Skip to main content
Glama

check_before_building

Search GitHub, npm, and Python for maintained projects matching your planned build. Get verified candidates and extend them instead of rebuilding from scratch.

Instructions

Run this BEFORE scaffolding a new project or a substantial new module. Searches GitHub, npm, and Python repos for existing projects that already do what's being proposed and filters out abandoned/unmaintained results. Returns verified-maintained candidates plus scoring instructions — the calling agent (you) must then judge semantic relevance itself and present at most 3 real alternatives with a concrete 'extend this instead of rebuilding' suggestion for each, per the returned 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
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?

Discloses that it searches GitHub, npm, and Python repos, filters out abandoned results, returns verified-maintained candidates with scoring instructions, and requires the agent to judge relevance and suggest alternatives. Since no annotations are provided, the description fully covers behavioral traits.

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 front-loaded with the most critical information (when to use, what it does) and each sentence adds value. However, it is somewhat lengthy; minor trimming could improve conciseness without losing substance.

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's moderate complexity, lack of output schema, and no sibling tools, the description provides complete context: what the tool does, how to invoke it (with keyword guidance), what it returns, and the agent's subsequent responsibilities. No gaps are evident.

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?

The input schema covers 100% of parameters with detailed descriptions, but the tool description adds significant context by emphasizing the importance of keyword selection and instructing the agent to supply keywords themselves. This goes beyond a baseline of 3.

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 new projects or modules, searching multiple repositories for existing solutions and filtering out abandoned ones. It distinguishes the tool as a pre-building search tool, which is specific and actionable.

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?

Explicitly tells when to use ('BEFORE scaffolding a new project') and provides detailed instructions on how to use it, including the requirement for the agent to supply precise keywords and the warning against generic terms. No siblings exist, so alternatives are not needed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/aradar46/reuse-before-generate'

If you have feedback or need assistance with the MCP directory API, please join our Discord server