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evaluate_file

Read a code file from disk, automatically detect its programming language, and submit it to 45 specialized judges for comprehensive evaluation with pattern detection and deep contextual analysis.

Instructions

Read a file from disk and submit it to the full Judges Panel for evaluation. Automatically detects the programming language from the file extension. All 45 judges review the code with pattern detection and deep contextual analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
configNoOptional inline configuration (same format as .judgesrc)
contextNoOptional context about the code — framework, use-case, deployment target.
filePathYesAbsolute or relative path to the file to evaluate.
languageNoOverride the detected language (e.g., 'typescript', 'python').
minConfidenceNoMinimum finding confidence to include (0-1, default: 0)
includeAstFindingsNoInclude AST/code-structure findings (default: true)
Behavior3/5

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

No annotations provided, so description carries burden. Discloses reading file, auto-detection, and full judge panel, but lacks details on side effects, authorization, rate limits, or return format. Partially transparent.

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

Conciseness5/5

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

Two sentences, no redundancy, front-loaded with action. Every sentence adds value.

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

Completeness2/5

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

No output schema or annotations; description omits result format and post-evaluation steps. With 6 parameters and many sibling tools, more context on what the tool returns and how to use results is needed.

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

Parameters3/5

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

Schema has 100% description coverage, so description adds little beyond stating config format similar to .judgesrc. Baseline score appropriate as schema already documents parameters.

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?

Description clearly states verb (read and submit), resource (file from disk), and behavior (auto-detect language, all 45 judges). Distinguishes from siblings like evaluate_code or evaluate_batch through focus on file reading.

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

Usage Guidelines3/5

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

Implies use for evaluating a file from disk, but no explicit when-to-use or when-not-to-use guidance. No mention of alternatives like evaluate_code (for code strings) or evaluate_project.

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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