Allows the server to use an installed GitHub Copilot CLI's model access to provide independent runtime-failure predictions with a line number, reason, and confidence score.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@predictive-debugger Find the riskiest files in src/"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Website and docs: predictivedebugger.dev
MCP server for finding likely runtime failures in JavaScript and TypeScript. Six tools help your coding agent 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.
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.
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@latestOn native Windows, from PowerShell:
claude mcp add --scope project predictive-debugger -- cmd /d /c npx -y predictive-debugger@latestUse --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@latestOn native Windows, from PowerShell:
codex mcp add predictive-debugger -- cmd /d /c npx -y predictive-debugger@latestFor 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.
Restart your agent and check /mcp for predictive-debugger and its six tools.
To check that the package downloads and print its version:
npx -y predictive-debugger@latest --versionRunning without --version starts a stdio server that waits for your agent's
messages. See setup help for local builds and troubleshooting.
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.Related MCP server: flowindex
Tool | What it does | Model call |
| Rank source files by risk density. Excludes tests by default. | No |
| Return complexity metrics, risk scores and contributing signals. | No |
| Find imports, reverse imports and connected test files, with source-line evidence. | No |
| Return log anomalies, ranked by severity and unusual wording. | No |
| Get an independent model verdict with a line number, reason and confidence. Supports batches. | Yes |
| Check which supported CLIs are installed and their sign-in status. | No |
Start with scan_project, then read the files it highlights. Use
map_dependencies to find related files and predict_failures when you want a
second opinion. Pass several paths as files to review them concurrently.
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 |
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 |
Per-file predictions cannot verify that several files agree or that a feature meets its requirements. The benefit of the sub-agent rule has not been measured.
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.
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.
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.
MIT.
Return static complexity metrics and a heuristic risk score (0-1) for a single JavaScript or TypeScript file. Deterministic and fast — no model call. Call this when reviewing a file to find out where the structural risk sits (nested loops, long functions, async boundaries, unguarded mutation) before reading the whole file yourself.
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | Absolute path to a .js/.jsx/.ts/.tsx file |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses that the tool is deterministic, fast, makes no model call, and is heuristic in nature. It also lists the risk categories it detects. It does not explicitly state read-only/no side effects, but 'Return...' strongly implies a pure analysis operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler: it front-loads the return value, defines the risk score range, names the risk categories, and gives usage context. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with no annotations and no output schema, this description is largely complete: it covers purpose, return concept, detected risk categories, and when to call it. The main gaps are the exact output shape and error behavior, which would matter more without an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter `file` is fully documented in the schema with its absolute-path requirement and allowed extensions, giving 100% schema description coverage. The tool description only reinforces that it is a single JavaScript or TypeScript file and adds no new format, default, or edge-case semantics, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the specific verb + resource: 'Return static complexity metrics and a heuristic risk score (0-1) for a single JavaScript or TypeScript file.' It clearly distinguishes this from sibling tools like scan_project or map_dependencies by emphasizing single-file analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit when-to-use guidance: 'Call this when reviewing a file to find out where the structural risk sits... before reading the whole file yourself.' However, it does not mention when not to use it or name specific sibling alternatives, so it stops short of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Score a log file's lines by severity and how unusual their wording is, and return the anomalies, worst first. Deterministic — no model call, no API key. Call this when you have a log file and want the handful of lines worth reading rather than the whole file.
| Name | Required | Description | Default |
|---|---|---|---|
| logFile | Yes | Absolute path to the log file | |
| threshold | No | Anomaly score cutoff, 0-1 (default 0.5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It usefully discloses that the tool is deterministic and requires no API key or model call, which addresses common LLM-tool concerns about cost, latency, and non-determinism. It does not disclose failure modes (e.g., unreadable file, encoding) or performance limits, which would push it higher.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, all useful. The key behavior (anomaly scoring and ordering) is front-loaded, followed by determinism note and a concrete usage trigger. No filler or repetition of the title.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers what the tool returns (anomalies, worst first), how it behaves (deterministic, no model call), and when to use it. Given there is no output schema and no annotations, a brief note on returned structure or error behavior would make it complete, but the current description is sufficient for an agent to decide to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents logFile (absolute path) and threshold (0-1, default 0.5) with descriptions. The description adds no parameter-specific semantics beyond what the schema provides, so a baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: score log lines by severity and wording unusualness, returning anomalies worst-first. This specific verb-resource pairing distinguishes it from siblings like analyze_file (generic file analysis) and predict_failures (prediction rather than anomaly extraction).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Call this when you have a log file and want the handful of lines worth reading rather than the whole file.' This gives a clear trigger condition. However, it doesn't explicitly contrast with siblings (e.g., when to choose analyze_file or scan_project instead), so it stops short of full when-not/alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Report which supported CLIs (Claude Code, Codex, GitHub Copilot) are installed and signed in. Call this to diagnose why predict_failures is failing.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does add useful context by clarifying that the tool checks both installation and sign-in status for a fixed set of CLIs. However, it does not explicitly state that the operation is read-only, whether it shells out to external processes, or what the report format looks like, so transparency is only partially addressed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no filler. The first sentence states what the tool does, and the second gives a concrete use case. It is front-loaded and every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, empty input schema, and single clear purpose, the description is largely complete. It names the exact providers and explains why the tool would be called. It could be slightly richer about output shape, but the description's 'Report...' wording already implies a readable report, and nothing critical is missing for invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema coverage is trivially 100% with an empty schema. Per the baseline rule for 0-parameter tools, the description does not need to add parameter-level semantics, and it correctly focuses on behavior rather than inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Report') and a concrete resource ('which supported CLIs are installed and signed in'), and it explicitly enumerates the providers: Claude Code, Codex, and GitHub Copilot. It also ties the tool to a diagnostic relationship with predict_failures, which helps distinguish it from the sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear, explicit trigger: 'Call this to diagnose why predict_failures is failing.' This is useful when-to-use guidance. It does not go as far as naming alternatives or stating when not to use it, but the condition is specific enough for an agent to select the tool appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find a file's local imports, reverse imports and tests connected by imports. Each relationship includes a source path and line as evidence. Static file relationships, not runtime callers or test coverage. Scans JavaScript/TypeScript including tests, with bounded work and explicit unresolved imports and scan limits. Deterministic; no provider call.
