Google Search Console Audit MCP

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v3.0.1
Disambiguation5/5
Each tool targets a distinct domain: audit report generation, bulk indexing check, single URL inspection, performance summary, detailed query data, site details, sitemap listing, and site list. No overlap in functionality.
Naming Consistency5/5
All tools follow the consistent 'gsc_' prefix plus descriptive snake_case noun or noun_verb pattern (e.g., gsc_audit, gsc_indexing_issues). No mixing of styles.
Tool Count5/5
8 tools is well-scoped for a Google Search Console audit server, covering audit, indexing, inspection, performance, queries, site details, sitemaps, and site listing without redundancy.
Completeness5/5
The tool set covers the full lifecycle of Search Console auditing: generating comprehensive reports, inspecting individual URLs, checking indexing, retrieving performance data, querying details, managing sitemaps, and listing sites. No obvious missing functionality.
Average 4.1/5 across 8 of 8 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 24 commits in the last 12 weeks
- Last stable release on August 22, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 provided. Description lacks behavioral details such as data aggregation, time zone handling, quota limits, or whether it returns cached or real-time data.
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?
Short, front-loaded with purpose, and uses clear parameter documentation. No superfluous 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?
Output schema exists, reducing need to describe returns. However, no annotations and lack of usage context make it incomplete for a complex tool; leaves agent uninformed about behavioral nuances.
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 coverage is 0%, but description provides format examples for site_url (https:// or sc-domain:) and date format (YYYY-MM-DD). This adds basic meaning, though could be more explicit about allowed patterns.
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 provides a 'Summary of site performance' with specific metrics (clicks, impressions, avg CTR, avg position). The verb 'overview' and metric list distinguish it from more detailed sibling tools like gsc_query, though not explicitly differentiated.
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 overview versus other tools. Does not mention prerequisites, use cases, or alternatives among siblings such as gsc_query for detailed queries.
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, and the description does not disclose behavioral traits such as rate limits, authentication requirements, or side effects. It only describes parameters without context on tool 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 structured as a clear list of parameter definitions with defaults and examples. It is mostly concise, though the 'Args:' section could be slightly more compact.
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?
An output schema exists, so return value explanation is not required. However, with no annotations, the description lacks behavioral context (e.g., pagination, response format). Adequate for a straightforward query tool but could be more 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%, so the description carries full burden. It adds meaningful context: site_url format examples, date format, dimensions list, row_limit default and max. This significantly aids in correct parameter 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?
Description states it provides a 'Search Console performance report (top queries and pages with metrics)', making the purpose clear. However, it does not differentiate from sibling tools like gsc_audit or gsc_performance_overview, which may also query performance 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. The description only explains what the tool does, not when to choose it over others such as gsc_audit or gsc_performance_overview.
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, and the description does not disclose behavioral traits such as being read-only, required permissions, rate limits, or response size. It only states the basic action.
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 very concise, consisting of two short sentences with the main action front-loaded. No redundant information.
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 has only one parameter and an output schema, the description is adequately complete: it explains the purpose and parameter. The output schema can handle details of return values.
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 for the 'site_url' parameter, including example formats ('https://example.com/' or 'sc-domain:example.com'), which is not present in the input schema (0% coverage).
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 action ('Get details') and the resource ('a specific site in Google Search Console'), distinguishing it from sibling tools like gsc_sites (likely listing sites) or gsc_audit (auditing).
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?
No explicit guidance on when to use this tool versus alternatives. Usage is implied: you need a site URL. No mention of when not to use or prerequisites beyond the parameter.
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 fails to disclose behavioral traits like authentication requirements, potential errors, or limitations. The term 'verified' could use more context.
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, front-loaded sentence with no wasted words. Every part earns its place.
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 simple list tool with an output schema, the description is mostly adequate. It could briefly mention the return type (e.g., list of site URLs), but the low complexity justifies a score of 4.
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 input schema has no parameters, and the description correctly reflects this. For a tool with 0 parameters, the baseline score is 4, and the description adds no unnecessary parameter info.
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 verb 'List' and the resource 'all verified sites in Google Search Console.' It effectively distinguishes itself from siblings like gsc_site_details, which focuses on a single site.
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 when needing a list of verified sites but provides no explicit guidance on when to use versus alternatives, such as gsc_site_details for individual site details.
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?
No annotations provided, so description carries full burden. Details that it runs multiple queries, detects issues, and generates an HTML report. Does not explicitly state it is non-destructive, but the read-only nature is implied by the generation of a report. Customization via branding.json is mentioned.
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?
Description is well-structured with a summary paragraph, an important note, and parameter list. Slightly verbose but each sentence adds value, such as explaining the report's features and customization.
