creator-seo-mcp
Provides tools to query Google Search Console data for SEO analysis, including striking distance keywords, page performance, content decay detection, cannibalization detection, and revenue-weighted opportunity ranking.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@creator-seo-mcpFind my best SEO opportunities this week."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
creator-seo-mcp
An MCP server for content creators that connects Google Search Console to your page content and ranks every SEO opportunity by estimated revenue, not vanity clicks.
Generic SEO tools surface raw GSC numbers. This one tells you which fix pays most, based on your actual display-ad RPM and affiliate commission rates.
What it does
Six tools, designed to work together in a full creator-SEO workflow:
Tool | Description |
| Queries ranking at positions 4-15 with real impression volume, the ranking page, and the gap to page 1 |
| Full GSC picture for one URL: clicks, impressions, CTR, position, and the queries driving it |
| Flags pages losing clicks or impressions month-over-month |
| Fetches a URL, reads title/meta/headings/body, compares against a target query, and proposes concrete edits |
| Detects queries where multiple pages compete, splitting authority |
| The headline call: combines all signals, weights by revenue, returns a single ranked action list |
Related MCP server: Google Search Console MCP Server
Install
uvx creator-seo-mcpOr with pip:
pip install creator-seo-mcpSetup
1. Google Search Console credentials
Follow docs/gsc-setup.md to:
Enable the Search Console API in Google Cloud
Create an OAuth 2.0 Desktop client
Download
credentials.json
2. Claude Desktop config
Add to your claude_desktop_config.json:
{
"mcpServers": {
"creator-seo-mcp": {
"command": "uvx",
"args": ["creator-seo-mcp"],
"env": {
"GOOGLE_CREDENTIALS_PATH": "/path/to/credentials.json",
"CREATOR_SEO_SITE_RPM": "15"
}
}
}
}3. Claude Code config
claude mcp add creator-seo-mcp uvx creator-seo-mcp \
-e GOOGLE_CREDENTIALS_PATH=/path/to/credentials.json \
-e CREATOR_SEO_SITE_RPM=154. Revenue config (optional but recommended)
Set your display-ad RPM so opportunities are ranked by real dollars:
export CREATOR_SEO_SITE_RPM=22 # your Mediavine/Raptive RPM
export CREATOR_SEO_AFFILIATE_CATEGORIES='{"gaming-deals": 2.0}'See .env.example for all options.
Example agent prompts
"Show me my top five revenue-weighted SEO opportunities for this week."
"Which of my posts are losing traffic compared to last month?"
"My Baldur's Gate 3 build guide is stuck on page 2. Audit it against its main keyword and tell me what to fix."
"Find any posts that are competing with each other for the same search term."
"What is the on-page gap between this article and the query it is trying to rank for?"
Two Average Gamers case study
Real output from get_top_opportunities, run live against Two Average Gamers (a top-200 gaming blog on Mediavine display ads and Amazon affiliate links) on 2026-08-25, over the trailing 28 days (392K impressions, 3.5K clicks per Search Console).
This is the actual "before" baseline, not a mockup. Dollar estimates use the default $15 RPM placeholder (set CREATOR_SEO_SITE_RPM to your real network RPM for accurate figures). Out of 147 scored opportunities across all three signal types, the top 5 by estimated revenue:
# | Type | Page | Query | Finding |
1 | Striking distance | "letterboxd for games" | Position 4.6, 97K impressions, 1.6-position gap to page 1 | |
2 | Cannibalization | same page + 3 others | "letterboxd for games" | 4 TAG pages splitting 99.8K impressions on the same query |
3 | Striking distance | "backloggd" | Position 5.5, 11.4K impressions, 2.5-position gap to page 1 | |
4 | Cannibalization | same page + 2 others | "backloggd" | 3 TAG pages splitting 15.1K impressions on the same query |
5 | Striking distance | "backloggd" | Position 5.5, 3.7K impressions, 2.5-position gap to page 1 |
analyze_content_decay also flagged 40 pages losing clicks month-over-month on this run, the worst being a 100% drop (10 clicks to 0) on a Palworld lawsuit post and a 61% drop on a Palworld tower-boss guide, both worth a content refresh before the traffic they had is gone for good.
