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save_scores

Save scoring results for GitHub hackathon submissions to persistent storage, updating existing entries and adding new ones to maintain a centralized scoring record.

Instructions

Save scoring results to data/scores.json.

Existing scores for the same Issue are overwritten (idempotent).
New Issues are appended.

Args:
    scores: List of scoring result dicts. Each must contain:
        - issue_number (int)
        - project_name (str)
        - track (str)
        - criteria_scores (dict[str, int]): per-criterion scores (1-10)
        - weighted_total (float): weighted total (0-100)
        - evidence (dict[str, str]): per-criterion evidence citations
        - confidence (str): 'high', 'medium', or 'low'
        - red_flags_detected (list[str]): red flag signals found
        - bonus_signals_detected (list[str]): bonus signals found
        - strengths (list[str])
        - improvements (list[str])
        - summary (str)

Returns:
    Summary dict (saved_count, updated_count, total_in_store, file_path).

Raises:
    OSError: If disk write fails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scoresYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: idempotent overwrite behavior for existing issues, append behavior for new issues, file path location, and potential OSError on disk write failure. It doesn't mention permissions, rate limits, or concurrency considerations, but covers the essential mutation behavior adequately.

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?

The description is appropriately sized and front-loaded with the core purpose first. The Args and Returns sections are well-structured. Some redundancy exists (e.g., 'List of scoring result dicts' could be tighter), but overall it's efficient with minimal waste.

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?

Given the complexity of the parameter structure (13 nested fields), no annotations, and an output schema present (though not shown), the description is remarkably complete. It fully documents the parameter semantics, return format, and error conditions. The presence of an output schema means the description doesn't need to explain return values in detail, and it covers all other aspects thoroughly.

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

Parameters5/5

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

The input schema has 0% description coverage (just 'Scores' as title), so the description fully compensates by providing detailed semantics for the single 'scores' parameter. It specifies the exact structure of each dict in the list with 13 required fields, their types, constraints (e.g., '1-10' for criteria_scores), and enumerations (confidence values). This adds substantial meaning beyond the bare 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 specific action ('Save scoring results') and resource ('to data/scores.json'), distinguishing it from sibling tools like generate_ranking_report or list_submissions which have different purposes. It explicitly describes the file operation rather than analysis or retrieval functions.

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?

The description implies usage through the 'Existing scores... overwritten' and 'New Issues are appended' statements, suggesting this is for persisting scoring results. However, it doesn't explicitly state when to use this tool versus alternatives or mention any prerequisites, leaving usage context somewhat implied rather than explicitly guided.

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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