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Macrocosmos MCP

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by macrocosm-os

create_gravity_task

Set up large-scale data collection from X (Twitter) or Reddit for up to 7 days; receive an email notification when the dataset is ready for download.

Instructions

Create a Gravity task for large-scale data collection from X (Twitter) or Reddit. Use this for collecting large datasets over time (up to 7 days). For quick queries (up to 1000 results), use query_on_demand_data instead.

The task registers on the network within 20 minutes and collects data for 7 days. You'll receive an email notification when the dataset is ready for download.

Parameters:

  • tasks (List[dict], REQUIRED): List of task objects, each containing:

    • platform (str): 'x' or 'reddit'

    • topic (str): The hashtag/subreddit to monitor

      • For X: MUST start with '#' or '$' (e.g., '#ai', '$BTC') - plain keywords are rejected!

      • For Reddit: subreddit name (e.g., 'r/MachineLearning')

    • keyword (str, optional): Additional keyword filter within the topic

      • Filters posts to only those containing this keyword

      • Example: topic='#Bittensor', keyword='dTAO' -> only #Bittensor posts mentioning 'dTAO'

  • name (str, optional): Name for the task (helps organize multiple tasks)

  • email (str, optional): Email address for notification when dataset is ready

  • redirect_url (str, optional): URL to redirect to from the email notification

Returns:

  • gravity_task_id: Unique identifier to track and manage the task

Examples:

  1. Basic collection: create_gravity_task( tasks=[{"platform": "x", "topic": "#ai"}], name="AI Tweets" )

  2. With keyword filter: create_gravity_task( tasks=[{"platform": "x", "topic": "#Bittensor", "keyword": "dTAO"}], name="Bittensor dTAO mentions" )

  3. Multiple platforms: create_gravity_task( tasks=[ {"platform": "x", "topic": "#ai", "keyword": "LLM"}, {"platform": "reddit", "topic": "r/MachineLearning"} ], name="AI Data Collection", email="user@example.com" )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
emailNo
tasksYes
redirect_urlNohttps://app.macrocosmos.ai/

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses important asynchronous behavior: the task registers within 20 minutes, collects data for 7 days, sends an email notification, and returns a gravity_task_id. It could also mention costs, cancellation, or failure handling, but the key lifecycle behavior is transparent.

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 well-structured and front-loaded: purpose, usage distinction, lifecycle, parameter details, returns, and examples. There is minor redundancy—'up to 7 days' appears twice and collection duration is restated—but overall each section earns its place and the examples are valuable.

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?

For a complex asynchronous collection tool with no annotations, the description covers purpose, when to use it, parameter semantics, behavior after invocation, and return value. The output schema exists, so return details need no further expansion. No critical information is missing for selecting and invoking the tool correctly.

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?

Schema coverage is 0%, and the schema only describes tasks as an array of objects with additionalProperties true. The description fully compensates by documenting the task object structure, platform values ('x' or 'reddit'), topic constraints (#/$ prefix for X, r/ for Reddit), keyword filtering behavior, and all optional parameters with examples.

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 first sentence states a specific verb and resource: 'Create a Gravity task for large-scale data collection from X (Twitter) or Reddit.' It also differentiates this from query_on_demand_data by contrasting long-running collection (up to 7 days) with quick queries, so an agent can tell them apart immediately.

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

Usage Guidelines5/5

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

Explicitly says when to use this tool: 'Use this for collecting large datasets over time (up to 7 days).' It also states an exclusion and routes to the alternative: 'For quick queries (up to 1000 results), use query_on_demand_data instead.' This is clear when/when-not guidance.

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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