Provides access to Google's 'People Also Ask' data through the AlsoAsked API, enabling hierarchical question retrieval for SEO research and content optimization.
Click on "Deploy 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., "@AlsoAsked MCP Server find People Also Ask questions for 'vegan protein sources' with depth 2"
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.
A Model Context Protocol (MCP) server for the AlsoAsked API, providing access to Google's "People Also Ask" data for SEO research and content optimization.
Search People Also Ask Questions: Get hierarchical PAA data for any search terms
Account Management: Check your API credits and account status
Flexible Search Options: Configure language, region, depth, and freshness
Rich Data Structure: Formatted results with question hierarchy and counts
Related MCP server: google-search-console
npm installnpm run buildSign up for an AlsoAsked Pro account
Generate an API key from your dashboard
Keep your API key secure
Add this to your Claude claude_desktop_config.json:
{
"mcpServers": {
"alsoasked": {
"command": "node",
"args": ["/path/to/your/alsoasked-mcp/dist/index.js"],
"env": {
"ALSOASKED_API_KEY": "your-api-key-here"
}
}
}
}Restart Claude Desktop to load the new MCP server.
The server provides three main tools:
Search for PAA questions with full control over parameters:
// Example: Search for marketing questions in Spanish for Mexico
{
"terms": ["digital marketing", "content strategy"],
"language": "es",
"region": "mx",
"depth": 3,
"fresh": true
}Convenient method for single-term searches:
// Example: Quick search for a single term
{
"term": "machine learning",
"depth": 2
}Check your account status and remaining credits:
// No parameters needed
{}Parameter | Type | Default | Description |
| string[] | required | Search terms to query |
| string | "en" | Language code (en, es, fr, etc.) |
| string | "us" | Region code (us, uk, ca, etc.) |
| number | 2 | Question hierarchy depth (1-3) |
| boolean | false | Fetch fresh vs cached results |
| boolean | false | Process asynchronously |
The server returns structured data with:
Question Hierarchy: Nested questions with levels
Search Metadata: Total questions, search terms
Account Info: Credits remaining, plan details
Formatted Output: Clean JSON structure for easy parsing
Ask Claude:
"Use AlsoAsked to find People Also Ask questions for 'sustainable energy' with depth 3"
"Get PAA data for SEO keyword research on 'home workout equipment' in the UK market"
"Check my AlsoAsked account credits and usage"
# Watch mode for development
npm run dev
# Build for production
npm run build
# Start the server
npm startPro Plan: $59/month with 1,000 queries included
Additional Credits: $0.03-$0.06 per query
API Efficiency: Use appropriate depth levels to control costs
MIT License - feel free to modify and distribute as needed.
Get account information including credits, plan details, and usage statistics
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 it 'gets' information (implying a read operation) but doesn't disclose behavioral traits like authentication requirements, rate limits, error conditions, or response format. This is inadequate for a tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose with no wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.
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 (0 parameters, no output schema), the description is minimally adequate but lacks completeness. It doesn't cover behavioral aspects like response format or error handling, which are important even for simple tools, especially with no annotations to fill gaps.
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 tool has 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description doesn't add param info, which is fine, but it does specify the types of account information retrieved (credits, plan details, usage statistics), adding some semantic context 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 clearly states the verb 'Get' and the resource 'account information', specifying what data is retrieved (credits, plan details, usage statistics). However, it doesn't differentiate from sibling tools, which appear unrelated (search tools vs account info).
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 is provided on when to use this tool versus alternatives or in what context. The description only states what it does, not when it should be used, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for "People Also Ask" questions related to search terms. Returns hierarchical question data from Google PAA.
| Name | Required | Description | Default |
|---|---|---|---|
| terms | Yes | Array of search terms to query | |
| language | No | Language code (e.g., "en", "es", "fr") | en |
| region | No | Region code (e.g., "us", "uk", "ca") | us |
| latitude | No | Latitude for geographic targeting (e.g., 40.7128 for NYC, 31.9686 for Texas) | |
| longitude | No | Longitude for geographic targeting (e.g., -74.0060 for NYC, -99.9018 for Texas) | |
| depth | No | Depth of question hierarchy (1-3) | |
| fresh | No | Whether to fetch fresh results or use cached data | |
| async | No | Whether to process request asynchronously |
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 mentions the tool returns hierarchical question data, but lacks details on rate limits, authentication needs, error handling, or whether it's a read-only or mutative operation. For a tool with 8 parameters and no annotation coverage, this is a significant gap in behavioral disclosure.
