#!/usr/bin/python

# Example check. Put this into local/share/check_mk/checks

# Inventory function without any specialities. Return
# None as a default parameter (meaning: no levels).
def inventory_foo(info):
    if len(info) > 0:
        return [(None, None)]

def check_foo(_no_item, params, info):
    # Step 1: extract the measured value from the agent output. Here
    # of course trivial
    value = float(info[0][0]) 

    # Step 2: Output the value (and maybe others) as performance data.
    # This is mandatory when using predictive levels. The name of the
    # value (here 'fooval') is needed later.
    perfdata = [("fooval", value)]

    # Step 3: Prepare some informative output text
    text = "%d running processes" % value

    # Step 4: let the helper function check_levesl() do the rest.
    # The params can be None (lo levels), a pair of fixed levels for
    # WARN/CRIT or a dictionary with the parameters for the prediction.
    # In return we get the resulting Nagios state and in case of prediciton
    # an extra text and extra perfdata (the predicted reference).
    state, extratext, extraperf = check_levels(value, "fooval", params)

    # Put that extratext together with our own text
    if extratext:
        text += ", " + extratext

    # Also append the extra performance data and return the stuff
    return state, text, perfdata + extraperf


check_info['foo'] = {
    "check_function"          : check_foo,
    "inventory_function"      : inventory_foo,
    "service_description"     : "FOO",
    "has_perfdata"            : True,  # must be true
    "group"                   : "foobar", # configuration group for WATO
}