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100Hires - AI ATS & Recruitment Software

by com.100hires

hires_create_candidate

Create a new candidate profile. Optionally link to a job/stage and attach the candidate's resume as extracted text. Used for imports, inbound forms, and enrichment workflows. When the user provides an attached resume (PDF/DOCX/etc.), parse the file content yourself from the chat context — LLMs natively extract text from uploaded files — and pass the extracted text via resume_text. Do NOT attempt to inline binary file data: host function-call serializers truncate arguments above ~20KB.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoCandidate city for location/timezone resolution.
emailNoCandidate email address. Used for deduplication.
phoneNoCandidate phone number.
stateNoCandidate state or region.
job_idNoJob ID to create an application for this candidate.
countryNoCandidate country name or ISO code.
profileNoKey-value map of profile field answers. Keys can be question text or question_id. Example: {"Current job title": "Senior Engineer"}.
stage_idNoPipeline stage ID for the initial application. Requires job_id.
timezoneNoIANA timezone (e.g. 'America/Los_Angeles'). Auto-resolved from city/country if omitted.
last_nameNoCandidate last name.
company_idNoTarget company ID. Required only when the API key has access to multiple companies.
first_nameNoCandidate first name.
resume_textNoPlain-text resume content extracted by the model from an attached PDF/DOCX/etc. Stored as a text/plain attachment on the candidate. Do not pass binary or base64 here — only the parsed text content.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations indicate the tool is not read-only (readOnlyHint=false) and not destructive (destructiveHint=false). The description adds value by explaining the resume text extraction behavior and warning about argument truncation for binary data, which are behavioral details beyond the annotations. No contradiction found.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured paragraph with front-loaded core action. All sentences provide value, covering purpose, use cases, and a key implementation note. It is appropriately sized and not verbose.

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 the tool's complexity (13 parameters, no output schema), the description covers the essential aspects: candidate creation, linking to job/stage, resume handling, and target use cases. It does not explain the return value, but that is acceptable without an output schema. It is complete enough for an agent to invoke 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?

Schema description coverage is 100%, so baseline is 3. The description adds meaning beyond the schema by explaining that resume_text should be extracted from file content, that job_id and stage_id can be used for linking, and that timezone auto-resolves from city/country if omitted. This additional context elevates the score.

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 action 'Create a new candidate profile' and specifies optional linking to a job/stage and resume attachment. It distinguishes from siblings like 'hires_create_application' by focusing on candidate profile creation, and provides specific use cases: imports, inbound forms, enrichment workflows.

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 provides clear guidance on when to use the tool (imports, inbound forms, enrichment workflows) and includes a critical directive on resume handling: parse text from chat context, do not inline binary data. It lacks explicit comparison to alternatives or when-not-to-use scenarios, but the context is sufficient for an agent.

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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TDQS

A3.7/5.0
Disambiguation4/5

Most tools have clearly distinct purposes due to specific entity and action combinations. However, with 133 tools, there is some overlap (e.g., multiple ways to move applications) and similar-sounding operations (e.g., batch_remove_tags vs remove_candidate_tag) that could cause confusion. The detailed descriptions help but the sheer number increases ambiguity.

Naming Consistency5/5

All tools follow a consistent 'hires_verb_noun' pattern with snake_case. Verbs are descriptive (create, list, get, delete, update, batch) and nouns match the domain entities (candidate, application, job, etc.). No mixing of conventions like camelCase or inconsistent verb styles.

Tool Count2/5

133 tools is excessive for a typical server scope. While a full-featured ATS requires many operations, this count suggests insufficient aggregation. Tools for similar entities (e.g., multiple update/delete variants) could be consolidated. The high number overwhelms the tool surface and increases complexity.

Completeness4/5

The tool set covers core CRUD operations for major entities (candidates, applications, jobs, companies, users, messages, forms, etc.) plus batch operations, webhooks, and advanced features like AI scoring and nurture campaigns. Minor gaps exist (e.g., no direct reporting/analytics tools), but most workflows can be executed.

Resources