Parse a CVSS v3.0/3.1 vector string into base score, severity and per-metric labels. Fully deterministic; no network I/O; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| vector | Yes | CVSS v3.0/3.1 vector string |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It explicitly states the tool is deterministic, performs no network I/O, and is free, which gives the agent confidence about side effects and cost. It also specifies the output components. However, it does not mention error behavior or handling of invalid vector strings, which would be useful but is not critical for a simple parse function.
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 one concise sentence followed by a short behavioral note. Both parts earn their place: the first states the purpose and output, the second adds key behavioral traits. There is no fluff or repetition, making it easy to scan and understand.
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 simple single-parameter parser, the description provides a good level of completeness: it covers behavior, cost, and output. With no output schema, mentioning the output components helps set expectations. It could be enhanced by noting return format or error handling, but overall it is adequate for the tool's complexity.
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 has 100% coverage: the only parameter 'vector' is already described as a 'CVSS v3.0/3.1 vector string'. The tool description repeats this without adding extra detail about format, examples, or syntax. Since the schema fully documents the parameter, the description adds no additional semantic value beyond the baseline.
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 purpose with a specific verb ('Parse') and resource ('CVSS v3.0/3.1 vector string'), and lists concrete outputs: base score, severity, per-metric labels. It distinguishes this from sibling tools like cve_lookup_get_cve or cve_lookup_search_cve, which focus on fetching or searching CVE data rather than parsing vector strings.
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 the tool is for when you have a CVSS vector string and need scores, but it does not explicitly say when to use this versus alternatives such as cve_lookup_get_cve. There is no mention of situations where this tool should not be used, nor any named alternatives. It provides basic context ('fully deterministic; no network I/O') but lacks explicit usage boundaries.
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 distinguished by domain prefixes (e.g., bid_watch, grant_watch) and specific action verbs. However, the high number of similarly structured watch tools could still cause confusion, though descriptions clarify exact purposes.
Every tool follows a consistent `domain_subdomain_action` pattern with underscores, e.g., `agent_audit_query`, `bid_watch_search`. Even long names like `commerce_catalog_agent_readiness_score` adhere to this structure.
With 147 tools, the server is far too broad, covering weather, carbon estimates, domain intel, and more—well beyond its stated 'Japan public-data ledgers' scope. This sheer volume overwhelms agents and dilutes focus.
The server offers many read-only tools for Japanese public data (bids, grants, licenses, etc.), but lacks create/update/delete operations for those domains. Additionally, numerous unrelated tools (e.g., carbon estimates, weather) feel tacked on, leaving gaps in core coverage.