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vipankumar87

MCP Multi-Tool Server

by vipankumar87

power

Calculate exponential values by raising a base number to a specified power for mathematical computations and data analysis.

Instructions

Raise a number to a power.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseYes
exponentYes

Implementation Reference

  • server.py:95-98 (handler)
    The handler function for the 'power' MCP tool. It is decorated with @mcp.tool() which registers it, and implements raising the base to the exponent power using the ** operator. The input schema is inferred from type annotations: base (float), exponent (float) returning float.
    @mcp.tool()
    def power(base: float, exponent: float) -> float:
        """Raise a number to a power."""
        return base ** exponent
Behavior2/5

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 mathematical operation but doesn't cover important traits like error handling (e.g., for negative bases with fractional exponents), performance characteristics, or output format. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves beyond the basic function.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a simple mathematical tool, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (basic math operation), no annotations, no output schema, and minimal parameter documentation, the description is incomplete. It doesn't explain the return value (e.g., that it outputs a number), error cases, or how it fits among sibling tools, leaving the agent with insufficient context for reliable use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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 mentions 'a number' and 'a power,' which loosely maps to the 'base' and 'exponent' parameters, but doesn't explain their roles, constraints (e.g., valid ranges), or interactions. This adds minimal semantic value beyond what's inferred from the schema titles, failing to adequately address the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Raise a number to a power' clearly states the verb ('raise') and resource ('a number'), making the purpose immediately understandable. However, it doesn't explicitly distinguish this tool from sibling mathematical operations like 'factorial' or 'square_root' beyond the basic mathematical concept, which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 like 'square_root' (which is a special case of exponentiation) or 'factorial' (which involves repeated multiplication). There's no mention of use cases, prerequisites, or comparisons to sibling tools, leaving the agent with minimal contextual direction.

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

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