Connect an AI agent with MCP

Teml exposes a remote Model Context Protocol endpoint. An AI agent can investigate customers, Issues, traces, logs, replay, incidents, and LLM cost without copying telemetry into prompts by hand.

Start with read-only access.

Create a key

In Settings → Access, create an API key with mcp:read. Give the key a name tied to the person, agent, or automation that will use it.

Do not reuse an application telemetry key. Store the MCP key in the client’s local secret or credential configuration and never commit it to a repository.

Endpoint

Configure a remote HTTP MCP server with:

URL:           https://api.teml.io/mcp
Authorization: Bearer tm_...

The connection is scoped to the organization represented by the API key.

Claude Code

claude mcp add --transport http teml https://api.teml.io/mcp \
  --header "Authorization: Bearer tm_..."

Run claude mcp list in a new session to confirm the server is connected. Protect the local client configuration because it contains the header value.

Other MCP clients

Create a remote HTTP server named teml, use the endpoint above, and set the bearer header through the client’s credential UI or local configuration. The client must support Streamable HTTP.

Verify with a safe question

Ask the agent:

Search for the most recently active customers and explain which Teml tools you used. Do not change anything.

Then ask a bounded production question such as:

What happened to buyer@acme.example in the last hour? Cite the underlying Teml evidence.

Tool results include deep links back to the dashboard so you can verify the answer.

Add writes only when required

Read access is sufficient for investigation. If an agent must acknowledge alerts, triage Issues, or manage another resource, create a separate key with mcp:write and only the required domain scopes. See Permissions and safe actions.