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mcp-linkedin-post-engager-capture

by mambalabsdev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Capture LinkedIn Posts and Commenters

capture_linkedin_posts_and_commenters
Idempotent

Retrieve indexed LinkedIn posts for profiles or company pages without LinkedIn access, returning permalink, author, excerpt, and decoded publish date as flat rows.

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

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. The actor's own spend cap for this run, in USD. The run stops before a charge would take its total past this amount, writes one notice row saying so, and keeps every row already charged. Leave it empty for no cap of your own; the maximum cost per run set in Apify is honored either way.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.4.0
    • addedInput schema / properties / max_charge_usd
      Added value: +{
+  "description": "Optional. The actor's own spend cap for this run, in USD. The run stops before a charge would take its total past this amount, writes one notice row saying so, and keeps every row already charged. Leave it empty for no cap of your own; the maximum cost per run set in Apify is honored either way.",
+  "minimum": 0,
+  "type": "number"
+}
  2. Changed8 schema fields changedv1.3.0
    • removedInput schema / additionalProperties
      Removed value: -false
    • removedInput schema / properties / collect_commenters
      Removed value: -{
-  "description": "Return one row per person who commented, with name, profile URL, comment text and comment likes. Turn it off to collect posts only, in which case commenters_collected comes back null rather than 0, because the actor did not look. Default: true.",
-  "type": "boolean"
-}
    • removedInput schema / properties / collect_reactors
      Removed value: -{
-  "description": "LinkedIn serves no reactor identities to a logged-out visitor, so this returns no reaction rows whatever you set. It exists so the limit is visible rather than silent, and it adds one notice row per run. Set it to false to suppress that row. Default: true.",
-  "type": "boolean"
-}
    • changedInput schema / properties / company_urls / description
      Previous value: -"LinkedIn company page URLs whose recent posts you want, for example https://www.linkedin.com/company/microsoft. Supply this, profile_urls, or both."New value: +"LinkedIn company page URLs, for example https://www.linkedin.com/company/microsoft. Supply this, profile_urls, or both."
    • removedInput schema / properties / max_engagers_per_post
      Removed value: -{
-  "description": "Caps the engager rows charged per post. LinkedIn shows about ten comments to a logged-out visitor, so ten is the platform ceiling and raising this above ten does nothing. Set it to 0 to pay for posts only. Default: 10.",
-  "maximum": 100,
-  "minimum": 0,
-  "type": "integer"
-}
    • changedInput schema / properties / posted_since / description
      Previous value: -"ISO date or timestamp, for example 2026-08-01. Posts published before this are skipped BEFORE anything is charged, so a scheduled run that finds nothing new costs the actor start and nothing else. Leave empty to take everything the page advertises."New value: +"ISO 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."
    • changedInput schema / properties / profile_urls / description
      Previous value: -"LinkedIn person profile URLs whose recent posts you want, for example https://www.linkedin.com/in/williamhgates. Country subdomains such as uk.linkedin.com are fine. Supply this, company_urls, or both."New value: +"LinkedIn 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."
    • removedInput schema / properties / use_residential_proxy
      Removed value: -{
-  "description": "Off by default, which is what the pricing assumes and what was measured. Turn it on only if LinkedIn starts refusing the platform's datacenter addresses. Apify bills residential bandwidth on top of this actor's events. Default: false.",
-  "type": "boolean"
-}
  3. First observedv1.0.1

TDQS

A4.4/5.0
Behavior5/5

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

Goes well beyond the annotations: no request is sent to LinkedIn, no cookie or account is used, results only count when the permalink names the source, coverage is bounded by the search index, and commenters/reactions/media were removed on 2026-10-12. It also discloses the APIFY_TOKEN requirement, credit consumption, degraded/degradation_reason flags, and that post_id is a stable activity URN.

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 the core behavior, then layered with caveats, each of which carries real decision value (coverage limits, row_type, removed features, credits). It is long, but nearly every sentence prevents a specific failure mode; only minor tightening is possible.

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 no output schema, the description fully describes the return shape (permalink, author, text excerpt, decoded publish date, row_type, post_id, degraded flags). Combined with the cost, auth, and coverage disclosures, an agent has everything needed to invoke and interpret this tool 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%, so all four parameters (company_urls, profile_urls, posted_since, max_charge_usd) are already fully documented in the schema, including the charge-cap behavior. The description adds no parameter-level detail beyond that, which is the correct baseline when the schema does the heavy lifting.

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 — it returns search-indexed LinkedIn posts for supplied person profiles or company pages as flat rows — and names the exact fields returned. It also proactively corrects the misleading tool name by stating that commenters are no longer returned. An agent knows precisely what comes back 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 concrete operating guidance: filter on row_type before loading a table, use posted_since to avoid charges, and expect posts to appear days or weeks after publishing with many never indexed. There are no sibling tools to route against, so explicit alternatives cannot be named, but the coverage caveats make the appropriate context clear.

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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