Add a candidate to one of your d.cv shortlists (creates the shortlist if it does not exist). Requires your d.cv API key. Use after search_talent when the user wants to save someone.
| Name | Required | Description | Default |
|---|---|---|---|
| handle | Yes | The candidate handle to add, e.g. "kukuh" | |
| shortlist | No | Shortlist name. Defaults to "From AI". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the side effect of creating a shortlist if it does not exist and mentions the API key requirement. However, it does not describe what happens on duplicate additions, potential errors, or reversibility. The description adds some useful behavioral context but leaves gaps in a mutation tool's behavior.
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 long, front-loaded with the primary action, and each sentence adds value: the first explains what the tool does and its side effect, the second provides usage context and a prerequisite. No filler or redundancy.
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 params, no output schema, no nested objects). The description covers its purpose, when to use, side effects, and authentication requirements. For the tool's low complexity, this is sufficient context for an agent to select and invoke 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 documents both parameters (handle and shortlist) with descriptions and a default for shortlist, achieving 100% schema description coverage. The description itself does not add further parameter-level detail beyond the schema, except implying that the shortlist parameter can be a new name due to auto-creation. The baseline of 3 is appropriate because the schema does the heavy lifting.
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 uses the specific verb 'Add' with the resource 'candidate to one of your d.cv shortlists', clearly distinguishing it from sibling tools like search_talent (searching) and save_search (saving searches). It also adds a clarifying side note that the shortlist is created if it does not exist, making the tool's function unambiguous.
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 explicitly states 'Use after search_talent when the user wants to save someone', giving a concrete trigger and workflow context. It also notes the API key requirement, which is a prerequisite. This provides clear guidance on when to use the tool, though it does not mention exclusions or alternatives beyond the implicit contrast with search_talent.
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.
There is significant overlap between tools: 'search' and 'search_talent' both search profiles with similar functionality, and 'fetch' and 'get_profile' both retrieve a full profile by handle. This creates ambiguity about which tool to use for a given task.
Naming conventions are inconsistent: some tools use bare verbs ('fetch', 'search'), some use verb-noun ('get_profile', 'save_search'), and one uses a multi-word phrase ('get_open_to_work'). The pair 'search' and 'search_talent' also breaks any consistent pattern.
Seven tools is a well-scoped number for a talent search server. It's not overwhelming or too sparse, and each tool has a place in the candidate discovery workflow.
The tool set covers the core workflow: searching, retrieving full profiles, filtering open-to-work candidates, adding to shortlists, and saving searches. However, the duplicate fetch/get_profile pair suggests one could be replaced with something like listing or managing shortlists.
