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@asyntai/mcp

@asyntai/mcp

Official
by asyntai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

Set the Real-Time Data Feed

set_data_feed
DestructiveIdempotent

Index a product feed for chatbot use, replacing the previous index and rebuilding in the background. Send an empty URL to remove the feed.

Instructions

Point the chatbot at a product feed and index it. This fetches the feed, checks it really is a feed, clears whatever was indexed before, and rebuilds the index in the background. Send an empty url to remove the feed. Needs the Standard plan or higher.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "set_data_feed"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesAddress of the product feed. Empty removes it.
enabledNoWhether the chatbot may use the feed.
website_idNoNumeric id of the website, from list_websites. Omit to use the account's primary website.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

A4.5/5.0
Behavior5/5

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

The description transparently states side effects: it fetches the feed, validates it, clears previous index, and rebuilds in the background. This aligns with the destructiveHint annotation and adds detail about the background operation, exceeding what annotations alone provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a few concise sentences with no filler, front-loading the primary action and then covering the removal case and prerequisite. It is easy to read and directly informative.

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 tool's moderate complexity and absence of an output schema, the description covers the process, side effects, special case, and prerequisite effectively. It provides sufficient context for an agent to decide when and how to call it.

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

Parameters3/5

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

The schema already has full descriptions for all three parameters (url, enabled, website_id), achieving 100% coverage. The description adds only slight emphasis on the URL behavior (empty removes), but it does not meaningfully enhance understanding beyond the schema, so baseline applies.

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 action: pointing the chatbot at a product feed and indexing it, plus the special case of removing the feed with an empty URL. This distinguishes it from related tools like set_data_feed_max and is specific enough to understand the core function.

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

Usage Guidelines4/5

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

It explains when to use the tool (to set or remove a product feed) and includes a conditional (empty URL for removal). However, it does not explicitly compare against alternatives like other feed-related tools, though the purpose is clear enough to avoid confusion.

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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