Outscraper MCP

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Outscraper Businesses Search

businesses_search

Search and filter normalized business records by location, category, and other structured fields to build lead lists. Supports natural-language queries and cursor-based pagination.

Instructions

Search Outscraper businesses using structured filters, a natural-language query, or both.

Best for:

building lead lists from normalized business records
filtering by country, state, city, type, and other structured business fields
paginated browsing with cursor when you want repeatable result navigation

Prefer this tool when:

you already know the geography, categories, or other business filters you want
you want the normalized /businesses dataset rather than raw Google Maps search behavior
you need stable field selection and cursor-based pagination

Use this instead of google_maps_search when:

you want the normalized /businesses API
you need field selection, filters, or cursor pagination

Note:

according to the current OpenAPI, /businesses is a synchronous endpoint in this MCP server
async-style execution controls are intentionally not exposed here
structured filters are the most reliable input mode
live Outscraper testing showed that free-form query parsing may fail with "Could not parse query into a valid request format."

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Result page size.
`query`	No	Optional natural-language business search query parsed by Outscraper.
`cursor`	No	Pagination cursor from the previous response.
`fields`	No	Specific fields to return for each business.
`filters`	No	Structured Outscraper /businesses filters JSON.
`include_total`	No	Whether Outscraper should include total matching count.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	Yes
`meta`	Yes
`async`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv0.2.5Sep 23, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

changedOutput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

First observedv0.2.3Aug 1, 2026

TDQS

A4.3/5.0

Behavior4/5

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 discloses that the endpoint is synchronous, that async-style execution controls are intentionally not exposed, and that free-form query parsing may fail with a specific error. It also notes that structured filters are the most reliable input mode. It doesn't mention pagination behavior beyond cursor support, but the schema already covers cursor. The description adds meaningful behavioral context beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections and front-loaded purpose. It is somewhat longer than necessary, but every section earns its place: the 'Best for' and 'Prefer this tool' sections provide actionable guidance, and the notes about synchronous behavior and query parsing failures are valuable. The bullet-point format makes it scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose, usage context, alternatives, and important behavioral caveats. With an output schema present, return values don't need to be explained. The only minor gap is that it doesn't describe the structure of the filters object in detail, but the schema's 'Structured Outscraper /businesses filters JSON' description plus the note about reliability is sufficient for an agent to proceed. Overall, it is complete for a tool with this complexity.

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 six parameters. The description adds context about the query parameter's reliability (may fail to parse) and emphasizes structured filters as the most reliable mode, which is useful. However, it doesn't add much detail about the filters object structure or fields parameter beyond what the schema provides. Baseline 3 is appropriate given full schema coverage.

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?

The description clearly states the tool searches Outscraper businesses using structured filters, natural-language query, or both. It explicitly distinguishes this from google_maps_search by noting it targets the normalized /businesses dataset rather than raw Google Maps search behavior. The verb 'search' plus the resource 'businesses' and the mention of structured filters makes the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit 'Best for' and 'Prefer this tool when' sections, and directly names the alternative google_maps_search with the condition for choosing this tool instead. It also includes a note about the synchronous endpoint and warns that free-form query parsing may fail, which helps the agent decide when to use structured filters. This is exemplary usage guidance.

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