Memprediksi kegagalan runtime pada kode JavaScript dan TypeScript untuk membantu coding agent mengidentifikasi file berisiko dan melakukan debug pada potensi error.
Juga dikenal sebagai SpeedosDK/predictive-debugger, predictive-debugger
- Stars
- 2
- Tools
- 7
- Unduhan, 30 hari
- —
- Versi terbaru
- 0.9.0
- Commit terakhir
- 27 Sep 2026
- Lisensi
- MIT
Apa itu Predictive Debugger MCP?
Predictive Debugger adalah MCP server. Memprediksi kegagalan runtime pada kode JavaScript dan TypeScript untuk membantu coding agent mengidentifikasi file berisiko dan melakukan debug pada potensi error. Server ini menyediakan 7 tool, seperti scan_project, analyze_file, check_types.
Ringkasan dibuat dari README proyek
Tentang Predictive Debugger MCP server
Fungsi utama dari Predictive Debugger adalah mengidentifikasi potensi kegagalan runtime dalam kode JavaScript dan TypeScript. Tool ini membantu coding agent dengan cara meranking file yang berisiko, menelusuri dependency, dan memberikan review model independen yang mencakup nomor baris spesifik serta alasan di balik prediksi error tersebut. Tool ini dirancang untuk developer yang menggunakan Claude Code, Codex, atau GitHub Copilot CLI, karena memanfaatkan akses model dan kuota penggunaan yang sudah ada pada command-line interface tersebut.
Pengguna dapat mengintegrasikan server ini ke dalam workflow lokal menggunakan npx untuk menjalankan package secara otomatis. Server ini menyediakan tujuh tool khusus, seperti scan_project untuk meranking densitas risiko, check_types untuk diagnostik compiler TypeScript, dan analyze_logs untuk menemukan anomali dalam file log. Meskipun static analysis dan dependency mapping dilakukan secara lokal, tool predict_failures akan mengirimkan source code dan konteks ke provider CLI yang terhubung untuk menghasilkan verdict. Tool ini mendukung berbagai tipe file termasuk JSX dan TSX, namun tidak mencakup komponen Vue dan Svelte.
Apa yang bisa Anda lakukan dengannya
- 1Identifikasi file source berisiko tinggiMinta agent Anda untuk menemukan file mana di direktori source yang memiliki densitas potensi risiko tertinggi.
- 2Telusuri dependency kodeMinta peta import dan file test yang terhubung untuk memahami bagaimana modul tertentu berinteraksi dengan bagian proyek lainnya.
- 3Deteksi error runtime dengan AIGunakan agent untuk melakukan review model secara independen guna menentukan baris kode spesifik yang kemungkinan besar menyebabkan kegagalan.
- 4Analisis log aplikasiInstruksikan agent untuk memindai file log guna mencari entri yang tidak biasa atau anomali dengan tingkat keparahan tinggi.
Ditulis berdasarkan README proyek. Setiap use case diperiksa terhadap kutipan dari README tersebut.
