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evascrape

Evascrape MCP server

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by evascrape

Find business leads on Google Maps

google_maps_leads

Extract local business leads from Google Maps with names, addresses, phones, websites, and emails. Build targeted prospect lists for sales or outreach.

Instructions

Find local businesses and their contact details from Google Maps: name, address, phone, website, rating, and the email address published on the business website where there is one. Use this for requests like "find 200 dentists in Austin with their emails", "get me restaurants in Berlin with phone numbers" or "build a list of plumbers near Manchester". This is the best starting point for local business prospecting. Costs $12 per 1,000 records from a prepaid balance; you are refunded for anything not delivered. Returns a job id, not the data: poll job_status, then call download.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoGoogle Maps interface language, e.g. "en", "de", "tr". Default "en".
locationYesWhere to search. A city, region or country. Example: "Austin, Texas".
list_nameNoName for the export file. Defaults to a name built from the search.
categoriesNoOptional extra category filter, comma separated.
min_ratingNoOnly include places at or above this Google rating.
skip_closedNoSkip permanently closed places. Default false.
search_termsYesWhat kind of business, comma separated. Example: "dentist" or "dentist, orthodontist".
total_recordsYesMaximum records to collect. Multiple of 100, minimum 100, maximum 50000. This is a ceiling, not a promise.
email_verificationNoVerify every email in real time for an extra $5 per 1,000. Default false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Goes well beyond the annotations: it discloses the async contract (returns a job id, not data) and the exact next tools, the cost model ($12 per 1,000 records, refunded for undelivered records), and an optional paid add-on ($5/1,000 for email verification). That is exactly the operational context the readOnlyHint=false / idempotentHint=false annotations cannot convey.

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?

Front-loads what is found and the output fields, then usage examples, then cost and async mechanics. Every sentence carries distinct information; no filler or restatement of the title.

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?

With no output schema and an asynchronous job model, the description supplies precisely the missing pieces: what the call returns (a job id), how to retrieve results (job_status then download), and what it costs. Nothing needed to invoke and complete the workflow is absent.

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 description coverage is 100%, so the schema already documents all nine parameters and the baseline is 3. The description adds only a marginal clarification that emails come from the business website, which is useful context but not parameter-level detail 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?

States a specific verb (find) and resource (local businesses and their contact details) and enumerates the returned fields. It is clearly distinguishable from sibling lead sources like linkedin_leads and apollo_export because it names Google Maps as the data source.

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?

Gives concrete triggering requests ("find 200 dentists in Austin with their emails", "get me restaurants in Berlin") and positions itself as "the best starting point for local business prospecting", plus the follow-up workflow (poll job_status, then download). It does not explicitly contrast itself with apollo_export or linkedin_leads, so the when-not-to-use case is missing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.