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FeedbackPulse MCP Overview

FeedbackPulse supports the Model Context Protocol (MCP), an open standard that lets AI assistants like Claude and ChatGPT read and act on your organisation’s feedback data. Instead of logging into the dashboard, you can ask questions in plain language and get answers drawn directly from your data.

Ask your AI assistant… What FeedbackPulse provides
“Show me our latest engagement survey results” Survey responses, scores, and completion rates
“What’s the completion rate for the Q1 review cycle by department?” Performance review progress and breakdowns
“Who are our top recognition recipients this quarter?” Recognition counts, kudos trends, top recipients
“Which employees haven’t completed their performance reviews?” Per-employee review status and overdue list
“Create a new onboarding survey template with these questions” Survey template creation (Admin + write scope)
“Show me all active employees in the Engineering department” Employee list with department and manager info

FeedbackPulse exposes 19 tools to connected AI assistants. For full parameter details, see the MCP Tools Reference.

Tool What it does Scope
list_surveys Lists surveys with status and completion rates mcp:use
get_survey_detail Full details for a specific survey mcp:use
get_survey_results Survey results, scores, and response statistics mcp:use
get_survey_summary AI-generated summary of survey themes mcp:use
create_survey_draft Creates a real, unlaunched survey draft from an existing template and returns a dashboard link to review and launch it mcp:write (Admin)
list_survey_templates Lists available survey templates mcp:use (Admin)
get_survey_template Full template detail including all questions mcp:use (Admin)
create_survey_template Creates a new survey template mcp:write (Admin)
update_survey_template Updates an existing survey template mcp:write (Admin)
Tool What it does Scope
list_review_cycles Lists review cycles with completion statistics mcp:use
get_review_cycle_summary Department-level completion breakdown for a cycle mcp:use
list_perf_reviews Individual performance reviews with status mcp:use
list_peer_reviews Peer reviews with status and reviewer info mcp:use
get_peer_review_stats Aggregated peer review ratings and completion rates mcp:use
Tool What it does Scope
get_recognition_stats Aggregate recognition metrics and top recipients mcp:use
list_recognitions Individual recognition records with feedback text mcp:use
Tool What it does Scope
list_employees Browse employees with department and manager info mcp:use
Tool What it does Scope
list_tokens Read-only audit of your organisation’s MCP personal API tokens: name, owner, last four characters, and last-used timestamps. Never returns token secrets. mcp:use (Admin)
get_activity_log Read-only audit trail of MCP tool calls: tool name, arguments, user, status, and timestamp mcp:use (Admin)

FeedbackPulse also provides four guided prompts: pre-built workflows that instruct your AI assistant to gather and analyse data in a structured way.

Prompt What it does Arguments
analyze_survey_results Response rates, sentiment themes, low-scoring questions, and recommendations department, survey_id, period
engagement_health_check Snapshot combining survey scores and recognition activity with red-flag alerts department, period
performance_review_prep Cycle completion status, peer feedback stats, and calibration notes review_cycle (required), employee_name
team_pulse Quick health check on a team’s survey participation and recognition frequency department, manager_name, period

To use a prompt, ask your AI assistant to run it by name, for example:

“Run the engagement health check for the Engineering department over the last 90 days.”

performance_review_prep requires a paid FeedbackPulse plan with performance reviews enabled.

Try these prompts once you’ve connected your AI assistant:

  1. “Run analyze_survey_results for the last 90 days.” The AI will retrieve your most recent surveys, compare response rates and scores, surface recurring themes, and flag low-scoring questions.

  2. “What’s the completion rate for the Q1 2026 review cycle, broken down by department?” The AI will pull the review cycle summary and present department-level progress.

  3. “Show me recognition stats for the Engineering department this quarter.” The AI will return total recognitions, kudos vs constructive breakdown, and top recipients.

  4. “Which employees haven’t started their performance reviews in the current cycle?” The AI will list reviews filtered by not_started status.

  5. “List all active employees in the Sales department.” The AI will return employees with their job titles, managers, and contact details.

  • AI assistants can only access data your account is authorised to see. Admins see all organisation data. Managers see their direct and indirect reports for review and employee tools, but Managers have no access to survey or recognition data through MCP at all, not even for their own reports.
  • Anonymous survey responses are protected. Individual text responses are withheld when the number of responses falls below the configured privacy threshold.
  • Write actions (creating survey drafts, and creating and updating survey templates) require Admin access and a token with the mcp:write scope.
  • No conversation content is stored by FeedbackPulse.

For full details, see our Privacy Policy (Section 11: API & MCP Access).

Choose your AI assistant to get started:

Having trouble? See MCP Connection Troubleshooting.

Questions? Contact us at support@feedbackpulse.com.