Pornhub MCP Server

Server Quality Checklist

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Latest release: v2.0.0
Disambiguation4/5
Most tools have distinct purposes focused on different types of trend analysis (categories, performers, platforms, historical, general search, and related searches). However, there is some overlap between 'search_trends' and the more specific comparison tools, as 'search_trends' can analyze similar keywords but with a broader scope, which could cause mild confusion.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with clear verb_noun structures (e.g., analyze_category_trends, compare_performers, historical_analysis). The naming is predictable and readable throughout the set, enhancing usability.
Tool Count5/5
With 6 tools, the server is well-scoped for analyzing adult content trends via Google Trends. Each tool serves a specific analytical function, and the count is neither too sparse nor bloated, fitting the domain effectively.
Completeness4/5
The tool set covers key aspects of trend analysis for adult content, including comparisons, historical data, and related searches. A minor gap is the lack of tools for real-time or predictive analytics, but the existing tools allow agents to handle most common analytical workflows in this domain.
Average 3.3/5 across 6 of 6 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 mentions the tool analyzes trends and returns 'popularity and growth' data, but lacks critical details: it doesn't specify data sources beyond 'Google search trends', rate limits, authentication requirements, error handling, or whether it's read-only (implied but not stated). For a tool with no annotation coverage, this leaves significant gaps in understanding its operational behavior.
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 well-structured and appropriately sized. It front-loads the purpose, uses clear sections for arguments and returns, and includes an example without unnecessary verbosity. Every sentence adds value, though the formatting with bullet-like sections could be slightly more polished for maximum clarity.
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 moderate complexity (3 parameters, no annotations, but with an output schema), the description is partially complete. It covers the basic purpose and parameters but lacks usage guidelines, detailed behavioral context, and integration with sibling tools. The presence of an output schema means return values don't need explanation, but overall, it's adequate yet has clear gaps for effective agent use.
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 adds meaningful context beyond the input schema, which has 0% description coverage. It explains that 'categories' are 'keywords' with a 'max 5' limit and provides an example, clarifies defaults for 'timeframe' and 'region', and notes the output includes 'popularity and growth'. However, it doesn't detail parameter formats (e.g., region code standards) or constraints beyond the example, leaving some ambiguity.
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: 'Analyze Google search trends for adult content categories.' It specifies the verb ('analyze'), resource ('Google search trends'), and domain ('adult content categories'), making it distinct from general trend analysis tools. However, it doesn't explicitly differentiate from sibling tools like 'search_trends' or 'historical_analysis' beyond the adult content focus.
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. It doesn't mention sibling tools like 'search_trends' or 'historical_analysis', nor does it specify prerequisites, exclusions, or appropriate contexts beyond the implied adult content domain. Users must infer usage from the purpose alone.
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 mentions the tool 'compares' trends and returns a 'comparative analysis,' but doesn't disclose critical traits such as data source limitations, rate limits, authentication needs, or whether it's a read-only operation. For a tool with no annotations, this leaves significant gaps in understanding its behavior and constraints.
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 purpose followed by parameter details and return information. Each sentence adds value without redundancy. However, the parameter explanations could be slightly more concise, and the structure includes a separate 'Args' and 'Returns' section, which is clear but not maximally efficient.
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 (3 parameters, no annotations, but has an output schema), the description is moderately complete. It covers the purpose and parameters well, and the output schema handles return values, so the description doesn't need to explain those. However, it lacks behavioral context and usage guidelines, making it incomplete for optimal agent understanding without additional structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains each parameter: 'platform_names' as a list of names with a max of 5 and an example, 'timeframe' as a time period with a default, and 'region' as a region code with a default. This compensates well for the low schema coverage, though it doesn't detail format specifics like valid region codes or timeframe syntax.
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: 'Compare Google search trends between different adult platforms.' It specifies the verb ('compare'), resource ('Google search trends'), and domain ('adult platforms'). However, it doesn't explicitly differentiate from sibling tools like 'analyze_category_trends' or 'compare_performers,' which might also involve comparisons in similar domains.
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. It doesn't mention sibling tools like 'compare_performers' or 'search_trends,' nor does it specify scenarios where this tool is preferred or excluded. Usage is implied by the purpose but lacks explicit context or exclusions.
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 mentions the tool returns 'Multi-year trend analysis showing growth, peaks, and patterns,' which gives some output context, but it lacks critical details: it doesn't specify data sources, rate limits, authentication needs, error conditions, or whether it's a read-only operation. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
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 well-structured and appropriately sized. It front-loads the purpose in the first sentence, then lists parameters and returns in clear sections. Every sentence adds value, with no redundant or vague phrasing. Minor improvements could include briefer examples or combining some details, but overall it's efficient.
