Faceup
FaceUp is an anonymous reporting tool designed for companies and schools, enabling employees and students to safely report issues and misconduct.
Verdict
Common use cases
- Generate monthly whistleblowing summaries for compliance
- Compare incident volumes across school campuses
- Identify trending concern categories in workplace reports
- Pull anonymized metrics for board presentations
- Track resolution times by report type
Integration
- Vendor
- Faceup
- Category
- other
- Auth
- API_KEY
- Tools
- 1
- Composio slug
faceup
Tools
- FaceUp GraphQL Statistics Query
Tool to execute a graphql query to retrieve faceup statistics. use when you need to fetch or filter metrics (e.g., by date range or school ids).
Setup
Setup guide
- 11. In Switchy, open your workspace settings and navigate to the MCP Integrations section. 2. Select 'Add Integration' and choose FaceUp from the list. 3. You'll be prompted to enter an API key — generate this from your FaceUp admin dashboard under API Settings or Developer Access (exact location depends on your FaceUp plan). 4. Paste the key into Switchy and click 'Connect'. 5. To verify the connection works, open any Space and type '@FaceUp show me total reports from the last 30 days' — if the MCP responds with a count or data table, you're connected. 6. For more complex queries, ask '@FaceUp what fields are available in the statistics schema' to discover filterable dimensions like school IDs, date ranges, or report categories.
What teammates see: by default, memories from Faceup are scoped to the Space (PROJECT visibility) - you can mark any memory PRIVATE or share it ORG-wide.
Works well with
Top models
Compatibility data appears once enough Spaces have used this MCP together with a given model.
How Switchy teams use it
Starter prompts
Monthly Report Summary
@FaceUp retrieve all reports submitted in the last 30 days, grouped by category, and show me the top five categories by volume.Open in a Space →
Cross-Location Comparison
@FaceUp compare total reports across all school IDs for the current quarter and highlight any location with more than 20% above average.Open in a Space →
Trend Analysis
@FaceUp show me monthly report totals for the last six months and calculate the month-over-month percentage change.Open in a Space →
Category Deep Dive
@FaceUp fetch all reports tagged as 'harassment' in the past 90 days and summarize the distribution by week.Open in a Space →
Schema Exploration
@FaceUp list all available fields and filters in the statistics schema so I can build a custom query.Open in a Space →
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
@Faceup show me total reports submitted in the last 30 days across all our schools
Based on FaceUp's statistics, your organization received 47 reports across all schools in the last 30 days. The breakdown shows 28 reports from School ID 12345, 12 from School ID 67890, and 7 from School ID 11223. The most common report categories were bullying (18 reports), safety concerns (14 reports), and mental health (9 reports). This represents a 12% increase compared to the previous 30-day period.
This example demonstrates the MCP's core read capability — querying aggregated metrics via GraphQL. The actual data structure and available filters depend on your FaceUp account configuration and the schools/organizations you have access to. Requires API key with read permissions for statistics endpoints.
@Faceup filter reports by school ID 12345 and show me trends for bullying-related submissions over the past quarter
For School ID 12345, bullying-related reports over Q1 show the following trend: January had 8 reports, February had 12 reports, and March had 14 reports — indicating a 75% increase quarter-over-quarter. Peak submission days were Mondays and Tuesdays. The data also shows 60% of these reports were submitted anonymously, and average response time from administrators was 2.3 days.
This showcases the MCP's filtering and time-series analysis capabilities. The GraphQL query can slice data by school, category, and date range. Note that granular breakdowns (like day-of-week patterns) depend on what fields FaceUp exposes in their statistics API — some derived metrics may require additional calculation on your end.
@Faceup compare report volumes between our three schools and identify which one needs more administrative attention this month
Comparing March report volumes: School A (ID 12345) received 34 reports with an average resolution time of 1.8 days. School B (ID 67890) received 19 reports, resolved in 2.1 days on average. School C (ID 11223) received 41 reports but shows a 4.7-day average resolution time — significantly slower than the others. School C also has 9 reports marked as high-priority that remain unresolved for over a week. Recommendation: prioritize administrative resources for School C to address the backlog and improve response times.
