Pre-build reality check for AI coding agents. Scans GitHub, Hacker News, npm, PyPI & Product Hunt — returns a 0-100 reality signal before you build. Supports quick (2 sources) and deep (5 sources) parallel search.
Scored across 1 tool
With only one tool, there is no possibility of selection confusion; idea_check has a single, clearly stated purpose (idea/competition validation) with explicit trigger phrases.
The lone name is clean snake_case and readable, but it follows a noun_verb shape (idea_check) rather than the more common verb_noun convention (check_idea), so it only weakly demonstrates a pattern.
A single tool is borderline thin for a server: idea validation naturally invites supporting capabilities (competitor lookup, market data, similar-product search) that are absent here, leaving the surface feeling minimal even if focused.
The core operation of checking whether an idea exists is covered, but there is no follow-up path—no way to retrieve competitor details, market context, or refine results—so agents can hit a dead end after the initial check.
Check if a product idea already exists before building it.
Use when users discuss new project ideas, ask about competition, market saturation, or whether something has been built before.
Trigger phrases: "has anyone built", "does this exist", "check competition", "is this idea original", "有沒有人做過", "市場上有類似的嗎", "幫我查這個點子"
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | en | |
| depth | No | "quick" (GitHub + HN, fast) or "deep" (all sources in parallel). | quick |
| idea_text | Yes | Natural-language description of the idea. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the transparency burden. It implies a non-destructive 'check' but does not explicitly describe data sources, rate limits, or that it performs only reads. The depth parameter in the schema mentions 'GitHub + HN' and 'all sources', adding some transparency, but the main description does not elaborate on these behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a one-sentence purpose, a short usage paragraph, and a bullet-like list of trigger phrases. It is front-loaded and every line earns its place without redundant fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, no siblings, and the presence of an output schema, the description covers essential context: purpose, when to use, and trigger examples. It stops short of being a 5 because the main description does not directly state data sources or any limitations, though the schema partially fills that gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67% (idea_text and depth have descriptions; lang does not). The description itself adds no parameter-specific detail, but the schema covers the important parameters. The lang parameter is self-explanatory via its enum and default. Overall, the description adds marginal value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Check if a product idea already exists before building it.' It uses a specific verb ('check') and resource ('product idea'), making the function unambiguous. Although there are no siblings to distinguish from, the purpose is immediately evident.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Use when users discuss new project ideas, ask about competition, market saturation, or whether something has been built before.' It also lists concrete trigger phrases in both English and Chinese, giving the agent clear signals for when to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
idea_check2 fields changedInput schema / properties / depth / descriptionAdded value: +"\"quick\" (GitHub + HN, fast) or \"deep\" (all sources in parallel)."Input schema / properties / idea_text / descriptionAdded value: +"Natural-language description of the idea."idea_check1 field changedInput schema / properties / langAdded value: +{
+ "default": "en",
+ "enum": [
+ "en",
+ "zh"
+ ],
+ "type": "string"
+}idea_check
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