Plausible Analytics MCP Server
Server Quality Checklist
Latest release: v0.1.1
- Disambiguation5/5
Each tool has a clearly distinct purpose: aggregate overview, breakdown by dimension, real-time visitors, time series trends, site listing, and a raw query for advanced needs. No two tools are likely to be confused.
Naming Consistency4/5Most tools follow a 'get-<resource>' pattern, but 'list-sites' deviates to 'list' and 'query' uses a bare verb. The pattern is readable and largely consistent, with minor exceptions.
Tool Count5/5Six tools cover the core analytics query needs (aggregate, breakdown, timeseries, real-time, site listing, raw query) without bloat. The count is well-scoped for the domain.
Completeness4/5The tool set covers the main analytics operations: summary, breakdown, trends, real-time, site list, and a raw query for custom requests. Missing explicit support for custom properties or goals, but the raw query compensates, so only minor gaps.
Average 3.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- Last stable release on
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- No high-severity vulnerability alerts
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This repository is licensed under MIT License.
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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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must cover behavioral traits. It states it returns a real-time count, which implies non-destructive read. However, it omits details like error handling (e.g., invalid site_id), rate limits, or whether the count is approximate. Basic transparency but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no wasted words. It is front-loaded with the action and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one param, no output schema), but the description does not specify the return format (e.g., integer, object). No mention of edge cases or performance. Adequate but could be more complete given the lack of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the parameter site_id has its own description. The tool description does not add new meaning beyond the schema, so baseline 3 applies. No extra constraints or context provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'number of people currently on a site', adding 'real-time' to specify immediacy. This distinguishes it from sibling tools like get-aggregate-stats (likely historical) or get-breakdown.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or comparison with sibling tools like get-timeseries or query.
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 provided. Description does not disclose read-only nature, potential limits, or output format. Minimal information beyond core purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no fluff, front-loaded with action and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple parameterless tool, but lacks details on response structure or pagination. Marginal given absence of output schema or annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so description cannot add parameter meaning. Baseline score of 4 applies as there is nothing to add.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the action (list) and resource (sites) with scope (all you have access to). Distinct from sibling tools that deal with analytics data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage when needing to see accessible sites. No explicit when-to-use or alternatives, but siblings are obviously different.
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 must fully disclose behavioral traits. It only states the basic function and usage, with no mention of read-only nature, side effects, rate limits, or authentication needs. As a 'get' tool, it's likely safe, but transparency is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no fluff. The first sentence front-loads the verb and resource, the second gives usage guidance. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description could explain return values, but the tool is straightforward and the schema covers parameters. The filters parameter is complex but schema describes it. The description is adequate for a summary tool, though adding output structure would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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. The description adds no extra meaning beyond listing example metrics; it does not clarify filter syntax or default behaviors beyond what the schema provides. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get', resource 'aggregate stats', and scope 'over a time period', mentioning example metrics like visitors, pageviews, bounce rate. It also distinguishes from siblings by saying 'Use this for summary/overview questions', which contrasts with breakdown or timeseries tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this for summary/overview questions', providing a clear usage context. However, it does not explicitly state when not to use it or mention alternatives like 'get-breakdown' for detailed breakdowns, though the sibling names imply differentiation.
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 states the tool returns data points but does not disclose any behavioral traits like rate limits, authentication needs, or side effects. For a read-only tool, this is minimal but acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core action and purpose. No unnecessary words, every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema is provided. The description says it returns data points broken by interval, but does not specify the structure (e.g., timestamps, value fields). For a tool that supports trend charts, this leaves ambiguity. Adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents all parameters. The description mentions 'time interval' which maps to the interval parameter but does not add significant meaning beyond the schema. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves traffic trends over time, broken by interval, and explicitly recommends it for trend analysis and charts, distinguishing it from sibling tools like get-aggregate-stats or get-breakdown.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It says 'Use this for trend analysis and charts,' giving a clear use case. However, it does not explicitly mention when not to use it or provide alternatives, which would be helpful given sibling tools exist.
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 implies a read-only operation (breaking down stats) with no side effects, but does not explicitly state safety or permission requirements. The description adequately conveys the tool is non-destructive but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally concise: two sentences, no redundant phrases, and front-loaded with the core action. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and the absence of an output schema, the description is sufficiently complete. It defines the purpose, inputs, and typical use cases. Subtle omissions (e.g., return structure) are minor; the tool is easily understood.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all parameters. The description adds value by naming common dimensions and use cases (e.g., 'event:page' for top pages), which helps users select appropriate parameters beyond what the schema alone provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool breaks down stats by a dimension, giving concrete examples like page, source, country, device, browser, OS, UTM tags. It explicitly lists use cases ('top pages', 'traffic sources', 'visitor countries'), effectively distinguishing it from siblings like get-aggregate-stats or get-timeseries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use the tool ('Use this for...'), with practical examples. However, it does not mention when not to use or explicitly name alternative tools, though sibling names imply different purposes.
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 provided, so description carries full burden. It implies read-only query but doesn't explicitly state behavioral constraints like idempotency, rate limits, or auth needs. Adequate but not detailed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, no redundant words; efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema and complex parameters, the description provides enough context for an agent to decide when to use this tool vs siblings, and covers the core purpose. Lacks return format details but acceptable for a raw API tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions cover 100% of parameters, so description adds no extra parameter-level info beyond mentioning example use cases like filters and dimensions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it executes a raw Plausible API v2 query for advanced use cases not covered by siblings, with specific examples like custom property breakdowns and behavioral filters.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use (advanced queries other tools don't cover) and implies alternatives by naming sibling tools, such as get-breakdown and get-timeseries.
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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