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say_hello

Greet a person by name using the GCP MCP server, which manages Google Cloud Platform resources through natural language commands in Claude Desktop without manual credential setup.

Instructions

Say hello to a person.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes

Implementation Reference

  • The async handler function for the 'say_hello' tool, decorated with @mcp.tool() which registers it with the MCP server. It takes a string name and returns a personalized greeting.
    @mcp.tool()
    async def say_hello(name: str) -> str:
        """Say hello to a person."""
        return f"Hello, {name}!"
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. It states the action ('say hello') but doesn't disclose behavioral traits like what 'say' means (e.g., output format, side effects, permissions needed). For a tool with no annotations, this is insufficient.

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 and front-loaded with a single sentence that directly states the purpose. There is no wasted text, making it efficient for quick understanding.

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 the tool's simplicity (1 parameter, no output schema, no annotations), the description is incomplete. It doesn't explain what 'say hello' returns or how it behaves, leaving gaps for the agent to guess. It should provide more context for proper usage.

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 implies a 'person' parameter but doesn't explicitly name or describe the 'name' parameter from the schema. The description adds minimal meaning beyond the schema, barely meeting the baseline.

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 with a specific verb ('say') and resource ('to a person'), making it immediately understandable. However, it doesn't differentiate from sibling tools, which are all unrelated GCP operations, so it doesn't need sibling differentiation but could be more specific about what 'say hello' entails.

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 doesn't mention any context, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone.

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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