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agentspan-ai

MCP Test Server

by agentspan-ai

string_lowercase

Converts any text string to lowercase, ensuring consistent casing for comparisons, formatting, and data normalization.

Instructions

Convert a string to lowercase.

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

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden, but for a trivial deterministic pure-function string transform there is little behavior to disclose. It does not mention locale/Unicode handling, null/empty input behavior, or error cases, which are the only non-obvious traits here.

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?

A single front-loaded sentence with zero waste. Every word earns its place for a tool this simple.

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?

An output schema exists, so return values need not be described, and the tool's complexity is minimal. The description is nearly sufficient, with only trivial gaps around edge-case input handling.

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%, but the single parameter 'text' is self-explanatory and the tool has only one input. The description adds no syntax, format, or constraint detail beyond the schema, so it is baseline-adequate rather than enriching.

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 (convert) and resource (string) with the target transformation (lowercase), which is unambiguous and readily distinguished from the sibling string_uppercase. It stops short of explicitly naming the sibling or scoping the behavior (e.g., locale handling), so it sits at a clear-but-not-differentiated 4.

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 statement of when to use this versus string_uppercase, string_reverse, or other string tools, and no prerequisites or exclusions. The tool's purpose is obvious enough to infer usage, but no explicit guidance is offered.

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