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AgentAvow Trust

Scan a package

scan_package
Read-only

Scan a published package (npm, PyPI, crates, Docker, or Hugging Face) with AgentAvow. Returns a 0-100 trust score, a safe / needs-review verdict, findings with remediation, and a signed attestation. Also reports repo-vs-artifact drift (files shipped that aren't in the source). Read-only; calls agentavow.com.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPackage name, e.g. 'chalk' (or 'org/model' for hf).
forceNoRe-scan now instead of returning the cached verdict (results cache ~1h). Use after a new version ships.
surfaceNoAlias for registry.
registryNoPackage registry: npm, pypi, crates, docker, or hf.
ecosystemNoAlias for registry.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and destructiveHint, so the safety profile is covered. The description adds genuinely useful behavioral context: it calls agentavow.com (an external remote dependency) and reports repo-vs-artifact drift. It repeats 'Read-only' from the annotation but also explains the return contents, so it goes beyond the structured fields.

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

Conciseness5/5

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

The description is three tight sentences: the action and scope are front-loaded, the return value is summarized, and the remote/read-only nature is stated at the end. No sentence is wasted and no information is buried.

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?

With no output schema, the description carries the full burden of explaining outputs, and it does so well: trust score range, verdict, findings with remediation, signed attestation, and drift reporting. It also covers the external call and read-only nature, making the tool's behavior sufficiently clear for correct invocation.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all five parameters, their aliases, and the force behavior. The description's ecosystem list reinforces the registry choices but does not add meaning beyond what the schema provides; baseline 3 is appropriate.

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 states a specific verb ('Scan'), a specific resource ('a published package'), and enumerates the supported ecosystems (npm, PyPI, crates, Docker, Hugging Face). It clearly differentiates from sibling tools like scan_repo and scan_mcp_server by focusing on published packages rather than source repositories or MCP servers.

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 makes the intended use case clear: it is for published packages across named registries, not for source repos or MCP servers. It does not explicitly name sibling alternatives or say 'use scan_repo instead for source code,' but the context is strong enough for an agent to route correctly.

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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