Cara menginstal Predictive Debugger MCP
Format diverifikasi 10 Nov 2026Instal Predictive Debugger MCP di Claude Code
$ claude mcp add predictive-debugger -- npx -y predictive-debuggerInstal Predictive Debugger MCP di Cursor
~/.cursor/mcp.json
{
"mcpServers": {
"predictive-debugger": {
"command": "npx",
"args": [
"-y",
"predictive-debugger"
]
}
}
}Instal Predictive Debugger MCP di VS Code
.vscode/mcp.json
{
"servers": {
"predictive-debugger": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"predictive-debugger"
]
}
}
}Instal Predictive Debugger MCP di Claude Desktop
claude_desktop_config.json
{
"mcpServers": {
"predictive-debugger": {
"command": "npx",
"args": [
"-y",
"predictive-debugger"
]
}
}
}Instal Predictive Debugger MCP di Codex
~/.codex/config.toml
[mcp_servers.predictive-debugger] command = "npx" args = ["-y", "predictive-debugger"]
Instal Predictive Debugger MCP di Gemini CLI
~/.gemini/settings.json
{
"mcpServers": {
"predictive-debugger": {
"command": "npx",
"args": [
"-y",
"predictive-debugger"
]
}
}
}Tool Predictive Debugger MCP 7
Terdeteksi dari READMEscan_projectanalyze_filecheck_typesmap_dependenciesanalyze_logspredict_failureslist_providers
| scan_project | — | |
| analyze_file | — | |
| check_types | — | |
| map_dependencies | — | |
| analyze_logs | — | |
| predict_failures | — | |
| list_providers | — |
Pengecekan otomatis
Berjalan 14 Nov 2026Laporan lengkap →Lolos checks otomatis kami. Ini bukan audit keamanan: baca kodenya dan berikan server akses paling terbatas yang dibutuhkannya. Cara kerja checks
- Source repository terhubung dan dapat dijangkauspeedosdk/predictive-debugger is public and reachablegithub.com/speedosdk/predictive-debugger ↗Lolos
- Paket menyebut repo ini sebagai sumbernyapredictive-debugger names speedosdk/predictive-debugger as sourcenpmjs.com/package/predictive-debugger ↗Lolos
- Provenance build (atestasi Sigstore)No build attestation published for this releaseTidak diperiksa
- Lisensi open source (OSI)MITspdx.org/licenses/MIT.html ↗Lolos
- Ada commit atau release dalam satu tahun terakhirLast commit or release 2026-09-27github.com/speedosdk/predictive-debugger ↗Lolos
- OpenSSF ScorecardTidak diperiksa
- Nama package tidak mirip dengan package populerNo popular package one edit away from predictive-debuggerLolos
- Tidak ada advisory kritis di paket maupun dependensinya1 resolved dependencies checked; no critical advisoryosv.dev ↗Lolos
- Tidak ada instruksi tersembunyi untuk model7 tool descriptions and the README read; no override phrases, credential paths or hidden charactersLolos
- Tools yang bersifat destruktif telah diberi labelNo tool named like delete, drop or execLolos
- Endpoint remote menjawab handshake MCPTidak diperiksa
README
Lihat di GitHubPredictive Debugger
Website and docs: predictivedebugger.dev
MCP server for finding likely runtime failures in JavaScript and TypeScript. Seven tools help your coding agent check types, rank risky files, trace dependencies, inspect logs and get an independent model review with a line number and reason.
Uses the Claude Code, Codex or GitHub Copilot CLI you already have installed. Prediction calls use that CLI's model access and usage allowance. No separate API key is needed. Your provider's terms apply to those calls; check them before use, especially on a Claude subscription. See Provider terms.
Setup
Requires Node.js 22 or later. For model predictions, install and sign in to at least one supported CLI. Python 3 is optional for log analysis.
1. Add the MCP server
Choose your agent below. npx downloads and runs the package automatically.
Run in your project on macOS, Linux or WSL:
claude mcp add --scope project predictive-debugger -- npx -y predictive-debugger@latest
On native Windows, from PowerShell:
claude mcp add --scope project predictive-debugger -- cmd /d /c npx -y predictive-debugger@latest
Use --scope user to make it available in every project.
For a user-level setup on macOS, Linux or WSL:
codex mcp add predictive-debugger -- npx -y predictive-debugger@latest
On native Windows, from PowerShell:
codex mcp add predictive-debugger -- cmd /d /c npx -y predictive-debugger@latest
For project-only setup or a longer startup timeout, see Codex configuration.
Add to .mcp.json in your project:
{
"mcpServers": {
"predictive-debugger": {
"command": "npx",
"args": ["-y", "predictive-debugger@latest"],
"tools": ["*"]
}
}
}
On native Windows, use "command": "cmd" and
"args": ["/d", "/c", "npx", "-y", "predictive-debugger@latest"].
For every project, see Copilot user-level setup.
2. Check the connection
Restart your agent and check /mcp for predictive-debugger and its seven tools.
To check that the package downloads and print its version:
npx -y predictive-debugger@latest --version
Running without --version starts a stdio server that waits for your agent's
messages. See setup help for local builds and troubleshooting.
3. Ask your agent about the code
Use Predictive Debugger to find the riskiest files in src/.
Show the imports and tests connected to src/services/orders.ts.
Check src/services/orders.ts for likely runtime failures.