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 moderate complexity (4 parameters, no annotations, but has an output schema), the description is partially complete. It covers the purpose and parameters well, and the output schema likely handles return values, reducing the need for detailed output explanation. However, it lacks behavioral context (e.g., data sources, limitations) and usage guidelines, making it adequate but with clear gaps for an agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics beyond the schema: it explains that 'keyword' can be a performer, platform, or category with examples ('Lana Rhoades', 'onlyfans', 'milf'), specifies constraints for 'start_year' ('2004 or later'), and clarifies defaults for 'end_year' and 'region'. This effectively documents all parameters, though it could provide more detail on format (e.g., region code standards).
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: 'Analyze historical trends for a keyword across multiple years.' It specifies the verb ('analyze'), resource ('historical trends'), and scope ('keyword across multiple years'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate this from sibling tools like 'analyze_category_trends' or 'compare_performers', 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 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. It doesn't mention sibling tools like 'analyze_category_trends' or 'compare_performers', nor does it specify prerequisites, exclusions, or typical use cases. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.
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 mentions the tool 'finds' data, implying a read-only operation, but doesn't specify aspects like rate limits, authentication needs, data sources, or error handling. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 well-structured and front-loaded, starting with a clear purpose sentence. It efficiently lists args and returns in bullet-like sections without unnecessary details. Every sentence adds value, making it appropriately sized and easy to parse.
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 moderate complexity (3 parameters, no annotations, but with an output schema), the description is somewhat complete. It covers the purpose and parameters but lacks behavioral details and usage guidelines. The output schema exists, so the description doesn't need to explain return values, but overall gaps in transparency and guidelines reduce completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context beyond the input schema, which has 0% description coverage. It explains 'base_keyword' with examples ('pornhub', 'onlyfans'), clarifies 'timeframe' as a time period with a default, and defines 'region' as a region code with a default. This compensates well for the schema's lack of descriptions, though it doesn't detail format constraints (e.g., region code standards).
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: 'Find trending and related searches for a keyword.' It specifies the verb ('find') and resource ('trending and related searches'), making the function evident. However, it doesn't explicitly differentiate from sibling tools like 'search_trends' or 'historical_analysis,' which might offer overlapping functionality, preventing a perfect score.
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. It mentions parameters like 'base_keyword' with examples but doesn't clarify scenarios where this tool is preferred over siblings such as 'analyze_category_trends' or 'compare_performers.' This lack of context leaves usage ambiguous.
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 the tool returns a 'comparative analysis showing which performer has higher search interest,' which gives some output context. However, it lacks details on rate limits, authentication needs, data freshness, or error handling. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
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 efficiently structured with clear sections: purpose statement, parameter explanations with examples, and return value description. Every sentence adds value without redundancy. It's appropriately sized and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no annotations, but with an output schema), the description is reasonably complete. It covers parameter semantics thoroughly, states the purpose clearly, and mentions the return value. The output schema likely details the 'comparative analysis,' so the description doesn't need to explain return values further. Minor gaps include lack of behavioral context and usage guidelines.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains each parameter: 'performer_names' as a list with a max of 5 and an example, 'timeframe' with default and format examples, and 'region' with default. This compensates well for the schema's lack of descriptions, though it doesn't cover all possible edge cases.
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: 'Compare Google search trends between different performers.' This specifies the verb ('compare'), resource ('Google search trends'), and target ('performers'). It distinguishes from siblings like 'analyze_category_trends' or 'compare_platforms' by focusing specifically on performers, though it doesn't explicitly contrast with them.
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. While it implicitly suggests use for comparing performer search trends, it doesn't mention when to choose this over siblings like 'search_trends' or 'historical_analysis', nor does it specify prerequisites or exclusions. Usage context is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by specifying parameter constraints (max 5 keywords, timeframe options back to 2004, region codes). It also describes the return format ('interest over time, regional data, and related queries'), though it doesn't mention rate limits or authentication needs.
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 well-structured with clear sections (Args, Returns) and uses bullet points efficiently. It's appropriately sized but could be slightly more concise by integrating some details into the opening sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a 3-parameter tool with no annotations, the description provides complete context: clear purpose, detailed parameter semantics, and output description. With an output schema present, it doesn't need to explain return values in depth, making this comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate fully. It provides detailed semantics for all 3 parameters: keywords with examples and max limit, timeframe with options and date range availability, and region with codes and defaults. This adds significant value beyond the bare schema.
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 a specific verb ('Search Google Trends') and resource ('for any keywords'), listing examples of keyword types. It distinguishes from siblings by focusing on general keyword search rather than specific comparisons or historical analysis.
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 for analyzing search trends with keywords, but doesn't explicitly state when to use this tool versus alternatives like 'compare_performers' or 'historical_analysis'. No exclusions or specific contexts are provided beyond the general purpose.
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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