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check_before_building

Searches GitHub, npm, and other registries for existing projects and competing products before you scaffold a new project, preventing redundant work.

Instructions

Run this BEFORE scaffolding a new project or a substantial new module. Searches GitHub, npm, GitLab, Show HN, optional Tavily web search, Python repositories when relevant, and one ecosystem registry for Rust, Ruby, PHP, or JVM projects. Returns both reusable projects and products the proposal would compete with, plus complete retrieval evidence. The calling agent (you) remains responsible for semantic relevance judgment and must follow the returned scoring instructions. REQUIRES you to supply keywords yourself (see its field description) — do not guess this tool can extract good search terms on its own; generic terms like 'mcp'/'agent'/'server' will bury results in noise.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queriesNoOptional high-quality intent inferred semantically by the calling agent: category names what this is, outcome says what it accomplishes, synonyms supplies distinct terminology maintainers or product makers may use, constraints supplies up to 8 must-have properties, priorities supplies up to 4 ordered preferences, and artifactType says whether the desired result is an application, hosted service, CLI, or library. Older callers may omit these optional fields; the server will infer conservative fallbacks.
keywordsYesREQUIRED: 3-4 precise search terms YOU infer from the description, using your own understanding of what the user actually means — do this especially when the description is vague, informal, or from a non-native speaker. Pick the concrete domain noun a maintainer would actually put in their README, not a generic category word: e.g. for 'thing that checks my code doesn't have secret keys by mistake' prefer ["git", "secrets", "detect", "leak"] over ["secret", "scanner", "detect", "git"] — 'scanner' is broad enough to pull in unrelated security-tool listicles, while 'leak'/'secrets' matches how gitleaks/trufflehog actually describe themselves. Avoid generic tooling-ecosystem words (mcp, agent, server, tool, app) unless the description has nothing more specific — they return noise (awesome-lists, unrelated MCP servers) rather than real competitors. Critically, favor the word a maintainer would use to describe WHAT THE TOOL IS over the word describing the USER'S PROBLEM: a real 'pretty JSON in the terminal' tool likely calls itself a 'viewer' or 'processor', not a 'pretty-printer'/'colorizer'; a real static-site link checker likely says it validates 'rendered HTML', not 'static site alt-text'. If your first guess doesn't match, mentally simulate the README of the tool you're picturing and pull words straight from that sentence.
descriptionYesPlain-language description of the project/module about to be built — what it does, not how. The more specific, the better the match quality.
Behavior4/5

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

No annotations are provided, so the description fully conveys behavioral traits. It lists sources searched, types of results returned, and warns about generic keywords. It also clarifies agent responsibility for relevance judgment. This is detailed and honest.

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 long, especially the keywords advice, but it is front-loaded with the core purpose. Each sentence is informative, but some repetition and length could be trimmed without losing meaning. Adequately structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose, search sources, result types, and agent responsibilities. However, it does not detail the return format (only mentions 'complete retrieval evidence'), and the tool is complex with a sibling tool that could have been contrasted more explicitly. Mostly complete but leaves some questions.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds significant value: for 'keywords', it gives extensive guidance on selecting effective terms with concrete examples. 'queries' is explained as optional and how to use it. 'description' is clarified as a plain-language requirement. This goes well beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Run this BEFORE scaffolding a new project or a substantial new module.' It specifies the sources searched and the outputs returned. The sibling tool 'reuse_before_generate' contrasts, making the distinct purpose clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to use the tool (before scaffolding) and provides detailed instructions on how to use it, including agent responsibilities and keyword selection. However, it does not explicitly state when not to use it or mention alternatives directly.

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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