Fetch a person's cv.json — their full machine-readable CV — by FreeCV slug or livelink.cv URL. Returns structured JSON: basics, work, education, skills, languages, availability, ats, verification, i18n.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Full cv.json URL (alternative to slug). | |
| slug | No | The slug, e.g. "ashley" (from livelink.cv/ashley). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does disclose the output shape (structured JSON with specific sections), which is useful behavioral context. However, it does not mention failure modes, authentication requirements, rate limits, or what happens when a slug/URL is invalid. This is adequate but not comprehensive.
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?
Two sentences with no filler. The core purpose is stated first, followed by a compact enumeration of returned fields. Every clause earns its place, and the structure is direct and scannable.
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 (2 optional params, no output schema), and the description compensates for the missing output schema by listing the JSON sections. It does not describe error behavior or specify which parameter to prefer, but the essential information needed to invoke fetch_cv with either identifier is present. Minor gaps keep it from a 5.
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 both parameters are already documented in the schema. The description adds minimal extra meaning beyond the schema, essentially restating that slug and URL are two alternative ways to fetch. This matches the baseline of 3 for full schema coverage.
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 identifies the action ('Fetch') and the resource ('a person's cv.json') and specifies both input identifier types (FreeCV slug or livelink.cv URL). The returned object is enumerated, which distinguishes it from siblings like get_cvjson_schema, search_open_to_work, and validate_cv without needing to inspect them.
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 use case is clear: retrieve a full machine-readable CV by slug or URL. It does not explicitly state when to prefer fetch_cv over siblings, but the context of 'fetch full CV' is distinct enough from schema retrieval, searching, or validation. No exclusions or alternative routing are provided, which keeps it just below 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.
Each tool has a clearly distinct purpose: fetching a CV, retrieving the schema, searching the open-to-work index, and validating a document. Even where fetch_cv and validate_cv both accept a slug/url, their outputs are completely different and unambiguous.
All tool names follow a consistent snake_case verb_noun pattern: fetch_cv, get_cvjson_schema, search_open_to_work, validate_cv. The verbs are descriptive and the naming style is uniform across the set.
Four tools is well-scoped for a read-only CV data server: retrieve, search, validate, and schema access. Each tool earns its place and there is no unnecessary redundancy.
The tool surface covers the core lifecycle for the domain: searching for candidates, fetching full CVs, understanding the schema, and validating documents. The integration between search results and fetch_cv via URLs creates a complete workflow with no obvious dead ends.
