Google Analytics MCP Server

Server Quality Checklist

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Latest release: v0.2.2
Disambiguation5/5
Each tool targets a distinct aspect of Google Analytics: account summaries, custom dimensions/metrics, property details, ads links, annotations, realtime reporting, and historical reporting. No overlap in purpose.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern in snake_case (get_*, list_*, run_*). No mixed conventions or ambiguous names.
Tool Count4/5
7 tools is appropriate for a Google Analytics MCP server, covering essential metadata retrieval and reporting. Slightly on the lower side but still reasonable.
Completeness4/5
The tool set covers account/property info, custom fields, ads links, annotations, and both realtime and historical reporting. Missing administrative features like data streams or user management, but core analytics is well-covered.
Average 3.5/5 across 7 of 7 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
- Last stable release on March 21, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
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, and the description does not disclose behavioral traits such as read-only nature, required permissions, or potential errors. The description only states it returns details, without further behavioral context.
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 (two sentences) and front-loaded with the main purpose. However, it could be slightly more structured for 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 lack of output schema, the description does not explain what 'details' are returned. For a simple tool with one parameter, it is adequate but incomplete regarding return value and behavior.
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 value beyond the input schema by specifying accepted formats for property_id (number or 'properties/' string). The schema only lists types, so this clarifies usage.
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 it returns details about a property, which distinguishes it from sibling tools like run_report or list_google_ads_links. However, it could be more specific about what details are included.
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. The description lacks context on prerequisites or scenarios where this tool is preferable.
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 disclose behavioral traits. It only says 'Returns annotations' without stating read-only nature, side effects, or authentication needs. The lack of output schema further reduces 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 well-structured with a purpose sentence, paragraph explanation, and Args section. It is slightly verbose but avoids rambling; each part adds context.
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?
For a simple list tool with one parameter, the description covers purpose and parameter format but lacks return structure or pagination details. Given no output schema, some completeness is missing.
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?
Despite 0% schema description coverage, the tool description explains property_id accepted formats (number or 'properties/'+number) in detail, significantly adding value beyond the schema's type-only definition.
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 it 'Returns annotations for a property' and explains what annotations are, but does not explicitly differentiate from sibling tools, which are distinct enough.
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 context on when annotations are used (e.g., service releases, marketing campaigns), giving implicit guidance, but lacks explicit 'when to use' vs. 'alternatives' statements.
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?
Without annotations, the description should disclose behavioral traits like read-only nature, rate limits, or authentication. It only mentions return of custom dimensions and metrics with no details on response format or side effects.
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 short and structured with a purpose statement and an Args section. It is front-loaded and succinct, though it could be slightly more informative without adding verbosity.
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?
While the parameter explanation is sufficient, the description lacks details about the output structure (e.g., field names, array vs object). Given no output schema, the agent might need more context to handle the response correctly.
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 meaning to the property_id parameter by specifying accepted formats (number or 'properties/' prefix), which is beyond the schema's bare anyOf type. However, only one parameter exists, so no gap.
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 returns custom dimensions and metrics for a property, using a specific verb and resource. It distinctively separates from sibling tools like get_account_summaries or run_report.
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 is provided on when to use this tool versus alternatives like get_property_details or list_google_ads_links. It lacks any context about typical use cases or when not to use it.
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?
The description extensively covers parameter behavior, especially filter independence and limitations, and includes notes on pagination and snake_case naming. However, it lacks information on response structure, error handling, and authentication, leaving gaps for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with headings and examples, but it is overly long and repetitive (e.g., filter notes repeated twice). Conciseness is compromised despite good organization.
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?
The description thoroughly covers parameter usage and filter limitations, but does not describe the response format or provide usage context relative to sibling tools. Missing output schema information further reduces completeness.
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?
With 0% schema coverage, the description compensates exceptionally well by providing detailed hints, examples, and links for each parameter including date_ranges, filters, order_bys, and more. This adds significant meaning beyond the bare schema types.
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 it runs a Google Analytics Data API report, specifying the primary function. However, it does not explicitly distinguish from the sibling tool 'run_realtime_report', relying on the name difference alone.
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 is provided on when to use this tool versus the sibling 'run_realtime_report' or other alternatives. The description focuses on parameter details rather than use case 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?
With no annotations, description implies read-only retrieval. However, it lacks details on potential size of returned data or any side effects. Basic behavioral context is present.
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?
Single sentence is concise and front-loaded. Every word adds value without redundancy.
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?
For a parameterless tool with no output schema, description provides sufficient context. Could mention it returns a list or summary, but current wording is adequate.
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?
No parameters exist, so baseline score of 4 applies. Description adds no parameter-level info, which is acceptable given zero parameters.
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?
Description clearly states it retrieves information about accounts and properties. Verb is 'retrieves' and resource is specific, distinguishing it from sibling tools like get_property_details which likely target a single property.
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 siblings. For example, it doesn't indicate that this is for account-level summaries versus property-level details.
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. It discloses the tool is a read operation (returns a list) but lacks details on pagination, response structure, or potential side effects. It adds some value beyond the name but is not rich.
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 extremely concise: three sentences with a clear front-loaded purpose and a bullet-like format for the parameter explanation. Every sentence adds value, and there is no wasted text.
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 output schema and no annotations, the description covers the input parameter and basic purpose. However, it omits details about the return format, pagination, or error conditions. For a simple list tool it is adequate but not fully complete.
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%, but the description explains the accepted formats for property_id (number or 'properties/' prefix), adding meaning beyond the schema's type constraints. This compensates for the lack of 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 it returns a list of links to Google Ads accounts for a property, using a specific verb ('Returns') and resource ('links to Google Ads accounts'). This distinguishes it from sibling tools like get_account_summaries or get_property_details, which serve different purposes.
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, nor does it mention any prerequisites or excluded scenarios. The usage is only implied by the purpose, without explicit when-to-use or when-not-to-use context.
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, the description carries the full burden. It discloses behaviors such as pagination (limit/offset), independent application of dimension and metric filters with complex limitations, and notes that realtime reports cannot use custom metrics. It also provides quota management guidance. While it doesn't explicitly state read-only behavior, the nature of a report implies it. Overall, it is transparent about key behavioral aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very long, but it is well-structured with sections, hints, and examples. However, the 'Notes' on filter combinations are repeated verbatim for both dimension_filter and metric_filter, which is redundant. Some trimming could improve conciseness while maintaining clarity.
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 complexity (9 parameters, nested objects, no output schema), the description is comprehensive. It covers parameter constraints, usage examples, and complex limitations. Missing are explicit error handling details and output format, but these are partially addressed by links to the API documentation. Overall, it is thorough and helps the agent use the tool correctly.
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?
The input schema has 0% description coverage, so the description fully compensates. It explains each parameter in detail, including accepted formats for property_id, allowed types for dimensions/metrics (with links to schemas), structure of filter expressions and order_bys with examples, and constraints for limit/offset. This goes far 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 runs a Google Analytics Data API realtime report. It specifies the resource (realtime report) and the action (runs), and the title reinforces 'realtime' to distinguish from the sibling 'run_report' which implies standard reports. The purpose is unambiguous.
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 rich context for using the tool, including parameter constraints and examples. However, it does not explicitly state when to use this tool versus the sibling 'run_report' or when not to use it. The usage is implied by the 'realtime' designation, but no direct comparison or exclusion criteria are given.
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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