MCP server providing Google Maps data, local business information, place details, and geolocation services for AI agents.
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Average 4.1/5 across 3 of 3 tools scored.
Each tool has a distinct purpose: lead generation, local search, and email validation. However, the inclusion of email validation alongside Google Maps tools may cause confusion about the server's core domain, but the tools themselves are easily distinguishable.
All tool names follow a consistent verb_noun pattern (generate_leads, search_local_businesses, validate_emails), using underscores and clear action-object pairs.
With only 3 tools, the server feels thin for a domain as broad as Google Maps and local business. The count is borderline but not extreme, especially given the inclusion of an unrelated email validation tool.
The server provides only two local business tools (search and lead generation), missing essential operations like getting detailed business info, directions, reviews, or location queries. The email validation tool is out of scope and does not fill any obvious gap in the local business domain.
generate_leadsAExtract 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') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds useful behavioral detail beyond the readOnlyHint and openWorldHint annotations by listing the exact return fields (company name, address, phone, website, category, review metrics) and the default result count ('20+ leads per query'). It does not contradict 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?
The description is three sentences with no fluff. It opens with the core action, lists output fields, then provides use cases and default 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 moderate complexity (4 flat params, no output schema), the description covers the essential context: what it does, what it returns, and typical use cases. It does not detail limitations like result quality or rate limits, but the presence of annotations and complete schema descriptions fill most 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?
Schema coverage is 100%, so the baseline is 3. The description adds minimal extra parameter insight: it mentions 'business category and geography' which maps to business_type and city/state, and the '20+ leads' default aligns with max_results, but it mostly relies on the schema's strong field descriptions.
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 extracts B2B lead lists from Google Maps by category and geography, with specific output fields. It is distinct from validate_emails, but does not explicitly differentiate from the similar-sounding search_local_businesses sibling, so it misses the highest bar.
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 clearly enumerates use cases: sales prospecting, market research, and building vendor lists. It gives clear context for when to use the tool, though it does not explicitly mention when not to use it or compare it directly to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_local_businessesASearch 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) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint and openWorldHint; the description adds output details (business name, address, rating, etc.) and notes open/closed status. It does not contradict annotations and gives a good picture of expected results, though it lacks caveats like rate limits.
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: purpose, return fields, and use cases. All are informative, but 'Returns open/closed status' could be folded into the list of return fields to reduce 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?
With no output schema, the description lists the key return fields and use cases, making it reasonably complete. It doesn't explain behavior when location is omitted, but for a read-only search with well-documented schema, this is sufficient.
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 covers all three parameters with detailed descriptions and examples (query, location, max_results). The description only references 'query and location' generically, adding no extra parameter semantics 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 states 'Search Google Maps for local businesses matching a query and location' with a specific verb, resource, and scope. It also enumerates return fields and differentiates from sibling tools like generate_leads and validate_emails.
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?
Provides explicit use cases ('restaurant discovery, service provider lookup, or competitive local analysis') but does not mention exclusions or alternative tools. The sibling tools are clearly different in purpose, so context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_emailsAValidate 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds behavioral detail about return values (pass/fail status, syntax errors, domain validity, SMTP result) and the checks performed (deliverability, mailbox existence), which goes beyond annotations. No contradictions with 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?
Two sentences: first defines actions and outputs, second gives usage guidance. No redundant content, front-loaded with the verb 'Validate and verify'. Highly concise and well-structured.
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 simple single-parameter schema, clear annotations, and no output schema, the description adequately explains the tool's behavior, return values, and intended use case. Nothing essential is missing for an agent to select and invoke 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 covers the single 'emails' parameter thoroughly with type, format examples, and description (100% coverage). The tool description reinforces the parameter purpose but adds no additional semantic detail beyond the schema, so baseline 3 applies.
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 as validating and verifying email addresses, with specific outcomes like syntax errors, domain validity, and SMTP verification. This distinguishes it from sibling tools (generate_leads and search_local_businesses) 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage context: 'Use before sending bulk emails to prevent bounces and protect sender reputation.' This clearly indicates when to use, though it does not mention exclusions or alternative tools, so it lacks the full when/when-not structure for a 5.
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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