Find unusual entries in logs/app.log.
Tools
| Tool | What it does | Model call |
|---|---|---|
scan_project |
Rank source files by risk density. Excludes tests by default. | No |
analyze_file |
Return complexity metrics, risk scores and contributing signals. | No |
check_types |
Return selected files' TypeScript compiler diagnostics using local project settings. | No |
map_dependencies |
Find imports, reverse imports and connected test files, with source-line evidence. | No |
analyze_logs |
Return log anomalies, ranked by severity and unusual wording. | No |
predict_failures |
Get an independent model verdict with a line number, reason and confidence. Supports batches. | Yes |
list_providers |
Check which supported CLIs are installed and their sign-in status. | No |
Start with scan_project and check_types, then read the files they highlight. Use
map_dependencies to find related files and predict_failures when you want a
second opinion. Pass several paths as files so small files share bounded model
calls and avoid repeating the CLI context for every file.
See the tool reference for parameters, result fields and limits.
The server's MCP instructions ask agents to check code they wrote in the current session from a fresh context. The routing depends on file count:
| Change | Requested check |
|---|---|
| One file, including a feature contained in one file | A fresh predict_failures call |
| Several files | A sub-agent scoped to the changed files and intended behavior, where the host supports it |
| Mechanical correction with one clear answer | Neither check required |
Grouped predictions retain per-file verdicts. They do not verify a feature's requirements. The benefit of the sub-agent rule has not been measured.
Privacy and limits
- Static analysis, dependency maps and log analysis run locally.
predict_failuressends source and bounded dependency context to your CLI's model provider. SetcalleeContext: falseto omit dependency context. - Credentials stay with the CLI. MCP tools can read paths the server process can access; project-scoped setup does not restrict file access. See the security model.
- JavaScript and TypeScript are supported, including JSX, TSX and decorators. Vue and Svelte single-file components are not supported. Files above 4 MB are rejected; large predictions may cover only selected declarations.
- Risk scores and predictions can be wrong. The benchmarks use development cases and do not establish accuracy on arbitrary repositories.
- Manually verified on Windows. CI covers Windows, macOS and Linux on Node 22 and 24; real CLI installations on macOS and Linux have not been manually verified.
VS Code preview
An unfinished extension can show findings in the Problems panel. It requires a local build and is not available on the Marketplace or as a prebuilt VSIX. See trying the extension.
Documentation
- predictivedebugger.dev: website, quickstart and guides.
- Advanced setup: provider login, project scope, updates and local builds.
- Tool reference: parameters, prediction results, dependency context and scoring.
- VS Code preview: development host, commands and settings.
- Benchmarks and method.
- Development and contributing. Bug reports are welcome; code contributions are not open yet.
- Security policy. Report vulnerabilities privately.
License
MIT.
Pertanyaan tentang Predictive Debugger MCP
Bagaimana cara menambahkan Predictive Debugger MCP ke Claude Code?
Jalankan claude mcp add predictive-debugger -- npx -y predictive-debugger di terminal Anda. Tambahkan --scope user agar tersedia di semua proyek.
Bagaimana cara menambahkan Predictive Debugger MCP ke Cursor?
Buka ~/.cursor/mcp.json dan tambahkan server di bawah mcpServers, menggunakan config Cursor yang ditunjukkan di bagian instalasi di atas.
Tool apa yang disediakan Predictive Debugger MCP?
Server ini menyediakan 7 tool, termasuk scan_project, analyze_file, check_types, map_dependencies, analyze_logs.
Apakah saya memerlukan API key terpisah untuk prediksi model?
Tidak, server ini menggunakan akses model dan kuota penggunaan dari CLI yang sudah Anda instal, seperti Claude Code, Codex, atau GitHub Copilot CLI.
Bahasa pemrograman apa saja yang didukung?
Server ini mendukung JavaScript dan TypeScript, termasuk JSX, TSX, dan decorators, namun tidak mendukung single-file component Vue dan Svelte.
Bagaimana server menangani privasi dan data?
Static analysis, dependency maps, dan log analysis dilakukan secara lokal. Hanya tool predict_failures yang mengirimkan konteks source code dan dependency ke model provider CLI Anda.