Consulting Engagement Bench Matching Handler

Role: Lead Workforce Automation Specialist in global management consulting operations.

Context

• Staff seniority level: {{consulting_tier}}
• Core skill taxonomy: {{skill_ontology}}
• Baseline utilization target: {{utilization_target_pct}}
• Corporate travel policy: {{travel_restriction_policy}}
• Allocation timeframe: {{scheduling_horizon_weeks}}
• Engagement budget ceiling: {{project_budget_band}}

Task

Author a lightweight Python tool schema and execution script that enables an autonomous staffing agent to query practitioner bench lists, verify availability against {{scheduling_horizon_weeks}}, check budget compliance within {{project_budget_band}}, and reserve project staff.

Method

1. Generate structured tool declarations for query_consultant_bench and reserve_practitioner.
2. Design input properties to accept requirements based on {{skill_ontology}} and seniority from {{consulting_tier}}.
3. Write Python functions that evaluate individual utilization against {{utilization_target_pct}} before returning availability.
4. Incorporate a policy check verifying whether candidate travel willingness meets {{travel_restriction_policy}}.
5. Compute total estimated cost across {{scheduling_horizon_weeks}} to ensure allocations remain within {{project_budget_band}}.
6. Build a tool dispatcher function that safely receives function names and arguments, returning typed response dicts.
7. Include sample data and a runnable test harness demonstrating a successful matching sequence.

Constraints

• Code MUST be fully self-contained Python without third-party heavy dependencies.
• MUST NOT recommend practitioners whose utilization violates the {{utilization_target_pct}} target threshold.
• Schema parameter properties MUST include detailed enum constraints where applicable.
• The script must return structured error messages for out-of-budget candidate selections.

Output format

1. Tool Schemas (JSON)

Standard function definition schemas ready for OpenAI or Anthropic tool configuration.

2. Dispatcher and Filter Script (Python)

Executable script with mock practitioner database and filter evaluation logic (70-110 lines).

3. Test Scenarios

Two input/output test runs: one successful match and one policy-rejected allocation.

Self-review

• Ensure {{consulting_tier}} and {{skill_ontology}} are explicitly filtered in the query tool.
• Confirm that {{travel_restriction_policy}} rules are checked before confirming reservations.
• Verify script handles zero-match scenarios without raising unhandled exceptions.