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Platano78

Smart-AI-Bridge

batch_analyze

Run a single question against files selected by glob patterns, then aggregate findings into a cross-file summary for multi-file audits.

Instructions

Run the SAME question against a glob of files, then aggregate the findings into one cross-file summary. Use for codebase-wide audits ('any SQL injection under src/**/handlers/*.js?'), per-feature reviews, or pre-merge sweeps. For ONE file, use analyze_file (cheaper). For NL search without a known file set, use explore. Set aggregateResults:false to get raw per-file results instead of the aggregated summary. Read-only: reads every matched file (capped by maxFiles) and makes one LLM call per file (parallel by default). Returns: shape depends on aggregateResults. aggregateResults:true (default): {success, status:'completed', filesAnalyzed, patterns, question, aggregatedSummary, aggregatedFindings:[strings], aggregatedActions:[strings], overallConfidence, perFileResults:[{filePath, summary, findingCount, confidence}], processing_time, tokens_saved}. aggregateResults:false: {success, status:'completed', filesAnalyzed, patterns, question, results:[full per-file analysis objects], processing_time}. Empty pattern match: {success, status:'no_files', message, patterns}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filePatternsYesGlob patterns or file paths (e.g., ["src/**/*.ts", "lib/*.js"])
questionYesQuestion to ask about each file
optionsNo
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses key behavioral traits: read-only operation, file reading capped by `maxFiles`, one LLM call per file, parallel execution by default, and detailed return shapes for both aggregate and non-aggregate modes. No annotations provided, so description carries full burden and meets it well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with purpose first, then usage guidelines, parameter details, and return descriptions. Some redundancy (repeats `aggregateResults` behavior multiple times), but overall each sentence adds value. Could be slightly more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Comprehensive coverage given tool complexity: explains when to use, parameter semantics, return types for two modes, error case (no files matched), and behavioral details (capped, parallel). No output schema, so description fully compensates.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds meaning beyond the input schema by explaining effects of `aggregateResults` (raw vs. aggregated), default parallelism, and capping via `maxFiles`. Schema describes 67% of parameters; description reinforces and adds context, though some details in description overlap with schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: running a single question across multiple files via glob patterns and aggregating results. It distinguishes from siblings by explicitly naming `analyze_file` for single files and `explore` for natural language search without known file sets.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance on when to use this tool (codebase-wide audits, per-feature reviews, pre-merge sweeps) and when to use alternatives (`analyze_file` for one file, `explore` for NL search). Also explains the `aggregateResults` option for raw per-file output.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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