get_postsGet Instagram user posts. Max - 12 Use end_cursor to get the rest of the data Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
| id_user | No | User ID | |
| end_cursor | No | The value of end_cursor for viewing the posts list |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
It conveys that this is a read operation (get) with a maximum of 12 posts per call, which is a behavioral constraint. The pagination mechanism is described. However, without annotations, it lacks details on authentication, error handling, or what the response includes, leaving some behavioral ambiguity.
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 brief but poorly structured, running ideas together without clear separation. It could be more readable with proper punctuation or bullet points. Still, it contains no fluff, so it earns a median score.
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 an output schema, the description should explain what the returned posts contain, but it only says 'posts' without detail. It also doesn't mention how to obtain id_user (e.g., via get_user_id) or handle missing users, making it incomplete for a complex Instagram tool.
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 description adds meaning to end_cursor by explaining its role ('to get the rest of the data'), which the schema only vaguely describes as 'for viewing the posts list.' The id_user parameter is already clear. Since schema coverage is 100%, the description still enriches the semantics of end_cursor.
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 clearly states the tool's function: 'Get Instagram user posts.' It specifies a limit ('Max - 12') and mentions pagination via end_cursor, which defines the scope precisely. This distinguishes it from sibling tools like get_posts_username by implying a user ID-based approach.
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 actionable guidance: it explains how to get all posts by using end_cursor ('Use end_cursor to get the rest of the data') and mentions billing per call. However, it does not explicitly compare with alternatives or state prerequisites, but the pagination instruction is valuable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools have overlapping or near-identical purposes (e.g., get_posts vs get_posts_username, get_reels_posts vs get_reels_posts_username). The distinction between get_post_info, get_post_info_v2, get_reel, and get_tv_info is unclear from descriptions alone. This will cause frequent misselection.
Naming is inconsistent: suffixes like '_username', '_hd', '_v2', '_id' appear sporadically, and the same resource type is named differently (e.g., 'posts' vs 'post_info' vs 'reels_posts' vs 'tv_posts'). Some tools are meta (get_requests, get_server) and deviate from the data-focused pattern. Overall, no clear naming convention.
With 40 tools, the set is overly large for an Instagram scraper. Many tools are near-duplicates differing only by input type (ID vs username), which could be consolidated. The count far exceeds the typical 3-15 range and feels bloated, though not extreme.
The tool set covers a comprehensive range of Instagram data: user info, posts, reels, TV, stories, highlights, comments, likes, followers, followings, hashtag/location/music search, and even server status. Despite some vague tools (get_additional_info, get_basic_engagement), it appears functionally complete for the domain.