get_upcoming_calendarUpcoming dividends, earnings and macro events that touch the book.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| household_id | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral scope: results are limited to future events that 'touch the book', which clarifies the filtering behavior beyond what annotations provide.
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, compact phrase with no filler. 'Upcoming' conveys the time window, the three event types convey content, and 'touch the book' conveys scope.
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?
An output schema exists and annotations cover side-effect safety, so the main remaining gap is parameter semantics. The description is nearly sufficient for a simple read-only tool, but without explaining `days` and `household_id`, the agent cannot reliably customize the request.
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 has 0% description coverage, and the description does not explain `days` or `household_id`. An agent can guess that `days` relates to the horizon and `household_id` selects the book, but the description fails to compensate for the schema's lack of parameter documentation.
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 specifies the resource: an upcoming calendar of dividends, earnings, and macro events scoped to 'the book'. Although it is a noun phrase rather than an explicit 'gets/returns' statement, it is specific enough to distinguish this from generic market-data or recent-results tools.
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 the use case: when an agent needs upcoming book-relevant dividends, earnings, or macro events. However, it does not explicitly mention when not to use it or name alternatives such as get_market_brief, get_recent_results, or get_today_brief, leaving comparison to inference.
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 distinct, but several pairs overlap in purpose: get_market_data and list_watchlist both return watchlist prices/signals, and the four 'brief' tools have fuzzy boundaries. Descriptions help, but an agent could easily select the wrong one without deeper context.
The dominant verb_noun pattern (get_* for details, list_* for collections) is clear and consistent in style. There are minor deviations like lookup_index_by_brand and run_technical_analysis, and several plural collections use get_, but these remain readable and mostly predictable.
39 tools is well past the 25+ threshold and represents a heavy surface for an agent to navigate. The broad wealth domain explains some of the count, but many granular getters could be consolidated or curated.
The set covers the advertised wealth surface well—portfolio, risk, credit, real assets, markets, geopolitics, watchlist, goals, family, and documents—but it is read-only and lacks obvious navigation endpoints like list_portfolios or detail views for goals/alerts. Agents can retrieve most data, but not all lifecycle operations or entity enumerations are present.