dex-data

read_feed

Convert any public RSS, Atom, or RDF feed into clean JSON with item details, ISO dates, and plain-text summaries for direct use in prompts.

Instructions

Read any public RSS, Atom or RDF feed as clean JSON: feed title, link and description, then items with title, real destination link, author, categories and the published date both verbatim and normalised to ISO 8601. Each summary is returned twice — the feed HTML as published, and a plain-text rendering safe to put straight into a prompt. An unparseable date is null, never an invented timestamp. Pair with url_to_markdown to read a linked article.

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": "read_feed"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes	Public http(s) URL of an RSS 2.0, Atom or RDF feed.
`limit`	No	Maximum items, 1..100. Default 20.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 7, 2026

TDQS

A4.1/5.0

Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses behavior: returns clean JSON, includes date normalization, null for unparseable dates, summaries in HTML and plain-text, and 'real destination link' implying redirect resolution. Missing auth/rate-limit details but these may be irrelevant for a read-only public feed tool.

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 a few sentences but dense with useful information, front-loading the purpose and then detailing output structure and date handling. No fluff, though it could be slightly more concise without losing value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with two parameters and no output schema, the description is fairly complete: it explains the output structure, date behavior, and even mentions a sibling tool. It doesn't cover error scenarios or edge cases (e.g., invalid URL), but these are not critical for a read-only feed fetcher.

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?

Schema description coverage is 100% for both parameters, so the baseline is 3. The description does not add extra meaning beyond the schema; it mentions the URL type but does not elaborate on limit or its default. Since the schema already documents them, this is acceptable.

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?

Description states a clear verb 'Read' and resource 'any public RSS, Atom or RDF feed' and specifies output as clean JSON with feed and item fields. It distinguishes itself from url_to_markdown by noting it reads the feed itself, not the linked article.

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?

Explicitly pairs with url_to_markdown for reading linked articles, providing a clear alternative and context. However, it doesn't give explicit 'when not to use' conditions beyond that pairing, but the context is sufficient for an agent to choose appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.