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ycjcl868

Fear & Greed Index MCP Server

by ycjcl868
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TypeScript

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and singular.

    Naming Consistency5/5

    The single tool name 'get_fear_greed_index' follows a clear verb_noun pattern, and with no other tools, consistency is inherently perfect.

    Tool Count2/5

    One tool is too few for a server's purpose, as it limits functionality and flexibility. A single tool feels thin and under-scoped, even for a focused domain like market sentiment.

    Completeness3/5

    The tool provides comprehensive data retrieval for the Fear & Greed Index, but there are notable gaps such as no historical data access, filtering, or comparison tools. This limits agents to a single, static operation.

  • Average 4.3/5 across 1 of 1 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

  • This repository is licensed under MIT License.

  • 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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden. It effectively describes the tool's behavior: it returns comprehensive sentiment analysis with specific components (main index, 7 indicators), each with score, rating, and timestamp. It doesn't mention rate limits, authentication needs, or data freshness, but provides substantial behavioral context for a read-only operation.

    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 well-structured and front-loaded with the core purpose, followed by detailed return value information. Every sentence adds value: the first states what it does, the second details the return structure, and the third directs to schema for field details. No wasted words.

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

    Completeness5/5

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

    Given the tool's complexity (returns multiple indicators with scores and ratings), the description provides complete context. It explains what the tool returns in detail, and since an output schema exists, it doesn't need to explain return values further. The combination of description and output schema coverage makes this fully adequate.

    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?

    The input schema has 0 parameters with 100% coverage, so the description doesn't need to compensate. The baseline for 0 parameters is 4, and the description appropriately focuses on output semantics rather than input parameters, which is correct given the tool's nature.

    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 the tool's purpose with specific verb ('Get') and resource ('US stock market Fear & Greed Index data'). It distinguishes what it returns ('comprehensive market sentiment analysis including the main composite index and 7 individual indicators') and provides details about the indicators. No siblings exist, so differentiation isn't needed.

    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 obtaining market sentiment data but doesn't provide explicit guidance on when to use it versus alternatives. Since there are no sibling tools, there's no need for differentiation, but it lacks context about when this tool is appropriate versus other market analysis methods.

    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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