OCM MCP Server

Instructions

Implementation Reference

• src/tools/prometheus.ts:74-179 (handler)

  Main handler function for the 'prometheus' tool. Connects to Prometheus thanos-querier, executes PromQL query (snapshot or range), groups results, applies unit conversion, and returns JSON-formatted data for charting.

  export async function prometheus({
    ql,
    data_type = "snapshot",
    group_by = "pod",
    unit = "auto",
    cluster = "default",
    start,
    end,
    step = "5m",
  }: {
    ql: string;
    data_type: "snapshot" | "range";
    group_by: string;
    unit: "auto" | "bytes" | "MiB" | "GiB" | "cores" | "millicores";
    cluster?: string;
    start?: string;
    end?: string;
    step?: string;
  }): Promise<CallToolResult> {
  
    let responseData: any[] = [];
  
    try {
      const { url, token } = await getPrometheusURL(cluster);
  
      const headers = { Authorization: token };
      const effectiveUnit = inferUnit(unit, ql);
  
      const httpsAgent = new https.Agent({ rejectUnauthorized: false });
  
      if (data_type === "range") {
        const response = await axios.default.get(`${url}/api/v1/query_range`, {
          headers,
          params: {
            query: ql,
            start,
            end,
            step,
          },
          httpsAgent,
          proxy: false,
        });
        responseData = response.data.data.result.map((series: any) => ({
          metric: series.metric,
          values: series.values.map(([timestamp, rawValue]: [number, string]) => [
            dayjs.unix(timestamp).toISOString(),
            Number(rawValue) / (1024 * 1024), // bytes → MiB
          ]),
        }));
      } else {
  
        const response = await axios.default.get(`${url}/api/v1/query`, {
          headers,
          params: { query: ql },
          httpsAgent,
          proxy: false,
        });
  
        responseData = response.data.data.result.map(
          (entry: { metric: { [x: string]: any; }; value: (string | number)[]; }) => (
            {
              [group_by]: entry.metric[group_by] || "value",
              value: transformValue(entry.value[1], effectiveUnit),
            }
          ));
      }
  
      // console.warn(responseData)
      if (responseData.length === 0) {
        return {
          content: [{
            type: "text",
            text: JSON.stringify({
              data: [],
              type: data_type,
              unit: effectiveUnit,
            }),
          }],
        };
        // return { data: [], type: data_type, unit: effectiveUnit };
      }
  
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(
              {
                data: responseData,
                type: data_type,
                unit: effectiveUnit,
              }),
          }
        ],
      };
    } catch (err: any) {
      console.error(responseData)
      console.error(err)
      return {
        content: [{
          type: "text",
          text: `Failed to query Prometheus: ${err.message || String(err)}`,
        }],
      };
    }
  }

• src/tools/prometheus.ts:16-70 (schema)

  Input schema (prometheusArgs using Zod) and description (prometheusDesc) for the prometheus tool.

  export const prometheusDesc = "Queries a Prometheus server (snapshot or range) and returns metrics formatted for charting."
  
  export const prometheusArgs = {
    ql: z.string().describe(
      "The PromQL query string to run against the Prometheus server."
    ),
  
    data_type: z
      .enum(["snapshot", "range"])
      .describe("Type of query: 'snapshot' (instant) or 'range' (time-series).")
      .default("snapshot"),
  
    group_by: z
      .string()
      .describe(
        "Label to group results by, such as 'pod' or 'namespace'. If not specified, behavior depends on the query (e.g., 'sum', 'avg')."
      )
      .default("pod"),
  
    unit: z
      .enum(["auto", "bytes", "MiB", "GiB", "cores", "millicores"])
      .describe(
        "Desired output unit. Use 'auto' to infer from the query content (e.g., memory → MiB, CPU → cores)."
      )
      .default("auto"),
  
    cluster: z
      .string()
      .describe(
        "Target cluster name in a multi-cluster environment. Defaults to the hub cluster if not provided."
      )
      .default("default")
      .optional(),
  
    start: z
      .string()
      .describe(
        "(range only) Start time in ISO 8601 format, e.g., '2025-06-06T00:00:00Z'."
      )
      .optional(),
  
    end: z
      .string()
      .describe(
        "(range only) End time in ISO 8601 format. Defaults to current time if not provided."
      )
      .optional(),
  
    step: z
      .string()
      .describe(
        "(range only) Resolution step (e.g., '30s', '5m', '1h'). Choose appropriately to keep the sample count under 200."
      )
      .optional(),
  };

• src/index.ts:45-49 (registration)

  Registration of the 'prometheus' tool in the MCP server using server.tool().

    "prometheus",
    prometheusDesc,
    prometheusArgs,
    async (args, extra) => prometheus(args) // ensure connectCluster matches (args, extra) => ...
  )

• src/tools/prometheus.ts:181-224 (helper)

  Helper function to resolve Prometheus URL and authentication token from Kubernetes custom object (OpenShift route).

