Search Posts
search_postsSearch LinkedIn posts using keywords or filters to retrieve relevant post data from the Fresh Linkedin Profile Data API.
Instructions
2 credits per call.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
search_postsSearch LinkedIn posts using keywords or filters to retrieve relevant post data from the Fresh Linkedin Profile Data API.
2 credits per call.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
v2.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only mentions credit cost and omits whether the operation is read-only, what it searches over, whether it returns posts or metadata, or any side effects. This is far below what an agent needs.
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?
The text is short, but brevity is not enough; it is under-specified. The single sentence conveys cost, not purpose or behavior, so it does not earn its place as a useful tool description.
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?
Even though there are no parameters, the tool is one of nearly forty siblings, and the description provides no functional or scoping information. An agent cannot reliably decide whether to call search_posts versus post-related siblings such as get_post_details, get_posts_reactions, or get_companys_posts.
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?
The input schema has zero parameters, so there is nothing for the description to document. The schema already fully captures input requirements, and the description does not need to compensate for missing parameter details.
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?
The description '2 credits per call' says nothing about what the tool does. The only hint of purpose is in the title, which is not part of the description. It also does not distinguish search_posts from many sibling tools like get_companys_posts, get_profiles_posts, or get_post_details.
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?
No guidance is given about when to use this tool instead of the many alternatives. The cost note is mildly useful operationally, but it does not explain context, preconditions, or which sibling to prefer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/BACH-AI-Tools/bach-fresh_linkedin_profile_data'
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