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Rz017

Tavily MCP Server

by Rz017

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, making it easy for an agent to select the appropriate tool based on the task.

    Naming Consistency5/5

    Both tools follow a consistent naming pattern with the prefix 'tavily-' followed by a descriptive action (extract, search). This uniformity makes the tool set predictable and easy to understand, with no deviations in style or convention.

    Tool Count2/5

    With only two tools, the server feels under-scoped for a web content and search domain. While the tools cover extraction and search, the lack of additional operations (e.g., summarization, filtering, or advanced analysis) limits functionality and may require agents to work around gaps, making the set feel incomplete for broader use cases.

    Completeness3/5

    The tools cover basic web content retrieval (extract and search), but there are notable gaps in the surface. For example, there are no tools for processing or analyzing the extracted content (e.g., summarization, translation, or sentiment analysis), which could hinder agents in performing comprehensive tasks beyond raw data collection.

  • Average 3.5/5 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.

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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 the full burden of behavioral disclosure. It mentions that the tool 'retrieves and processes raw content,' which implies read-only behavior, but does not specify rate limits, authentication needs, error handling, or what 'processes' entails (e.g., formatting, filtering). For a tool with no annotations, this leaves significant gaps in understanding its operational traits.

    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 concise and front-loaded, stating the core purpose in the first clause. It uses two sentences efficiently to cover functionality and ideal use cases without unnecessary details. However, it could be slightly more structured by explicitly separating purpose from guidelines, but overall it's well-sized and avoids waste.

    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 tool has no annotations, no output schema, and 3 parameters, the description is incomplete. It lacks details on behavioral aspects (e.g., rate limits, errors), output format, and deeper usage contexts. For a tool that extracts web content, which can involve complexities like handling dynamic pages or authentication, the description does not provide enough information for an agent to use it effectively without additional 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 already documents all parameters thoroughly. The description does not add any additional meaning or context beyond what the schema provides (e.g., it doesn't explain the implications of 'basic' vs 'advanced' extraction or when to include images). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract either.

    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 search, though the distinction could be more explicit. The description is not tautological and provides meaningful context about use cases.

    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 through phrases like 'ideal for data collection, content analysis, and research tasks,' which suggests when to use it. However, it lacks explicit guidance on when to choose this tool over 'tavily-search' or any alternatives, and does not mention exclusions or prerequisites. The guidance is present but not comprehensive.

    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 of behavioral disclosure. It mentions 'real-time results' and 'customizable parameters,' which adds useful context about timeliness and flexibility. However, it does not disclose critical behavioral traits such as rate limits, authentication requirements, error handling, or pagination behavior. The description is not misleading but lacks depth for a tool with 11 parameters and no output schema.

    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 appropriately sized and front-loaded, with three sentences that each earn their place. The first sentence states the core purpose, the second explains capabilities and parameters, and the third provides usage context. There is zero waste, and the structure efficiently conveys essential information without redundancy or fluff.

    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 tool's complexity (11 parameters, no annotations, no output schema), the description is adequate but has clear gaps. It covers purpose and high-level usage but lacks details on behavioral traits like rate limits or error handling. Without an output schema, the description does not explain return values, which is a significant omission for a search tool. It is complete enough for basic understanding but insufficient for full operational clarity.

    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 already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'customizable parameters for result count, content type, and domain filtering,' but does not provide additional syntax, format details, or usage examples. This meets the baseline of 3 when the schema does the heavy lifting, but the description could have enhanced understanding of parameter interactions.

    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: 'A powerful web search tool that provides comprehensive, real-time results using Tavily's AI search engine.' It specifies the verb ('search'), resource ('web content'), and distinguishes from its sibling 'tavily-extract' by focusing on search rather than extraction. The description explicitly mentions what it returns ('relevant web content') and its primary use case ('gathering current information, news, and detailed web content analysis').

    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 this tool: 'Ideal for gathering current information, news, and detailed web content analysis.' It implies usage scenarios but does not explicitly state when not to use it or name alternatives. While it distinguishes from 'tavily-extract' by context (search vs. extraction), it lacks explicit guidance on choosing between them or other potential search 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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  • Evaluate tool definition quality.

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