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sanhao-global-experts

kids-reading-plan

Save hours every week. This kids reading plan 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

C2.2/5.0
Behavior2/5

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

With no annotations, the description carries the full burden and does not meet it. It never says whether the tool returns a prompt template, a generated plan, or static reference text, nor whether invoking it mutates or stores anything; 'No setup' is the sole behavioral hint.

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?

Only two sentences, but the second half is off-topic marketing ('restaurant/brand growth') that actively misdirects rather than earning its place. The front-loaded line is a benefit claim, not a statement of function.

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?

There is no output schema and no explanation of the return artifact, which is critical for a toolkit/playbook-style tool whose whole value is the content it emits. Combined with the absent usage guidance and the contradicting domain claim, an agent lacks what it needs to call this confidently.

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 single optional 'query' parameter is fully documented in the schema (100% coverage), so the baseline is 3. The description adds no meaning about what scoping the query supports or what happens when it is omitted.

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 first half names a resource ('kids reading plan toolkit') but never states a verb or what invoking it actually produces. The second clause then claims it is an 'operating playbook for restaurant/brand growth,' which contradicts the kids-reading-plan name and leaves the agent unable to tell what domain this tool serves.

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?

No when-to-use, when-not-to-use, or alternative is named, despite dozens of adjacent siblings such as bedtime-story, homework-companion, k12-homework-system, and brand-fnb-playbook. The only contextual remark, 'works in any LLM chatbot,' describes the hosting environment, not selection criteria.

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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