rss-digest-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool has a clear, distinct purpose: fetch_feed for a single feed, get_digest for aggregated multi-feed digests with filtering, and load_opml for importing feed lists. No overlap or ambiguity.
Naming Consistency5/5All tool names follow the verb_noun pattern with lowercase underscores (fetch_feed, get_digest, load_opml), providing a predictable and consistent naming convention.
Tool Count5/5With 3 tools, the server is well-scoped for its purpose of RSS feed digestion. Each tool fills a necessary role without being too few or too many.
Completeness5/5The tools cover the full workflow: loading feeds (load_opml), fetching a single feed (fetch_feed), and building a filtered digest across multiple feeds (get_digest). No obvious gaps for the stated competitive-intelligence digest purpose.
Average 4/5 across 3 of 3 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 9 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that results are ordered newest first and that summary_max_chars can shorten summaries. However, lacks details on caching, error behavior, or rate limits. With no annotations, description carries full burden but is only partially transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first clearly states purpose, second adds parameter detail. No filler words. Efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists; description only vaguely mentions 'items' without specifying structure. No error handling or edge case info. Leaves significant gaps for a tool with 3 parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must compensate. It explains summary_max_chars behavior and default, but does not elaborate on url or limit beyond schema basics. Provides some value but not comprehensive.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Fetch' and the resource 'RSS/Atom feed', and specifies 'latest items (newest first)'. This distinguishes the tool from siblings like 'get_digest' and 'load_opml'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. No context about prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations being provided, the description covers key behaviors: deduplication by link, sorting by newest first, handling of undated items, truncation of summaries, and the structure of the returned dict. It also explains default values and edge cases (e.g., pass 0 to disable time filter). The description provides sufficient transparency for a read-only aggregation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear opening line, a labeled 'Args' section, and a final sentence describing the return value. It uses a consistent format and avoids unnecessary words. However, it could be slightly more concise by consolidating some parameter details (e.g., default values are already in the schema).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, no output schema, no annotations), the description is fairly complete. It explains return value structure ('items', 'count', success count, 'errors') and covers key edge cases (undated items, dedup, sorting). It does not provide example output or error handling beyond per-feed errors, but it is sufficient for most use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description carries the full burden. It explains each parameter's purpose and behavior: 'feeds' (RSS/Atom URLs with examples), 'keywords' (case-insensitive filtering, behavior when omitted), 'hours' (time filter with undated items handling), 'max_items' (cap after dedup+sort), and 'summary_max_chars' (truncation with ellipsis). This adds substantial meaning beyond the schema's basic titles and types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool builds a competitive-intelligence digest from multiple RSS/Atom feeds, specifying the action (build a digest) and the resource (feeds). It implicitly distinguishes from siblings 'fetch_feed' (single feed) and 'load_opml' (import OPML) by focusing on aggregation and filtering.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the parameters and their defaults, which implies usage scenarios (e.g., filtering by keywords, time range). However, it does not explicitly state when to use this tool versus alternatives like 'fetch_feed' or 'load_opml', nor does it provide when-not-to-use guidance. The usage context is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry full burden. It discloses read-only nature and local file access. Could mention validation or error behavior, but sufficiently transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with purpose, no wasted words. Efficient structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple tool: covers purpose, usage, and read-only behavior. Missing return format details but partly addressed by listing feed title and xmlUrl.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and description does not elaborate on the path parameter beyond implying it's a file path. No format, requirements, or constraints added.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads a local OPML file and returns the feed list with title and xmlUrl. It distinguishes from siblings like fetch_feed and get_digest.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises to use for bulk-onboarding from OPML exports and then pass URLs to get_digest. Provides clear when-to-use context.
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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- Evaluate tool definition quality.
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