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sanhao-global-experts

fund-invest-beginner

Save hours every week. This fund invest beginner 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

C2.3/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the behavioral burden, but it only says the toolkit is "battle-tested" and requires "no setup." It does not disclose what the tool returns, whether it performs analysis or only supplies prompt text, or any limits on use.

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

Conciseness3/5

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

The description is short, but it leads with marketing copy ("Save hours every week") rather than the tool's function. The remaining sentences are compact but vague, so the structure wastes its most prominent position.

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?

Given one optional parameter, no annotations, and no output schema, the description should be complete enough to tell an agent what invocation produces and when to use it. Instead it provides only promotional framing and does not explain the returned prompt/playbook format or how the optional query is used.

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 documented in the schema as scoping the analysis, and schema coverage is 100%, so the parameter is structurally explained. The description adds no further meaning about how the query changes behavior or output.

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 specific operation; it calls itself a "toolkit" that supplies a "prompt and operating playbook" for restaurant/brand growth, which sits oddly with the name fund-invest-beginner. It does not clearly distinguish the tool from the many sibling branding, F&B, or investment-related tools.

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 guidance, no exclusions, and no named alternatives. "No setup, works in any LLM chatbot" describes portability, not the task conditions that should trigger this tool over 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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