositories with exact stars, licenses, and clone commands.
• Dual surface: Works as an interactive terminal CLI for developers and as a stdio MCP server for autonomous coding agents (Claude Code, Antigravity).

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Quickstart

Installation

cargo install jev-scout

Or build from source:

git clone https://github.com/AkashPriyadarshii/jev-scout.git
cd jev-scout
cargo build --release

Setup API Key

jev-scout requires a TypeSafe AI API key:

export TYPESAFE_API_KEY="your_typesafe_key"

Usage

Search for repositories matching natural language concepts:

# Search crates and repositories
jev-scout "fast sqlite tui in rust"

# Filter by ecosystem
jev-scout "headless browser without chromium" --ecosystem rust

# Output raw JSON for scripts and agents
jev-scout "token efficient grep for coding agents" --json

# Show all candidates, skip weak-match filtering
jev-scout "sqlite tui" --no-filter

MCP Server (for AI coding agents)

Start jev-scout as a stdio MCP server exposing the scout_repos tool, then point any MCP client at it:

# Claude Code
claude mcp add jev-scout -- bash -c "export TYPESAFE_API_KEY=$TYPESAFE_API_KEY && jev-scout --mcp"

# pi
# add an mcpServers entry: {"command": "jev-scout", "args": ["--mcp"], "env": {"TYPESAFE_API_KEY": "..."}}

The scout_repos tool takes query (required), plus optional ecosystem (all/github/crates), limit (1-10), and strict (filter weak matches, default true). Results include structuredContent for typed consumption.

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How it works

User Query: "fast sqlite tui in rust"
   │
   ├─► 1. Grounded Search (GitHub REST API + crates.io)
   │      Pulls top candidate repos with stars, licenses, and commit dates.
   │
   ├─► 2. Speculative Fan-out Call (POST https://api.typesafe.ai/v1/systemone)
   │      Evaluates all candidates in a single ~1s call with typed primitives:
   │      - Score(fit): 1 (unrelated) to 4 (exact architectural fit)
   │      - Noul(modern): Calibrated probability of active maintenance
   │      - Choice(best_match): Single top candidate
   │
   └─► 3. Deterministic Ranking & Output
          Sorts by confidence-weighted score (score * confidence).
          Renders terminal cards or JSON.

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CLI Flags

Flag	Short	Default	Description
--ecosystem	-e	all	Target ecosystem (all, github, crates)
--limit	-n	5	Maximum number of ranked results to return
--json	-j	false	Output machine-readable JSON to stdout
--no-filter		false	Show all candidates, skip weak-match filtering
--mcp		false	Start as a stdio Model Context Protocol (MCP) server
--help	-h		Print help information
--version	-v		Print version

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Architecture

jev-scout/
├── Cargo.toml          # Rust dependencies: ureq, lexopt, serde
├── README.md           # Documentation
├── CLAUDE.md           # Development rules
├── AGENTS.md           # Agent directives
├── STATE.md            # Active project state
├── CHANGELOG.md        # Version history
├── src/
│   ├── main.rs         # Lexopt argument parsing and terminal display
│   ├── jev.rs          # TypeSafe API client (Choice, Score, Noul)
│   ├── search.rs       # Candidate retriever (GitHub REST + crates.io)
│   └── mcp.rs          # Stdio JSON-RPC 2.0 MCP server handler
└── tests/
    └── mock_test.rs    # Offline unit tests using recorded fixtures

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Latency (honest numbers)

Measured live on an Intel Core i3 / Windows 11 / broadband, against the real APIs:

• Cold (new process): ~2.0-2.3s. The floor is two serial external legs: GitHub search API ~0.9s + Jev API ~1.0s (raw curl floor of 1.0s even for a single question). Search runs in parallel threads; the Jev call is a single fan-out request.
• Warm (MCP session repeat, TTL 60s caches): ~0.3s. Both search results and Jev evaluations are cached in-process.
• Filters and ranking run instantly once candidates are fetched.

Non-Goals

• No text generation: jev-scout does not write code summaries or essays. It returns verified repo metadata and typed scores.
• No heavy local databases: Zero SQLite, Postgres, or caching daemons required.
• No browser automation: No Chromium, Playwright, or web scraping dependencies.
• No paid search engines: Relies on official GitHub and crates.io APIs rather than paid search proxies.

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Ecosystem

• design-genius - Design systems and UI craft documentation.
• akash-design-engineering - Advanced front-end architecture and animations.
• tdlib-android - Precompiled TDLib for Android with zero-dependency builds.
• kharcha - India-first offline UPI expense tracker.

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Author

Akash Priyadarshi
Patna, Bihar, India

• GitHub: AkashPriyadarshii
• Portfolio: akashpriyadarshi.vercel.app
• LinkedIn: akash-priyadarshi-1aa51b37a
• Resume: akashpriyadarshii.github.io/Resume

Social

• X / Twitter: @Akash__ydv001
• Threads: @akash.priyadarshii
• Instagram: @akash.priyadarshii
• Reddit: u/DragonfruitWeak2801

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Zero hallucinations, verified repositories, sub-second decisions.