Social Fetch

List LinkedIn profile posts

linkedin_profiles_posts_list

Read-only

List posts from one specific LinkedIn person profile by URL (not a keyword search — use linkedin.posts.search.list to search across public posts). Returns a list (use cursor when paginated).

Input Schema

TableJSON Schema

Name	Required	Description
`url`	Yes	LinkedIn profile URL or vanity handle whose public posts should be listed.
`limit`	No	Maximum posts to return (default 10).
`context`	Yes	Describe the user's underlying goal in one sentence — not the tool you are calling.
`endDate`	No	Optional end of the date range for posts to include. Must be a valid ISO-8601 date-time. For profile URLs, date filtering applies only to LinkedIn articles.
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`startDate`	No	Optional start of the date range for posts to include. Must be a valid ISO-8601 date-time. For profile URLs, date filtering applies only to LinkedIn articles.
`conversation_id`	No	Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.
`onlyAuthoredPosts`	No	When true, return only posts created by the profile owner.

Schema Changelog

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

Changed5 schema fields changedSep 5, 2026

removedInput schema / additionalProperties
```
Removed value: -false
```

addedInput schema / properties / context

Added value: +{
+  "description": "Describe the user's underlying goal in one sentence — not the tool you are calling.",
+  "type": "string"
+}

addedInput schema / properties / conversation_id

Added value: +{
+  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "url"
-]New value: +[
+  "url",
+  "context",
+  "llm_model"
+]

Changed1 schema field changedAug 27, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

Changed1 schema field changedJul 29, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```

Changed7 schema fields changedJul 29, 2026

changedInput schema / properties / endDate / description

Previous value: -"Optional end of the date range for posts to include."New value: +"Optional end of the date range for posts to include. Must be a valid ISO-8601 date-time. For profile URLs, date filtering applies only to LinkedIn articles."

removedInput schema / properties / limit / anyOf

Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "number"
-  }
-]

addedInput schema / properties / limit / maximum
```
Added value: +200
```
addedInput schema / properties / limit / minimum
```
Added value: +1
```
addedInput schema / properties / limit / type
```
Added value: +"integer"
```

changedInput schema / properties / onlyAuthoredPosts / anyOf

Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "boolean"
-  }
-]New value: +[
+  {
+    "type": "boolean"
+  },
+  {
+    "enum": [
+      "0",
+      "1",
+      "true",
+      "false"
+    ],
+    "type": "string"
+  }
+]

changedInput schema / properties / startDate / description

Previous value: -"Optional start of the date range for posts to include."New value: +"Optional start of the date range for posts to include. Must be a valid ISO-8601 date-time. For profile URLs, date filtering applies only to LinkedIn articles."

First observedJul 20, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already cover read-only safety (readOnlyHint=true) and data volatility (openWorldHint=true), and the description adds value beyond them by disclosing the return shape ('Returns a list') and pagination behavior ('use cursor when paginated'). No contradction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with zero waste: the core action and scope come first, followed by the exclusion/alternative and the return/pagination note. Every clause earns its place, and the most decision-relevant information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only list tool with 100% schema coverage and no output schema, the description covers the essential operational facts: scope, exclusion, alternative routing, and pagination. It could go further by describing post fields or the onlyAuthoredPosts behavior, but those are partially covered by the rich schema descriptions and openWorldHint annotation.

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 each of the 8 parameters (url, limit, context, startDate, endDate, conversation_id, llm_model, onlyAuthoredPosts) is already documented in the schema. The description does not add parameter-level meaning beyond confirming the URL-driven scope, which matches the baseline of 3.

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 ('List posts from one specific LinkedIn person profile by URL') and explicitly distinguishes itself from keyword search by naming the alternative (linkedin.posts.search.list). An agent can immediately tell this apart from sibling tools like linkedin_company_posts_list and linkedin_people_search_list without inspecting their schemas.

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?

Provides explicit when-to-use ('one specific LinkedIn person profile by URL'), when-not-to-use ('not a keyword search'), and the named alternative ('use linkedin.posts.search.list'). This directly resolves the most likely confusion point — profile-scoped listing vs public keyword search — with no inference required.

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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TDQS

B3.4/5.0

Disambiguation5/5

Each tool is clearly scoped to a specific platform and action (e.g., facebook_post_get vs instagram_post_get). Descriptions explicitly differentiate similar tools across platforms, and within-a-platform tools like tiktok_search_videos_list vs tiktok_search_hashtag_list have clear disambiguation notes.

Naming Consistency5/5

All 167 tools follow a strict `platform_resource_action` pattern (e.g., youtube_video_comments_list). No mixing of styles—snake_case throughout, with consistent verb ordering (get, list, search, etc.).

Tool Count2/5

The server has 167 tools, which is far beyond the typical well-scoped range of 3-15. While the broad multi-platform scope justifies many tools, this extreme number makes the tool surface overwhelming and difficult for an agent to navigate efficiently.

Completeness4/5

The tool set covers a wide range of platforms and operations including profile retrieval, post/video fetching, comments, search, transcripts, and ad library access. Minor gaps exist (e.g., no Facebook events or LinkedIn messaging), but the surface is comprehensive for a read-only data aggregation use case.

List LinkedIn profile posts

Input Schema

Schema Changelog

TDQS

Glama MCP Gateway

TDQS

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