Get User Info by id_user
get_infoGet Instagram user info by id_user Billing per call: 1 Credits.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id_user | No | User ID |
get_infoGet Instagram user info by id_user Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
| id_user | No | User ID |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions billing per call (1 credit), which is a useful operational detail, but it does not disclose any other behavioral aspects such as rate limits, data freshness, or whether the info is basic or full. It adds minimal context beyond the function name.
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 very short and front-loaded with the key purpose. The billing note is an extra fact that may be useful but is somewhat tangential. It is not overly verbose, so it earns a 4 for efficiency, though it could be slightly more informative.
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 and no annotations, the description leaves out critical details: what fields are returned, whether it requires authentication, potential error cases, and how it differs from similar tools. For a simple one-parameter tool, it is minimally adequate but not complete for an agent to use confidently.
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 the schema already documents the parameter. The description adds no further meaning to the parameter beyond what the schema provides. Baseline of 3 is appropriate. It mentions the billing aspect, but that is not parameter-specific.
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 states it gets Instagram user info by id_user, which is a specific verb+resource. However, it does not clarify what specific information is returned, and it is not strongly differentiated from siblings like get_info_username or get_additional_info. The functionality is clear but the scope is vague.
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 on when to use this tool versus others like get_info_username or get_additional_info. The description simply says 'Get Instagram user info by id_user' without stating context or exclusions, so an agent cannot determine when this is the preferred choice.
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.