check_reply
Check if GYOTAK staff has replied to a previously submitted inquiry.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| inquiryId | Yes | Inquiry ID (e.g. "INQ-042") |
GYOTAK Fish Market
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A3.9/5.0
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 of signaling side effects. The verb 'Check' implies a read-only, non-destructive operation, which is useful. However, it does not disclose the return shape, idempotency, or any authentication/rate-limit implications, leaving some behavioral ambiguity.
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, direct sentence with no filler. The action and target are front-loaded, making it immediately scannable for an agent.
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?
With one fully documented parameter and no output schema, the description covers the invocation trigger (previously submitted inquiry) but does not clarify the tool's return value (e.g., boolean vs. reply content). This is the main completeness gap, though the tool is simple enough that an agent can still infer basic 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?
The input schema already fully documents inquiryId with type and example, so schema coverage is 100%. The description adds only the context that the inquiry was previously submitted; no additional parameter semantics are necessary or 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?
The description uses a specific action ('Check') and a concrete resource ('GYOTAK staff reply to a previously submitted inquiry'). It clearly distinguishes this tool from siblings like ask_gyotak (submission) and check_payment_status (payment), even without naming alternatives.
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 situates the tool in a clear workflow: after an inquiry has been submitted, check whether staff has replied. It does not explicitly name alternatives or exclusions, but the 'previously submitted inquiry' context is unambiguous and provides enough guidance for an agent to know when to invoke it.
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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TDQS
A3.9/5.0
Disambiguation4/5
Each tool has a clear role in the customer, ordering, payment, or verification flow. The only potential confusions (get_catalog vs search_fish, get_catch_reports vs verify_catch) are explicitly differentiated in their descriptions.
Naming Consistency5/5
All tools follow a consistent verb_noun snake_case pattern using verbs like get, create, place, verify, check, and ask. Brand-specific tools like ask_gyotak and contact_gyotak still fit the same convention.
Tool Count4/5
18 tools is slightly on the heavy side for a single server, but the scope spans sales, payments, customer management, and blockchain traceability, so most tools have distinct jobs. It is above the ideal range but not bloated.
Completeness4/5
The core commerce loop (register, order, pay, confirm) and traceability verification are covered well. Missing order cancellation, guest order status retrieval, or customer profile editing are minor gaps that agents can usually work around.