SharpEdge MCP Server

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

No annotations are provided, so the description carries the full burden. It discloses that the tool provides educational explanations and mentions specific methods (de-vigging, sharp book reference lines, Kelly Criterion), which adds useful context. However, it does not cover behavioral aspects like response format, potential rate limits, authentication needs, or error handling, leaving gaps for a tool with no annotation coverage.

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 front-loaded with the core purpose in the first sentence, followed by a targeted user benefit. Both sentences earn their place by clarifying the tool's educational nature and audience, with no redundant or vague phrasing. It efficiently conveys necessary information without excess verbiage.

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 tool's low complexity (one optional parameter, no output schema, no annotations), the description is reasonably complete. It covers the purpose, usage context, and key topics, which suffices for an educational tool. However, without annotations or output schema, it could benefit from more behavioral details (e.g., response format) to achieve full completeness.

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 100% description coverage, with the 'topic' parameter fully documented via enum and description. The description does not add specific parameter semantics beyond what the schema provides, but it implicitly supports the parameter by mentioning topics like 'de-vigging' and 'Kelly Criterion.' With high schema coverage and only one optional parameter, a baseline of 3 is appropriate; the description's alignment with parameter topics justifies a slightly higher score.

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 explicitly states the tool's purpose: to provide 'a clear, educational explanation of what +EV (positive expected value) betting is, how it works, and how SharpEdge AI uses de-vigging, sharp book reference lines, and the Kelly Criterion to find profitable edges.' It uses specific verbs ('Get', 'explain') and resources (+EV betting concepts), and clearly distinguishes from sibling tools like get_features or get_sample_edges by focusing on educational content rather than data retrieval.

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 provides clear context for when to use this tool: 'Great for users who want to understand the math behind profitable sports betting.' It implicitly suggests this is for educational purposes rather than operational tasks like get_live_stats or get_sample_edges. However, it does not explicitly state when not to use it or name specific alternatives among siblings.

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