Clicky MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting different analytics dimensions: domain visitors, page traffic, top pages, total visitors, and traffic sources. There is no overlap in functionality, making it easy for an agent to select the correct tool without confusion.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with 'get_' prefix and descriptive nouns (e.g., get_domain_visitors, get_page_traffic). This uniformity enhances readability and predictability across the toolset.
Tool Count5/5
With 5 tools, the server is well-scoped for analytics retrieval, covering key metrics like visitors, pages, and traffic sources. Each tool earns its place without being overly sparse or bloated, fitting typical use cases effectively.
Completeness4/5
The toolset provides comprehensive read-only coverage for analytics data, including filtering and segmentation options. A minor gap exists in lacking write or configuration tools (e.g., setting up analytics), but this is reasonable for a data retrieval-focused server, and agents can work around this limitation.
Average 3/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed in the last 6 months
- 4 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 full burden but only states it 'gets' data, implying a read-only operation without confirming safety or permissions. It mentions optional segmentation but doesn't disclose behavioral traits like rate limits, authentication needs, or what happens if parameters are invalid. This leaves significant gaps for a tool with 5 parameters.
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 ('Get visitors filtered by domain') and adds a useful detail ('with optional segmentation data'). There is zero waste, and every word earns its place.
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 no annotations and no output schema, the description is incomplete for a tool with 5 parameters. It doesn't explain return values, error conditions, or behavioral constraints like rate limits or authentication requirements. For a data retrieval tool in analytics, more context is needed to 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?
Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description adds minimal value beyond the schema by mentioning 'optional segmentation data', which loosely relates to the 'segments' parameter but doesn't provide additional meaning or context. 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 verb ('Get') and resource ('visitors filtered by domain from Clicky analytics'), specifying the filtering mechanism. It distinguishes from siblings like 'get_total_visitors' by mentioning domain filtering, but doesn't explicitly contrast with 'get_traffic_sources' or 'get_page_traffic' which might also involve visitor data.
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 on when to use this tool versus alternatives like 'get_total_visitors' or 'get_traffic_sources' is provided. The description mentions optional segmentation data but doesn't clarify when segmentation is beneficial or when other tools might be more appropriate.
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 states the tool retrieves traffic data but doesn't mention any behavioral traits such as rate limits, authentication requirements, data freshness, or what the output format looks like (e.g., metrics like pageviews, sessions). This is a significant gap for a tool with no annotation coverage.
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 directly states the tool's purpose without unnecessary words. It's front-loaded and every part earns its place, making it easy to parse quickly.
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 a traffic data tool with no annotations and no output schema, the description is incomplete. It lacks details on what traffic data is returned (e.g., metrics, format), behavioral aspects like permissions or limits, and differentiation from sibling tools, leaving the agent with insufficient context for effective 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 minimal value beyond the input schema, which has 100% coverage with clear descriptions for all three parameters (url, start_date, end_date). It implies URL-based filtering but doesn't provide additional context like URL format constraints or date range implications. 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 with a specific verb ('Get') and resource ('traffic data for a specific page'), and it specifies the filtering mechanism ('by filtering with its URL'). However, it doesn't explicitly differentiate this tool from its siblings like 'get_top_pages' or 'get_total_visitors', which likely provide different scopes or aggregations of traffic data.
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 its siblings (e.g., 'get_domain_visitors', 'get_top_pages', 'get_total_visitors', 'get_traffic_sources'). It mentions filtering by URL but doesn't explain alternative scenarios or exclusions, leaving the agent to infer usage from tool names 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions retrieving 'top pages' but doesn't specify what 'top' means (e.g., by views, visits, or other metrics), how results are ordered, if there's pagination, rate limits, authentication needs, or error handling. This leaves significant gaps in understanding the tool's behavior 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words or fluff. It's front-loaded with the core action and resource, making it easy to parse quickly, which is ideal for conciseness in tool definitions.
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 lack of annotations and output schema, and the description's minimal detail, it's incomplete for a tool that likely returns complex analytics data. It doesn't explain what 'top pages' entails, the structure of the output, or behavioral aspects like data freshness or limitations, leaving the agent with insufficient context to use the tool effectively beyond basic parameter input.
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%, with clear documentation for each parameter (start_date, end_date, limit) including formats, constraints, and defaults. The description adds no additional semantic details beyond implying date-range usage, so it meets the baseline of 3 where the schema handles most of the parameter explanation without extra value from the description.
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 ('Get') and resource ('top pages') with the source ('from Clicky analytics') and scope ('for a date range'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_page_traffic' or 'get_total_visitors', which might also involve date ranges or page-related data, leaving some ambiguity about uniqueness.
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 'get_page_traffic' or 'get_traffic_sources', nor does it mention any prerequisites, exclusions, or specific contexts. It only states what the tool does, without indicating appropriate scenarios or comparisons with siblings.
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 full burden for behavioral disclosure. It states what the tool does but doesn't describe how it behaves: no information about authentication requirements, rate limits, error handling, or what the return value looks like (e.g., is it a single number, a structured object?). For a data retrieval tool with zero annotation coverage, this leaves significant gaps in understanding 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core purpose without unnecessary words. It's appropriately sized for a simple retrieval tool and front-loads the essential information ('Get total visitors'). Every word earns its place.
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 lack of annotations and output schema, the description is incomplete for effective tool use. While the purpose is clear, it doesn't address key contextual elements: what format the result returns, whether authentication is needed, any rate limits, or how it differs from sibling tools. For a data retrieval tool in an analytics context, more operational context would be helpful.
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 input schema fully documents both parameters (start_date and end_date) with format patterns. The description adds no additional parameter semantics beyond implying date-range filtering. This meets the baseline expectation when schema does the heavy lifting, but doesn't provide extra context like date range constraints or timezone handling.
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 verb ('Get') and resource ('total visitors'), and specifies the data source ('from Clicky analytics'). It distinguishes itself from siblings by focusing on total visitor counts rather than domain-specific, page-level, or source-level metrics. However, it doesn't explicitly contrast with sibling tools like 'get_domain_visitors' which might also provide visitor counts.
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 'get_domain_visitors' or 'get_traffic_sources'. It doesn't mention prerequisites, limitations, or specific use cases. The agent must infer usage from the tool name 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 an optional filter but does not describe the return format, pagination, rate limits, authentication needs, or whether this is 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the main purpose and includes the optional filter. There is no wasted language, and it is appropriately sized for the tool's complexity.
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 incomplete for a tool with three parameters. It covers the basic purpose and optional filtering but lacks details on return values, error handling, or behavioral traits. This is adequate as a minimum but has clear gaps in providing a full context for the agent.
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 (start_date, end_date, page_url) with descriptions and formats. The description adds marginal value by mentioning the optional filter by page URL, but does not provide additional semantics beyond what the schema specifies, such as examples or constraints.
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 verb ('Get') and resource ('traffic sources breakdown from Clicky analytics'), making the purpose evident. It distinguishes from siblings by focusing on traffic sources rather than visitors, page traffic, or top pages. However, it doesn't explicitly contrast with sibling tools like 'get_domain_visitors' or 'get_page_traffic' beyond the resource type.
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 the optional filter by page URL, suggesting it can be used for overall traffic sources or per-page analysis. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_page_traffic' or 'get_domain_visitors', and does not mention prerequisites or exclusions.
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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