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | Source file, absolute or relative to directory | |
| depth | No | Import hops in each direction, default 1 | |
| limit | No | Total neighboring files to return, default 50 | |
| maxFiles | No | Maximum source files to discover, default 1000 | |
| directory | Yes | Project directory; source outside this directory is excluded |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses determinism ('no provider call'), bounded work, and explicit handling of unresolved imports and scan limits. It implies a read-only analysis by describing static relationships. It could explicitly state that it does not modify files, but the nature of the tool strongly implies it. This is good transparency for a tool with no annotation safety hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, no filler. The main purpose is front-loaded, followed by scope and behavior clarifications. Every sentence adds value: purpose, exclusions and language, and determinism/limits. It is concise and well-structured for an agent to quickly grasp.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (5 params, no output schema), the description covers purpose, scope, exclusions, behavior (deterministic, bounded), and even mentions output evidence ('source path and line'). It does not describe the exact return structure or error handling, but for a static analysis tool that is not overly complex, this is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents each parameter (depth, limit, maxFiles, directory, file). The description adds only a general mention of 'bounded work and explicit unresolved imports,' which indirectly relates to parameters but does not introduce new semantics beyond the schema. Baseline of 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a precise verb and resource: 'Find a file's local imports, reverse imports and tests connected by imports.' It further distinguishes itself from siblings by explicitly stating it deals with static file relationships, not runtime callers or test coverage, and specifies the language scope (JavaScript/TypeScript). This clearly sets it apart from tools like predict_failures or analyze_logs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides a clear exclusion: 'not runtime callers or test coverage,' which tells the agent when not to use it. It also notes the bounded work and scan limits, implying a use case of controlled analysis. However, it does not explicitly name alternative tools for the excluded scenarios, so guidance is slightly implied rather than fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Combine static analysis with a second-opinion verdict from the signed-in Claude Code, Codex, or GitHub Copilot CLI, returning the most likely runtime failure with a line number and reason. status distinguishes actionable, uncertain, no-finding, and unavailable results. checked lists the bug categories the model reports having considered, so a clean file weighed against the whole catalogue is distinguishable from one where it stopped early; it is a self-report, and an empty list means no coverage was reported. Pass multi: true to get every finding the model can demonstrate, ranked, in a findings array instead of one verdict — experimental, and more findings per call is also more surface for false positives per call. Treat it as a defect only when actionable is true; that applies the measured score >= 0.7 precision gate. This spawns another model and takes 5-15 seconds, so only call it when you specifically want an independent second opinion. If you are yourself reviewing the code, use analyze_file and read the source instead. Reviewing several files? Pass them all as files in one call rather than calling once per file: the verdicts run concurrently, so the batch costs the same and takes about as long as a single file.