Update (2026-08-25): the title/H1 edits for the two striking-distance opportunities above are live. Re-running audit_page_onpage against the real pages confirms it:
Page | Target query | In title? | In H1? | Remaining suggestions |
"letterboxd for games" | ✅ | ✅ | none | |
"backloggd" | ✅ | ✅ | none | |
"backloggd" | ✅ | ✅ | none |
No position or traffic movement to report yet, and that's expected, not a null result: Search Console's data has a 2-3 day freshness lag, so a same-day re-pull still covers the identical 2026-07-26 to 2026-08-22 window as the original baseline above, before any of these edits existed. Google also needs to re-crawl and re-rank the pages, which typically takes longer than the raw data lag. This section will be updated again once a GSC pull actually covers the post-edit period (realistically 2-3 weeks out) with the real position and click change, not before.
Contributing
Issues and PRs welcome. See CONTRIBUTING.md for guidelines.
License
MIT
Available Tools
8 toolsanalyze_content_decayA
Compare recent vs prior period for every page and flag declining traffic. Results are sorted worst decliners first (most negative percent change at top).
| Name | Required | Description | Default |
|---|---|---|---|
| site_url | Yes | GSC property URL | |
| min_clicks | No | Minimum clicks in recent period to include | |
| recent_days | No | Length of the recent period in days | |
| compare_days | No | Length of the comparison period in days |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behavioral traits. It reveals sorting order (worst decliners first) but does not mention nondestructive behavior, authentication needs, rate limits, or output format. The description adds some context (comparison logic) but leaves gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long with zero wasted words. It front-loads the core action and includes the key sorting behavior. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema, the description does not need to explain return values. It covers the core function, sorting, and implied use case. With only one required parameter and a clear purpose, the description is nearly complete, though it could mention required data sources or assumptions (e.g., GSC access) to be fully self-contained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 4 parameters are fully described in the schema (100% coverage), so the description adds minimal additional meaning. It mentions the comparison periods (recent vs prior) but does not elaborate on how recent_days and compare_days map to those periods. The sorting note is present but applies to output, not parameters. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool compares recent vs prior periods for every page and flags declining traffic, with results sorted by worst decliners first. This specific verb-resource combination distinguishes it from siblings like get_page_performance or get_top_opportunities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for analyzing content decay but lacks explicit guidance on when to use this tool versus alternatives like get_page_performance or find_cannibalization. No exclusions or prerequisites are mentioned, so it's adequate but not proactive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
audit_page_onpageB
Fetch a page, parse its title/meta/headings/body, and return concrete edit suggestions for the target query.
| Name | Required | Description | Default |
|---|---|---|---|
| page_url | Yes | Full URL of the page to audit | |
| target_query | Yes | The search query you want this page to rank for |
Output Schema
| Name | Required | Description |
|---|---|---|
| h1 | Yes | |
| url | Yes | |
| title | Yes | |
| headings | Yes | |
| word_count | Yes | |
| suggestions | Yes | |
| target_in_h1 | Yes | |
| target_density | Yes | |
| target_in_title | Yes | |
| meta_description | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions fetching and parsing, but omits behavioral traits such as read-only nature, error handling (e.g., page not found), rate limits, or permission requirements. For a tool without annotations, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence clearly conveys the action, inputs, and output. No wasted words; front-loaded with the verb 'Fetch'. Appropriate for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (not shown) and only two parameters, the description is minimally adequate. However, it lacks usage guidance and behavioral context. For a tool that suggests edits, it could mention that suggestions are action-oriented or that the tool is read-only.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds no additional parameter-level meaning beyond the schema. Both parameters (page_url, target_query) are self-explanatory from the description, but no extra semantic detail is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a page, parses title/meta/headings/body, and returns concrete edit suggestions for a target query. It distinguishes from siblings like get_page_performance (performance metrics) and analyze_content_decay (content decay) by focusing on on-page SEO suggestions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 (e.g., analyze_content_decay, get_page_performance). The description implies usage for optimizing a page for a query, but lacks when-not-to-use or comparative context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_cannibalizationB
Find queries where multiple pages are competing, splitting authority and confusing Google. Results are sorted by total impression volume.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Lookback window in days | |
| site_url | Yes | GSC property URL | |
| min_impressions | No | Minimum total impressions across competing pages |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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 side effects (e.g., destructive potential), authentication needs, or rate limits. It only mentions the output sorting, leaving safety and operational behavior unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the primary purpose. Every word adds value; there is no redundancy or fluff. It is efficient and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity and the presence of an output schema, the description provides minimal context. It explains the core problem and sorting, but omits details about return format, pagination, or limitations. It is adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All parameters are described in the input schema, so baseline is 3. The description adds that results are sorted by total impression volume, which relates to min_impressions but does not provide new syntactic or semantic details beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's purpose: finding queries where multiple pages compete (cannibalization). It uses specific verb 'find' and resource 'queries with competing pages'. It distinguishes from sibling tools like get_page_performance or get_top_opportunities, which do not address cannibalization.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 does not mention prerequisites, when not to use it, or how it compares to siblings like 'analyze_content_decay' or 'audit_page_onpage'. The usage context is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_page_performanceA
