Allows the server to fetch hackathon submission data from GitHub issues and post automated feedback comments using the GitHub CLI.
Integrates with GitHub Copilot in VS Code to orchestrate specialized agents for evaluating submissions and generating ranking reports.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Saiten MCP Server score all submissions and generate a ranking report"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Submission Track: 🎨 Creative Apps — GitHub Copilot
MIT License — see LICENSE.
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).
| Name | Required | Description | Default |
|---|---|---|---|
| top_n | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses key behavioral traits: it generates a file (saves to reports/ranking.md), reads from a specific data source (data/scores.json), and produces a structured output. However, it doesn't mention error handling, file overwriting behavior, or performance characteristics like execution time or resource usage, leaving gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear opening sentence stating the core action, followed by brief context on data source and report content, and ending with explicit sections for Args and Returns. Every sentence adds value without redundancy, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter with good description coverage and an output schema (Returns section details the result dict structure), the description is largely complete. It covers purpose, basic behavior, parameter meaning, and output format. Minor gaps include lack of error cases or dependencies on the data/scores.json file format, but overall it provides sufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for the single parameter 'top_n' by explaining it controls 'Number of top entries to highlight' with a default value. Since schema description coverage is 0% (the schema only provides title and type), the description fully compensates by clarifying the parameter's purpose and default, though it could specify constraints like minimum/maximum values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Generate a Markdown ranking report and save to reports/ranking.md'), identifies the input source ('Reads scoring results from data/scores.json'), and specifies the output content ('overall ranking, per-track ranking, and individual evaluation summaries'). This distinguishes it from sibling tools like get_scoring_rubric or list_submissions which retrieve rather than generate reports.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning it reads from data/scores.json, suggesting it should be used after scoring data is available. However, it doesn't explicitly state when to use this tool versus alternatives like save_scores or get_submission_detail, nor does it provide exclusion criteria or prerequisites beyond the implied data dependency.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| track | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 behaviors: it reads from a YAML file, returns a structured rubric dict, and raises specific exceptions (FileNotFoundError, ValueError). This covers file I/O, output structure, and error conditions without contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose, followed by Args, Returns, and Raises sections. Every sentence earns its place by providing essential information without redundancy, structured for quick scanning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (1 parameter, file I/O), no annotations, and an output schema present, the description is complete. It explains the purpose, parameter semantics, return structure, and error handling, compensating for the lack of annotations and low schema coverage effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate fully. It adds significant meaning beyond the bare schema by explaining the 'track' parameter's purpose, listing valid enum values (creative-apps, reasoning-agents, enterprise-agents), and linking it to the file path. This provides complete parameter context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Return') and resource ('scoring rubric for the specified track'), and distinguishes it from siblings by focusing on rubric retrieval rather than submissions, rankings, or scores. It explicitly mentions loading from a specific YAML file path, making the operation concrete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying the track names and file path, but does not explicitly state when to use this tool versus alternatives like 'list_submissions' or 'get_submission_detail'. It provides clear input requirements but lacks comparative guidance with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| issue_number | Yes |
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 behaviors: it parses template sections, hides GitHub usernames during scoring but retains them for reports, fetches READMEs from GitHub repositories, and raises RuntimeError on command execution failure. However, it doesn't mention rate limits, authentication needs, or pagination behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by behavioral details and parameter/return documentation. Every sentence adds value without redundancy, and the Args/Returns/Raises sections are efficiently organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no annotations and no output schema, the description provides good coverage: purpose, behavioral traits, parameter meaning, and error handling. It could be more complete by detailing the return dictionary structure or authentication requirements, but it's substantially adequate given the context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and only one parameter, the description adds significant value by explaining that 'issue_number' is 'The Issue number to fetch', providing context beyond the schema's title. It doesn't specify format constraints or examples, but compensates adequately for the schema gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('fetch detailed submission data', 'parses each section', 'returns scoring data') and identifies the resource ('Issue number'). It distinguishes from siblings by focusing on detailed parsing of individual submissions rather than listing, generating reports, or managing rubrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'for the specified Issue number' and mentioning scoring data, but doesn't explicitly state when to use this tool versus alternatives like list_submissions or generate_ranking_report. No exclusions or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| track | No | ||
| state | No | all |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 return format (list of dictionaries with specific fields), error conditions (RuntimeError on gh command failure), and the fact this fetches data rather than modifies it. It doesn't mention rate limits or authentication requirements, but provides solid behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Perfectly structured with purpose statement, parameter explanations, return format, and error conditions in clear sections. Every sentence adds value with zero redundancy. The information is front-loaded with the core purpose first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, list output), no annotations, but with output schema present, the description provides complete context: purpose, parameter semantics, return format, and error handling. The output schema handles return structure details, so the description focuses on semantic context appropriately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description fully compensates by explaining both parameters: track with specific enum values and 'all' default behavior, and state with enum values and default. It adds crucial semantic meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Fetch' and resource 'list of Agents League submissions', making the purpose specific and unambiguous. It distinguishes from siblings like get_submission_detail (single submission) and generate_ranking_report (analytical output).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through parameter explanations (track filtering, state filtering), but doesn't explicitly state when to use this tool versus alternatives like get_submission_detail. It provides clear filtering capabilities but lacks explicit sibling comparison guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| scores | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0generate_ranking_reportget_scoring_rubricget_submission_detaillist_submissionssave_scoresScored across 5 tools
Each tool has a clearly distinct purpose with no overlap. generate_ranking_report creates reports, get_scoring_rubric fetches scoring criteria, get_submission_detail retrieves specific submission data, list_submissions lists submissions with filtering, and save_scores stores scoring results. The tools cover different aspects of the scoring workflow without ambiguity.
All tools follow a consistent verb_noun naming pattern (generate_ranking_report, get_scoring_rubric, get_submission_detail, list_submissions, save_scores). The verbs are appropriate and descriptive, and the snake_case convention is maintained throughout without deviation.
With 5 tools, this server is well-scoped for managing a scoring system for submissions. The tools cover essential operations like listing, retrieving details, accessing rubrics, saving scores, and generating reports, which is appropriate for the domain without being excessive or insufficient.
The toolset provides complete coverage for the scoring workflow domain. It includes operations for listing submissions, fetching details and rubrics, saving scores, and generating reports, covering the full lifecycle from data retrieval to output without any obvious gaps that would hinder an agent.
Run hackathons end to end: events, teams, submissions, judging and winners.
Manage repositories, users, releases, and automate GitHub workflows
Browse, search and scan a public leaderboard of AI-generated GitHub repos.
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
