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agentspan-ai

MCP Test Server

by agentspan-ai

string_uppercase

Convert text to uppercase to standardize casing for comparisons, display, or test data.

Instructions

Convert a string to uppercase.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.4

TDQS

C2.9/5.0
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, yet it discloses nothing about behavior beyond the basic transformation: no note on locale/Unicode casing rules, handling of non-ASCII characters, null/empty input, or that the operation is a pure, side-effect-free function. For a trivial tool this is a minor gap, but it is still undisclosed.

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?

A single short sentence with no filler and the operation front-loaded. It is efficient, though so terse that nothing beyond the operation is conveyed.

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

Completeness3/5

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

With an output schema present, return values need not be explained, and the tool is simple enough that a brief description suffices. Still, no edge-case behavior (empty string, Unicode, non-string input) is covered, leaving gaps for an agent reasoning about unusual inputs.

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

Parameters3/5

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

Schema description coverage is 0% for the single 'text' parameter, but the description's phrase 'a string' loosely identifies the input as the string to convert. It adds little beyond the schema's type declaration, which is roughly the minimum viable level for a one-parameter tool.

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 states a clear verb ('Convert') and target ('a string to uppercase'), so the operation is unambiguous. It does not mention the sibling string_lowercase or otherwise differentiate, but the name plus description is specific enough for an agent to identify the tool.

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?

There is no guidance on when to use this versus the many sibling string/encoding tools, and no preconditions or exclusions are stated. The only implicit cue is the tool name itself.

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