Maple MCP server

Connect Claude, Cursor and other AI agents to your Maple telemetry over the Model Context Protocol: endpoints, authentication, and every tool the server exposes.

Maple runs a hosted Model Context Protocol (MCP) server. An AI agent connected to it can list services, search traces and logs, triage error issues, read and edit dashboards, and manage alert rules in your organization.


Endpoint (US)	`https://api.maple.dev/mcp`
Endpoint (EU)	`https://api.eu.maple.dev/mcp`
Transport	Streamable HTTP
Manifest (`server.json`)	maple.dev/.well-known/mcp.json · api.maple.dev/.well-known/mcp.json
Registry name	`dev.maple/maple`
Auth	Maple API key as Bearer token, or OAuth 2.1
Rate limit	120 requests per 10 seconds per key or user; over it, `429` with `Retry-After: 10`

Use the endpoint of your organization’s region. The examples below use the US endpoint; for an EU organization replace api.maple.dev with api.eu.maple.dev.

Connecting a client

Any MCP client that speaks Streamable HTTP can connect. Settings → MCP in the dashboard shows the endpoint for your region and ready-made configuration for common clients. There are two ways to authenticate. Both are described in more detail on Authentication.

API key. Create a key under Settings → MCP or Settings → API Keys and pass it as a Bearer token:

{
	"mcpServers": {
		"maple": {
			"type": "http",
			"url": "https://api.maple.dev/mcp",
			"headers": { "Authorization": "Bearer maple_ak_…" }
		}
	}
}

For Claude Code: claude mcp add --transport http maple https://api.maple.dev/mcp --header "Authorization: Bearer maple_ak_…".

OAuth. Clients that support the MCP authorization flow need no key. The server publishes its protected-resource metadata at https://api.maple.dev/.well-known/oauth-protected-resource/mcp and its authorization server metadata at https://api.maple.dev/.well-known/oauth-authorization-server. Dynamic client registration and PKCE (S256) are supported, so the client needs only the endpoint URL. It opens a browser sign-in on first use.

Requests are scoped to the organization the key belongs to, or the one chosen at sign-in. A user in several organizations picks one during the OAuth consent step.

Tools

The server exposes the tools below. Read-only tools are marked read; the rest change data in your organization. tools/list returns the current set with full parameter schemas.

Services and discovery

Tool	Access	What it does
`list_services`	read	Active services with throughput, error rate and P95 latency
`diagnose_service`	read	One service’s health metrics, Apdex, top errors, recent traces and logs
`get_service_top_operations`	read	A service’s top operations (endpoints or spans), ranked by a chosen metric
`service_map`	read	Service-to-service dependencies with call counts, error rates and latency per edge
`compare_periods`	read	Error rate, throughput and P95 across two periods, overall and per service, with regressions flagged
`explore_attributes`	read	Attribute keys and their values for filtering; `source=services` lists environments and commit SHAs
`list_metrics`	read	Custom metrics with type, unit, monotonicity and data volume
`describe_warehouse_tables`	read	Tables and columns available to raw SQL, with sorting keys and notes on units and casing

Traces, logs and queries

Tool	Access	What it does
`search_traces`	read	Find traces by service, duration, error status, HTTP method, span name or one span attribute
`find_slow_traces`	read	The slowest traces with p50, p95, min and max for context
`inspect_trace`	read	Span tree and logs for one trace
`inspect_span`	read	Full attribute set for one span; decodes messages and tool calls on AI agent spans
`search_logs`	read	Log entries, newest first, filtered by service, severity, body text, trace or span
`mine_log_patterns`	read	Cluster log messages into templates with counts and a severity and service breakdown
`query_data`	read	Aggregate traces, logs, metrics or product events into a time series or top-N breakdown
`run_sql`	read	Run one read-only ClickHouse `SELECT`; see the SQL reference

