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sanhao-global-experts

ir-tech-gen-tool

Turn expertise into output. This tech gen 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

C2.2/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden, and it says nothing about whether this generates a prompt, a document, or a plan, what permissions or inputs it needs, or whether output is deterministic. The claim that it 'includes the complete prompt, usage guide, and edge-case handling' hints at a bundled prompt pack but not at what the caller actually receives.

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 of marketing copy ('professional results in minutes') that consume the entire budget without conveying scope, inputs, or return shape. Front-loading is absent: the most substantive phrase, 'prompt pack', arrives buried in the second sentence.

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, no output schema, and an ambiguous name, the description is the only source of context and it leaves the core questions unanswered: what gets generated, for whom, and how it differs from the check/tpl variants. The 'restaurant and brand teams' framing is inconsistent with the sibling set.

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 one optional parameter with 100% schema description coverage ('Optional user query to scope the analysis'), so the schema already documents it fully. The description adds no meaning beyond that, which lands at the baseline for high coverage.

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 says it is a 'tech gen tool prompt pack' that 'turns expertise into output', which restates the name without defining the resource. It never says what 'tech' means here (the 'ir-' sibling family implies investor relations) or what artifact is produced, so an agent cannot distinguish it from ir-tech-check-tool or ir-tech-tpl-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?

No when-to-use or when-not-to-use guidance appears, and no sibling alternative is named. The only audience cue, 'restaurant and brand teams', actively misdirects given the surrounding IR/compete/KB/voice tool family.

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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