Skip to main content
Glama

sanhao-global-experts

expense-reply-tool

AI-powered restaurant & brand intelligence pack. Instantly generate a ready-to-use expense reply tool workflow - no prompt engineering needed. Ships as a full prompt kit with step-by-step instructions and a 30-day support promise.

Input Schema

NameRequiredDescriptionDefault
queryNoOptional user query to scope the analysis.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

D1.7/5.0
Behavior1/5

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

With no annotations, the description carries the full behavioral burden, and it discloses nothing: not whether the tool is read-only, what permissions are needed, what it produces, or what side effects occur. The 'full prompt kit' and '30-day support promise' language is marketing, not behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, all promotional, none of which front-loads what the tool actually does. Every clause ('instantly generate', 'no prompt engineering needed', 'ships as a full prompt kit') is filler from an agent's perspective, though it is at least short.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema and no annotations, so the description must explain the tool's behavior and return value — and it does neither. An agent reading it cannot determine what input produces what output, making the definition unusable for correct invocation.

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 tool has a single optional 'query' parameter with 100% schema description coverage, so the schema already explains its meaning. The description adds nothing about how the query scopes the generated expense reply, so the baseline of 3 applies.

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

Purpose2/5

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

The description calls it an 'AI-powered restaurant & brand intelligence pack' that will 'generate a ready-to-use expense reply tool workflow', which only obliquely implies the tool drafts expense-related replies. It never states a concrete verb+resource like 'draft a reply to an expense inquiry', and with nine sibling tools in the expense-* family (expense-gen-tool, expense-write-tool, expense-polish-tool, etc.) there is no way to tell this one apart.

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

Usage Guidelines1/5

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

There is no when-to-use or when-not-to-use guidance at all. The text only makes sales claims ('no prompt engineering needed', '30-day support promise') and never tells the agent how this differs from expense-write-tool, expense-gen-tool, or the other *-reply-tool siblings like email-reply-tool.

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.