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Workforce-Cloud-Tech

Recruit CRM MCP Server

Prepare Client Brief

prepare_client_brief
Read-only

Prepare a client briefing before calls or meetings. Get talking points, account health, open jobs, pipeline status, feedback blockers, and recommended follow-ups from Recruit CRM data.

Instructions

Prepares a client/account briefing for recruiters before a client call, client meeting, hiring-manager check-in, account review, business-development outreach, or client follow-up. Produces talking points, account health, open jobs, primary and secondary client contacts, assigned-candidate pipeline status, client-feedback blockers, candidates waiting on feedback, recent notes / meetings / tasks / call logs, pitched candidates for the contact, relationship risks, and recommended follow-ups. Inputs are exact Recruit CRM contact_slug or company_slug; display names are not accepted as slug values. Returns summaries and recommended follow-ups only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_slugNoClient company slug to prepare for. Provide company_slug, contact_slug, or both.
contact_slugNoClient contact slug to prepare for. Provide contact_slug, company_slug, or both.
lookback_daysNoActivity lookback window in days. Default 30.
max_open_jobsNoMaximum open jobs to include. Default 5.
include_activityNoInclude notes / meetings / tasks / call-log activity summaries. Default true.
include_contact_infoNoOpt-in flag. When true, contact summaries can include email, contact_number, and linkedin. Default false.
max_related_contactsNoMaximum related company/job contacts to resolve. Default 15.
include_pipeline_summaryNoInclude assigned-candidate pipeline summaries for each open job. Default true.
feedback_wait_days_thresholdNoDays in a client-facing stage before flagging a candidate as waiting on client feedback. Default 3.
max_assigned_candidates_per_jobNoMaximum assigned candidates to inspect per included job. Default 50.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobsYes
scopeYes
clientYes
errorsYes
activityYes
coverageYes
brief_typeYes
account_healthYes
pitched_candidatesYes
recommended_followupsYes
suggested_talking_pointsYes
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: it requires exact slugs, produces only summaries and follow-ups (not full data), and includes configurable lookback and limits. This helps the agent set expectations.

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 dense but well-structured: opening purpose, then output components, then input constraints, then return scope. It is longer than the calibration examples but every sentence adds value; the list of outputs is long but necessary given the tool's breadth.

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?

Given the tool's complexity (10 parameters, many output sections), the description, full param schema, and output schema together provide complete guidance. It covers what the tool does, when to use it, input requirements, output scope, and doesn't need to explain return values since an output schema exists.

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?

Schema description coverage is 100%, with each parameter having a clear description and default. The tool description reinforces the slug input constraint but does not add new per-parameter semantics beyond the schema. 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 uses a specific verb ('Prepares a client/account briefing') and clearly identifies the resource (Recruit CRM client/company data) and the use context (before client calls, meetings, etc.). It lists concrete outputs and distinguishes itself from sibling CRUD/search tools, which are all narrower operations.

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 when to use the tool (before client calls, meetings, check-ins, etc.) and gives input guidance (exact contact_slug or company_slug, not display names). Does not explicitly mention alternatives or when-not-to-use, but the context is clear enough 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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