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Chetto983

Mathematical Calculator MCP Server

by Chetto983

summation

Computes the sum of a function over a specified range by evaluating an expression at each integer step.

Instructions

Calculates the summation of a function from start to end.

Args: expression: The expression of function x as a string. start: The starting value of the summation. end: The ending value of the summation.

Returns: On success: {"result": } On error: {"error": }

Examples: >>> summation("x**2", 0, 10) {'result': 385}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNo
startNo
expressionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.1

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden, and it does disclose the success shape {'result': ...} and the error shape {'error': ...}, which is real behavioral value. It does not address precision, numeric vs symbolic evaluation, or any limits/validation on the expression string (e.g. whether arbitrary code is evaluated), which is a notable gap for a tool taking an expression string.

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

Conciseness4/5

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

Front-loaded purpose sentence followed by clean Args/Returns/Examples sections; nothing is redundant. The section headers are slightly verbose relative to the tiny surface, keeping it short of a 5.

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

Completeness4/5

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

There is no output schema, but the description supplies the return dictionary shape for both success and error plus a concrete example, which is what an agent needs to interpret results. Only minor gaps (defaults, precision or validation behavior on the expression string) remain.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate and it does: it documents all three parameters, clarifying that expression is 'the expression of function x as a string' and defining start and end as the summation bounds. This is meaningfully more than the bare schema titles, though it doesn't state that start/end default to 0 and 10.

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?

States a specific verb and resource ('Calculates the summation of a function from start to end'), which is clearly distinguishable from continuous siblings like integrate. However, it does not explicitly contrast itself with any sibling tool, so the agent must infer that this is the discrete counterpart to integrate.

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

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied through the signature and the worked example, which show exactly how to call it. There is no explicit statement of when to prefer this over integrate, calculate, or solve_equation, nor any prerequisites or exclusions.

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