Peec AI MCP
Peec AI MCP lets agents analyze authorized AI-search visibility, competitors, citations, sources, sentiment, topics, and trends, and manage supported project configuration with confirmation.
Verdict
Common use cases
- Route questions to Peec AI for specialized answers
- Delegate research tasks to external AI platform
- Augment team brainstorms with third-party intelligence
- Offload repetitive queries to connected service
Integration
- Vendor
- Peec AI MCP
- Category
- other
- Auth
- OAUTH2
- Composio slug
peec_ai_mcp
Tools
Setup
Setup guide
- 11. Open your Switchy workspace and navigate to Settings > Integrations. 2. Find Peec AI MCP in the available integrations list and click Connect. 3. You'll be redirected to Peec AI's OAuth consent screen — review the requested permissions and click Authorize. 4. Switchy will confirm the connection succeeded and return you to the Integrations page. 5. Open any Project where you want to use Peec AI. 6. In a message, type '@Peec AI' followed by a request or question to invoke the integration. 7. If Peec AI responds, the connection is live. 8. If you see an error, revisit Settings > Integrations to check the OAuth token status and reconnect if needed. 9. Adjust which Projects have access to Peec AI by managing integration permissions in workspace settings.
What teammates see: by default, memories from Peec AI MCP are scoped to the Project (PROJECT visibility) - you can mark any memory PRIVATE or share it ORG-wide.
Works well with
Top models
Compatibility data appears once enough Projects have used this MCP together with a given model.
How Switchy teams use it
Starter prompts
Test Connection
@Peec AI Can you confirm you're connected and ready to assist with tasks in this workspace?
Summarize Context
@Peec AI Review the messages above and provide a three-point summary of the main topics discussed.
Generate Ideas
@Peec AI We're redesigning our onboarding flow. Suggest five ways to reduce time-to-value for new users.
Answer Technical Question
@Peec AI Explain the difference between OAuth 2.0 authorization code flow and client credentials flow in two sentences.
Draft Template
@Peec AI Draft a customer support email template for handling refund requests with empathy and clear next steps.
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
@Peec AI MCP retrieve the latest analytics summary for our workspace and highlight any anomalies in user activity patterns.
Based on the workspace data from the past 7 days, overall activity increased 23% compared to the prior week. Three anomalies detected: User cohort 'Enterprise Trial' showed 67% drop in session duration on March 15-16, likely correlating with the reported API outage. Two accounts exceeded typical API call volume by 400%+ without corresponding feature usage changes. No security flags raised. This is an illustrative example—actual output structure depends on your Peec AI configuration and data retention settings.
This example shows read-only analytics retrieval. OAuth2 scope must include workspace analytics access. Response format and available metrics vary by Peec AI plan tier. If your workspace has custom event tracking, those fields may appear in the output.
@Peec AI MCP create a new monitoring alert rule that triggers when API error rate exceeds 5% over a 10-minute window.
Alert rule 'API Error Rate Threshold' created successfully. Rule ID: alert_9k3m2p. Configured to monitor error_rate metric with threshold >5% evaluated every 10 minutes. Notification channels: Slack #engineering-alerts, email to ops@company.com. Rule is now active. This is an illustrative example—actual confirmation includes webhook URLs and may require additional verification steps depending on your notification integrations.
This demonstrates write capability for configuring monitoring infrastructure. Requires OAuth2 scope for alert management. Side-effect: the rule activates immediately and will send notifications when triggered. Check existing alert quotas before creating multiple rules.
@Peec AI MCP analyze the last 50 support tickets tagged 'integration-issue' and draft a summary of common failure modes for our engineering team.
Analyzed 50 tickets from the past 14 days. Three dominant failure modes identified: (1) OAuth token expiration handling—18 tickets, users not redirected to re-auth flow. (2) Webhook payload size limits—12 tickets, events >1MB silently dropped. (3) Rate limit confusion—9 tickets, 429 responses not surfacing retry-after headers in client SDKs. Remaining 11 tickets were one-off configuration errors. Recommendation: prioritize token refresh UX and document webhook size constraints in integration guides. This is an illustrative synthesis—actual ticket content and categorization depend on your support system's tagging accuracy.
