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RSSAmp: Recent posts on a topic

rssamplifier_topic_posts
Read-onlyIdempotent

[RSSAmp] What the feeds covering a topic have actually published lately, newest first — the river rather than the roster. Note the difference from get_topic, which lists who covers the subject; this lists what they wrote. Drawn from the feeds most strongly filed under the topic, so a very broad keyword is a sample rather than a census. A comma-separated list ("business, saas, crypto", up to 10) is one river across all of them.

Served by RSSAmp (https://rssamp.com/mcp).

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": {
      "keyword": "<keyword>"
    },
    "name": "rssamplifier_topic_posts"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoDefault 50.
keywordYesThe topic, in any spelling — or several, separated by commas.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive, openWorld), and the description adds real behavior: newest-first ordering, the sampling caveat for broad topics, and that a comma-list is merged into one result stream. It stops short of stating response shape or pagination behavior beyond 'newest first'.

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?

Front-loaded with the core action, then the sibling distinction, then the caveat and multi-keyword rule. The 'river rather than the roster' metaphor is compact and reinforces the get_topic contrast rather than padding it.

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?

With no output schema, the description correctly implies the return is recent posts newest-first, which is enough to call it confidently. It could say a bit more about what a post record contains or how results behave at the 200 limit, but nothing critical is missing for a two-parameter read tool.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3, but the description adds information the schema lacks: the comma-separated keyword list is capped at 10 and is treated as a single combined river rather than separate queries. The 'up to 10' constraint in particular is not derivable from the schema.

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?

States a concrete verb and resource ('what the feeds covering a topic have actually published lately, newest first') and explicitly contrasts with get_topic ('lists who covers the subject; this lists what they wrote'), so an agent can separate the two without opening either schema.

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?

Names the alternative tool and the condition that selects it, plus a meaningful caveat about when the output is representative ('a very broad keyword is a sample rather than a census') and how multi-keyword input behaves. Nothing essential is left to inference.

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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