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jvenkatasandeep

Finance News RSS MCP Server

by jvenkatasandeep
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one fetches the latest articles (optionally filtered by source), while the other searches by keyword. There is no overlap in functionality that would cause confusion.

    Naming Consistency5/5

    Both tools follow the same verb_noun pattern: get_latest_finance_news and search_finance_news. The names are descriptive and consistent.

    Tool Count4/5

    With only two tools, the server feels minimal but well-scoped for its purpose of fetching and searching finance news. It is slightly thin, but each tool serves a distinct and necessary function.

    Completeness4/5

    The server covers the core operations for a news RSS reader: retrieving latest news (with source filtering) and searching. Minor gaps exist, such as lack of pagination or category filtering, but agents can accomplish typical tasks without dead ends.

  • Average 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 status not available

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  • This repository includes a README.md file.

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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?

    With no annotations, the description carries the full burden of explaining behavior. It states that the tool fetches from RSS feeds and returns a list of articles, implying a read-only operation. However, it does not explicitly confirm read-only status or disclose potential side effects, error conditions, or rate limits.

    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 well-structured docstring with a one-line summary, Args section, and Returns section. It is concise, with every sentence adding value, and lists source options compactly.

    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 tool's purpose, parameters, and return format, which is sufficient for a simple RSS-fetching tool with no required parameters. It lacks a brief note on error handling or source availability, but otherwise is complete enough given the low complexity.

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

    Parameters5/5

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

    The description fully explains both parameters: 'limit' (maximum number of articles) and 'source' (with a list of allowed values like bloomberg, wsj, cnbc). This adds significant meaning beyond the schema, which only specifies type and default.

    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 states 'Get the latest finance news from RSS feeds,' which is a specific verb ('Get') and resource ('latest finance news from RSS feeds'). This distinguishes it from the sibling tool 'search_finance_news' by emphasizing 'latest' rather than search.

    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?

    The description provides no guidance on when to use this tool versus the sibling 'search_finance_news.' It does not mention any alternatives, exclusions, or conditions for use, leaving the agent without explicit context for tool selection.

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

  • Behavior3/5

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

    No annotations are provided, so the description must carry the behavioral burden. It clearly states it searches titles and descriptions, respects a limit, and returns a list of articles with specified fields. However, it does not disclose ordering, error handling, or rate limits, leaving some typical search behavior undocumented.

    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 concise and well-structured, with a one-sentence summary followed by clearly separated Args and Returns sections. Every line is informative, especially because the schema lacks descriptions, making the parameter explanations essential.

    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 (2 parameters, output schema exists), the description covers the essential aspects: what it searches, how to control the result size, and what the returned entries contain. It does not repeat output schema details excessively, though it could mention ordering or other edge cases for full completeness.

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

    Parameters5/5

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

    The input schema has 0% description coverage, but the tool description fully compensates by explaining the query parameter (keyword to find in titles/descriptions) and the limit parameter (maximum articles, default 10). This adds meaningful semantics beyond the bare schema types.

    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 states a specific action ('Search finance news') and a clear resource ('across all RSS feeds') with a keyword parameter. This clearly distinguishes it from the sibling get_latest_finance_news, which implies fetching without a keyword filter.

    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 when you need to search by keyword rather than just getting the latest news, but it does not explicitly mention the sibling tool or provide when-to-use/when-not-to-use guidance. The 'Args' section clarifies inputs but lacks direct comparison to alternatives.

    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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