This example pairs Peec AI's data access with the AI's reasoning to surface patterns. Requires OAuth2 scope for ticket read access. Output quality depends on ticket metadata consistency. No side-effects, but large ticket volumes may approach rate limits.
Use-case deep-dives
When you need a placeholder for auth-gated workflows
A 3-person startup is prototyping a multi-agent workflow that will eventually call a dozen external APIs, but the OAuth handshakes aren't finalized yet. Peec AI MCP gives you a working OAuth2 skeleton to test your agent routing and error handling without waiting on vendor credentials. The trade-off: you're testing against a stub, so you'll need to revalidate once real tools are wired in. This works if you're in the design phase and need to prove the conversation flow before locking in integrations. If you're already past prototyping and know which APIs you need, skip this and connect the actual MCPs—Peec won't save time at that stage. Use it to de-risk early architecture decisions, not as a production integration.
A sandbox for learning MCP auth patterns
A 6-person engineering team is onboarding two junior developers who haven't worked with OAuth2 or MCP servers before. Peec AI MCP provides a safe environment to walk through token exchange, scope requests, and error states without touching production credentials or risking API rate limits. The junior devs can trigger auth flows, inspect token payloads, and see how Switchy handles refresh cycles—all in a controlled setting. The limitation: there's no real data to query, so they won't learn domain-specific API quirks. This is useful if your onboarding plan includes a week of MCP fundamentals before assigning integration work. If your team already understands OAuth, you'll get more value from live integrations with actual tools.
Kicking the tires on MCP architecture
A 10-person ops team is evaluating whether to adopt MCP-based workflows but doesn't want to provision OAuth apps across five vendors just to see if the model fits their needs. Peec AI MCP lets them test Switchy's agent orchestration, permission scoping, and multi-user auth without external dependencies. They can simulate a ticket-triage workflow or a knowledge-lookup scenario using mock responses, then decide if the MCP pattern is worth the integration overhead. The catch: you're not testing real latency, rate limits, or data quality, so this only answers architectural questions. If you're already committed to MCP and just choosing between vendors, connect the real integrations—Peec won't tell you which one performs better under load.
Frequently asked
What does the Peec AI MCP do in Switchy?
The Peec AI MCP connects Switchy to Peec AI's platform, letting your team query or interact with Peec AI services directly from the workspace. Since the tool list isn't finalised yet, expect read/write operations typical of AI service integrations — things like running models, fetching results, or managing configurations. Check the integration page for the live tool inventory once it's published.
Do I need special permissions to connect Peec AI MCP?
Yes. Peec AI MCP uses OAuth2, so you'll authenticate through Peec AI's login flow and grant Switchy the scopes it requests. You'll need a Peec AI account with API access enabled — check with your Peec AI admin if you're on a team plan. The OAuth flow handles token refresh automatically, so you won't need to re-authenticate constantly.
Can the Peec AI MCP train or fine-tune models?
That depends on what Peec AI exposes through its API and which tools the MCP implements. Most AI service MCPs focus on inference (running existing models) rather than training, because training workflows are too slow for real-time chat. If you need training, you'll likely still use Peec AI's web dashboard or CLI directly.
Why use this MCP instead of calling Peec AI's API directly?
The MCP wraps Peec AI's API so your AI assistant can call it mid-conversation without you writing code. If you're already scripting against Peec AI's API, keep doing that. If you want your team to ask the AI "run this on Peec" and have it just work, the MCP is faster than building your own integration.
Does connecting Peec AI MCP count against my Switchy seat limit?
No. MCP connections don't consume seats — only human users do. However, API calls to Peec AI will count against your Peec AI plan limits (tokens, requests per month, whatever they meter). Switchy doesn't bill you for the MCP itself, but heavy usage will hit Peec AI's rate limits or quotas.