Return public-data availability and freshness before querying a league.
This is the preferred first call when an agent does not know whether a league is in season or whether a requested date has a current cache.
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | ||
| league | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 declare readOnlyHint and idempotentHint, covering mutability and side effects. The description adds that it returns 'availability and freshness' but does not detail what those mean or describe output structure. It meets the bar given annotations but adds limited new behavioral insight.
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 sentences, direct and without excess. Every word adds value: action ('return'), scope ('public-data availability and freshness'), and usage context ('preferred first call').
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, so return value description is not required. However, the description omits any mention of the two input parameters and does not describe how they affect results (e.g., date optionality, league filtering). For a simple tool, this is a moderate completeness gap.
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 description coverage is 0%, meaning the description adds no parameter explanations. The two parameters (date, league) are not mentioned despite being enums with clear names. The agent must infer usage from names only, which is a significant gap.
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 it returns public-data availability and freshness, and explicitly frames it as the preferred first call for checking league season state or cache freshness. This distinguishes it from sibling tools like get_league_schedule or get_performance_summary.
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 explicitly says 'preferred first call when an agent does not know whether a league is in season or whether a requested date has a current cache,' providing clear when-to-use guidance. It does not mention when not to use or alternatives, but the context is sufficient.
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 tools occupy overlapping historical/performance territory: get_track_record, get_pick_history, get_premium_history, get_projection_history, and get_performance_summary all query resolved picks or performance, and get_pick_history even instructs callers to prefer get_track_record. While the long descriptions help, the name-level boundaries are unclear and an agent selecting by tool name could easily choose the wrong history endpoint.
Tool names follow a consistent get_<noun> snake_case convention, with search_entities as the only deviation but still a verb_noun form. The naming pattern makes the API predictable and scannable.
Twenty tools is on the heavy side for a single MCP server and falls in the borderline 16-25 range. The count is not absurd, but several history/record tools could likely be consolidated into fewer endpoints.
The server covers the main analytics workflow well: data status, methodology, schedules, projections, premium content, performance, historical records, profiles, and subscription details. Minor gaps exist (e.g., no single all-league schedule endpoint, no generic search across all data types), but core querying needs are satisfied.