Wrong Location

search_humans returns a location that doesn’t match where the person actually is. Here’s why and how to improve accuracy.

Why it happens

SERP noise

Location is extracted from LinkedIn SERP snippets and profile data. Google’s snippet for a LinkedIn profile might say “Greater Chicago Area” when the person recently moved to Austin. LinkedIn location data reflects what the user last updated — it can be months or years out of date.

Non-structured data

LinkedIn doesn’t always surface location in a consistent format. Sometimes the snippet contains the company headquarters instead of the person’s location. For example, searching for a remote employee at Airbnb might return “San Francisco” (HQ) instead of their actual city.

Multiple locations

People who work across multiple offices or travel frequently may have ambiguous location signals. The system picks the most frequently mentioned location, which may not be the one you expect.

How to get better results

Provide company_name

Adding company_name improves location accuracy because the system can disambiguate between multiple people with the same name and select the one associated with the right company.

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// Less accurate
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await search_humans({ name: "David Solomon" });
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// More accurate
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await search_humans({ name: "David Solomon", company_domain: "goldmansachs.com" });

Provide location as a hint

If you already know the person’s approximate location, pass it as a parameter. This helps the system prioritize SERP results from that area.

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await search_humans({
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  name: "Melanie Perkins",
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  company_name: "Canva",
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  location: "Sydney"
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});

Cross-reference with interests

search_interests sometimes surfaces location signals from recent posts (e.g. “Speaking at SXSW in Austin next month”). These can be more current than LinkedIn profile data.

What to expect

Location accuracy depends on the person’s web presence. Public figures and active LinkedIn users tend to have accurate locations. People with minimal online presence may have outdated or missing location data.

If location is critical for your workflow (e.g. finding nearby reps), consider using the location as a starting point and expanding your search radius.