Clarity AI MCP

@clarity-ai what patterns do you see in our recent API error logs that might indicate a performance bottleneck?

Based on the error patterns in your logs, I'm seeing a cluster of timeout errors (HTTP 504) concentrated around the `/api/v2/search` endpoint between 14:00-15:30 UTC. These correlate with database query times exceeding 8 seconds when the result set contains more than 10,000 records. The pattern suggests missing indexes on the `created_at` and `user_id` columns in your `search_cache` table. Additionally, 23% of these timeouts occur immediately after cache invalidation events, indicating the cold-cache query path needs optimization.

This example shows Clarity AI analyzing log data to surface actionable insights. The MCP would need read access to your logging infrastructure or exported log files. Response quality depends on log verbosity and structure — sparse logs yield generic advice. No side effects; this is read-only analysis.

@clarity-ai create a monitoring dashboard config that tracks our checkout flow conversion rate and alerts if it drops below 85%

I've generated a monitoring configuration for your checkout flow. The dashboard tracks four key metrics: cart-to-checkout transition (current: 91%), checkout-to-payment transition (88%), payment success rate (96%), and overall conversion (77%). The alert rule triggers when the 15-minute rolling average falls below 85%, with a 5-minute cooldown to prevent notification spam. Configuration is exported in Prometheus/Grafana format and includes suggested label filters to exclude test transactions. You'll need to wire this into your existing observability stack.

Demonstrates Clarity AI generating infrastructure-as-code for observability. The output is a starting template, not a production-ready config — you must adapt it to your metric naming conventions and alerting channels. No automatic deployment; this creates configuration files you review and apply manually.

@clarity-ai compare our current test coverage against the modules that caused production incidents in the last quarter and recommend where to add tests

Cross-referencing your test suite with incident reports reveals three high-risk gaps. The `payment-processor` module caused 4 incidents but has only 62% line coverage, with zero integration tests for the retry logic that failed twice. The `user-session` module (3 incidents) lacks tests for concurrent session handling, which caused the March 12 outage. The `email-queue` worker has 89% coverage but no tests for the rate-limiting code that triggered cascading failures on April 3. I recommend prioritizing integration tests for payment retries, concurrency tests for session management, and chaos tests for queue backpressure scenarios.

This synthesis example pairs incident data with code coverage metrics to prioritize testing efforts. Requires access to both your test reports and incident tracking system. The recommendations are directional — Clarity AI identifies gaps but doesn't write the tests. Most valuable when you have structured incident postmortems with root-cause tags.

When no-auth MCPs fit fast-moving product teams

A 3-person startup runs weekly discovery sessions where the founder, designer, and engineer whiteboard user flows and validate ideas against external data. The Clarity AI MCP requires no OAuth setup or API keys, so anyone on the team can spin up a workspace and start querying without waiting on IT or credential handoffs. This works when the team needs quick answers during live calls and the MCP's scope covers the research domain they're exploring. The trade-off: no-auth usually means rate limits or narrower tool coverage, so if your sessions involve heavy data pulls or need audit trails tied to individual users, you'll hit the ceiling fast. For teams under 5 people doing exploratory work a few times a week, the zero-friction setup wins.

Why tool-light MCPs struggle in high-volume support

A 6-person support team fields 40 tickets a day and wants an MCP to surface help docs, past ticket resolutions, and product FAQs during live chats. The Clarity AI MCP's current tool count is zero, which means it can't yet hook into ticket systems, search internal wikis, or pull structured data from your knowledge base. Without those integrations, the MCP becomes a conversational layer with no operational leverage—your team still context-switches to Zendesk or Notion for every answer. This scenario needs an MCP with at least 3-5 tools covering search, ticket lookup, and doc retrieval. If Clarity AI adds those tools later, revisit this use case; until then, support workflows need a more connected integration.

When simplicity beats feature depth for onboarding

A 10-person remote agency onboards 2-3 contractors per quarter and wants a shared AI workspace where new hires can ask process questions without pinging the team lead every hour. The Clarity AI MCP's no-auth model means you can add someone to the workspace in 30 seconds—no credential provisioning, no admin approval flow. This fits onboarding because new hires need conversational access to company context, not deep tool integrations, in their first two weeks. The boundary: if your onboarding involves live demos of internal tools or requires the MCP to pull data from Slack, GitHub, or your project tracker, the lack of tools will frustrate people by day three. For pure Q&A onboarding at small scale, the setup speed is the feature.