Who it is forA developer using a coding agent such as Codex who wants the agent to gather runtime evidence on its own hypotheses.
The problemAgents guess at performance causes without measured runtime data, and wiring up profilers for each session is tedious.
The outcomeThe agent can call named Flameox analysis and capture tools through MCP and reports bounded, traceable evidence for its hypotheses.
What you need first
- Node.js (for npx) or uv (for uvx) installed
- Python 3.12 or newer for the local development path
- An absolute path to an existing artifact file, or an explicit command to run from an absolute working directory
Steps
- Run
npx flameox@latest setupand choose your client, for example--client codex --yesfor automation. - Restart or reconnect the MCP client as the setup output reports.
- Run
uvx flameox mcp inspectto confirm the available tool catalog, or use--tool TOOL_NAMEfor a focused schema. (inferred, check the README) - Ask the agent to use the Flameox tools for a specific hypothesis, then check that the returned evidence lists its limitations.
Prompt for your agent
Using the flameox repo's MCP server, capture runtime evidence for the hypothesis <hypothesis> on the target <target command> with its working directory <absolute path>. Return the bounded evidence, coverage, and limitations for each claim. Verify that each claim is labeled observed, derived, or inferred and that no causal or speedup claim is made without a separate experiment.
Caveat. MCP work belongs to the live request, so no detached job survives a restart and long captures must finish while the session is open.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for morluto/flameox and "Connect a coding agent to Flameox runtime evidence"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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