Local SEO Data

Official

Overview Schema Source Related Servers Discussions(1)Admin

bing_local

Read-only

Checks how businesses rank in Bing local results for a keyword and location, returning rank, name, address, phone, website, and hours. Use it to track Bing and Copilot local visibility.

Instructions

Checks how businesses rank in Bing's local results for a keyword. Use when the question is about Bing, Bing Places, Copilot, or ChatGPT local recommendations — ChatGPT's web layer draws on Bing, so this is the closest measurable proxy for it. Returns rank, name, address, phone, website, hours, coordinates, and platform_id. Bing publishes no ratings or review counts on local results, so rating is null and reviews_count is 0 — that is a platform limitation, not missing data. claimed is always null: Bing gives no claimed/unclaimed signal. Costs 3 credits.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "keyword": "<keyword>",
      "location": "<location>"
    },
    "name": "bing_local"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Results to return. Default: 20
`keyword`	Yes	Search keyword (e.g. "plumber")
`location`	Yes	City and state (e.g. "Orchard Park, NY")

Output Schema

Name	Required	Description	Default
`keyword`	Yes
`results`	Yes
`location`	Yes
`platform`	Yes
`total_results`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.1.0Oct 22, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the readOnly/openWorld annotations, it discloses the returned field set, explains the counterintuitive null rating, reviews_count=0, and claimed=null as platform limitations rather than data gaps, and states the cost (3 credits). This is exactly the context annotations cannot carry.

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?

The purpose is front-loaded in the first sentence, and every following sentence carries actionable payload — proxy rationale, return fields, null-field caveats, cost. No filler or restatement of the name.

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 an output schema present, the description need not enumerate return values, yet it pre-empts the most likely agent confusion (null rating, reviews_count=0, claimed=null). Combined with annotations covering safety and cost noted explicitly, nothing needed to call it correctly is missing.

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% and every parameter (keyword, location, limit) is documented in the schema itself. The description adds no parameter-specific syntax or constraints, so the baseline 3 applies.

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 states a specific verb ('Checks how businesses rank') and a specific resource ('Bing's local results for a keyword'), and the Bing framing cleanly distinguishes it from siblings like apple_maps, local_pack, and organic_serp. An agent knows exactly what this tool produces without opening the schema.

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?

It gives concrete usage conditions — Bing, Bing Places, Copilot, or ChatGPT local recommendations — and explains the proxy rationale. It stops short of naming a specific alternative (e.g. the Google equivalent) or an explicit when-not-to-use case, so it is clear context without full routing.

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