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sanhao-global-experts

consumer-claim-coach

Save hours every week. This consumer claim coach toolkit gives you a battle-tested prompt and operating playbook for restaurant/brand growth. No setup, works in any LLM chatbot.

Input Schema

NameRequiredDescriptionDefault
queryNoOptional user query to scope the analysis.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2/5.0
Behavior2/5

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

No annotations exist, so the description carries the full burden, and it discloses nothing about what happens on invocation, whether any state changes, what the response looks like, or what a 'playbook' return consists of. 'No setup' is the only behavioral statement and it is a sales promise rather than operationally useful information.

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?

Two sentences, both marketing-oriented ('Save hours every week'), with the benefit claim front-loaded instead of the purpose. Nothing is wasted in length but the content that is present is low-information.

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?

With no annotations and no output schema, the description must explain purpose and returns, and it does neither. An agent has no basis to know what invoking this tool yields or when it should be preferred over the many nearby brand and content 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?

There is a single optional 'query' parameter with 100% schema description coverage, so the schema already explains it. The description adds no meaning about how the query scopes the analysis, which is acceptable only because the schema does the work.

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 never states a verb or concrete action. It says the tool provides 'a battle-tested prompt and operating playbook' for 'restaurant/brand growth', which is closer to marketing copy than a statement of what the tool produces or does for the 'consumer claim' resource named in the title. An agent cannot distinguish this from the many brand/marketing siblings.

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 guidance, no exclusion of cases, and no named alternative among the hundreds of sibling tools. 'Works in any LLM chatbot' is a compatibility claim, not usage direction.

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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