Read the current responses for a Whenly event and get the best meeting times (the slots where the most people are free). Pass the event slug (the code after /e/ in the event_url).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The 7-char event code from the event_url (the part after /e/). |
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 behavioral weight. It discloses that the operation is read-based and that it computes best times, but it does not describe output format, error cases, or explicitly confirm no side effects beyond the word 'Read'.
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?
Two concise sentences with no filler. The first sentence front-loads purpose and outcome, and the second provides the exact parameter guidance needed.
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?
For a simple one-parameter read tool with no output schema, the description covers what it does and how to construct the call. It stops short of describing the response shape, but the core usage context is adequately complete.
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 param is already well-documented. The description restates that the slug is the part after /e/ in the event_url, matching the schema exactly without adding new 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?
The description clearly states the tool reads responses for a Whenly event and derives best meeting times. It names a specific verb and resource, and the contrast with the sibling create_event makes the distinction obvious.
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 makes the use case clear: pass an event slug to read responses and get best times. It does not explicitly state when not to use this tool, but the single sibling create_event implies the read-vs-create split without needing an explicit exclusion.
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.
The two tools have completely distinct purposes: create_event sets up a new scheduling poll, while get_results reads responses from an existing poll. There is no ambiguity or overlap between them.
Both tool names follow the same verb_noun snake_case pattern: create_event and get_results. This is consistent and predictable.
Two tools feels thin for a server, though the narrow workflow of creating an event and then polling for results is cohesive. Each tool earns its place, but the surface is minimal.
The create-and-poll workflow is complete for the basic group scheduling use case, but there are no update or delete capabilities for events. Agents can work around mistakes by creating new events, so the gap is minor.