Full GSC picture for one URL: clicks, impressions, CTR, position, and top queries.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Lookback window in days | |
| page_url | Yes | Full URL of the page to analyze | |
| site_url | Yes | GSC property URL |
Output Schema
| Name | Required | Description |
|---|---|---|
| ctr | Yes | |
| page | Yes | |
| clicks | Yes | |
| position | Yes | |
| impressions | Yes | |
| top_queries | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It does not disclose behavioral traits like read-only nature, rate limits, or what happens if no data exists. While outputs are listed, the mechanism and constraints (e.g., maximum top queries) are omitted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one sentence, front-loaded with the key purpose ('Full GSC picture for one URL'), and lists specific outputs succinctly. Every word is useful with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema, the description covers return values adequately by naming the metrics. However, it could mention default lookback window or data recency expectations, but overall it is sufficiently complete for its moderate complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers all three parameters with descriptions (days, page_url, site_url), achieving 100% coverage. The description does not add any additional parameter meaning beyond what is already in the schema; it focuses on outputs. Baseline of 3 is appropriate as no added value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a 'full GSC picture for one URL' including specific metrics: clicks, impressions, CTR, position, and top queries. This verb+resource combination ('get page performance') is direct and distinguishes it from sibling tools like 'get_striking_distance_keywords' or 'analyze_content_decay', which have different focuses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for a single URL deep dive but provides no explicit guidance on when to choose this tool over alternatives (e.g., 'use this for raw performance, not for trend analysis'). No when-not-to-use or crossover with siblings is mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_striking_distance_keywordsA
Find queries ranking in striking distance of page 1 (positions 4-15 by default). Results are sorted by impression volume so the highest-potential opportunities come first.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Lookback window in days | |
| limit | No | Max results to return | |
| site_url | Yes | GSC property URL, e.g. https://example.com/ | |
| position_max | No | Maximum average position | |
| position_min | No | Minimum average position | |
| min_impressions | No | Minimum impressions to include |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the default position range (4-15) and sorting by impression volume, but it does not explain how parameters like position_min/max override defaults or if the tool is read-only. The behavioral description is adequate 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the core purpose and a key behavior (sorting). Every word earns its place, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 6 parameters and an output schema, the description is brief but covers the main concept. However, it does not explain how parameters influence results (e.g., adjusting position range or impression threshold), leaving some gaps for a complete understanding. The presence of an output schema reduces the need for return value description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add meaning beyond the schema; it only reiterates the default range (4-15) already present in the schema's default values for position_min and position_max. No additional parameter context is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds queries 'ranking in striking distance of page 1' with a default range of positions 4-15, and results are sorted by impression volume. This specific verb+resource combination distinguishes it from siblings like 'get_top_opportunities' which likely targets top positions rather than near-top.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used for identifying high-potential queries near page 1, but it does not explicitly state when to use this tool versus alternatives (e.g., 'get_top_opportunities' or 'get_page_performance'). No exclusions or conditions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_top_opportunitiesA
The headline call. Combines striking-distance keywords, content decay, and cannibalization, weights each by estimated revenue, and returns a single ranked action list. Revenue estimates use your configured RPM and affiliate multipliers.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Lookback window in days | |
| limit | No | Max opportunities to return | |
| site_rpm | No | Override display ad RPM in USD (default from env) | |
