One-line summary: This server exposes a single tool that takes LinkedIn person profiles and/or company pages and returns the posts a search engine has indexed for them, as flat rows with permalink, author, excerpt and publish date — no LinkedIn request, cookie or account involved.
Look up posts by person profile — pass one or more LinkedIn profile URLs (e.g. https://www.linkedin.com/in/williamhgates), including country subdomains like uk.linkedin.com.
Look up posts by company page — pass one or more company URLs (e.g. https://www.linkedin.com/company/microsoft); you can supply profiles, companies, or both in the same run.
Filter by publish date before being charged — posted_since takes an ISO date/timestamp; posts older than it are skipped before any billing occurs.
Cap your spend — max_charge_usd sets a run-level USD ceiling; the run stops before crossing it, emits one max_charge_reached notice row, and keeps rows already charged.
Get structured, flat output rows — each post row carries post_id (stable activity URN), post_permalink, author_name, author_url, author_type, post_text (search-engine excerpt) and published_at; a post only counts when its permalink names the source, so attribution is not spoofed.
Distinguish real results from run messages — rows are typed post or notice (filter on row type before loading a table); each row also carries degraded and degradation_reason, and a refused search surfaces as a degraded notice rather than "no posts".
Trust error handling — invalid input, bad token, exhausted balance, timeout, or a non-dataset run all come back as explicit tool errors rather than empty results.
Note the limits — requires an APIFY_TOKEN and consumes Apify credits; commenters, reaction/comment counts and media are no longer returned; coverage is only what the search index holds, so posts lag by days or weeks and many never appear; the tool is read-only, non-destructive, idempotent, and unofficial/unaffiliated with LinkedIn.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-linkedin-post-engager-capture Show me recent posts and commenters from Bill Gates' LinkedIn"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for the Mamba Labs LinkedIn Post Tracker actor on Apify.
Point it at LinkedIn person profiles or company pages. It returns the posts a search engine has indexed for each one, as flat rows: permalink, author, a text excerpt, and the publish date. No request is sent to LinkedIn, and no cookie, account, or credential is used.
Changed in 1.3.0. The actor stopped fetching linkedin.com on 2026-10-12. Commenter rows, reaction and comment counts, media, and the inputs collect_commenters, collect_reactors, max_engagers_per_post, and use_residential_proxy are gone. Posts now come from search results, so coverage is what the index holds: posts appear days or weeks after publishing, and many never appear. The tool keeps its name, capture_linkedin_posts_and_commenters, so existing clients keep working.
npx -y @mambalabsdev/mcp-linkedin-post-engager-capture{
"mcpServers": {
"mamba-linkedin-post-engager-capture": {
"command": "npx",
"args": ["-y", "@mambalabsdev/mcp-linkedin-post-engager-capture"],
"env": { "APIFY_TOKEN": "your-apify-token" }
}
}
}Get an Apify token at console.apify.com/account/integrations.
Related MCP server: LinkedIn Data API MCP Server
capture_linkedin_posts_and_commentersLinkedIn profiles and company pages in, the posts a search engine has indexed for them out.
Input | Type | Required | Meaning |
| array of strings | one of the two | Person profiles, for example |
| array of strings | one of the two | Company pages, for example |
| string | no | ISO date. Posts published before this are skipped before anything is charged. |
| number | no | Spend cap for the run, in USD. The run starts no post whose charge would cross it and writes one |
post is one row per indexed post: post_id, post_permalink, author_name, author_url, author_type, post_text (the search engine's excerpt), published_at (decoded from the post id).
notice is a run-level or source-level message. Filter these out before loading a table.
A post counts only when its permalink names the source, for example linkedin.com/posts/stripe_... for linkedin.com/company/stripe. A refused search comes back as a degraded notice, never as "no posts".
You are charged per post collected, plus a small actor start fee. Notice rows are free. posted_since filters before anything is charged.
Event | Price per event (USD) | Fires when |
| $0.00005 | Charged when the Actor starts running, one event per GB of memory. |
| $0.002 on the Free plan, down to $0.0017 on higher Apify plans | One LinkedIn post returned from search results, with its permalink, author, text excerpt and publish date. |
The actor's Apify page carries the current prices.
It is a thin client for the Apify actor. It passes your input through and returns the actor's output unchanged. Every behavior described above lives in the actor, not here.
It reads only public search engine results. It sends no request to LinkedIn, holds no session cookie, and uses no LinkedIn account.
Post rows from a person profile name that person. When you run this you are the data controller for the personal data it returns and Apify is the processor. Your lawful basis is yours to establish; nothing here is legal advice.
This actor is unofficial and is not affiliated with, sponsored by or endorsed by LinkedIn or Microsoft.
Errors are surfaced, never swallowed. An invalid input, an invalid token, an exhausted balance, a timeout, or a run that returns anything other than a dataset all come back as an explicit tool error rather than as an empty result.
The actor is on the Apify Store. This wrapper is MIT licensed.
Built by Mamba Labs
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.
| Name | Required | Description | Default |
|---|---|---|---|
| company_urls | No | LinkedIn company page URLs, for example https://www.linkedin.com/company/microsoft. Supply this, profile_urls, or both. | |
| posted_since | No | 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. | |
| profile_urls | No | 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. | |
| max_charge_usd | No | 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. |
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.4.0capture_linkedin_posts_and_commenters1 field changedInput schema / properties / max_charge_usdAdded 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"
+}v1.3.0capture_linkedin_posts_and_commenters8 fields changedInput schema / additionalPropertiesRemoved value: -falseInput schema / properties / collect_commentersRemoved 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"
-}Input schema / properties / collect_reactorsRemoved 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"
-}Input schema / properties / company_urls / descriptionPrevious 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."Input schema / properties / max_engagers_per_postRemoved 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"
-}Input schema / properties / posted_since / descriptionPrevious 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."Input schema / properties / profile_urls / descriptionPrevious 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."Input schema / properties / use_residential_proxyRemoved 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"
-}v1.0.1capture_linkedin_posts_and_commentersScored across 1 tool
There is only one tool, so there is no possibility of selecting the wrong one. Its purpose (capture indexed LinkedIn posts for given profiles/pages) is stated unambiguously.
The single name uses a clear snake_case verb_noun form, which is internally consistent. However, it still advertises 'commenters' even though the description says commenter data was removed, making the name stale and slightly misleading.
The server exposes exactly one tool for a very narrow, read-only capture operation, so the count is defensible but thin. An agent gets no separate means to list sources, filter row types, or fetch metadata without parsing the single flat output.
For a read-only 'capture posts from a search index' domain, one tool covers the whole lifecycle and returns stable post_id, row_type, and degradation flags. Gaps are minor: no dedicated source-validation or post-filtering operation, and the name's promise of commenters is no longer fulfilled.
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