Veezee: LinkedIn people & company data for agents

Get a LinkedIn person profile

linkedin_get_profile

Read-only

Fetch one person's LinkedIn profile. identifier accepts a profile URL, the slug after /in/ (e.g. 'williamhgates'), or a urn:li:fsd_profile URN; URLs are cleaned automatically. Always returns the overview (name, headline, location, current position, follower counts) plus up to 2 requested sections from about|experience|education|skills at no extra cost; each section beyond 2 adds 2 credits (max 4 sections). Costs 4 credits base. If you only have a name, use linkedin_search_people first; this tool does not search. Results from linkedin_search_people with is_anonymous=true cannot be fetched here; treat them as 'someone matching this exists' and stop. Companies belong to linkedin_get_company.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`sections`	No	Extra profile sections. First 2 are included in the base price.
`freshness`	No	recent (default) serves cached data from the last few hours when available; realtime forces a live fetch for +2 credits (refunded if we fall back to cached data). Trial keys are cached-only and reject realtime with TRIAL_CAP_EXCEEDED; paying upgrades this same key to unlock it.	recent
`identifier`	Yes	Profile URL, slug (after /in/), or urn:li:fsd_profile URN.
`max_credits`	No	Spend ceiling for this one call. The call is rejected (nothing charged) if its quote exceeds this. Only the quote is ever reserved, never this ceiling.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`usage`	Yes
`common`	Yes
`entity`	Yes
`platform`	Yes
`freshness`	Yes
`data_as_of`	Yes
`canonical_url`	Yes
`schema_version`	Yes
`platform_fields`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

AddedJul 21, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Discloses cost structure (4 credits base + 2 per extra section beyond 2), caching behavior (recent/realtime), trial key limitations, and credit refund policy. Annotations (readOnlyHint, openWorldHint) are supported and not contradicted.

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?

Description is comprehensive yet efficient; each sentence adds information. Minor redundancy in listing sections again, but overall well-structured with front-loaded main purpose.

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?

Combined with full schema coverage, annotations, and output schema (implied), the description covers all necessary context: identifier types, cost, caching, limits, alternatives, and failure conditions.

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?

Input schema covers all 4 parameters with descriptions (100% coverage). The description adds value by explaining sections cost, freshness credit implications, and max_credits as a rejection threshold, enriching beyond 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?

The description clearly states 'Fetch one person's LinkedIn profile' and specifies three accepted identifier formats (URL, slug, URN). It differentiates from siblings by directing name-based lookups to linkedin_search_people and company profiles to linkedin_get_company.

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?

Explicitly advises when to use alternative tools: 'If you only have a name, use linkedin_search_people first' and 'Companies belong to linkedin_get_company.' Also explains that anonymous search results cannot be fetched, and provides conditions for realtime vs cached fetches.

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

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