This example pairs the MCP's data retrieval with AI reasoning to surface actionable insights. The MCP provides raw statistics; the AI synthesizes trends and flags outliers. Useful for oversight roles, but remember the MCP is read-only — it cannot assign tasks or send alerts to administrators. You'll need to act on these insights manually or through other integrations.
Use-case deep-dives
When FaceUp MCP streamlines compliance reporting cycles
A 3-person student services team at a district office pulls monthly incident metrics for board meetings. The FaceUp MCP is the right call here: one API key, one GraphQL tool, and you can filter by date range or school ID without logging into the vendor dashboard. The team runs the same query each month, drops the output into a shared doc, and moves on. This works cleanly for districts managing 5-20 schools where the query shape stays consistent. If you're aggregating across 50+ schools or need custom breakdowns beyond what the GraphQL schema exposes, you'll hit the tool's ceiling fast and end up scripting exports anyway. For standard monthly pulls at small-to-mid district scale, the MCP saves 15 minutes per cycle and keeps the data in your workspace.
FaceUp MCP handles ad-hoc metric pulls, not deep investigation
A campus safety coordinator at a university needs to spot trends in anonymous reports across residence halls. The FaceUp MCP works for quick checks: filter by hall ID, pull counts for the past 90 days, compare to last semester. It's faster than opening the vendor portal for one-off questions during a weekly sync. The limitation is the single GraphQL tool—if you need to cross-reference incident text, export attachments, or join with external datasets, the MCP doesn't help. It's a read-only stats layer, not an investigation workbench. For coordinators who mostly need high-level counts and date-range filters, the MCP fits. If your workflow involves case-by-case review or multi-source correlation, keep the vendor dashboard open and use the MCP only for the numeric snapshot.
When FaceUp MCP supports light touchpoint cadences
A 2-person HR team at a 200-employee company runs quarterly pulse checks on anonymous feedback volume. The FaceUp MCP is borderline useful here: the GraphQL tool can pull submission counts and filter by date, but the schema is built for school-centric use cases (school IDs, not department tags). If your FaceUp instance maps cleanly to organizational units, the MCP gives you a quick quarterly snapshot without switching contexts. If your setup uses custom taxonomies or you need sentiment breakdowns, the single-tool MCP won't cut it—you'll export CSVs from the vendor UI instead. For teams running lightweight quarterly reviews where raw counts answer the question, the MCP keeps the workflow in Switchy. For richer analysis, it's a pass.
Frequently asked
What does the Faceup MCP do in Switchy?
The Faceup MCP lets your AI agents query Faceup's whistleblowing and reporting statistics via GraphQL. Agents can pull metrics filtered by date range or school IDs, making it useful for compliance teams that need to surface incident trends or generate reports without manually logging into Faceup's dashboard.
Do I need admin access to connect Faceup?
You need an API key from Faceup, which typically requires admin or developer permissions in your Faceup account. The MCP uses API_KEY auth, so whoever connects it must have the rights to generate or access that key. Check with your Faceup account owner if you're unsure.
Can the Faceup MCP submit new reports or respond to whistleblowers?
No. The MCP only reads statistics through a GraphQL query tool. It can't create reports, reply to submissions, or modify case statuses. If you need to act on incidents, you'll still use Faceup's web interface or their full API directly.
Why use this MCP instead of Faceup's dashboard or API?
The MCP is faster for ad-hoc questions your team asks in Switchy chat — "How many reports did we get last month?" gets answered instantly without switching apps. For deep analysis or case management, Faceup's dashboard or a custom API integration is still the better choice.
Who on the team should connect the Faceup MCP?
Whoever owns compliance reporting or has access to your Faceup API key. Since the MCP exposes statistics, make sure the person connecting it understands your data-privacy policies around incident metrics. The connection itself doesn't count against Switchy seat limits.