Search for a Norwegian business and get its commitment profile from public data (Brønnøysund Register Centre). Returns real commitment signals: longevity, financial health, employee count, and overall commitment score (0-100). Data source: Norwegian government registers — free, verified, unfakeable.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Business name to search for (e.g. 'Peppes Pizza', 'Equinor') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the data source (Brønnøysund Register Centre), the nature of the data ('real commitment signals', 'verified, unfakeable'), and what is returned. It doesn't mention limitations or output format details, but overall provides meaningful behavioral context.
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 two sentences, front-loaded with the core action and output, followed by data source and quality attributes. No filler or repetition.
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?
Given the tool's simplicity (one parameter, no output schema), the description covers the essential aspects: what it searches, what it returns, and where the data comes from. Missing are specifics like response format or edge cases, but these are not critical for such a straightforward lookup tool.
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 100%, so the schema already documents the only parameter ('query'). The tool description adds context about Norwegian business but no additional syntax, formatting, or examples beyond the schema. Baseline 3 is appropriate.
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 verb ('Search'), the resource ('Norwegian business'), and the specific output ('commitment profile') with concrete signals (longevity, financial health, employee count, commitment score). It differentiates from sibling lookup tools by specifying Norwegian business data and government sources.
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 the tool: when you need a commitment profile for a Norwegian business. It doesn't explicitly mention alternatives (like lookup_business_by_org) or exclusions, so it falls slightly short of a 5.
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.