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atlas_start_fit_match

Start an async fit match for a single candidate against a job description (8 credits). Returns task_id and analysis_id. Poll with careerproof_task_status, then fetch result with careerproof_task_result (result_type='fit_match'). Requires context_id from atlas_list_contexts, candidate_id from atlas_list_candidates, and jd_text (from atlas_list_jds or raw text). For multiple candidates, use atlas_start_fit_rank or atlas_start_jd_fit_batch instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jd_textYesJob description text to match against (from atlas_list_jds content or raw text)
context_idYesContext ID from atlas_create_context or atlas_list_contexts
candidate_idYesCandidate ID from atlas_upload_candidate or atlas_list_candidates

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Adds critical behavioral context beyond annotations: discloses credit cost (8 credits), async nature requiring polling, and specific return values (task_id and analysis_id). Annotations only indicate it's a non-destructive write operation; the description explains the full lifecycle and cost model.

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?

Five tightly constructed sentences with zero redundancy. Front-loaded with purpose and cost, followed by return values, workflow, prerequisites, and alternatives. Every sentence conveys essential information for tool selection and invocation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Thoroughly covers the async operation pattern despite lacking an output schema. Explains what to expect (task_id/analysis_id), how to proceed (polling), credit cost, and prerequisite data requirements. Complete for an async task initiation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the baseline is 3. The description reinforces parameter requirements by listing them as prerequisites with their source functions, though it omits some source options present in the schema (e.g., omits atlas_create_context for context_id). No additional semantic depth provided beyond 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?

Explicitly states the action ('Start an async fit match'), target resource (single candidate against job description), and cost (8 credits). Clearly distinguishes from sibling batch tools by specifying 'single candidate' and explicitly naming alternatives for multiple candidates.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit workflow guidance (poll with careerproof_task_status, then fetch with careerproof_task_result), prerequisite data sources (context_id from atlas_list_contexts, etc.), and clear alternative tools for different use cases (atlas_start_fit_rank for multiple candidates).

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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