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StevenFengLi

tavily-mcp

by StevenFengLi
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: tavily-extract is for extracting content from specific URLs, while tavily-search is for performing web searches with customizable parameters. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent 'tavily-verb' pattern (tavily-extract and tavily-search), making them predictable and easy to identify. The naming is uniform and follows the same style throughout.

    Tool Count2/5

    With only 2 tools, the server feels thin for a web content and search domain. While the tools cover extraction and search, the scope suggests more operations (e.g., filtering, summarization, or advanced querying) could be included to provide a more complete surface.

    Completeness3/5

    The server covers basic web content retrieval (extract and search), but there are notable gaps. For example, it lacks tools for processing or analyzing the extracted content (e.g., summarization, translation, or sentiment analysis), which could limit agent workflows in research or data analysis tasks.

  • Average 3.6/5 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

  • 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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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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?

    No annotations are provided, so the description carries full burden. It mentions 'retrieves and processes raw content' but lacks details on rate limits, authentication needs, error handling, or what 'processes' entails (e.g., cleaning, formatting). This is a significant gap for a web extraction tool with no structured safety hints.

    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 adds context without waste. Every phrase ('powerful web content extraction tool', 'ideal for...') contributes meaningfully to understanding the tool's role.

    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?

    Given no annotations and no output schema, the description is moderately complete but lacks behavioral details (e.g., rate limits, errors) and output specifics. For a 3-parameter tool with 100% schema coverage, it adequately covers purpose but falls short on operational context.

    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 parameters. The description adds no specific parameter semantics beyond what's in the schema, such as explaining 'extract_depth' implications or 'include_images' output format. Baseline 3 is appropriate as the schema does the heavy lifting.

    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: 'retrieves and processes raw content from specified URLs' with specific verbs and resources. It distinguishes from the sibling 'tavily-search' by focusing on extraction rather than searching, though the distinction could be more explicit.

    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 contexts ('data collection, content analysis, and research tasks') but does not explicitly state when to use this tool versus 'tavily-search' or provide any exclusions. The input schema hints at usage for LinkedIn with 'advanced' extraction, but this is not in the description itself.

    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?

    With no annotations provided, the description carries the full burden. It mentions the tool is 'powerful' and provides 'real-time results', but lacks details on rate limits, authentication needs, error handling, or response format. It partially compensates by noting it's for 'current information' and 'analysis', but behavioral traits are not fully disclosed.

    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 appropriately sized and front-loaded, starting with the core function and key features. It uses three sentences efficiently, though the last sentence could be slightly more concise by integrating 'Ideal for' into the preceding context.

    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?

    Given the complexity (11 parameters, no annotations, no output schema), the description is moderately complete. It covers the purpose and high-level usage but lacks details on behavioral aspects like rate limits or output structure, which are important for a search tool with many parameters.

    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 schema description coverage is 100%, so the baseline is 3. The description adds minimal value beyond the schema by mentioning 'customizable parameters for result count, content type, and domain filtering', but does not elaborate on parameter interactions or provide additional semantic context not already in the schema descriptions.

    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 performs web search using Tavily's AI engine, specifying it returns comprehensive, real-time results with customizable parameters. It distinguishes itself from the sibling tool 'tavily-extract' by focusing on search rather than extraction, though the sibling's function isn't detailed here.

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

    Usage Guidelines4/5

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

    The description provides clear context for when to use the tool ('gathering current information, news, and detailed web content analysis'), but does not explicitly state when not to use it or mention alternatives like the sibling tool 'tavily-extract' for comparison.

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

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