Tavily MCP Load Balancer

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
The tool set has significant overlap and ambiguity, particularly between 'search' and 'tavily-search', which appear to be identical based on their descriptions. Additionally, 'tavily-crawl', 'tavily-extract', and 'tavily-map' all involve web content processing, making it unclear when to use one over the others without more specific distinctions in their purposes.
Naming Consistency3/5
The naming is mixed: four tools use a consistent 'tavily-' prefix with descriptive suffixes (crawl, extract, map, search), but one tool is named simply 'search' without the prefix. This deviation breaks full consistency, though the overall pattern is still readable and not chaotic.
Tool Count4/5
With 5 tools, the count is reasonable for a web search and content analysis server, aligning well with typical scopes. It's slightly under the ideal range but not overly thin, as each tool (despite overlaps) covers different aspects of web interaction.
Completeness3/5
The server covers core web operations like search, crawling, extraction, and mapping, but there are notable gaps. For example, there's no tool for updating or managing saved searches, filtering results beyond basic parameters, or handling authentication for restricted content, which could limit agent workflows in more advanced scenarios.
Average 3.4/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'retrieves and processes raw content' but doesn't mention critical behavioral traits like rate limits, authentication needs, error handling, or what 'processes' entails (e.g., cleaning, structuring). For a web extraction tool with potential complexity, this leaves significant gaps in understanding how it behaves beyond basic functionality.
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 concise with two sentences that efficiently state the tool's purpose and ideal use cases. It's front-loaded with the core functionality. While it could be slightly more structured (e.g., separating purpose from guidelines), there's minimal waste, and every sentence adds value.
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 web content extraction (5 parameters, no output schema, no annotations), the description is incomplete. It lacks details on output format, error cases, performance characteristics, and how it differs from sibling tools. Without annotations or output schema, the description should compensate more to help an agent use it effectively, but it provides only basic functional overview.
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 fully documents all 5 parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain 'extract_depth' or 'format' further). Baseline 3 is appropriate when the schema does the heavy lifting, though the description could have provided higher-level context about parameter interactions.
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 resource. It mentions use cases like 'data collection, content analysis, and research tasks' which helps clarify intent. However, it doesn't explicitly differentiate from sibling tools like 'tavily-crawl' or 'tavily-search', which likely have overlapping web content functionality.
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 alternatives like 'tavily-crawl' or 'tavily-search'. It mentions it's 'ideal for data collection, content analysis, and research tasks', but this is generic and doesn't help an agent choose between sibling tools. There are no explicit when/when-not statements or named alternatives.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'real-time results' and 'customizable parameters,' but lacks critical details such as rate limits, authentication requirements, error handling, or what the output format looks like (since there's no output schema). For a complex tool with 15 parameters, this is insufficient.
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 efficiently structured in two sentences, front-loaded with the core purpose and key features. It avoids redundancy, though it could be slightly more concise by integrating the 'ideal for' clause into the first sentence.
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's complexity (15 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain the return format, error conditions, or behavioral constraints like rate limits or data freshness. For a search tool with rich parameters, more contextual guidance is needed to help the agent use it effectively.
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 schema already documents all 15 parameters thoroughly. The description adds minimal value beyond the schema, mentioning 'customizable parameters for result count, content type, and domain filtering' but without specific details. Baseline 3 is appropriate when 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 performs web search using Tavily's AI engine, providing real-time results. It specifies the verb ('search') and resource ('web content'), but doesn't explicitly differentiate from sibling tools like 'tavily-search' or 'tavily-crawl', which likely have overlapping functionality.
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 mentions the tool is 'ideal for gathering current information, news, and detailed web content analysis,' which implies usage contexts. However, it doesn't provide explicit guidance on when to use this tool versus the sibling tools (tavily-crawl, tavily-extract, tavily-map, tavily-search), leaving the agent to infer differences.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'real-time results' and 'customizable parameters' but lacks critical behavioral details such as rate limits, authentication requirements, error handling, pagination behavior, or what happens when parameters conflict. For a complex 15-parameter tool with no annotation coverage, this is a significant gap in behavioral transparency.
