get_cvjson_schema
Return the official cv.json JSON Schema (the format spec) — use it to understand or generate a compliant cv.json.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
cv.json — open CV data
TDQS
A4.4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of conveying behavior. The verb 'Return' signals a read-only retrieval, and specifying that the result is the official JSON Schema defines the output. For a zero-parameter getter, no further side-effect disclosure is necessary.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the resource being returned and then adds a useful purpose clause. There is no filler or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no output schema, the description adequately communicates both the payload and the intended use. It could have explicitly mentioned that no arguments are required or compared itself to validate_cv, but nothing essential is missing for calling it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no properties and schema description coverage is 100%, setting a baseline of 4 for zero-parameter tools. The description correctly adds no parameter details because none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Return') and a specific resource ('the official cv.json JSON Schema'), and clarifies that it is the format spec. This makes it clearly distinct from sibling tools like fetch_cv, search_open_to_work, and validate_cv, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states when to use the tool: to understand or generate a compliant cv.json. It doesn't explicitly list exclusion conditions or compare to alternatives, but the intended use case is clear enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A4.4/5.0
Disambiguation5/5
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.
Naming Consistency5/5
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.
Tool Count5/5
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.
Completeness5/5
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.