Validate a cv.json document against the official cv.json schema. Provide the document (cvjson, object or JSON string) OR a slug/url to fetch and validate. Returns { valid, errors[] }.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Fetch and validate this cv.json URL. | |
| slug | No | Fetch and validate this slug's live cv.json. | |
| cvjson | No | The cv.json document to validate (object or JSON string). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the behavioral burden. It discloses the return shape '{ valid, errors[] }' and indicates that it can fetch from a slug/url, but it does not explicitly state whether the operation is read-only, whether remote fetching happens server-side, or any authentication/rate-limit considerations. Adequate but not detailed.
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 with no filler. The core purpose is front-loaded, and the second sentence provides the necessary call contract and return shape efficiently.
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 low-complexity validation tool with three straightforward parameters and no output schema, the description covers the essential contract: accepted input forms, the OR relationship, and the return shape. It is only slightly incomplete in not guiding the agent toward sibling tools or noting potential side effects of URL fetching.
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 baseline is 3. The description adds value by grouping the parameters into mutually exclusive alternatives ('document ... OR slug/url') and clarifying that cvjson can be an object or JSON string, which the schema structure alone does not fully convey.
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?
States a specific verb and resource: 'Validate a cv.json document against the official cv.json schema.' This clearly distinguishes it from siblings like fetch_cv and get_cvjson_schema, which are about retrieving data or the schema itself.
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 explains the two invocation modes: pass a cvjson document directly or provide a slug/url to fetch. However, it does not explicitly contrast this tool with siblings like fetch_cv or get_cvjson_schema, so the agent must infer when validation is preferred over those alternatives.
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.
