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Veezee: LinkedIn people & company data for agents

Get recent posts by a person or company

linkedin_get_posts
Read-only

Fetch the recent LinkedIn posts of one person or one company. identifier accepts a profile or company URL, a slug, a person URN, or a company website domain like 'microsoft.com'; the entity type is detected automatically. A domain resolves to its verified company first, exactly like linkedin_get_company: it QUOTES base+4 credits (set max_credits accordingly) and the surcharge is refunded at settlement for already-known domains, so they settle at the base price. Company URNs and numeric company ids are search-filter inputs, not fetch identifiers: use the company slug, URL, or domain here. Returns one page of posts (text, created_at, author, likes, comments_count, shares, is_repost, url) with a cursor for older posts. Costs 4 credits per page. Use this for 'what has X been posting', voice-of-company research, or activity checks before outreach. Not for reading one specific post you already have a URL for, and not for keyword search across LinkedIn; neither is supported in v1.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cursorNoCursor from a previous page for older posts.
freshnessNorecent (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
identifierYesPerson or company URL, slug, URN, or company website domain.
max_creditsNoSpend 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
NameRequiredDescriptionDefault
usageYes
commonYes
entityYes
platformYes
freshnessYes
data_as_ofYes
canonical_urlYes
schema_versionYes
platform_fieldsYes

Schema Changelog

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

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

The description extensively discloses behaviors beyond annotations, including pricing (4 credits per page, surcharge for domains, refund), caching behavior (recent vs realtime), and identifier auto-detection. No contradiction with readOnlyHint annotation.

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 reasonably concise, front-loaded with core purpose, then detailed parameters and usage. Every sentence serves a purpose, though slightly verbose due to pricing details.

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?

The description is comprehensive, covering all aspects: purpose, identifier types, pricing, caching, pagination, and usage guidance. Output schema handles return values, making the description complete for the tool's 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 coverage is 100%, but the description adds significant context beyond schema, such as how identifier resolves domains, surcharge refund logic, and max_credits function. This enhances understanding of parameter behavior.

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 fetches recent LinkedIn posts for one person or company, using specific verbs and resource. It distinguishes from siblings by explicitly stating it is not for reading a single post or keyword search.

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?

The description explicitly states when to use (e.g., 'what has X been posting', voice-of-company research, activity checks) and when not to use (not for specific post or keyword search). It does not name alternative sibling tools but provides clear usage boundaries.

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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