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Workforce-Cloud-Tech

Recruit CRM MCP Server

Analyze Job Pipeline

analyze_job_pipeline
Read-only

Diagnose a job's hiring pipeline by analyzing candidate distribution, idle/at-risk candidates, and bottleneck stages to surface recommended next actions.

Instructions

Diagnoses a single Recruit CRM job's hiring pipeline: stage-by-stage candidate distribution, days_in_current_stage per active candidate (sourced from assignment-level stage_date — ~7 API calls), idle and at-risk candidates, bottleneck stage verdict, recent notes / meetings / tasks tied to the job, and suggested next actions. Set include_time_metrics=true to add time-to-hire, time-to-stage, and time-to-first-action metrics (fetches per-candidate history for capped active and Placed candidates, typically +25–55 extra calls). Requires a job_slug; resolve from search_jobs if only a job name is available. Candidate slugs in the response can be linked as https://app.recruitcrm.io/candidate/{candidate_slug}. Call logs are not included because the Recruit CRM API does not support filtering call logs by job.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_slugYesJob slug to analyze (e.g. 16734937272590003vEM). Resolve from search_jobs if only a job name is available.
start_pageNoFirst assignment page in the 3-page window (~300 candidates) analyzed by this call. Default 1. Pass next_window.start_page from a prior response to analyze the next batch.
include_activityNoInclude notes / meetings / tasks counts tied to this job (3 extra parallel calls). Default true. Auto-skipped when start_page > 1 to avoid redundant calls on follow-up windows.
idle_days_thresholdNoDays since a candidate's last hiring-stage movement before flagging as idle. Default 14.
include_time_metricsNoCompute time-to-hire, time-to-stage, and time-to-first-action metrics. Default false. When false, relies on stage_date for days_in_current_stage (~7 API calls). When true, fetches per-candidate history for capped active and Placed candidates (typically +25–55 extra calls), populating the time_metrics block.
max_active_candidatesNoCap on active candidates whose stage history is fetched when include_time_metrics is true (each costs one /candidates/{slug}/history call). Default 25. Ignored when include_time_metrics is false (no active histories fetched).
max_placed_candidatesNoCap on Placed (terminal) candidates whose stage history is fetched when include_time_metrics is true (each costs one /candidates/{slug}/history call). Default 25. Ignored when include_time_metrics is false.
terminal_stage_labelsNoStage labels treated as terminal (excluded from active analysis). Default ["Placed","Rejected","Offer Declined","Withdrawn"]. Override only when the account uses non-standard pipeline labels.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobYes
errorsYes
activityYes
pipelineYes
truncatedYes
bottleneckYes
time_metricsYes
idle_candidatesYes
suggested_actionsYes
Behavior5/5

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

Even though annotations already mark readOnlyHint=true and destructiveHint=false, the description adds substantial behavioral detail: API call counts (~7 baseline, +25–55 with time metrics), the data source for days_in_current_stage (stage_date), auto-skipping of include_activity when start_page > 1, and capping of fetched candidates. These are valuable operational facts not visible in annotations.

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?

The description is dense but well-organized: main purpose first, then optional metrics, prerequisite, linking convention, and a final limitation. Every sentence contributes useful information without redundancy or filler.

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?

For a tool with 8 parameters, an output schema, and a complex analysis workflow, the description covers purpose, usage context, behavioral costs, prerequisites, optional extensions, and limitations. It is fully sufficient for an agent to decide when to use it and what to expect.

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?

The input schema already provides 100% parameter coverage with detailed descriptions (defaults, behavior, cost implications). The main description adds minimal semantic meaning beyond the schema—though it does contextualize include_time_metrics and API call trade-offs, it largely restates what the schema already documents. Baseline 3 is appropriate.

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 opens with a specific verb and resource: 'Diagnoses a single Recruit CRM job's hiring pipeline.' It enumerates concrete outputs (stage-by-stage candidate distribution, days_in_current_stage, idle/at-risk candidates, bottleneck verdict, notes/meetings/tasks, next actions), which clearly distinguishes it from sibling tools like search_jobs or get_job_assigned_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?

The description provides explicit guidance on when to use this tool: it requires a job_slug and instructs to 'resolve from search_jobs if only a job name is available.' It also states a limitation and exclusion: 'Call logs are not included because the Recruit CRM API does not support filtering call logs by job.' This helps the agent understand scope and alternatives.

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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