cv.json — open CV data

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.

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.

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.

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.

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.

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.

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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and does so well. It discloses the privacy/access boundary ('contact details are never returned (reach candidates via their portfolio)'), the source scope ('only already-public profiles are indexed'), and the output intent ('lean candidate cards'). It also reveals behavioral context like 'LIVE index' to signal freshness.

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 dense sentences cover the purpose, filter dimensions, output shape, workflow with fetch_cv, and privacy caveat. The most important distinction—shortlist without fetch_cv—is front-loaded, and no sentence is filler.

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 12-parameter search tool with no output schema and no annotations, this description is notably complete. It tells the agent what cards contain, includes URLs returned, explains how to use the result with fetch_cv, and discloses the key limitations. Sorting and pagination are already documented in the schema, so nothing material is missing.

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 schema already explains every parameter and enum. The description summarizes filter categories ('skills, role/title, location, and recruiter facets') but adds no parameter-level detail beyond what the schema provides. Baseline 3 is appropriate.

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 names a specific verb ('Search') and a distinct resource ('FreeCV's LIVE index of real people who are open to work'), then specifies what it returns and how it differs from fetch_cv. It clearly distinguishes this tool from the sibling tools by stating it is enough to shortlist without calling fetch_cv.

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?

Usage guidance is explicit: search here to shortlist, then 'Call fetch_cv on a result to read the complete structured CV.' It also clarifies the boundary that only public profiles are indexed and contact details are never returned, so an agent knows when this tool is relevant and when to follow up with fetch_cv.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.