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reuse_before_generate

Searches GitHub, npm, GitLab, and other registries for existing projects and competitive products before building, providing scored results to inform reuse decisions.

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.
Behavior5/5

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

Despite no annotations, the description thoroughly discloses the tool's behavior: it searches multiple platforms, returns both reusable projects and competing products along with retrieval evidence, and requires the agent to follow scoring instructions. It does not explicitly state read-only nature, but the description implies it is a search/retrieval tool with no side effects, which is sufficient for transparency.

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

Conciseness4/5

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

The description is well-structured with key information front-loaded. It is somewhat long but every sentence adds value, explaining the tool's function, sources, outputs, and usage responsibilities. Minor redundancy could be trimmed, but overall it is efficient for the complexity.

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

Completeness5/5

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

Given the tool has 3 parameters (2 required), nested objects, and no output schema, the description is highly complete. It covers what the tool does, what sources it searches, what it returns, and the agent's responsibilities. The agent can confidently use this tool without ambiguity.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds substantial meaning beyond the schema. The keywords parameter has a detailed explanation with examples of how to formulate precise terms and avoid noise. The queries object's fields are explained even though optional. This significantly aids correct parameter usage.

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: to be run before scaffolding a new project or module, and it searches multiple repositories for reuse. It distinguishes itself by listing the specific sources and the output including both reusable projects and competing products. The sibling 'check_before_building' implies different usage, so this tool's scope is well defined.

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

Usage Guidelines5/5

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

The description explicitly says 'Run this BEFORE scaffolding a new project or a substantial new module.' It also provides guidance on when not to rely on the tool for keyword extraction, warning against generic terms. It sets expectations for the agent's responsibility in relevance judgment and following scoring instructions, making usage context very clear.

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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