Query verified behavioral commitment data for a domain. Returns aggregated signals: unique verified visitors, repeat visit rate, and average time spent. These prove real human engagement — harder to fake than reviews or content.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | The domain to query (e.g. 'example.com'). Will be normalized to lowercase without protocol or path. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden. It discloses that the data is 'verified' and returns 'aggregated signals,' which adds meaningful context beyond a simple read operation. However, it does not mention operational details such as whether the data is cached, potential rate limits, or errors on unknown domains. Some context is provided, but not exhaustive.
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 description is three sentences: the first states the action, the second lists the return values, and the third explains its value proposition. Every sentence earns its place without redundancy. It is front-loaded with the core purpose and remains concise.
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?
The tool is simple with one parameter and no output schema. The description sufficiently explains what the tool returns and why it is useful, covering the return values in enough detail. However, because there is no output schema, it could benefit from a brief note on output format or error behavior, which is a minor gap.
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?
With 100% schema coverage, the input schema already documents the 'domain' parameter thoroughly, including normalization. The description adds no extra meaning about parameters, so the baseline of 3 applies. It neither enhances nor detracts from the schema's clarity.
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 clearly states the tool's function: 'Query verified behavioral commitment data for a domain.' It lists specific outputs (unique verified visitors, repeat visit rate, average time spent), which distinguishes it from sibling lookup tools that focus on business, packages, or repositories. The verb and resource are specific, making its purpose 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?
The description provides clear context for when to use this tool: when you need to prove real human engagement, as it says the data is 'harder to fake than reviews or content.' However, it does not explicitly state exclusions or compare directly to alternatives like 'use this instead of lookup_business.' Thus it has clear context but lacks explicit when-not guidance.
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.
Most tools are clearly distinct by ecosystem and action, but the pair audit_github_repo and lookup_github_repo could be confused since both operate on GitHub repos (one audits dependencies, the other profiles the repo itself). The descriptions help, but the naming is similar enough to cause occasional misselection.
All tools follow a consistent verb_noun pattern: 'audit_' for dependency auditing, 'lookup_' for single-entity profiles, plus 'get_api_key' and 'query_commitment'. The 'lookup_business_by_org' variant is a clear sub-pattern, not a deviation.
12 tools is well-scoped for a multi-ecosystem supply chain risk scoring server. Each tool covers a distinct ecosystem or operational function (auditing, lookup, API key management, domain commitment), and none feel redundant.
Coverage is strong: flat audits for npm/PyPI/Cargo/Go, npm dependency tree traversal, GitHub repo audits, and single-package lookups across all major ecosystems. Minor gaps include no explicit tool for managing the monitoring/alerts feature mentioned in get_api_key, and no dependency tree traversal for non-npm ecosystems, but these are acceptable workarounds.