Skip to main content
Glama

echo

Test JIRA issue formatting by echoing input text with optional case transformation to verify markdown-to-ADF conversion accuracy.

Instructions

Echo back the input text with optional case transformation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
transformNo

Implementation Reference

  • Core handler function implementing the echo tool logic: echoes input text with optional upper/lower case transformation and returns MCP TextContent.
    def echo(text: str, transform: Optional[str] = None) -> types.TextContent:
        """
        Echo the input text back to the caller with optional case transformation.
        
        Args:
            text: The text to echo back
            transform: Optional case transformation ('upper' or 'lower')
            
        Returns:
            TextContent: The transformed text as MCP TextContent
        """
        if transform == "upper":
            result = text.upper()
        elif transform == "lower":
            result = text.lower()
        else:
            result = text
            
        return types.TextContent(
            type="text",
            text=result,
            format="text/plain"
        ) 
  • Registration of the 'echo' tool via @mcp_server.tool decorator. Includes a thin wrapper function that delegates to the core echo implementation.
    @mcp_server.tool(
        name="echo",
        description="Echo back the input text with optional case transformation",
    )
    def echo_tool(text: str, transform: Optional[str] = None) -> types.TextContent:
        """Wrapper around the echo tool implementation"""
        return echo(text, transform)
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states the tool echoes input text with optional transformation, but doesn't disclose behavioral traits like whether it's read-only, has side effects, rate limits, or error handling. The description is minimal and lacks necessary operational context.

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 extremely concise (one sentence) and front-loaded with the core purpose. Every word earns its place, with no wasted text. It efficiently communicates the essential functionality without unnecessary elaboration.

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 no annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how transformations work, or any behavioral aspects. For a tool with two parameters and no structured documentation, this minimal description leaves significant gaps.

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%, so the description must compensate. It mentions 'input text' and 'optional case transformation,' which map to the two parameters (text and transform), but doesn't explain what 'transform' accepts (e.g., uppercase, lowercase) or provide examples. It adds some meaning but doesn't fully compensate for 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 clearly states the tool's purpose: 'Echo back the input text' specifies the verb (echo) and resource (input text), and 'with optional case transformation' adds detail about functionality. However, it doesn't distinguish from sibling tools (Jira-related tools), which is expected since this is a simple utility tool in a different domain.

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. It mentions 'optional case transformation' but doesn't specify what transformations are available or when to apply them. There's no context about prerequisites, limitations, or relationship to sibling tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/codingthefuturewithai/mcp_jira'

If you have feedback or need assistance with the MCP directory API, please join our Discord server