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sanhao-global-experts

expense-audit-tool

AI-powered restaurant & brand intelligence pack. Instantly generate a ready-to-use expense audit 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

C2.2/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden, yet it only discloses packaging claims (prompt kit, step-by-step instructions, support promise). It says nothing about what the tool actually returns, whether it mutates anything, whether it is read-only, or how the optional query influences output.

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, and the leading content is generic marketing rather than the tool's function. The actual operation is buried mid-sentence, and 'Ships as a full prompt kit with step-by-step instructions and a 30-day support promise' is commercial copy that does not help an agent invoke the tool.

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?

For a zero-required-parameter tool with no output schema and no annotations, the description should at minimum explain what is produced and how the query is used. Instead it describes a purchasable product bundle, leaving an agent unable to predict the return value or decide between this and the sibling expense-* tools.

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 100% for the single optional 'query' parameter, so the schema already defines its meaning. The description adds no further semantics about scope, format, or examples for the query, 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 is product marketing ('AI-powered restaurant & brand intelligence pack', '30-day support promise') rather than a statement of what the tool does. 'Expense audit' appears only as part of the phrase 'generate a ready-to-use expense audit tool workflow', which restates the name without specifying the operation, its input domain, or how it differs from siblings like expense-check-tool or expense-gen-tool.

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?

There is no when-to-use, when-not-to-use, or alternative routing guidance. 'No prompt engineering needed' is a sales claim, not a usage condition, and nothing tells the agent when to pick this over the many neighbouring expense-* and *-audit-tool siblings.

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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