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sanhao-global-experts

recruit-tpl-tool

Turn expertise into output. This recruit tpl tool prompt pack helps restaurant and brand teams produce professional results in minutes. Includes the complete prompt, usage guide, and edge-case handling.

Input Schema

NameRequiredDescriptionDefault
queryNoOptional user query to scope the analysis.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.1/5.0
Behavior2/5

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

No annotations exist, so the description must carry the full behavioral burden and it does not: no mention of what input it consumes, whether it mutates state, what permissions are needed, or what the caller receives. The only behavioral hint ("includes the complete prompt, usage guide, and edge-case handling") reads as package contents rather than observable runtime behavior.

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?

Only three sentences, but the first two are promotional filler ("in minutes", "professional results") and the useful detail is buried last. It is short enough not to be bloated, yet the word budget is spent on persuasion rather than 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?

With no annotations and no output schema, the description must explain what the tool returns and how it differs from its many recruit-* and *-tpl-* siblings; it does neither. The domain mismatch with the restaurant framing further undermines the agent's ability to call it correctly.

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 parameter whose schema description already fully covers it (100% coverage), so the baseline of 3 applies. The description adds no meaning about how a query scopes the analysis 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 description restates the name ("recruit tpl tool prompt pack") and wraps it in marketing slogans ("Turn expertise into output"), never stating a concrete verb+resource. It never says whether this returns a template artifact, generates a recruiting deliverable, or fills a template, so an agent cannot distinguish it from recruit-gen-tool, recruit-write-tool or recruit-check-tool.

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?

No when-to-use, when-not-to-use, or alternative is given, and the only scoping cue ("restaurant and brand teams") points at an unrelated domain for a tool named recruit, which actively misleads selection. With a dozen recruit-* siblings, the absence of routing guidance is disqualifying.

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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