Cost of a meeting from attendee count or per-role rates. Returns total, burn per minute, per attendee, and annualised cost if it recurs.
| Name | Required | Description | Default |
|---|---|---|---|
| minutes | Yes | Meeting length in minutes | |
| currency | No | Label only (default USD) | |
| attendees | Yes | Head count, or [{role,count,hourly_rate}] | |
| recurrence | No | daily | weekly | biweekly | monthly | |
| hourly_rate | No | Default loaded hourly rate (default 75) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral transparency burden. It discloses the conditional 'annualised cost if it recurs', the input flexibility (attendee count or per-role rates), and the exact outputs returned. This is sufficient for a simple calculation tool, though it does not describe error handling or edge cases.
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?
Single sentence, front-loaded with purpose, followed by a compact output list. Every word contributes value; no filler or repetition of schema details. Excellent structure for the information conveyed.
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 output schema, the description adequately explains return values. It also conveys the two input modes and the recurrence condition. The schema covers parameter details, so overall the tool is well-specified. A minor gap is not mentioning that currency is label-only (though the schema already covers this), so the description is not misleading.
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 coverage is 100%, so the baseline is 3. The description adds mild meaning by linking attendees to 'per-role rates' and recurrence to 'annualised cost', but does not explain formatting or defaults beyond what the schema already provides. No significant additional parameter insight is offered.
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?
Description clearly states the tool calculates meeting cost from attendee count or per-role rates, and enumerates the outputs (total, burn per minute, per attendee, annualised cost). This distinguishes it from all sibling tools, which are unrelated or focus on other calculations.
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 intended use case is implied by the purpose statement but not stated explicitly. No guidance is given for when to choose this tool over alternatives, nor any exclusions or prerequisites. The 'cost of a meeting' framing is self-evident enough to infer usage, but explicit when-to-use context is missing.
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.
Most tools are clearly distinct, but there is overlap between local ops tools (gbrain_get/list/search, host_memory_get/set, forge_loop_status, list_mesh_snapshot, etc.) and public status/product tools (forge_status, popular_tools) which could confuse an agent. Descriptions help by tagging local ops, but the boundaries remain blurred.
All tool names use snake_case and mostly follow a verb_noun or noun_verb pattern (e.g., get_product, list_live_products, csv_to_markdown). A few names like now_iso and lorem_ipsum deviate from the verb-first style, but overall the naming is predictable and consistent.
With 40 tools, the server is overloaded for a coherent set. Many are simple utility functions that could be consolidated, and the mix of generic utilities, product APIs, and local ops adds unnecessary bulk, pushing the count well above the comfortable range.
The server lacks a clear domain, making it impossible to assess lifecycle coverage. While it offers many utilities, there is no coherent surface—missing common operations for any single category (e.g., no CRUD, no file handling) and many tools feel randomly assembled rather than forming a complete workflow.