Evaluate arbitrary-precision math expressions exactly to fix LLM arithmetic errors. Supports operators and functions like sqrt, sin, log.
Evaluate an arbitrary-precision math expression exactly (fixes LLM multi-digit arithmetic errors). Supports +,-,*,/,^,sqrt(),sin(),log(), etc.
| Name | Required | Description | Default |
|---|---|---|---|
| expression | Yes | Math expression, e.g. "123456789 * 987654321" or "sqrt(2)^10" |
Changes observed during successful MCP inspections.
v2.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It adds valuable behavioral context: exactness, arbitrary precision, and the supported operators/functions, which go beyond the schema's bare parameter description. It doesn't mention error handling or edge cases, but for a simple math tool this is adequate.
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?
Two sentences, front-loaded with the primary purpose and then a compact list of supported operations. Every word earns its place; no fluff.
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?
For a single-parameter tool with no output schema, the description is complete enough: it states what it does, why it exists (fixing arithmetic errors), and what operations it supports. It could mention return format or error handling, but these are not critical for this simple utility.
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 100% – the schema already provides an example for the expression parameter. The description adds supported function context but doesn't substantially change parameter meaning beyond what the schema conveys, so the baseline of 3 is appropriate.
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 evaluates arbitrary-precision math expressions exactly, with a specific verb ('evaluate') and resource ('math expression'). It also mentions fixing LLM arithmetic errors, which differentiates it from sibling tools like compute_statistics or factorial.
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 gives clear context for when to use it: whenever exact arithmetic is needed, especially for multi-digit numbers prone to LLM errors. It doesn't explicitly exclude alternatives, but the purpose is specific enough to route an agent correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.