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terellcodes

Tavily Web Search MCP Server

by terellcodes
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

42%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation2/5

    The two tools have overlapping purposes, as both retrieve stock price data, with 'get_comprehensive_stock_data' including additional metrics and news. An agent could easily confuse them or misselect 'check_stock_price' when more comprehensive data is needed, leading to inefficiency or incomplete results.

    Naming Consistency3/5

    The tool names follow a mixed convention: 'check_stock_price' uses a verb_noun pattern, while 'get_comprehensive_stock_data' uses a verb_adjective_noun pattern. This inconsistency in naming styles (e.g., 'check' vs. 'get', and structure) reduces predictability, though the names are still readable and descriptive.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for a 'Tavily Web Search MCP Server', which suggests broader web search capabilities. The tools are narrowly focused on stock data, lacking general search or other domain-specific functions, making the count too low for the implied purpose.

    Completeness2/5

    The server has significant gaps in its tool surface. As a web search server, it lacks tools for general web searches, news retrieval, or other common search functions beyond stocks. The stock-related tools themselves are incomplete, missing operations like historical data, company info, or portfolio management, limiting agent effectiveness.

  • Average 2.9/5 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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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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves current stock prices, implying a read-only operation, but doesn't mention any behavioral traits like rate limits, data freshness, error handling, or authentication needs. This leaves significant gaps in understanding how the tool behaves in practice.

    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, clear sentence that efficiently conveys the core functionality without any wasted words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

    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?

    Given the lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It doesn't address behavioral aspects, parameter details, or what the tool returns (e.g., price format, timestamp, error responses). For a tool with one parameter but no structured documentation, more context is needed to ensure reliable use.

    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?

    The schema description coverage is 0%, so the description must compensate for the lack of parameter documentation. It mentions 'stock symbol' as the input, which aligns with the single parameter in the schema, but doesn't add any semantic details beyond what's implied by the parameter name (e.g., format examples like 'AAPL' or 'GOOGL', validation rules, or what constitutes a valid symbol).

    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 tool's purpose with a specific verb ('Get') and resource ('current price of a stock symbol'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from its sibling tool 'get_comprehensive_stock_data', which might offer more detailed information beyond just the current price.

    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 its sibling 'get_comprehensive_stock_data'. It lacks any context about alternatives, prerequisites, or exclusions, leaving the agent to infer usage based solely on the tool name and description.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what data is returned but doesn't cover critical aspects like rate limits, authentication needs, data freshness, error handling, or whether it's a read-only operation. For a data-fetching tool with zero annotation coverage, this is a significant gap.

    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, efficient sentence that front-loads the core purpose. Every word earns its place by specifying the action, resource, and included data types without redundancy or fluff.

    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?

    Given the complexity of fetching comprehensive stock data, the lack of annotations, no output schema, and minimal parameter guidance, the description is incomplete. It doesn't address how data is returned, error cases, or operational constraints, making it inadequate for reliable tool invocation.

    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 input schema has 1 parameter with 0% description coverage, and the description doesn't mention parameters at all. However, since there's only one parameter ('stock_symbol'), the baseline is 4, but it's reduced to 3 because the description doesn't add any semantic context (e.g., format examples like 'AAPL' or 'MSFT', or validation rules) beyond what's implied by the schema's title.

    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 'Get' and the resource 'comprehensive stock data', specifying it includes price, financial metrics, and recent news. This distinguishes it from the sibling tool 'check_stock_price', which likely focuses only on price. However, it doesn't explicitly state how it differs from the sibling beyond listing data types.

    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 'check_stock_price'. It doesn't mention alternatives, prerequisites, or specific contexts for use, leaving the agent to infer based on the tool name and data scope alone.

    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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