get_account
Get account details. Free.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| account_id | Yes | Account UUID |
Get account details. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| account_id | Yes | Account UUID |
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 must carry the full burden of behavioral disclosure. Apart from the ambiguous "Free" (which may imply no cost), the description does not reveal whether this is a read-only operation, any permissions needed, or what response to expect. The verb "Get" implies read-only but is not explicit.
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, consisting of two short sentences. "Get account details" is essential, while "Free." is an extra but potentially useful hint. There is no redundancy or verbose language, which earns a high score for conciseness, even though it is minimal.
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?
The tool has no output schema, no annotations, and only one parameter, but the description is notably sparse. It does not explain what "account details" includes, how the response is structured, or how it differs from related account tools. This leaves significant ambiguity for an agent deciding whether to invoke it.
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 fully documents the only parameter (account_id as an account UUID), so the schema coverage is 100%. The description adds no additional meaning about the parameter, and the baseline of 3 applies because the schema already provides adequate semantics.
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?
"Get account details" clearly states a specific verb and resource, indicating this tool retrieves information about a single account. It distinguishes from the many sibling tools through its name, but the description does not explicitly differentiate it from similar tools like get_account_metrics or get_workspace, though the resource is clear.
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 does not mention any context, exclusions, or prerequisites, leaving the agent to infer usage solely from the name and schema.
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.
Every tool targets a distinct resource and action: account vs video vs folder vs analysis vs radar. Even similar tools like analyze_video and get_analysis are clearly separated by creation vs retrieval.
Most names follow a verb_noun pattern (track_video, get_account, create_folder). Minor deviations like 'growth_trends' and 'radar_history' are noun phrases but remain readable and predictable.
26 tools is slightly above the ideal range, but the server covers a broad domain with distinct sub-areas (accounts, videos, folders, analytics, AI analysis, radar), so the count is justified rather than bloated.
Core lifecycle operations are covered: track/untrack accounts and videos, list/get details, analytics, folders, and AI analysis. Minor gaps exist (no delete folder, no remove-from-folder), but they don't break primary workflows.