Check a software package and its dependency tree for open-source licence risk before adding it to a project.
ecosystem examples: pypi, npm, maven, cargo
use_case examples: hosted-saas distributed-closed-source
| Name | Required | Description | Default |
|---|---|---|---|
| package | Yes | ||
| version | Yes | ||
| use_case | No | hosted-saas | |
| ecosystem | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It implies a read-style check and mentions dependency-tree scope, but it does not state that the operation is read-only, whether network calls are involved, what output is returned, or what 'risk' means in practice.
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?
The core purpose is front-loaded in one clear sentence, followed by compact example lists that directly support parameter usage. There is no filler or redundant information.
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?
With no annotations, no output schema, and 0% schema coverage, important context is missing: there is no description of return values, no side-effect or safety disclosure, and use_case semantics are only hinted at through examples. An agent might invoke it, but it would not know what result to expect or how to interpret the output.
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 description coverage is 0%, so the description must compensate. It partially does by listing ecosystem examples (pypi, npm, maven, cargo) and use_case examples (hosted-saas, distributed-closed-source), but it does not explain the meaning of use_case or specify version format, leaving some parameters underspecified.
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?
The description states a specific action ('Check') applied to a concrete resource ('a software package and its dependency tree') for a clear objective ('open-source licence risk before adding it to a project'). Even without siblings, the tool's function is unmistakable.
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?
It explicitly frames when to use the tool: 'before adding it to a project.' It also provides ecosystem and use_case examples that guide input selection. With no sibling tools, absence of alternative routing guidance is acceptable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
With only one tool, there is no possibility of confusion or overlap. The tool's purpose is unambiguous.
The single tool name 'check_software_license' follows a clear verb_noun pattern and is consistent with common CLI conventions.
One tool feels thin for a server, but the narrow purpose of license checking may justify a single focused operation. It sits at the borderline of appropriateness.
The tool covers the core license-checking workflow including dependency trees, but lacks ancillary capabilities like policy configuration or report generation. Minor gaps exist but the primary use case is addressed.