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 with zero waste, front-loaded with the core purpose and followed by the return type. Every word earns its place, making it highly efficient and easy to parse.
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 (8 parameters, no annotations, no output schema), the description is minimal but covers the basic purpose and return data. However, it lacks details on output format, error conditions, or behavioral traits, leaving gaps for an AI agent to fully understand how to use it correctly.
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 schema fully documents all 8 parameters. The description adds no additional parameter semantics beyond what's in the schema, such as explaining interactions between parameters or providing examples. Baseline 3 is appropriate when the schema does the heavy lifting.
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 specific action ('Search for'), resource ('People Also Ask' questions), and outcome ('Returns hierarchical question data from Google PAA'). It distinguishes from sibling tools like 'get_account_info' and 'search_single_term' by focusing on hierarchical PAA data rather than account info or single-term search.
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 obtaining hierarchical PAA data related to search terms, but does not explicitly state when to use this tool versus alternatives like 'search_single_term' or provide any exclusions or prerequisites. Usage context is inferred rather than clearly defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for PAA questions for a single term (convenience method)
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes | Single search term | |
| language | No | Language code | en |
| region | No | Region code | us |
| latitude | No | Latitude for geographic targeting | |
| longitude | No | Longitude for geographic targeting | |
| depth | No | Depth of question hierarchy |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions it's a 'convenience method' which suggests simplicity, but doesn't disclose rate limits, authentication needs, error conditions, or what the search returns (format, pagination, etc.). For a search tool with 6 parameters, this is inadequate.
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 extremely concise - a single sentence that efficiently conveys the core purpose and key constraint (single term). Every word earns its place, and it's front-loaded with the main action.
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?
For a search tool with 6 parameters and no annotations or output schema, the description is insufficient. It doesn't explain what PAA questions are, what format results return, how geographic targeting works with region/language parameters, or the implications of the depth parameter. The 'convenience method' hint is helpful but doesn't compensate for missing context.
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 all parameters are documented in the schema. The description adds no specific parameter semantics beyond implying 'single term' focuses on the 'term' parameter. This meets the baseline when schema coverage is high, but doesn't provide additional value like explaining parameter interactions or constraints.
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 verb ('Search for') and resource ('PAA questions'), specifying it's for a single term. However, it doesn't explicitly differentiate from the sibling tool 'search_people_also_ask' - both appear to search PAA questions, though this one is described as a 'convenience method' which hints at a simpler alternative.
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 context by calling it a 'convenience method' for single-term searches, suggesting it's simpler than alternatives. However, it doesn't explicitly state when to use this versus 'search_people_also_ask' or provide clear exclusions or prerequisites for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
get_account_infosearch_people_also_asksearch_single_termScored across 3 tools
The tools have overlapping purposes, as both 'search_people_also_ask' and 'search_single_term' appear to retrieve PAA questions, with the latter described as a 'convenience method' for single terms. This creates ambiguity about when to use each, though the descriptions provide some differentiation. The 'get_account_info' tool is clearly distinct, focusing on account metadata.
All tool names follow a consistent verb_noun pattern with snake_case, such as 'get_account_info', 'search_people_also_ask', and 'search_single_term'. This uniformity makes the tool set predictable and easy to parse, with no deviations in naming conventions.
With only 3 tools, the set feels thin for a server focused on PAA search functionality, as it lacks operations like filtering, saving results, or managing search history. The count is too low to adequately cover the domain, making it seem incomplete for practical use.
The tool set is severely incomplete for a PAA search domain, offering only basic search and account info retrieval. Missing are essential operations such as updating search parameters, deleting or exporting results, or handling batch searches, which limits agent workflows and creates dead ends.
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