Skip to main content
Glama
Pangolin-spg

Pangolinfo Amazon Data MCP

search_local_maps

Find local businesses by keyword and coordinates to research physical-store coverage, competitor presence, and offline channel density. Returns names, addresses, ratings, and review counts.

Instructions

[Local Maps via Google Maps] Local-business search (data source: Google Maps; use must comply with Google Terms of Service). Search local businesses at a given lat/lng — returns name, address, rating, review count, etc. Use when: user says "Y businesses in city X" / "local retail research" / "offline channel distribution" / "coffee shops/supermarkets/wholesalers in area" / "physical-store coverage density"; offline competitor/channel research; gauging physical-supply density of a category in a region. Don't use: for e-commerce listings (Amazon series); for global trends (use keyword_trends); for Google search results (use ai_search). Returns: data.organicResults[{ place_id, name, about, rating, number_of_reviews, borough, street_addr, city, postal_code, ... }]. Pair with: ↑ query (business keyword) + latitude/longitude/zoom (zoom 1=world, 13=city, 21=single building); ↓ presentation-focused, downstream rarely consumes. Cost: ~1.5 points/call, ~5s. Tips: zoom 13 (city, default) gives you a whole neighborhood; zoom 17+ narrows to one street.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zoomNoMap zoom level, 1=world, 13=city, 21=building. Default 13.
limitNoMax results to return (1-100).
queryYesLocal search query. Examples: 'coffee shop' / 'wholesale electronics' / '电子产品批发' / 'pet store'.
languageNoBCP-47 language code, e.g. 'en', 'zh-CN'.en
latitudeYesLatitude of search center. Examples: 37.7822 (San Francisco) / 40.7128 (New York) / 34.0522 (Los Angeles).
longitudeYesLongitude of search center. Examples: -122.4642 (San Francisco) / -74.0060 (New York) / -118.2437 (Los Angeles).
clientSourceNo调用来源标记。仅由 Pangolinfo Skill 传 skill;普通 MCP 调用省略即可。

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the data source (Google Maps), a Terms-of-Service compliance obligation, cost (~1.5 points/call) and latency (~5s). It stops short of stating quota/rate-limit ceilings or failure modes on empty geocodes, so it is strong but not exhaustive.

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?

Labeled sections (Use when / Don't use / Returns / Pair with / Cost / Tips) make it scannable and front-loaded with purpose. The opening sentence's partial return list ('name, address, rating, review count') is then restated in the Returns block, a small redundancy in an otherwise dense, purposeful text.

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?

No output schema exists, so the description correctly enumerates the return shape (data.organicResults with place_id, name, about, rating, borough, city, etc.). With 7 parameters and only 3 required, it documents zoom/query/lat/lng but is silent on limit, language, and clientSource, leaving minor gaps for a search tool of this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3, but the description adds practical meaning: zoom 13 yields a whole neighborhood while 17+ narrows to one street, and it pairs query with latitude/longitude/zoom as the dial that controls result scope. It adds less for limit, language, and clientSource, which remain schema-only.

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 and resource ('Local-business search') and pins the scope to a lat/lng center returning name, address, rating, review count. It explicitly names what it is not (e-commerce Amazon series, keyword_trends, ai_search), so an agent can route correctly without opening a schema.

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?

Contains explicit 'Use when' triggers (local business counts, offline channel/competitor research, physical-store density) and a 'Don't use' block that routes to three named sibling tools for the cases this tool does not cover. Both directions are covered with concrete examples.

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