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Glama

FabTally Guard

by io.github.zvmzaretsky

Vet a dependency before install (paid $0.003)

deps_check

PAID $0.003 (x402, USDC on Base). Vet a software package BEFORE installing it. Returns existence, latest-vs-requested, deprecation/yank markers, known CVEs from OSV.dev, typosquat similarity to popular names, SPDX license, and age/download signals with a plain-English risk verdict. Args: ecosystem (npm|pypi|crates), name, version (optional). Without payment returns the x402 challenge; pass x_payment to settle.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPackage name, e.g. 'lodash', 'requests', 'serde'.
versionNoSpecific version to check for CVEs (optional; defaults to latest).
ecosystemYesPackage ecosystem: npm | pypi | crates.
x_paymentNox402 payment payload (base64) for this PAID check. If supplied it is forwarded as the X-PAYMENT header to settle the call and return the real result instead of a 402 challenge. Omit to get the price challenge first.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Even though no annotations are provided, the description discloses key behavioral details: payment requirement, return contents, and the 402 challenge flow. It doesn't mention error conditions or rate limits, but the core behavior is well covered.

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 dense but well-structured with a clear payment notice, a purpose statement, a return-value list, and argument summary. Every sentence contributes useful information, though it could be slightly more compact.

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?

Despite no output schema, the description enumerates the exact return fields and the payment challenge behavior, making it complete for agent decision-making. It covers the tool's complexity well.

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 covers all parameters (100%). The description adds meaningful context for x_payment by explaining the payment/settlement flow and clarifying that version is optional, which goes beyond the schema's basic descriptions.

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: 'Vet a software package BEFORE installing it' and lists the specific signals returned (existence, CVEs, license, etc.). It distinguishes itself from sibling tools (json_repair, url_read, x402_preflight) which have entirely different functions.

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?

It explains when to use (before installing) and provides the payment usage flow ('Without payment returns the x402 challenge; pass x_payment to settle'). It doesn't explicitly mention alternatives, but sibling tools are unrelated, so no exclusion is needed.

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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TDQS

A4.6/5.0
Disambiguation5/5

Each tool targets a clearly distinct function: package vetting, JSON repair, URL fetching, and x402 endpoint verification. There is no overlap in intent or output, making selection unambiguous.

Naming Consistency5/5

All tool names follow a consistent snake_case object_action pattern (deps_check, json_repair, url_read, x402_preflight). This maintains uniformity and predictability across the set.

Tool Count5/5

With exactly four tools, the server is well-scoped and every tool earns its place. There is no bloat or trivial filler, and the count falls squarely within the ideal 3-15 range.

Completeness4/5

Each tool is functionally complete for its individual purpose, but the set lacks batch operations or a way to manage/retrieve prior checks. These are minor gaps that agents can work around with scripting or external state.

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