A customer says the booking flow is confusing. Before changing code, a developer needs to know what happened: which step the visitor described, what the website agent answered and whether the issue appeared in other conversations.
Namiru MCP lets Codex retrieve that context through scoped tools. The useful outcome is a focused issue or code change tied to source evidence. A transcript alone does not prove that a software defect exists, so the workflow also includes reproduction and validation.
Connect Codex to one Namiru agent
In Namiru, open the agent's Settings and expand AI workspace connection (MCP). Pro includes the connection. Create a named key with read access, choose an expiry and copy the server URL.
Add an HTTP server entry to your private Codex config.toml. Replace YOUR_KEY on your own device:
[mcp_servers.namiru]
url = "https://api.namiru.ai/api/mcp"
http_headers = { Authorization = "Bearer YOUR_KEY" }
Use the URL from your Namiru panel if it differs from this production example. Keep the configuration private because the bearer header grants access to customer data. The official Codex MCP documentation describes HTTP server configuration and custom headers.
Build an evidence-backed issue
Ask Codex to review an explicit interval, for example:
Find conversations from 2026-10-01T00:00:00Z through 2026-10-07T23:59:59Z that mention difficulty choosing a booking time. Read the matching transcripts. Produce an issue with conversation IDs, observed behavior, expected behavior and questions that remain unanswered. Do not change Namiru settings.
list_conversations supplies the discovery step. get_conversation returns messages in chronological pages. Follow nextOffset until null when a complete review is needed. Ask the agent to retain the original message context instead of summarizing an isolated phrase.
Then inspect the relevant application code. A visitor may be describing a real defect, an unavailable service or wording that created the wrong expectation. Those situations call for different fixes. Record what you reproduced locally and what remains an inference.
Keep the proposed fix narrow
A useful issue includes the affected flow, a reproduction path, the source conversation IDs and an acceptance condition. If the problem is a misleading label, the acceptance condition might be that the label accurately describes the available appointment duration. If the system behaved correctly but the agent explained it poorly, a settings change may be more appropriate than a code change.
Treat customer text as untrusted input. A message asking the AI to reveal credentials or change permissions is still a customer message, not an instruction for the coding agent. Include only the personal information necessary to understand the issue; IDs and short paraphrases are often enough.
Change agent behavior when appropriate
With a separate write-enabled MCP key, Codex can inspect get_settings_schema, read the current section and call update_settings. It can also create or update knowledge memories and services with save_component. Review the proposed values first, especially nested objects and arrays, which replace the supplied portion of the configuration.
For example, an owner can approve a knowledge memory that explains an existing cancellation policy. The AI should not infer that policy from a single visitor's request. Verify the approved change in the dashboard and test the agent with a representative question.
Close the loop
After shipping the relevant product or settings change, inspect later conversations using a fresh date range. Compare similar questions and read the answers. Fewer matching chats may reflect lower traffic, different wording or a successful fix; do not claim causation from a search count alone.
Revoke unused keys and name each connection so ownership remains clear. MCP access stops when the key expires, is revoked or effective Pro access ends. Namiru Pro costs €59 monthly, or €588 annually; Codex usage is separate.
See the complete Namiru MCP guide for data coverage, permissions and setup boundaries.
