search_post_by_keyword
Search LinkedIn posts using specific keywords to find relevant content and discussions within the platform.
Instructions
Search Post by Keyword
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Search LinkedIn posts using specific keywords to find relevant content and discussions within the platform.
Search Post by Keyword
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Search Post by Keyword' implies a read-only query operation, but it doesn't specify whether it returns all matching posts, is paginated, has rate limits, requires authentication, or what the output format is. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 description is extremely concise with just three words, but this brevity leads to under-specification rather than efficiency. It's front-loaded but lacks necessary detail. While it avoids waste, it fails to provide essential context, making it minimally adequate but not helpful.
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?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., list of posts, metadata), how results are structured, or any behavioral aspects like error handling. For a search tool with no structured data support, this leaves critical gaps for an AI agent to understand its operation.
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 0 parameters with 100% coverage, so no parameters need documentation. The description mentions 'keyword' which might imply a parameter, but since the schema explicitly defines no parameters, this doesn't add value or cause confusion. With zero parameters, the baseline is 4 as no parameter semantics are needed.
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 'Search Post by Keyword' is a tautology that essentially restates the tool name without adding meaningful context. It specifies the verb 'search' and resource 'post' with a 'keyword' qualifier, but lacks specificity about what type of posts (e.g., social media, blog, forum) or how the search operates. It doesn't distinguish from siblings like 'search_post_by_hashtag' or 'get_profiles_posts' beyond the keyword aspect.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose it over 'search_post_by_hashtag' for keyword-based searches versus hashtag-based ones, or when to use it instead of 'get_profiles_posts' for retrieving posts. There's no context about prerequisites, limitations, or typical use cases.
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/bachai-li-data-scraper'
If you have feedback or need assistance with the MCP directory API, please join our Discord server