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luutuankiet

FreshRSS MCP Server

by luutuankiet

freshrss_get_digest_compact

Retrieve a compact, category-balanced digest of unread articles in plain text. Each line uses ~10 tokens to list short ID, title, and feed, enabling token-efficient agent curation.

Instructions

Get an ultra-compact category-balanced digest as plain text.

Returns ~10 tokens per article vs ~100 for JSON format. Each line: [short_id] Title (Feed) Grouped by category with counts.

Designed for agent curation workflows where token efficiency matters. After selecting articles, use mark_stream_read + mark_unread to keep only your picks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.5

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the plain text output, token count (~10 vs ~100), line format, and grouping. It does not mention side effects (since it's a get, that's implied) or any other behavioral caveats, but it adds useful context about the workflow. This is adequate but not comprehensive.

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 five sentences, starting with the primary purpose and then adding concise details. It front-loads the core functionality and keeps each sentence informative, with minimal waste. It could be slightly tighter, but it's well-structured and efficient.

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?

The description covers the output format, a concrete use case, and the suggested follow-up actions, making it sufficient for a simple retrieval tool. It doesn't mention default behaviors (e.g., show_read defaults to false) but those are in the schema. For the tool's complexity, it's complete enough.

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 provides full descriptions for all four parameters (show_read, categories, per_category, include_uncategorized), so the description doesn't need to repeat them. The description's mention of 'category-balanced' indirectly relates to categories and per_category, but it adds no explicit parameter-level meaning beyond the schema. Baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get an ultra-compact category-balanced digest as plain text', giving a specific verb and resource. It further specifies the output format and token efficiency, which helps distinguish it from general article retrieval. However, it doesn't explicitly name a sibling tool to differentiate from, so it falls short of a 5.

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

Usage Guidelines3/5

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

It says 'Designed for agent curation workflows where token efficiency matters', which provides a clear context for when to use. It also suggests a follow-up workflow with mark_stream_read and mark_unread. But it does not explicitly state when NOT to use this tool or name alternative tools for different scenarios, leaving some inference required.

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