| Name | Required | Description | Default |
|---|---|---|---|
| file | No | Absolute path to a .js/.jsx/.ts/.tsx file | |
| files | No | Absolute paths to review in one call, run concurrently. Prefer this over one call per file when checking a change set: the verdicts are independent, so a batch bills the same as the same files one at a time but finishes in roughly the time of the slowest one. Replies carry a `results` array in the order given. Supersedes `file`. | |
| model | No | Model override passed to the CLI | |
| multi | No | Return every finding the model can demonstrate, ranked by score, rather than the single most likely one (default false). Experimental: the precision gate was measured on one-finding replies, so `actionable` is less well characterised here. | |
| logFile | No | Optional log file to fold into the combined score | |
| verbose | No | Include the static metric counts and the full log breakdown (default false) | |
| provider | No | Which CLI to ask (default: whichever is installed) | |
| concurrency | No | Verdicts in flight at once for a batch (default 4). Lower it if the provider starts rate-limiting. | |
| calleeContext | No | Also send bounded imported definitions and referenced type contracts so the model can check dependency behavior (default true). Turning this off reduces input tokens but removes that evidence. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and fully discloses behavior: it spawns another model and takes 5-15 seconds, `checked` is a self-report (empty means no coverage), and `actionable` gates on a 0.7 precision gate. It also flags `multi` as experimental and notes that more findings increase false-positive surface. This is exceptional transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is longer than typical but highly structured: core purpose first, then output semantics, then usage guidance and batching tips. Every sentence adds unique information, though it could be tightened slightly without losing substance. It's efficient but borderline verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 9 parameters, no output schema, and heavy reliance on external model calls, the description covers all essential aspects: what it returns (status, checked, actionable), how to interpret `actionable` (precision gate), performance (5-15 seconds), batching behavior, and alternatives. Nothing an agent needs is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds significant semantics beyond auto-generated docs. It explains the `file` vs `files` relationship (files supersedes file, concurrency), the trade-offs of `multi` (experimental, precision gate less characterized), and `concurrency` usage for rate-limiting. This enriches parameter meaning substantially.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('predict'), a defined resource ('most likely runtime failure with a line number and reason'), and a distinguishing mechanism ('second-opinion verdict from Claude Code, Codex, or GitHub Copilot CLI'). It clearly differentiates from siblings like analyze_file, which reviews code directly rather than via an external model.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is given: only call for an independent second opinion, and 'If you are yourself reviewing the code, use analyze_file and read the source instead.' It also advises batching multiple files into one call versus per-file calls, and discloses that a batch costs the same. This directly addresses when and when not to use the tool relative to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Walk a directory and rank its JavaScript/TypeScript files by risk density — how concentrated the failure-prone code is, not how big the file is. Test files are left out by default; pass includeTests to rank them too. Deterministic and fast — no model call. Call this at the start of a code review to decide which files are worth your attention, instead of reading the tree in arbitrary order.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of files to return (default 50) | |
| verbose | No | Include the raw metric counts for every file (default false) | |
| directory | Yes | Absolute path to the directory to scan | |
| includeTests | No | Rank test files too — *.spec.*, *.test.*, and anything under __tests__/test/tests/spec/__mocks__ (default false). They rank high for a structural reason rather than a real one: mocked awaits read as async complexity. Turn this on to audit a suite's own complexity. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that it is deterministic, fast, and makes no model call, which is valuable for setting expectations. It also explains the 'includeTests' behavior and how test files are ranked for structural reasons. This goes beyond basic operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the core purpose and metric. Each sentence adds value: purpose, exclusion of tests, efficiency, and usage guidance. The parameter detail is left to the schema, and the description doesn't repeat schema text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 params and no output schema, the description covers the key aspects: what it does, when to use, default behavior, and param semantics. It doesn't describe the return format explicitly, but that's not strictly necessary given no output schema and the simplicity of the tool. Minor gap: it could mention what happens if directory doesn't exist or is empty.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are documented. The description adds value by explaining the impact of 'includeTests' in detail ('mocked awaits read as async complexity') and hinting at 'verbose' and 'limit'. It also adds context on default behavior (test files left out by default), which is not in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it walks a directory and ranks JavaScript/TypeScript files by risk density, distinguishing it from simple file listing. It also differentiates from sibling tools by focusing on ranking files by risk, not mapping dependencies or analyzing a single file. The title is consistent and the description elaborates on the specific metric.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly directs when to use it: 'Call this at the start of a code review to decide which files are worth your attention.' It also implies when not to use it by mentioning 'instead of reading the tree in arbitrary order.' However, it doesn't explicitly compare against sibling tools like analyze_file or map_dependencies, though the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.8.2analyze_fileanalyze_logslist_providersmap_dependenciespredict_failuresscan_projectScored across 6 tools
Most tools have clearly distinct scopes: dependencies, single-file metrics, project-wide ranking, log anomaly detection, provider diagnostics, and model-backed failure prediction. The only minor overlap is between analyze_file and scan_project (both assess risk, but at different granularities) and predict_failures (which uses a model for verdicts).
All six tools follow the same verb_noun snake_case pattern: map_dependencies, analyze_file, list_providers, scan_project, analyze_logs, predict_failures. No mixed conventions, aliases, or inconsistent verb choices.
Six tools is a well-scoped size for a predictive debugger. Each tool covers a distinct part of the workflow (dependency mapping, file analysis, project scanning, log analysis, provider setup, and failure prediction) without bloat or redundancy.
The tool surface covers the full predictive debugging workflow: scan to find risky files, analyze individual file risk, map dependencies for context, read logs for runtime anomalies, and get model-backed failure predictions. Minor gaps exist—no tool to directly inspect test coverage or historical failures—but these are workable around.
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