get_assistants_token_usage
Track and monitor token consumption across all AI assistants to manage usage and optimize costs.
Instructions
Get token usage across all assistants
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Track and monitor token consumption across all AI assistants to manage usage and optimize costs.
Get token usage across all assistants
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states what the tool does, not how it behaves. It doesn't disclose if this is a read-only operation, requires authentication, has rate limits, returns aggregated or detailed data, or any side effects. For a tool with zero annotation coverage, this is a significant gap in behavioral context.
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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for a no-parameter tool, making it easy for an agent to parse quickly.
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 no annotations and no output schema, the description is incomplete for a tool that likely returns structured data (token usage). It doesn't explain what 'token usage' entails (e.g., counts, types, time ranges) or the return format, leaving the agent with insufficient context to use the tool effectively.
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 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, earning a baseline score of 4 for not adding unnecessary information beyond the schema.
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 'Get token usage across all assistants' clearly states the action (get) and resource (token usage), specifying scope (across all assistants). It distinguishes from sibling tools like 'get_assistant_usage' which likely focuses on a single assistant, though not explicitly named. It lacks full sibling differentiation for a perfect score.
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 like 'get_assistant_usage' or other usage-related tools. It implies a broad scope but doesn't specify prerequisites, timing, or exclusions, leaving the agent to infer usage context.
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/AiAgency-Now/MCP-VoiceAI-WhiteLabel'
If you have feedback or need assistance with the MCP directory API, please join our Discord server