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 with three sentences that efficiently convey the tool's purpose and key features. It's front-loaded with the core functionality and follows with supporting details. While every sentence earns its place, the third sentence could be slightly more specific about the 'detailed web content analysis' aspect.
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 (15 parameters, no annotations, no output schema), the description is insufficiently complete. It doesn't explain what the tool returns (only mentioning 'relevant web content' vaguely), doesn't address potential parameter conflicts or dependencies, and provides minimal guidance on when to use which parameters. For such a rich parameter set with no structured output documentation, the description should do more to guide effective usage.
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 description mentions 'customizable parameters for result count, content type, and domain filtering,' which adds some context beyond the schema. However, with 100% schema description coverage, the schema already comprehensively documents all 15 parameters. The description provides only high-level categorization without adding meaningful semantic details about specific parameters or their interactions.
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 performs web search using Tavily's AI engine and returns results, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'search' or 'tavily-crawl', only mentioning it's 'ideal for gathering current information, news, and detailed web content analysis' without sibling comparison.
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 provides implied usage guidance by stating the tool is 'ideal for gathering current information, news, and detailed web content analysis,' which suggests appropriate contexts. However, it doesn't explicitly state when to use this tool versus the sibling tools like 'search', 'tavily-crawl', 'tavily-extract', or 'tavily-map', nor does it provide any exclusion criteria or alternative recommendations.
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 effectively describes the crawling behavior ('expands like a tree', 'following internal links'), scope control ('how deep and wide it goes'), and focus guidance. However, it doesn't mention important behavioral aspects like rate limits, authentication requirements, error handling, or what the output format looks like.
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 perfectly sized at three sentences, each earning its place. It's front-loaded with the core purpose, followed by expansion behavior, and ending with control capabilities. Zero wasted words or redundant information.
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 of a 10-parameter web crawling tool with no annotations and no output schema, the description provides adequate high-level context but lacks details about output format, error conditions, performance characteristics, or specific use cases. For a tool this complex, more behavioral and output information would be helpful despite the excellent schema coverage.
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 10 parameters thoroughly. The description adds some high-level context about controlling depth, breadth, and focusing on specific sections, which aligns with parameters like max_depth, max_breadth, and categories/select_paths. However, it doesn't provide additional semantic meaning beyond what's already in the comprehensive 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's purpose with specific verbs ('initiates a structured web crawl', 'expands like a tree', 'following internal links') and identifies the resource ('starting from a specified base URL'). It distinguishes this crawl tool from sibling tools like 'search' or 'extract' by emphasizing its tree-based expansion approach.
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 about when to use this tool: for structured crawling starting from a base URL with tree-like expansion. It mentions controlling depth, breadth, and focusing on specific site sections. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools.
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?
No annotations are provided, so the description carries the full burden. It describes the tool as 'powerful' and for 'discovering and analyzing,' which implies it performs read-only operations, but doesn't specify behavioral traits like rate limits, authentication needs, or potential impacts on target websites. The description adds value by explaining the mapping purpose but lacks detailed behavioral disclosure.
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: it starts with the core purpose, then elaborates on use cases. Every sentence earns its place by adding value without redundancy, making it efficient and well-structured.
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 (9 parameters, no annotations, no output schema), the description is somewhat complete but has gaps. It explains the tool's purpose and use cases well, but without annotations or output schema, it doesn't cover behavioral aspects or return values, leaving the agent to infer details from the schema alone.
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 schema already documents all 9 parameters thoroughly. The description doesn't add specific parameter semantics beyond what the schema provides, such as explaining how 'categories' interact with mapping or the implications of 'max_depth.' Baseline 3 is appropriate since the schema does the heavy lifting.
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: 'creates a structured map of website URLs' with specific verbs like 'discover and analyze site structure, content organization, and navigation paths.' It distinguishes from siblings like 'search' or 'extract' by focusing on mapping and structural analysis rather than general search or content extraction.
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: 'Perfect for site audits, content discovery, and understanding website architecture.' It doesn't explicitly mention when not to use it or name alternatives among siblings, but the context strongly implies it's for structural mapping rather than other tasks like searching or crawling without mapping.
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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