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faf_auto

Scans project manifests like package.json or Cargo.toml to auto-fill the .FAF dependency stack slots with real dependencies, returning detected values and the updated score.

Instructions

Scan your manifests (package.json, Cargo.toml, pyproject.toml, go.mod…) and fill the project.faf stack slots from real dependencies — no hardcoded defaults. Returns what was detected and the updated score. Use this for the technical context; use faf_go for the human 6Ws it can't detect, and faf_enhance to have an AI refine the result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNoProject path. Sets session context for subsequent calls.
forceNoForce overwrite existing files
Behavior4/5

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

Description discloses that the tool is not read-only (fills stack slots), returns detection results and updated score, and has a force parameter to overwrite files. Annotations already indicate not readOnly, not destructive. No contradictions. However, the path parameter's session-context-setting behavior is not mentioned in the description, only in the schema.

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?

Three concise sentences front-load the action (scan manifests, fill stack slots), describe return values, and provide usage guidance. No filler words; every sentence earns its place.

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

Completeness4/5

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

Given two parameters, no output schema, and moderate complexity, description covers purpose, return values, and usage context via sibling differentiation. Missing details on error conditions or prerequisites, but sufficient for effective use.

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 coverage is 100% with clear descriptions for both parameters (path: session context; force: overwrite). The tool description does not add additional semantics beyond the schema, so baseline of 3 is appropriate.

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 the tool scans manifests to fill project.faf stack slots from real dependencies, avoiding hardcoded defaults. It explicitly distinguishes from siblings by naming faf_go and faf_enhance for different purposes, making selection unambiguous.

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?

Explicitly says 'Use this for the technical context; use faf_go for the human 6Ws it can't detect, and faf_enhance to have an AI refine the result.' This provides direct when-to-use and when-not-to-use guidance with named alternatives.

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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