Checking the server
get_serverChecking the server status Billing per call: 1 Credits.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
get_serverChecking the server status Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 burden. It says 'Checking' which implies a read-only operation, but it does not explicitly state safety or specify what 'status' means or what the response contains. It does mention billing per call, which is a useful behavioral detail, but lacks disclosure of output structure, error behavior, or side effects.
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: 'Checking the server status Billing per call: 1 Credits.' It front-loads the purpose and adds cost information in a compact manner. There is no filler or redundant text, making it optimally sized.
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 simplicity (no params, no output schema), the description is minimally adequate. It tells what the tool does and the cost, but it does not explain what 'status' includes (e.g., uptime, latency, version) or how to interpret results. Since there is no output schema, such details would be valuable for completeness, earning a mid-range score.
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 tool has zero parameters, so the schema is fully covered by definition. According to the rubric, a 0-parameter tool baselines at 4. The description adds nothing about parameters because there are none, so it neither helps nor hurts.
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 'Checking the server status', which is a specific verb and resource. It is distinct from sibling tools like get_business or get_posts, all of which pertain to social media data. This tool is the only one about server status, so its purpose is unambiguous.
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 or alternatives. It does not mention prerequisites, scenarios where this is appropriate, or differentiate it from other tools. It simply states what it does without any contextual usage advice.
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.