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RichFerry

Production MCP Template

by RichFerry

system_echo

Read-onlyIdempotent

Echo structured payloads with request metadata for client integration tests.

Instructions

Echo structured payloads with request metadata for client integration tests.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYes
tagsNo
metadataNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYes
tagsNo
metadataNo
request_idNo
client_idNo

Implementation Reference

  • The main tool handler function for 'system_echo'. It accepts a message string, optional tags and metadata, and returns an EchoResult with the echoed payload plus request_id and client_id from the context.
    async def system_echo(
        message: str,
        ctx: Context[Any, Any, Any],
        tags: list[str] | None = None,
        metadata: dict[str, str] | None = None,
    ) -> EchoResult:
        """Echo structured payloads with request metadata for client integration tests."""
        with container.metrics.observe_tool("system_echo"):
            return EchoResult(
                message=message,
                tags=tags or [],
                metadata=metadata or {},
                request_id=ctx.request_id,
                client_id=ctx.client_id,
            )
  • Registration of the 'system_echo' tool via the @server.tool decorator with name='system_echo', readOnlyHint, idempotentHint, and openWorldHint=False.
    @server.tool(
        name="system_echo",
        annotations=ToolAnnotations(readOnlyHint=True, idempotentHint=True, openWorldHint=False),
  • ModuleDescriptor returned by the register() function lists 'system_echo' as one of the tools this 'system' module exports.
    return ModuleDescriptor(
        name="system",
        title="System",
        summary="Core health, metadata, and basic integration diagnostics.",
        tags=["core", "ops", "introspection"],
        maturity="stable",
        tools=["system_health_check", "system_echo", "system_capability_catalog"],
        resources=["server://build-info", "server://health", "server://instructions"],
        prompts=["system_triage_incident"],
    )
  • EchoResult model (the return type of system_echo) with fields: message, tags, metadata, request_id, client_id.
    class EchoResult(TemplateModel):
        message: str
        tags: list[str] = Field(default_factory=list)
        metadata: dict[str, str] = Field(default_factory=dict)
        request_id: str | None = None
        client_id: str | None = None
  • The MODULE_REGISTRARS dictionary maps module names to register functions; 'system' maps to register_system from system.py.
    MODULE_REGISTRARS: dict[str, ModuleRegistrar] = {
        "system": register_system,
        "workspace": register_workspace,
        "jobs": register_jobs,
        "design": register_design,
    }
Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description's safety profile is covered. The description does not add behavioral context beyond aligning with annotations; it does not describe side effects or limitations.

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, concise sentence that is front-loaded and contains no redundancy. Every word adds value.

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?

Output schema exists, so return value details are not needed. However, the description omits parameter semantics and how tags/metadata are handled, leaving some ambiguity for a testing tool. It is barely adequate.

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?

Schema description coverage is 0%, so the description must compensate. It does not explain the purpose of 'message', 'tags', or 'metadata'. The word 'echo' hints that inputs are returned, but no specific semantics for parameters are provided.

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

Purpose5/5

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

Description clearly states the tool echoes structured payloads with request metadata for client integration tests. The verb 'echo' and context 'client integration tests' differentiate it from sibling tools like system_capability_catalog or system_health_check.

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?

Description implies usage for testing but provides no explicit guidance on when to use or when to prefer alternatives like workspace_read_text or jobs_get_status. It lacks exclusions or context about when not to use.

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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