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mcp-gtm-suite

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TypeScript
Remote

Capture LinkedIn Posts and Commenters

capture_linkedin_posts_and_commenters
Idempotent

Retrieve search-indexed LinkedIn posts for person profiles or company pages as flat rows with permalink, author, excerpt and publish date, without using LinkedIn cookies or accounts.

Instructions

Point it at LinkedIn person profiles or company pages and it returns the posts a search engine has indexed for each one, as flat rows: permalink, author, a text excerpt, and the publish date decoded from the post id. No request is sent to LinkedIn and no cookie or account is used. A result counts only when its permalink names the source, so posts are never attributed to the wrong author. Coverage is what the search index holds: posts appear days or weeks after publishing and many never appear. Commenters, reaction counts, comment counts and media are no longer returned (removed 2026-10-12, they came only from LinkedIn pages). Rows carry row_type post or notice; filter on row_type before loading a table. post_id is the activity URN and is stable across runs. Every row carries degraded and degradation_reason. Requires an APIFY_TOKEN and consumes Apify credits. Read only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_urlsNoLinkedIn company page URLs, for example https://www.linkedin.com/company/microsoft. Supply this, profile_urls, or both.
posted_sinceNoISO date or timestamp, for example 2026-08-01. Posts published before this are skipped BEFORE anything is charged. Leave empty to take everything the index holds.
profile_urlsNoLinkedIn person profile URLs, for example https://www.linkedin.com/in/williamhgates. Country subdomains such as uk.linkedin.com are fine. Supply this, company_urls, or both.
max_charge_usdNoOptional spending cap for this call, in USD, passed to the actor as max_charge_usd. The run stops before any charge that would take it past this amount and keeps every row already charged. Leave empty to rely only on the maximum charge per run set on your Apify account.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.14.0
    • addedInput schema / properties / max_charge_usd
      Added value: +{
+  "description": "Optional spending cap for this call, in USD, passed to the actor as max_charge_usd. The run stops before any charge that would take it past this amount and keeps every row already charged. Leave empty to rely only on the maximum charge per run set on your Apify account.",
+  "minimum": 0,
+  "type": "number"
+}
  2. Addedv1.1.1

TDQS

A4.4/5.0
Behavior5/5

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

Annotations supply openWorldHint=true, idempotentHint=true, destructiveHint=false and readOnlyHint=false, but the description adds far more: no LinkedIn request/cookie/account, permalink-based attribution guarantee, index coverage latency ('days or weeks', 'many never appear'), removed fields with a date, row_type post/notice semantics, stable post_id, per-row degraded/degradation_reason, APIFY_TOKEN requirement and credit consumption. The closing 'Read only.' sits in mild tension with readOnlyHint=false, but the description itself discloses the credit side effect, so the two are complementary rather than contradictory.

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?

Front-loaded with what the tool does, then caveats, then output shape. It is dense but nearly every sentence carries distinct, decision-relevant information (coverage limits, attribution rule, removed fields). Slightly long, but not padded.

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?

There is no output schema, so the description carries the full burden of describing returns — and it does: flat rows with permalink, author, excerpt, decoded publish date, row_type, post_id and degradation fields. Auth, cost, and coverage constraints are all covered, leaving nothing an agent needs to invoke it correctly.

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 the four parameters (company_urls, profile_urls, posted_since, max_charge_usd) are already richly documented in the schema, including the pre-charge skip semantics of posted_since. The description adds no parameter-level detail beyond what the schema states, so the baseline of 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?

States a specific verb and resource ('Point it at LinkedIn person profiles or company pages and it returns the posts a search engine has indexed'), plus the exact row shape returned. It also distinguishes itself from generic LinkedIn tools by specifying the mechanism (search-index based, no LinkedIn request), so an agent can tell what it is 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?

Gives clear usage context ('Point it at LinkedIn person profiles or company pages') and practical guidance ('filter on row_type before loading a table'). However, it never names an alternative sibling (e.g., map_company_social_presence, track_publication_cadence) or states when NOT to use it, so routing is left to inference.

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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