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crowdcent

CrowdCent MCP Server

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by crowdcent
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Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
CROWDCENT_API_KEYYesYour CrowdCent API key (get one at crowdcent.com)

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
list_all_challengesB
List all available challenges.

Returns:
    Dictionary containing list of challenges with their details
get_challenge_infoB
Get detailed information about the current challenge.

Returns:
    Dictionary containing challenge details
switch_challengeC
Switch to a different challenge.

Args:
    challenge_slug: The slug of the challenge to switch to

Returns:
    Success message
list_training_datasetsB
List all available training datasets for the current challenge.

Returns:
    Dictionary containing list of training datasets
download_training_datasetB
Download a specific training dataset.

Args:
    version: The version string of the training dataset (e.g., '1.0', '2.1') or 'latest'
    dest_path: Absolute path where to save the dataset, must end with .parquet

Returns:
    Success message or error
download_inference_dataA
Download inference data for a specific period.

Args:
    release_date: The release date in 'YYYY-MM-DD' format or 'current' or 'latest'
    dest_path: Absolute path where to save the data, must end with .parquet
    poll: Whether to wait for the inference data to be available before downloading
    poll_interval: Seconds to wait between retries when polling
    timeout: Maximum seconds to wait before raising TimeoutError (None waits indefinitely)

Returns:
    Success message or error
submit_predictions_from_fileB
Submit predictions from a Parquet file.

Args:
    file_path: Absolute path to the predictions file, must end with .parquet
    slot: Submission slot number (1-based, default: 1)

Returns:
    Dictionary with submission details
submit_predictions_from_dataframeC
Submit predictions from a JSON representation of a dataframe.

Args:
    df: dataframe containing predictions data
    slot: Submission slot number (1-based, default: 1)

Returns:
    Dictionary with submission details
list_submissionsA
List recent submissions.

Args:
    period: Optional filter for submissions by period:
            - 'current': Only show submissions for the current active period
            - 'YYYY-MM-DD': Only show submissions for a specific inference period date

Returns:
    Dictionary containing list of submissions
get_submissionC
Get details about a specific submission.

Args:
    submission_id: The ID of the submission

Returns:
    Dictionary containing submission details
download_meta_modelB
Download the consolidated meta model for the current challenge.

Args:
    dest_path: Absolute path where to save the meta model, must end with .parquet

Returns:
    Success message or error
get_training_dataset_infoB
Get detailed information about a specific training dataset.

Args:
    version: The version string of the training dataset (e.g., '1.0', '2.1') or 'latest'

Returns:
    Dictionary containing dataset details
get_inference_data_infoB
Get detailed information about a specific inference data period.

Args:
    release_date: The release date in 'YYYY-MM-DD' format or 'current' or 'latest'

Returns:
    Dictionary containing inference data details

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.4/5.0

Scored across 13 tools

Disambiguation5/5

Each tool has a clearly distinct purpose targeting specific resources and actions. For example, download_inference_data vs get_inference_data_info provide complementary but non-overlapping functionality, and the two submission tools handle different input formats without ambiguity.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with snake_case throughout. The naming convention is predictable: download_*, get_*, list_*, submit_*, and switch_* prefixes clearly indicate action types, making the tool set highly readable and systematic.

Tool Count5/5

With 13 tools, this server is well-scoped for its apparent domain of challenge management and data handling. Each tool serves a specific purpose in the workflow, from data retrieval to submission management, without redundancy or obvious omissions in the count.

Completeness4/5

The tool set provides comprehensive coverage for core challenge workflows, including data access, information retrieval, submission handling, and challenge switching. A minor gap exists in update/delete operations for submissions or challenges, but agents can work effectively with the available CRUD-like operations.

Maintenance

ActivitySlowing
ResponsivenessNo issues

Resources