Google Maps & Local Business MCP Server
Server Details
MCP server providing Google Maps data, local business information, place details, and geolocation services for AI agents.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
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Tool Definition Quality
Average 3.2/5 across 3 of 3 tools scored.
While `validate_emails` is clearly distinct, `generate_leads` and `search_local_businesses` overlap significantly—both retrieve business contact information from Google Maps. The distinction between 'B2B scraping' and 'local search' is subtle and may confuse agents about which to use for retrieving business data.
All tools follow a consistent `verb_noun` pattern using snake_case (`generate_leads`, `search_local_businesses`, `validate_emails`). The naming convention is predictable and uniform throughout the set.
Three tools is borderline thin for a 'Google Maps & Local Business' server. The inclusion of `validate_emails`—a generic utility not specific to Google Maps—suggests the surface area is minimally scoped, feeling more like a lead-generation utility than a comprehensive Maps integration.
The set covers a narrow lead-generation workflow (search, scrape, validate) but lacks core Google Maps operations like geocoding, directions, or detailed place lookups by ID. For general Maps usage, notable gaps exist; for lead-gen specifically, it is functional but shallow.
Available Tools
3 toolsgenerate_leadsBRead-onlyInspect
Extract B2B lead lists from Google Maps by business category and geography. Returns company name, full address, contact phone, website, business category, and review metrics. Use for sales prospecting, market research, or building vendor lists. Returns 20+ leads per query by default.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | City where businesses are located (e.g. 'Denver', 'New York', 'San Francisco') | |
| state | No | State or region abbreviation (e.g. 'CO', 'NY', 'CA') | |
| max_results | No | Number of leads to generate (default 20, recommended for data quality) | |
| business_type | Yes | Industry or business category to target (e.g. 'HVAC contractors', 'dental clinics', 'software development firms') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses scraping method and contact info retrieval, but omits operational risks, rate limits, or failure modes given null annotations.
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 is front-loaded and efficient, though extreme brevity leaves critical gaps given lack of structured metadata.
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?
Mentions output (contact info) compensating for missing output schema, but inadequate for parameter guidance given simple schema structure.
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 0% schema description coverage, description fails to compensate by explaining any of the 4 parameters (business_type, city, state, max_results).
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?
Clear specific action (scraping Google Maps) and resource (B2B leads with contact info), but lacks explicit differentiation from sibling 'search_local_businesses'.
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 to use vs alternatives, limitations, or appropriate contexts for scraping.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_local_businessesBRead-onlyInspect
Search Google Maps for local businesses matching a query and location. Returns business name, complete address, star rating, review count, phone number, website URL, and business category. Use for restaurant discovery, service provider lookup, or competitive local analysis. Returns open/closed status.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Business type or name to find (e.g. 'plumbers near me', 'Thai restaurants', 'Starbucks') | |
| location | No | Geographic location as city, zip code, or address (e.g. 'Los Angeles, CA', '90210', '1600 Pennsylvania Ave') | |
| max_results | No | Number of business results to return (default 10, max 50 for large searches) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses return value structure (names, addresses, ratings, etc.) which compensates for missing output schema, but omits rate limits, auth, or caching behavior.
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?
Two concise sentences: first states purpose, second states return values; no fluff.
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?
Adequate for a simple 3-parameter tool without output schema, though parameter explanations are missing.
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 has 0% description coverage and description fails to compensate by explaining what 'query', 'location', or 'max_results' represent.
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?
Clear specific verb ('Search') and resource ('Google Maps for local businesses'), implicitly distinct from lead generation and email validation siblings.
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 to use versus siblings (generate_leads) or explicit use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_emailsARead-onlyInspect
Validate and verify email addresses for deliverability, format compliance, and mailbox existence. Returns pass/fail status per email, syntax errors, domain validity, and SMTP verification result. Use before sending bulk emails to prevent bounces and protect sender reputation.
| Name | Required | Description | Default |
|---|---|---|---|
| emails | Yes | Array of email addresses to validate for syntax, domain, and deliverability |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses validation dimensions (syntax, domain, deliverability) but lacks operational details like error handling, rate limits, or return value structure given no annotations or output schema.
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?
Extremely concise at two sentences; front-loaded with purpose first, behavioral details second; no wasted words.
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?
Missing description of return values or status indicators which would be essential given no output schema exists, though tool complexity is low.
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?
Implies input is a list of emails matching the schema, but with 0% schema description coverage, fails to explicitly document the 'emails' parameter name, constraints, or expected format.
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?
Specific verb (validate) and resource (email addresses) clearly stated, and functionally distinct from lead generation and business search siblings.
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 versus alternatives or when not to use (e.g., syntax-only validation scenarios).
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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