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 the tool (multiple queries, HTML report, customization), the description is comprehensive. It covers input constraints, behavior, output (via output schema existence), and user guidance. No obvious gaps.
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?
Despite 0% schema coverage, the description provides clear parameter semantics for all 5 parameters via an 'Args' section, including default for output_dir. Adds meaning beyond the input schema's type-only information.
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?
Description states 'Generate a complete HTML SEO audit report for a Search Console property' with specific verbs and resource. Lists multiple queries and distinguishes from sibling tools like gsc_performance_overview and gsc_query by aggregating multiple analyses into one report.
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?
Explicitly instructs to ask user for date range if not specified, preventing default assumptions. Provides clear context for when to use (full audit), but does not explicitly mention alternatives or when not to use.
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, description takes full burden. It explains the broad output structure (indexStatusResult, mobileUsabilityResult, etc.) and mentions quota constraints. Slightly lacks detail on authentication or error conditions, but overall transparent about 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?
Description is well-structured: overview sentence, then bullet-style breakdown of return fields, followed by usage guidelines and quota note. Front-loaded with purpose, no redundant sentences. Appropriate length for the complexity.
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 an output schema exists (context: 'Has output schema: true'), description provides additional context on return sub-objects, parameter formats, and usage. Could include error handling or rate limit retry suggestions, but overall complete for a URL inspection tool.
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 has 0% coverage (no descriptions), but description adds significant detail: site_url format (sc-domain: vs URL-prefix), requirement for authenticated access, page_url being fully qualified under site_url. This adds meaning beyond the schema's type-only fields.
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?
Description clearly states 'Full URL Inspection for a single page' with specific details (index, mobile, rich results, AMP). Distinguishes itself from sibling gsc_indexing_issues by noting it provides 'deep detail for one URL' vs 'quick indexed-or-not check across many URLs.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit usage recommendation: 'Use this when you need deep detail for one URL; for a quick indexed-or-not check across many URLs use gsc_indexing_issues instead.' Also provides quota limits (60 req/min, 2000/day), aiding agent decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the description fully discloses behavioral traits: rate limits (60 req/min, 2000 req/day), partial failure handling (error field for failed URLs while rest succeed), and return field semantics (verdict, coverageState, etc.).
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 fairly long but well-structured: a concise summary, then usage guidance, quota note, args, and returns. Every sentence adds value; however, the return field enumeration could be slightly more compact without losing clarity.
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?
Despite having an output schema (unseen), the description independently details the return format and error behavior, making it self-contained. It covers parameter semantics, quota limits, and sibling differentiation, leaving no critical gaps for the agent.
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%, but the description adds substantial meaning: it explains the 'site_url' parameter format ('sc-domain:' vs 'https://' prefix) and specifies that 'pages' must be fully qualified URLs belonging to the property. This goes well 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 it performs a 'bulk indexing check' for multiple pages via the URL Inspection API, and explicitly distinguishes from the sibling tool 'gsc_inspect_url' which handles single-page inspection. This gives a specific verb-resource pairing with clear differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use (verify indexing status of a batch of URLs) and when-not-to-use (for full single-page payload, use gsc_inspect_url). It also includes quota notes to guide reasonable usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explicitly states 'this tool is read-only' and explains scope limitations. The output structure is fully documented (path, lastSubmitted, lastDownloaded, etc.), leaving no ambiguity about the tool's behavior or return format.
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 with a clear verb-first sentence, a usage block, a note about read-only, and labeled Args/Returns sections. Every sentence earns its place, and there is no fluff. Despite the detail, it remains concise.
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 tool has one parameter and an output schema, the description goes beyond necessity by detailing the return fields and usage context. It provides complete guidance for an agent to understand the tool's purpose, parameters, behavior, and limitations, making it fully contextual.
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 a single required parameter 'site_url' with no description (0% schema coverage). The description compensates fully by explaining the format: 'Domain property format "sc-domain:example.com" or URL-prefix format "https://example.com/".' This adds critical meaning beyond the 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 begins with a specific verb and resource: 'List all sitemaps submitted for a property, with errors/warnings and timestamps.' This clearly distinguishes the tool from its siblings (e.g., gsc_performance_overview, gsc_inspect_url) by focusing on sitemap listing and audit details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear when-to-use scenarios: 'Use this to check which sitemaps Google knows about... Useful for auditing sitemap hygiene before crawl-budget work or to confirm a newly submitted sitemap was picked up.' It also explicitly states when not to use it: 'Submitting or deleting sitemaps requires the webmasters (read-write) scope, which this server intentionally does not request.' This offers excellent guidance against misuse.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![google-search-console-mcp MCP server](https://glama.ai/mcp/servers/acamolese/google-search-console-mcp/badges/card.svg)](https://glama.ai/mcp/servers/acamolese/google-search-console-mcp)

Score Badge

Copy to your README.md:

[![google-search-console-mcp MCP server](https://glama.ai/mcp/servers/acamolese/google-search-console-mcp/badges/score.svg)](https://glama.ai/mcp/servers/acamolese/google-search-console-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/acamolese/google-search-console-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server