get_eventGet a single scheduled event by its UUID (the last path segment of an event URI). Returns full event details including name, times, status, location, and event membership.
| Name | Required | Description | Default |
|---|---|---|---|
| uuid | Yes | Scheduled event UUID (last segment of the event URI). | |
| _apiKey | Yes | Calendly personal access token (sent as Bearer token) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "_apiKey": "your-calendly-api-key",
+ "uuid": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the description's main addition is explaining the UUID extraction and return fields. This adds useful context without contradicting annotations.
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 two sentences, front-loaded with the purpose, and contains no unnecessary fluff. Every sentence adds value.
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 get-by-ID operation, the description covers input (UUID extraction method) and output (list of returned fields) adequately. No output schema is needed, and annotations fill the safety profile.
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%, with both parameters well-described in the schema. The description adds minimal extra meaning beyond the schema, only restating the UUID source. Baseline of 3 is appropriate.
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 retrieves a single scheduled event by its UUID, specifying the verb 'Get' and the resource 'scheduled event'. It explicitly distinguishes from siblings like list_scheduled_events by focusing on a single event.
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 implies usage when you have a UUID and need full event details, providing clear context. However, it does not explicitly exclude other use cases or compare to alternatives like list_scheduled_events.
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.
Several research tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route to the same underlying catalog. Similarly, bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread form a dense prediction-market cluster with fuzzy boundaries. The few Calendly tools are distinct, but they are drowned out by duplicative families.
Most names use snake_case and a noun-based pattern (e.g. polymarket_edges, entity_profile), and several follow verb_noun (list_event_types, list_scheduled_events, resolve_entity). But there are standalone verbs (forget, recall, subscribe) and inconsistent verb styles (ask_ vs list_ vs scan_), so the pattern is readable but not predictable.
36 tools is far too many for a server ostensibly named Calendly: only 5 tools relate to Calendly events and invitees, while 31 tools belong to an unrelated Pipeworx data-research platform. The count is bloated by a second domain bolted onto the server, making the surface hard to navigate for its apparent purpose.
For the Calendly domain implied by the server name, the surface is missing core operations: there is no create/update/cancel/delete event, no cancel or reschedule flow, no invitee management beyond listing, and no event-type creation or editing. The Pipeworx side is broader, but even it has gaps like no bulk data export or direct raw-tool invocation.