Predicts runtime failures in JavaScript and TypeScript code to help coding agents identify risky files and debug potential errors.
Also known as SpeedosDK/predictive-debugger, predictive-debugger
- Stars
- 2
- Tools
- 7
- Downloads, 30 days
- —
- Latest version
- 0.9.0
- Last commit
- Sep 27, 2026
- License
- MIT
What is Predictive Debugger MCP?
Predictive Debugger predicts runtime failures in JavaScript and TypeScript code to help coding agents identify risky files and debug potential errors. It exposes 7 tools, such as scan_project, analyze_file, check_types.
Summary generated from the project's README
About Predictive Debugger MCP server
Identifying potential runtime failures in JavaScript and TypeScript code is the primary function of Predictive Debugger. It assists coding agents by ranking risky files, tracing dependencies, and providing independent model reviews that include specific line numbers and reasons for predicted errors. This tool is designed for developers using Claude Code, Codex, or GitHub Copilot CLI, as it utilizes the existing model access and usage allowances of those installed command-line interfaces.
Users can integrate the server into their local workflow using npx to run the package automatically. The server provides seven specialized tools, such as scan_project for ranking risk density, check_types for TypeScript compiler diagnostics, and analyze_logs for finding anomalies in log files. While static analysis and dependency mapping occur locally, the predict_failures tool sends source code and context to the connected CLI provider to generate verdicts. It supports various file types including JSX and TSX, though it excludes Vue and Svelte components.
What you can do with it
- 1Identify high-risk source filesAsk your agent to find which files in your source directory have the highest density of potential risks.
- 2Trace code dependenciesRequest a map of imports and connected test files to understand how a specific module interacts with the rest of the project.
- 3Detect runtime errors with AIHave an agent perform an independent model review to pinpoint specific lines of code likely to cause failures.
- 4Analyze application logsInstruct an agent to scan log files for unusual entries or high-severity anomalies.
Written from the project's README. Every use case is checked against a quote from it.
How to install Predictive Debugger MCP
Formats verified 10 Nov 2026Install Predictive Debugger MCP in Claude Code
$ claude mcp add predictive-debugger -- npx -y predictive-debuggerInstall Predictive Debugger MCP in Cursor
~/.cursor/mcp.json
{
"mcpServers": {
"predictive-debugger": {
"command": "npx",
"args": [
"-y",
"predictive-debugger"
]
}
}
}Install Predictive Debugger MCP in VS Code
.vscode/mcp.json
{
"servers": {
"predictive-debugger": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"predictive-debugger"
]
}
}
}Install Predictive Debugger MCP in Claude Desktop
claude_desktop_config.json
{
"mcpServers": {
"predictive-debugger": {
"command": "npx",
"args": [
"-y",
"predictive-debugger"
]
}
}
}Install Predictive Debugger MCP in Codex
~/.codex/config.toml
[mcp_servers.predictive-debugger] command = "npx" args = ["-y", "predictive-debugger"]
Install Predictive Debugger MCP in Gemini CLI
~/.gemini/settings.json
{
"mcpServers": {
"predictive-debugger": {
"command": "npx",
"args": [
"-y",
"predictive-debugger"
]
}
}
}Predictive Debugger MCP tools 7
Detected from the READMEscan_projectanalyze_filecheck_typesmap_dependenciesanalyze_logspredict_failureslist_providers
| scan_project | — | |
| analyze_file | — | |
| check_types | — | |
| map_dependencies | — | |
| analyze_logs | — | |
| predict_failures | — | |
| list_providers | — |
Automated checks
Ran Nov 14, 2026Full report →Passed our automated checks. That is not a security audit: read the code and give the server the narrowest access it needs. How checks work
- Source repository linked and reachablespeedosdk/predictive-debugger is public and reachablegithub.com/speedosdk/predictive-debugger ↗Pass
- Package names this repository as its sourcepredictive-debugger names speedosdk/predictive-debugger as sourcenpmjs.com/package/predictive-debugger ↗Pass
- Build provenance (Sigstore attestation)No build attestation published for this releaseNot checked
- Open source license (OSI)MITspdx.org/licenses/MIT.html ↗Pass
- Commit or release in the last yearLast commit or release 2026-09-27github.com/speedosdk/predictive-debugger ↗Pass
- OpenSSF ScorecardNot checked
- Package name not close to a popular packageNo popular package one edit away from predictive-debuggerPass
- No critical advisory in the package or its dependencies1 resolved dependencies checked; no critical advisoryosv.dev ↗Pass
- No hidden instructions to the model7 tool descriptions and the README read; no override phrases, credential paths or hidden charactersPass
- Destructive tools are labelledNo tool named like delete, drop or execPass
- Remote endpoint answers the MCP handshakeNot checked
README
View on 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.
Predictive Debugger MCP questions
How do I add Predictive Debugger MCP to Claude Code?
Run claude mcp add predictive-debugger -- npx -y predictive-debugger in your terminal. Add --scope user to make it available in every project.
How do I add Predictive Debugger MCP to Cursor?
Open ~/.cursor/mcp.json and add the server under mcpServers, using the Cursor config shown in the install section above.
What tools does Predictive Debugger MCP provide?
It exposes 7 tools, including scan_project, analyze_file, check_types, map_dependencies, analyze_logs.
Do I need a separate API key for model predictions?
No, the server uses the model access and usage allowance of your already installed CLI, such as Claude Code, Codex, or GitHub Copilot CLI.
What programming languages are supported?
The server supports JavaScript and TypeScript, including JSX, TSX, and decorators, though Vue and Svelte single-file components are not supported.
How does the server handle privacy and data?
Static analysis, dependency maps, and log analysis are performed locally. Only the predict_failures tool sends source and dependency context to your CLI's model provider.