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dibun75
by dibun75

get_drift_report

Detects context drift between AI's loaded snapshot and current codebase by comparing file hashes or timestamps, ensuring the AI uses up-to-date context.

Instructions

    Detects context drift between the AI's loaded snapshot and the current codebase.

    Two detection modes:
    - **Hash-based** (preferred): Pass ``file_hashes`` as ``{file_path: sha256_hash}``.
      V.I.S.O.R. compares the provided hashes against the indexed ``file_hash`` column.
      If they differ, the file has changed since the agent last read it.
    - **Timestamp-based** (fallback): Compares ``loaded_at`` against ``file_changelog``
      entries when hashes are not supplied.

    Args:
        context_files: List of file paths the agent currently holds in context.
        loaded_at:     ISO-8601 timestamp of when the agent loaded its context.
        file_hashes:   Optional dict mapping file_path → sha256 hash the agent last saw.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
loaded_atYes
file_hashesNo
context_filesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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 details how detection works (comparing hashes or timestamps) and implies read-only access. No contradictions exist, but it omits potential side effects or prerequisites like indexing.

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?

The description is concise and well-structured, with a brief purpose statement, clear mode descriptions, and a parameter list. Every sentence adds value without redundancy, making it efficient for an AI agent to parse.

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 the tool's complexity (two modes, 3 parameters), the description is fairly complete. It explains detection logic and parameter semantics, but does not mention the output format (though an output schema exists) or prerequisites like indexing. Overall, it meets the minimum for effective use.

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?

The schema has 0% description coverage, so the description must add meaning. The Args section provides clear explanations for all three parameters (context_files, loaded_at, file_hashes), including types and purpose, which compensates well for the schema gap.

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 detects context drift between an AI's snapshot and the current codebase, using a specific verb ('detects') and resource ('context drift'). While it does not explicitly distinguish from sibling tools, the purpose is unambiguous and matches the tool's name.

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

Usage Guidelines4/5

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

The description explains two modes of operation (hash-based preferred, timestamp-based fallback) and hints at when to use each. However, it lacks explicit guidance on when not to use the tool or how it compares to siblings, leaving some ambiguity for the agent.

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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