Resolve team or player names before requesting a profile.
Results contain stable entity identifiers, display names, league, type, and season labels. Public profile coverage is currently NBA, CBB, NHL, and NFL.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| league | No | ||
| entity_type | No | all |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds value by stating the result structure (identifiers, display names, etc.) and noting coverage (NBA, CBB, NHL, NFL). No contradictions with 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 extremely concise—two sentences that front-load the main use case and then detail result contents and coverage. Every sentence adds value without redundancy.
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 the presence of an output schema (which covers return values) and well-named parameters, the description provides adequate context about the tool's scope and result contents. It could be improved by explicitly linking the league parameter to coverage, but overall it is sufficient for a search tool.
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 is rich with descriptive parameter names, enums, and defaults (e.g., query, league, entity_type, limit). However, the description does not explain any parameters beyond mentioning coverage, and schema description coverage is 0%. The schema bears the explanatory burden, which is adequate but leaves the description's contribution minimal.
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's purpose: 'Resolve team or player names before requesting a profile.' It also lists the result contents (stable IDs, display names, league, type, season labels) and coverage, making it easy for an agent to understand its function. It differentiates from sibling tools like get_player_profile which are for fetching profiles after obtaining identifiers.
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 positions the tool as a precursor to profile requests: 'Resolve team or player names before requesting a profile.' This gives clear usage context. However, it does not specify when not to use it or list alternatives, so it lacks complete exclusion guidance.
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 have clearly distinct purposes, but some overlap exists (e.g., get_todays_projections vs get_game_recommendation and get_track_record vs get_pick_history vs get_performance_summary). However, the detailed descriptions help differentiate them.
All tools follow a consistent 'get_*' or 'search_*' verb_noun pattern with snake_case. The only deviation is 'search_entities', which is a natural fit for a search operation.
19 tools is well-scoped for a sports betting analytics server, covering metadata, data status, schedules, projections, performance, subscriptions, and profiles without being excessive.
The tool surface covers core analytics workflows (projections, track record, performance, methodology) plus supporting operations (brand, status, subscriptions, search). Minor gaps like league standings or team statistics are outside the primary scope.