Errors

Tool	Access	What it does
`find_errors`	read	Errors grouped by type with counts, affected services and a stable fingerprint
`error_detail`	read	Sample traces and correlated logs for one error fingerprint
`list_error_issues`	read	Error issues with workflow state, counts, assignee and lease holder
`list_error_issue_events`	read	An issue’s audit log: state changes, claims, comments, agent notes and fix proposals
`list_error_incidents`	read	Time-bounded flare-ups under an error issue (first seen and regressions)
`claim_error_issue`	write	Take a lease on an issue so other agents do not duplicate the work
`release_error_issue`	write	Give up your lease, optionally moving the issue to another state
`transition_error_issue`	write	Move an issue to another workflow state
`set_issue_severity`	write	Set or clear an issue’s triage severity
`comment_on_error_issue`	write	Add a comment or agent note to an issue’s timeline
`propose_fix`	write	Record a proposed fix and move the issue to `in_review`
`link_pull_request`	write	Attach a GitHub pull request to an issue; Maple verifies the fix after it merges
`register_agent`	write	Register an LLM agent so it can claim and transition issues. Needs a signed-in user, not an API key
`update_error_notification_policy`	write	Choose whether error incidents notify alert destinations, and which ones. Org admins only

Alerting

Tool	Access	What it does
`list_alert_rules`	read	Alert rules with severity, signal type and condition
`get_alert_rule`	read	One rule’s full configuration: thresholds, filters, evaluation settings and destinations
`list_alert_destinations`	read	Notification destinations with id, type and delivery health
`list_alert_incidents`	read	Triggered alert incidents, open and resolved, with the last observed value
`get_incident_timeline`	read	Trigger, notification and resolution times for one rule’s incidents, or all rules’
`list_alert_checks`	read	A rule’s recent evaluations with observed value, threshold and sample count
`preview_alert_rule`	read	Replay a saved or draft rule over past data: each window’s verdict and skip reason
`create_alert_rule`	write	Create an alert rule from a template or from signal type, comparator and threshold
`update_alert_rule`	write	Change the fields you pass on an alert rule
`delete_alert_rule`	write	Delete an alert rule and its incident history. Requires `confirm=true`

Dashboards

Tool	Access	What it does
`list_dashboards`	read	Dashboards with widget counts and timestamps
`get_dashboard`	read	One dashboard’s full configuration, every widget included
`describe_dashboard_schema`	read	What a widget can be: panel types, data sources, units, aggregations and display options
`inspect_chart_data`	read	Run a widget’s query and report row counts, series statistics and a health verdict
`create_dashboard`	write	Create a dashboard from a template, simplified widget specs or full JSON
`update_dashboard`	write	Change a dashboard’s name, description or time range
`add_dashboard_widget`	write	Add one widget, from the query builder, raw SQL or a funnel
`update_dashboard_widget`	write	Replace one widget with a full widget object
`remove_dashboard_widget`	write	Remove one widget
`reorder_dashboard_widgets`	write	Move or resize widgets
`replace_dashboard_widgets`	write	Replace every widget in one validated write

Session replays and product events

Tool	Access	What it does
`search_sessions`	read	Find browser session replays by user, client, errors, duration or what happened in them
`get_session_transcript`	read	A session replay as a text transcript of navigation, clicks, console, network and errors
`get_session_traces`	read	The backend traces a browser session produced
`list_product_events`	read	Product event names with how often each fired and how many sessions and people it reached
`query_funnel`	read	Conversion funnel over product events, with step counts, conversion and drop-off

Agent sessions

Tool	Access	What it does
`list_agent_sessions`	read	AI agent sessions with agent, models, LLM and tool calls, failures, tokens and cost
`get_agent_session`	read	One agent session: verdict, checks, findings, timing, turns, tokens and cost
`get_agent_tools_overview`	read	Calls, failures and latency per agent tool, compared with the previous window
`get_agent_tool_error`	read	One agent tool failure group with affected sessions and sample calls

Setup and source code

Tool	Access	What it does
`audit_setup`	read	Check the whole organization setup: alert routing, error notifications and per-service ingest coverage
`get_instrumentation_recommendations`	read	Span and resource attribute problems found in your live data
`list_source_repositories`	read	Repositories connected through the GitHub integration
`search_source_code`	read	Search one connected repository through GitHub code search
`read_source_file`	read	Read a line range from a file in a connected repository

The source-code tools only return results for repositories the organization’s GitHub App installation can access.

Feedback

Tool	Access	What it does
`send_maple_feedback`	write	Tell the Maple team about a bug, a missing capability or a misleading doc: its kind, the agent, and why

The same feedback can be sent over the REST API with POST /v2/agent_feedback.

Prompts and resources

The server also provides three prompts (incident_triage, latency_analysis, debug_errors) and one resource, maple://instructions, which explains Maple’s data model to the model.

Maple API: the REST API, with its OpenAPI spec
Authentication: API keys and OAuth
Limits: query ranges and rate limits
llms.txt: the machine-readable index of this site