Invocations

Invocations track ALL tool calls, not just failures. When enabled, desire_path becomes a comprehensive telemetry system for AI tool usage.

The Difference

• Desires: Only failed tool calls
• Invocations: Every tool call (success or failure)

Invocations give you the full picture: success rates, usage patterns, which tools get hammered, which never get touched, and how sessions unfold over time.

Enabling Invocations

Turn on full tracking when initializing:

dp init --source claude-code --track-all

The --track-all flag activates invocation recording. Now every tool call flows into your desire_path database, not just the failures.

What Gets Captured

Each invocation records:

• source: Which AI system made the call (e.g., “claude-code”)
• instance_id: Specific AI session or conversation
• host_id: Machine where the call happened
• tool_name: The tool that was called
• is_error: Boolean — did it succeed or fail?
• error: Error message if is_error is true (null otherwise)
• cwd: Working directory during the call
• timestamp: When it happened
• metadata: Additional context as JSON (optional)

Viewing Invocation Stats

Get aggregated statistics:

dp stats --invocations

This might show:

TOOL NAME       TOTAL   SUCCESS   FAILED   SUCCESS RATE
Read            324     320       4        98.8%
Bash            156     142       14       91.0%
Glob            89      89        0        100.0%
Edit            67      63        4        94.0%
read_file       47      0         47       0.0%

Insights immediately visible:

• Read works reliably (98.8% success)
• Bash has issues (14 failures worth investigating)
• read_file fails every time (needs aliasing or building)

Exporting Invocation Data

Pull raw data for deeper analysis:

dp export --type invocations --format json > invocations.json

Now you can:

• Load into analytics tools
• Build custom dashboards
• Track trends over time
• Correlate with other metrics

The Source Plugin System

Invocations use desire_path’s plugin architecture. Each AI tool has its own parser:

dp init --source claude-code --track-all   # Claude Code invocations
dp init --source aider --track-all          # Aider invocations
dp init --source custom-ai --track-all      # Your custom AI tool

The source plugin handles:

• Parsing that AI tool’s specific output format
• Extracting tool call data
• Recording to the desire_path database

Different AI tools structure their telemetry differently. Plugins normalize everything into a common schema.

Use Cases

Success Rate Monitoring

Which tools are fragile? If Bash fails 10% of the time, maybe error handling needs work.

Usage Patterns

Which tools actually get used? You might discover Claude Code calls Read 10x more than anything else — worth optimizing.

Session Timelines

Replay how a conversation unfolded: “First it read the file, then globbed for tests, then ran bash commands.” Debug AI reasoning by seeing the sequence.

Hallucination Detection

Tools with 0% success rate are hallucinations. But you already knew that from desires. What’s new: tools with 50% success rate might have naming collisions or API confusion.

When To Enable Invocations

Don’t Enable If…

• You only care about failures (desires are enough)
• Storage or performance is constrained (invocations generate more data)
• You’re just getting started (start simple, expand later)

Do Enable If…

• You’re building production AI tooling (comprehensive telemetry matters)
• You want to optimize tool implementations (need success data to measure improvements)
• You’re analyzing AI behavior patterns (full session data reveals reasoning flows)
• You’re running experiments (A/B testing tool changes requires success metrics)

The Full Picture

Desires tell you what’s broken. Invocations tell you what’s working, how often, and why.

Together, they turn desire_path from a failure tracker into an AI observability platform. You’re not just fixing problems — you’re understanding how AI assistants interact with your system at every level.

More data. Richer insights. Better tools.