Look up known vulnerabilities for a package via the free OSV.dev API, optionally filtered to a specific version. Read-only; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Package name | |
| version | No | Optional package version to filter to | |
| ecosystem | Yes | Package ecosystem (e.g. npm, PyPI, Go) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It explicitly states 'Read-only' and 'price 0.0 (free)', which are important safety and cost traits. It also mentions the underlying OSV.dev API, giving context about external service dependency. However, it does not disclose potential rate limits or error behavior, so it is not fully transparent.
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 short sentences, front-loaded with the action and resource. Every word earns its place: it states the purpose, the optional filter, the read-only nature, and the cost. No wasted words 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?
For a read-only lookup tool with no output schema, the description adequately covers purpose, key attributes, and context. It does not specify the return format or what happens when no advisories are found, which would be useful for an agent to set expectations, but the simplicity of the tool makes this 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?
The input schema already provides descriptions for all three parameters (name, version, ecosystem) with 100% coverage. The tool description adds context about the OSV.dev API and optional version filtering, but does not add syntactic details beyond what the schema already states. Baseline 3 is appropriate because the schema does the heavy lifting.
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 looks up known vulnerabilities for a package via the OSV.dev API, which is a specific verb and resource. However, it does not explicitly differentiate from sibling CVE lookup tools like cve_lookup_search_cve or cve_lookup_get_cve, though the phrase 'for a package' and 'OSV.dev API' provide implicit distinction.
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 implies when to use this tool (when needing package vulnerability information) but provides no explicit guidance on alternatives or when not to use it. It does not mention that other tools like cve_lookup_search_cve should be used for CVE ID lookups, leaving the usage context only implied.
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 have clearly distinct purposes, especially the ledger-watch groups with domain prefixes. However, there are near-duplicate utilities such as content_authenticity_domain_reputation and domain_intel_reputation, and fx_tax_convert overlaps with price_oracle_fx_rate/convert. The sheer number of tools also increases the chance of selecting the wrong one, though descriptions are generally clear.
The naming is largely consistent with a <domain>_<action> or <domain>_<watch>_<action> pattern, and all names use snake_case. Minor deviations include standalone names like entity_search, kyb_report, verify_receipt, and the confusing singular/plural pair of sanction_watch_* and sanctions_screen_*. Overall, the pattern is predictable.
With 172 tools, this server is far beyond the typical well-scoped range. It bundles dozens of unrelated utility domains (weather, carbon, CVE, geo, etc.) alongside the Japan public-ledger watches, making it unwieldy for an agent to navigate. This extreme count is a major coherence problem.
For the core Japan public-ledger domain, coverage is excellent: each ledger has search, get, timeline, recent_changes, and verify_ledger, plus cross-ledger entity_search and temporal_query. The unrelated utility areas are also fairly complete for their own purposes, but the server's scope is so broad that some utilities are duplicated (e.g., multiple currency converters). Overall, no critical dead ends in the main domain.