MCP Math 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 of behavioral disclosure. It states the algorithm used but does not describe key behaviors: computational complexity, typical use cases (e.g., for large numbers), limitations (e.g., memory usage), or output format. This is inadequate for a tool that performs a non-trivial mathematical computation.

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 and front-loaded: it directly states the tool's purpose in one sentence, with no wasted words. The parenthetical domain/category adds minimal but relevant context without redundancy. Every sentence earns its place, making it efficient and well-structured.

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 complexity of the algorithm, lack of annotations, no output schema, and 0% schema description coverage, the description is incomplete. It does not explain the tool's behavior, parameter meanings, or result format, which are essential for an AI agent to use it correctly. The conciseness comes at the cost of necessary detail.

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 3 parameters (g, h, n) with 0% description coverage in the schema. The description does not explain what these parameters represent (e.g., g as generator, h as target, n as modulus), their constraints, or how they relate to the discrete logarithm problem. This leaves the parameters entirely undocumented, failing to compensate for the schema 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 the tool's purpose: 'Solve discrete logarithm using Baby-step Giant-step algorithm.' It specifies the verb ('solve'), resource ('discrete logarithm'), and method ('Baby-step Giant-step algorithm'), which is more specific than just the tool name. However, it does not explicitly differentiate from sibling tools like 'discrete_log_naive', though the algorithm name implies a distinction.

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 no guidance on when to use this tool versus alternatives. It mentions the algorithm and domain/category ('arithmetic, modular_arithmetic'), but does not specify scenarios, prerequisites, or comparisons with sibling tools such as 'discrete_log_naive'. This leaves the agent without explicit usage instructions.

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