  export async function getPrometheusURL(cluster?: string): Promise<{ url: string; token: string }> {
    const cacheKey = cluster || "default";
  
    if (prometheusCache.has(cacheKey)) {
      return prometheusCache.get(cacheKey)!;
    }
  
    const kubeConfigFile = await getKubeconfigFile(cluster)
  
    const kc = new KubeConfig();
    if (kubeConfigFile) {
      kc.loadFromFile(kubeConfigFile);
    } else {
      kc.loadFromDefault();
    }
  
    const customApi = kc.makeApiClient(CustomObjectsApi);
    const res = await customApi.getNamespacedCustomObject({
      group: "route.openshift.io",
      version: "v1",
      namespace: "openshift-monitoring",
      plural: "routes",
      name: "thanos-querier"
    });
  
    const host = (res as any)?.spec?.host;
    if (!host) {
      throw new Error(`Failed to retrieve Prometheus route from cluster ${cluster}.`);
    }
  
    const user = kc.getCurrentUser();
    const token = user?.token;
    if (!token) {
      throw new Error(`No token found in KUBECONFIG for cluster ${cluster}.`);
    }
  
    const result = {
      url: `https://${host}`,
      token: `Bearer ${token}`,
    };
  
    prometheusCache.set(cacheKey, result);
    return result;
  }

• python/multicluster_mcp_server/tools/prometheus.py:13-134 (handler)

  Python implementation of prometheus tool handler, decorated with @mcp.tool for automatic registration. Uses prometheus_api_client for queries.

  @mcp.tool(description="Query Prometheus metrics from a specific cluster and format the results for Recharts visualization.")
  def prometheus(
      ql: Annotated[str, Field(description="The PromQL query string to run against the Prometheus server.")],
      data_type: Annotated[str, Field(description="Type of query: 'snapshot' for instant or 'range' for time-series.")] = "snapshot",
      group_by: Annotated[str, Field(description="Label to group results by, such as 'pod' or 'namespace'.")] = "pod",
      unit: Annotated[str, Field(description="The desired output unit: 'auto', 'bytes', 'MiB', 'GiB', 'cores', or 'millicores'.")] = "auto",
      cluster: Annotated[Optional[str], Field(description="The target cluster name. Defaults to the hub cluster.")] = None,
      start: Annotated[
          Optional[str],
          Field(description="(Only for data_type='range') Start time in ISO 8601 format, e.g., '2025-06-06T00:00:00Z'.")
      ] = None,
      end: Annotated[
          Optional[str],
          Field(description="(Only for data_type='range') End time in ISO 8601 format. Defaults to now if not provided.")
      ] = None,
      step: Annotated[
          Optional[str],
          Field(description="(Only for data_type='range') Query resolution step (e.g., '30s', '5m', '1h').")
      ] = "5m",
  ) -> Annotated[dict, Field(description="Formatted result including Recharts-compatible data or error message.")]:
      try:
          def infer_unit(unit: str, query: str) -> str:
              if unit != "auto":
                  return unit
              q = query.lower()
              if "memory" in q or "bytes" in q:
                  return "GiB"
              elif "cpu" in q:
                  return "cores"
              return "raw"
  
          def transform_value(value: float, unit: str) -> float:
              value = float(value)
              if unit == "MiB":
                  return value / (1024 ** 2)
              elif unit == "GiB":
                  return value / (1024 ** 3)
              elif unit == "millicores":
                  return value * 1000
              return value
  
          # Set up cluster access
          kubeconfig_file = None
          if cluster and cluster != "default":
              kubeconfig_file = get_kubeconfig_file(cluster)
              if not validate_kubeconfig_file(kubeconfig_file):
                  kubeconfig_file = setup_cluster_access(cluster)
                  if not kubeconfig_file:
                      raise FileNotFoundError(f"KUBECONFIG for cluster '{cluster}' does not exist.")
  
          pc = prom_connect(kubeconfig=kubeconfig_file)
          effective_unit = infer_unit(unit, ql)
  
          # Query data
          if data_type == "range":
              end_dt = parse_datetime(end) 
              start_dt = parse_datetime(start) 
              result = pc.custom_query_range(
                  query=ql,
                  start_time=start_dt,
                  end_time=end_dt,
                  step=step
              )
          else:
              result = pc.custom_query(query=ql)
              
          if len(result) == 0:
              return {
                "data": [],
                  "type": data_type,
                  "unit": effective_unit
              }
  
          # Format result
          recharts_data = []
          if data_type == "snapshot":
              df = MetricSnapshotDataFrame(result)
              recharts_data = [
                  {
                      "name": row.get(group_by, "unknown"),
                      "value": transform_value(row["value"], effective_unit)
                  }
                  for _, row in df.iterrows()
              ]
          elif data_type == "range":
              df = MetricRangeDataFrame(result)
              df["value"]=df["value"].astype(float)
              # df.index= pandas.to_datetime(df.index, unit="s")
              df["name"] = df.index
              
              columns_to_keep = ["name", "namespace", "pod", "value", group_by]
              columns_to_keep = list(dict.fromkeys(columns_to_keep))
              df = df[[col for col in columns_to_keep if col in df.columns]].copy()
              
  
              for ts, group in df.groupby("name"):
                if isinstance(ts, pandas.Timestamp):
                  entry = {"name": ts.isoformat()}
                else:
                  # entry["name"] = ts.isoformat()
                  entry = {"name": ts}
                for _, row in group.iterrows():
                    key = row.get(group_by, "unknown")
                    entry[key] = transform_value(row["value"], effective_unit)
                recharts_data.append(entry)
          else:
              raise ValueError("Invalid data_type. Must be 'snapshot' or 'range'.")
          print({
              "data": recharts_data,
              "type": data_type,
              "unit": effective_unit
          })
          
          return {
              "data": recharts_data,
              "type": data_type,
              "unit": effective_unit
          }
  
      except Exception as e:
          return {"not get the data": str(e)}