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by kula-ai

create_candidate

Create a candidate in Kula with required first name and either email or LinkedIn profile. Optionally add to a job pipeline, tags, skills, and custom fields.

Instructions

Create a new candidate in Kula. At least one of email or linkedin_url (in social_urls) is required. Optionally link to a job pipeline via job_id and job_stage_id. Returns the created candidate object.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
first_nameYesCandidate's first name (required)
last_nameNoCandidate's last name
emailNoCandidate's email address. Required unless linkedin_url is provided.
phone_numberNoCandidate's phone number
tagsNoComma-separated tag names to attach
skillsNoComma-separated skill names to attach
job_idNoJob ID to add this candidate to a pipeline
job_stage_idNoStage ID within the job pipeline (use with job_id)
candidate_source_idNoSource ID — get IDs from list_sources
credited_to_user_idNoUser ID to credit for this candidate
social_urlsNoSocial profile URLs. Include linkedin here if not providing email.
locationNoCandidate location using places IDs
additional_infoNoCustom field values as key-value pairs
Behavior3/5

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

No annotations are provided, so the description bears the full burden. It discloses the creation action and return of the object but does not mention side effects, permissions, or rate limits. Adequate but could add more behavioral context.

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?

Two sentences, no filler. First sentence states purpose and required condition, second adds optional linking and return value. Extremely concise and structured.

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?

Given 13 parameters with nested objects and no output schema, the description covers the key constraints and usage. It could elaborate on the return object structure, but overall it provides sufficient context for a creation tool.

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?

Schema description coverage is 100%, baseline is 3. The description adds value by clarifying the condition that email or linkedin_url is required, and the optional linking to a job pipeline, which goes beyond the schema.

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 clearly states the verb 'Create' and the resource 'new candidate in Kula'. It differentiates from siblings like update_candidate and get_candidate by specifying creation and linking to job pipelines.

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?

Explicitly states the required condition: 'At least one of email or linkedin_url (in social_urls) is required.' Also mentions optional linking to job pipeline. Does not provide when-not-to-use or alternatives, but the context is clear.

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

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