Skip to main content
Glama

get_service_status

Check whether MeatSpace is available and when to use a human. Call this when deciding whether to escalate a subjective or high-consequence choice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the core behavior (checking availability and escalation policy) but omits details like whether it performs network calls, auth requirements, or returns a structured result. The behavioral claim is consistent with the tool's name, but it is not very rich.

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 concise with two sentences, front-loading the primary purpose and then adding usage context. Every word adds value; no redundancy.

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?

Given the tool's simplicity (no params, no output schema, simple purpose), the description is almost complete. It explains what the tool does and when to use it. It lacks a note about the return format, but the context of a status check may be inferred.

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

Parameters4/5

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

The tool has zero parameters and the schema is empty, so the description cannot add parameter details. The provided description explains the tool's function, which is sufficient; the baseline for zero-parameter tools is 4.

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?

The description clearly states the tool's action: 'Check whether MeatSpace is available and when to use a human.' It specifies the resource (MeatSpace) and a distinct purpose (escalation guidance), differentiating it from siblings like ask_human and provision_api_key.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage context: 'Call this when deciding whether to escalate a subjective or high-consequence choice.' It does not list alternative tools or when-not-to-use conditions, but the guidance is clear enough for an agent to choose this tool over its siblings.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: ask_human for human interaction, get_service_status for service availability, and provision_api_key for key creation. No overlapping functionality.

Naming Consistency5/5

All tools use a consistent verb_noun pattern in snake_case: ask_human, get_service_status, provision_api_key. The naming is clear and predictable.

Tool Count4/5

With 3 tools, the server is minimal but covers the core workflow of human interaction. However, additional tools for managing API keys or retrieving ask results would be beneficial.

Completeness2/5

The tool set lacks essential features: no way to retrieve a pending ask result, no key management (list/revoke), and no tool to cancel or update an ask. Agents will likely fail to complete workflows involving delayed human responses.