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altinakseven

Tavily MCP Server

by altinakseven
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JavaScript
Remote

Server Quality Checklist

58%
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 confusion or overlap between tools. The single tool 'web_search' has a clearly defined and distinct purpose, making it impossible for an agent to misselect between non-existent alternatives.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'web_search' follows a clear verb_noun pattern, but with no other tools to compare against, there can be no inconsistency in the set.

    Tool Count2/5

    A single tool is too few for a server named 'Tavily MCP Server', which implies broader web search capabilities. While 'web_search' covers the core function, typical search servers might include tools for advanced queries, filtering, or result analysis, making this feel thin and limited in scope.

    Completeness2/5

    The tool surface is severely incomplete for a web search domain. It lacks obvious gaps such as tools for refining searches (e.g., by date or source), handling pagination, or accessing cached content, which could lead to agent failures when trying to perform comprehensive search tasks.

  • Average 3.1/5 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

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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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions the API provider (Tavily) and output format (results with snippets), but lacks details on rate limits, authentication needs, error handling, or performance characteristics that would help the agent anticipate behavior.

    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 and key output details. Every word contributes essential information with zero waste, making it optimally concise.

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

    Completeness3/5

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

    For a 6-parameter tool with no annotations and no output schema, the description is adequate but minimal. It covers the basic purpose and output format, but lacks depth on behavioral traits, usage context, or richer operational details that would enhance agent understanding.

    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?

    Schema description coverage is 100%, so the schema fully documents all 6 parameters. The description adds no parameter-specific information beyond what's in the schema, meeting the baseline for high coverage without additional value.

    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 action ('Search the web') and resource ('using Tavily API'), specifying it returns 'relevant search results with content snippets'. It's specific about the API provider and output format, though without sibling tools, differentiation isn't applicable.

    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 is provided on when to use this tool versus alternatives. The description doesn't mention any prerequisites, constraints, or typical use cases, leaving the agent with no contextual direction for tool selection.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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