| site_url | Yes | GSC property URL |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description reveals the tool is a read-style operation (combining data, no side effects mentioned) and discloses that revenue estimates use configured RPM and affiliate multipliers. However, it omits details like idempotency, required permissions, or error handling, which is acceptable for a non-destructive tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with no waste: the first sentence captures the core function, the second explains the weighting mechanism, and the third specifies revenue estimation. Information is front-loaded and every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is complex (combining three signals with revenue weighting) and has an output schema, so the description focuses on inputs and behavior. It could mention the output format or any prerequisites, but the presence of an output schema reduces the burden. Overall, it is sufficiently complete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema coverage is 100%, so baseline is 3. The description adds value by explaining that site_rpm can override the default RPM from the environment, clarifying its role in revenue estimation beyond the schema. Other parameters are adequately covered by the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool combines three distinct signals (striking-distance keywords, content decay, cannibalization), weights them by estimated revenue, and returns a single ranked action list. This specific verb+resource combination distinguishes it from sibling tools like get_striking_distance_keywords or analyze_content_decay.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (for a consolidated revenue-weighted opportunity list) and lists sibling tools that cover individual components. However, it does not explicitly state when NOT to use it or provide direct comparisons, leaving some inference to the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
health_checkA
Check that the server is running and list your verified GSC properties.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| status | Yes | |
| version | Yes | |
| token_status | No | |
| verified_sites | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description implies read-only behavior ('check', 'list') and does not suggest destructive actions. No annotations are provided, so the description carries the full burden, and it adequately conveys the safe, informational nature of the tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is direct and without unnecessary words. It is front-loaded with the action and immediately understandable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, has output schema), the description is complete. It covers the purpose and does not need to explain return values since an output schema exists.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero parameters and 100% schema coverage trivially, the description provides all necessary meaning. It clearly states what the tool does, adding value beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear purpose: checking server status and listing verified GSC properties. It uses specific verbs and resources, which helps differentiate it from sibling tools that focus on analysis or specific functionalities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when or why to use this tool over alternatives. Siblings like 'list_sites' may have overlapping functionality, but the description does not clarify when to prefer health_check.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_sitesA
List all Google Search Console properties you have access to.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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 only states 'list all properties you have access to,' without disclosing any behavioral traits like auth requirements, side effects, or rate limits. The description is minimal and lacks transparency beyond 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence that directly conveys the tool's purpose. There is no wasted content, and the information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and an output schema exists, the description is adequate for a simple list operation. It covers the core functionality, but could be slightly more complete by mentioning the output type or any limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, and schema description coverage is 100% (empty schema). The description adds no parameter information, but with no parameters, baseline score 4 is appropriate. No additional meaning is needed beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all Google Search Console properties the user has access to. The verb 'list' and resource 'Google Search Console properties' are specific and distinct from sibling tools which focus on keywords, performance, and audits.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (when needing to list accessible properties) but does not explicitly state when not to use it or mention alternatives. Sibling tools suggest other actions, but no direct guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Most tools have distinct purposes (e.g., keyword distance, performance, decay, audit, cannibalization). Only health_check and list_sites overlap slightly as both list GSC properties, but health_check primarily serves as a status check.
Tools generally follow a verb_noun pattern (list_sites, get_striking_distance_keywords, analyze_content_decay). health_check deviates (noun_noun) and 'audit_page_onpage' uses an awkward preposition, but overall pattern is mostly consistent.
8 tools cover the core SEO workflow (health, sites, opportunities, performance, decay, audit, cannibalization, and a combined report) without being excessive. The scope is well-calibrated for creator SEO use.
The set covers key analysis areas: opportunity identification, page performance, content decay, on-page audit, and cannibalization. Missing are execution tools (e.g., update page) and broader keyword research, but the core analytical workflow is complete.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Turn Search Console data into SEO actions, content, publishing, indexing, and AI insights.
Turns SEO insight into page changes: keyword research, SERP and rank data, rewrites you approve.
All-in-one Shopify SEO. Rank on Google and in AI search. Find what's broken and fix it in chat.
Open-source SEO manager for coding agents: keyword research, content PRs, rank + Search Console.
Related MCP Servers
- AlicenseNot gradedqualityCmaintenanceEnables querying Google Search Console and Google Analytics 4 through natural language, with tools for SEO analysis like anomaly detection, cannibalization detection, and opportunity scoring.291MIT
- AlicenseBqualityCmaintenanceConnects Google Search Console to AI assistants, enabling SEO data analysis through natural language conversations.32MIT
- AlicenseAqualityBmaintenanceTurns Google Search Console into an SEO copilot by enabling natural language queries for search analytics, URL inspection, sitemap management, and opportunity discovery.16MIT
- AlicenseNot gradedqualityCmaintenanceEnables natural language analysis of Google Search Console data through Claude, with pre-built tools for quick wins, cannibalization detection, content decay, and more.Apache 2.0
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
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/Yoshyaes/creator-seo-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server