x402-gated safety checker for npm and PyPI packages. Built for AI coding agents to call before running npm install or pip install — flags nonexistent/typosquatted names, known CVEs (OSV.dev), and suspiciously new or low-download packages. $0.005 USDC per check on Base mainnet
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Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.6/5 across 1 of 1 tools scored.
Only one tool exists, so there is no ambiguity or potential for misselection between different tools.
With a single tool, naming is trivially consistent. The name 'check_package_safety' follows a clear verb_noun pattern.
A single tool is borderline for the server's purpose of checking npm and PyPI package safety. While it covers the basic need, the surface feels thin and could benefit from additional tools like separate checks per ecosystem or advanced reporting.
The tool performs its stated function of checking package safety. Minor gaps might include lack of detailed vulnerability information or batch checking, but for a single-purpose query tool, the coverage is largely adequate.
check_package_safetyAInspectCheck whether an npm or PyPI package is safe to install. Costs $0.005 USDC via x402 per call.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Exact package name to check for safety before installing | |
| ecosystem | Yes | Package ecosystem to check |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose all behaviors. It mentions cost but fails to specify the return format (e.g., boolean, string) or what 'safe' means, leaving a critical gap for agent decision-making.
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?
Single concise sentence with additional cost info, no wasted words.
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?
Despite simple tool with 2 params, lack of output schema means description should explain return values. Missing return format and definition of 'safe' makes it incomplete for an agent to use correctly.
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 has 100% description coverage, so baseline 3. The description does not add significant meaning beyond the schema; cost is extra but not param-related.
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?
Clearly states verb 'Check' and resource 'package safety' with specific ecosystems 'npm or PyPI'. No siblings to distinguish, so purpose is unambiguous.
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?
Implies usage before installing npm/pypi packages, and mentions cost. No explicit when-not or alternatives, but context is clear given no sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
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Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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