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kwp-lab

rss-reader-mcp

by kwp-lab
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.8

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one fetches a list of feed entries, the other fetches the full content of a single article. There is no overlap.

    Naming Consistency5/5

    Both tools use a consistent verb_noun pattern with 'fetch_' prefix, making them predictable and easy to understand.

    Tool Count2/5

    With only two tools, the server feels very thin for an 'rss-reader' purpose. A typical reader would include tools for managing feeds, subscriptions, or browsing, making this count too low.

    Completeness1/5

    The tool set is severely incomplete. There are no tools for feed management (e.g., add, list, remove feeds), no search or filter capabilities, and no persistence. The server cannot function as a full RSS reader.

  • Average 3.4/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  ]
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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?

    Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds minimal behavioral context (fetching from URL), which is already implied by the input schema. No contradictions.

    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 single, well-formed sentence with no fluff. It front-loads the key action and resource. Could be slightly improved with more detail, but remains concise.

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

    Completeness2/5

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

    No output schema exists, so the description should explain the return value (e.g., list of entries with fields). It does not. Annotations cover safety but not functional completeness. The sibling tool is named but no usage differentiation.

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

    Parameters2/5

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

    Schema coverage is 0%. The description hints at the 'url' parameter ('from a given URL') but does not explain the 'limit' parameter or its purpose (e.g., max number of entries). The description adds insufficient meaning beyond the schema.

    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 the verb (Fetch) and resource (RSS feed entries) with a source URL. It distinguishes from the sibling tool 'fetch_article_content' by focusing on feed entries rather than individual article content.

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

    Usage Guidelines2/5

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

    No guidance on when to use this tool vs. the sibling tool. Does not specify prerequisites or contexts where fetching feed entries is appropriate or inappropriate.

    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?

    Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds that output is Markdown formatted, which is beyond what annotations provide. No behavioral contradictions are present.

    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 single sentence of 12 words, front-loaded with the key action 'Fetch and extract', and contains no superfluous information.

    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?

    Given the tool's simplicity (one parameter, no output schema, and comprehensive annotations), the description provides enough context: it specifies the action, resource, and output format. However, it could mention the return structure or error handling for full completeness.

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

    Parameters2/5

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

    With 0% schema description coverage, the description does not add significant meaning to the 'url' parameter beyond the schema's format: uri constraint. It only mentions 'from a URL', which is minimal additional context.

    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 uses the verb 'Fetch and extract' with the resource 'article content' and specifies the output format 'Markdown'. It distinguishes itself from the sibling tool 'fetch_feed_entries', which likely fetches lists of entries rather than individual article content.

    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?

    The description implies usage for extracting article content from a URL but does not explicitly state when not to use it or provide guidance on choosing between this tool and the sibling 'fetch_feed_entries'.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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