Sports Betting MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses that picks are 'AI-generated' and include 'confidence and edge scores', which adds useful context. However, it doesn't mention rate limits, authentication needs, data freshness (e.g., how often updated), or error conditions, leaving behavioral gaps for a read operation.

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 well-structured with a purpose statement, args section, and returns section. It's front-loaded with key information. However, the 'Returns' section could be more concise by integrating with the purpose statement, and some details like 'visual bet slip cards' are repeated.

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 no annotations and no output schema, the description provides basic purpose, parameter info, and return structure. However, for a tool with potential complexity (AI-generated picks, multiple sports, visual elements), it lacks details on output format (e.g., JSON structure), error handling, and how 'top pick per sport' is determined, making it minimally adequate.

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%, so the description must compensate. It provides the 'sport' parameter's purpose ('Filter by sport'), valid values ('nba', 'nhl', 'ncaab', or 'all'), and default ('all'). This adds meaningful semantics beyond the bare schema, though it doesn't explain format constraints or edge cases.

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's purpose: 'Get today's AI-generated picks with confidence and edge scores.' It specifies the resource (AI-generated picks) and includes additional functionality (visual bet slip cards). It distinguishes from siblings like 'get_top_pick' (singular) and 'get_pending_picks' (pending vs. today's).

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 for retrieving today's picks, but doesn't explicitly state when to use this vs. alternatives like 'get_top_pick' or 'get_pending_picks'. No guidance on prerequisites or exclusions is provided, leaving the agent to infer context from tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.