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 mentions the tool checks for a constant difference but does not specify behavioral traits like error handling (e.g., for invalid inputs), performance characteristics, or output format (e.g., boolean result). This leaves significant gaps in understanding how the tool behaves in practice.

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 concise and front-loaded, stating the core purpose in a single sentence. The additional domain and category information is brief and does not add unnecessary length. However, the lack of detail on parameters and usage slightly reduces efficiency, as more information could improve clarity without sacrificing brevity.

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 (a tool with two parameters, no annotations, and no output schema), the description is incomplete. It fails to explain parameter semantics, behavioral traits, or output expectations. For a tool that performs a mathematical check, more context on input format, tolerance usage, and result interpretation is needed to be fully helpful.

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 0% description coverage, so the description must compensate. It does not explain the parameters 'sequence' (an array of strings) or 'tolerance' (a number with default 1e-9), such as what the sequence represents (e.g., numeric values as strings) or how tolerance affects the arithmetic check. This lack of semantic detail makes parameter usage unclear.

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: 'Check if a sequence is arithmetic (constant difference between consecutive terms).' It specifies the verb ('Check'), resource ('sequence'), and condition ('constant difference between consecutive terms'). However, it does not explicitly differentiate from sibling tools like 'arithmetic_sequence' or 'arithmetic_series', which may have overlapping or related functionality, preventing a score of 5.

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 lacks context such as prerequisites, when-not-to-use scenarios, or comparisons to sibling tools like 'is_geometric' or other sequence analysis tools. The domain and category tags ('Domain: arithmetic, Category: general') are too vague to serve as meaningful usage guidelines.

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