Predictive Debugger

MCP serverAI & models

Lets your agent predict which lines of JavaScript or TypeScript code might fail before running it.

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About this server

Predicts where JavaScript and TypeScript code will fail at runtime, for coding agents.

Getting started

  1. Save this item in Your setup as a reference.
  2. Read the source or reference documentation for its setup requirements. Saving it here does not connect it to your AI.
  3. Check this page for availability before trying to install it through ahel.

From the project's README

As published by speedosdk/predictive-debugger in README.md.

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

ToolWhat it doesModel call
scan_projectRank source files by risk density. Excludes tests by default.No
analyze_fileReturn complexity metrics, risk scores and contributing signals.No
check_typesReturn selected files' TypeScript compiler diagnostics using local project settings.No
map_dependenciesFind imports, reverse imports and connected test files, with source-line evidence.No
analyze_logsReturn log anomalies, ranked by severity and unusual wording.No
predict_failuresGet an independent model verdict with a line number, reason and confidence. Supports batches.Yes
list_providersCheck 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:

ChangeRequested check
One file, including a feature contained in one fileA fresh predict_failures call
Several filesA sub-agent scoped to the changed files and intended behavior, where the host supports it
Mechanical correction with one clear answerNeither 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_failures sends source and bounded dependency context to your CLI's model provider. Set calleeContext: false to 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

License

MIT.

Signals

GitHub stars
1
Last commit
Sep 2026
Weekly downloads
104
Weekly_downloads
310 weekly_downloads
Advanced
Delivery
predictive-debugger MCP server → your ahel connector (mcp.ahel.ai) → your AI.
Item type
mcp-server
Key
io-github-speedosdk-predictive-debugger
Source
github.com/speedosdk/predictive-debugger