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Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
GITHUB_TOKENNoGitHub token used for authentication. Note that gh CLI usually manages its own auth; this is typically only set for CI environments.

Capabilities

Features and capabilities supported by this server

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
list_submissions

Fetch the list of Agents League submissions.

Args: track: Track name to filter by. ``"creative-apps"`` | ``"reasoning-agents"`` | ``"enterprise-agents"`` | None (all) state: Issue state. ``"open"`` | ``"closed"`` | ``"all"`` Returns: A list of submission summaries. Each element is a dictionary containing issue_number, title, track, project_name, repo_url, created_at, has_demo. Raises: RuntimeError: When gh command execution fails.
get_submission_detail

Fetch detailed submission data for the specified Issue number.

Parses each section of the Issue template and returns scoring data. GitHub Username is hidden during scoring to eliminate bias, but retained as the github_username field for report output. If repo_url points to a GitHub repository, the README is also fetched. Args: issue_number: The Issue number to fetch. Returns: A dictionary containing detailed submission information. Raises: RuntimeError: When gh command execution fails.
get_scoring_rubric

Return the scoring rubric for the specified track.

Loads the YAML file ``data/rubrics/{track}.yaml`` and returns the scoring criteria (name, weight, description, scoring_guide). Args: track: Track name. ``"creative-apps"`` | ``"reasoning-agents"`` | ``"enterprise-agents"`` Returns: Rubric dict with track, track_display_name, criteria (list), total_weight, score_range, and notes. Raises: FileNotFoundError: If the YAML file for the track does not exist. ValueError: If the track name is invalid.
save_scores

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.
generate_ranking_report

Generate a Markdown ranking report and save to reports/ranking.md.

Reads scoring results from data/scores.json and produces a report containing overall ranking, per-track ranking, and individual evaluation summaries. Args: top_n: Number of top entries to highlight (default: 10). Returns: Result dict (report_path, total_scored, top_n, top_entries).

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

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