davsclaus commented on code in PR #25052: URL: https://github.com/apache/camel/pull/25052#discussion_r3637048096
########## dsl/camel-jbang/camel-jbang-mcp/src/main/java/org/apache/camel/dsl/jbang/core/commands/mcp/RouteCostEstimateTools.java: ########## @@ -0,0 +1,317 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.camel.dsl.jbang.core.commands.mcp; + +import java.util.ArrayList; +import java.util.Comparator; +import java.util.LinkedHashMap; +import java.util.List; +import java.util.Map; +import java.util.regex.Matcher; +import java.util.regex.Pattern; + +import jakarta.enterprise.context.ApplicationScoped; + +import io.quarkiverse.mcp.server.Tool; +import io.quarkiverse.mcp.server.ToolArg; +import io.quarkiverse.mcp.server.ToolCallException; + +/** + * MCP Tool for estimating API costs of a Camel route based on its component usage. + * <p> + * Particularly useful for AI pipelines where Bedrock, Textract, and S3 calls have per-unit pricing. Provides + * per-execution cost estimates and projections at a given throughput. + * <p> + * Cost data is approximate and based on published AWS pricing as of 2025. Actual costs depend on model, region, and + * volume tiers. + */ +@ApplicationScoped +public class RouteCostEstimateTools { + + private static final Pattern SCHEME_PATTERN = Pattern.compile( + "(?:uri:\\s*[\"']?|from:\\s+[\"']?|to:\\s+[\"']?|toD:\\s+[\"']?)([a-zA-Z][a-zA-Z0-9+.-]*):(?://)?", + Pattern.MULTILINE); + + private static final Map<String, ComponentCostProfile> COST_PROFILES = buildCostProfiles(); + + @Tool(annotations = @Tool.Annotations(readOnlyHint = true, destructiveHint = false, openWorldHint = false), + description = "Estimate API costs for a Camel route based on its component usage. " + + "Analyzes a YAML or XML route definition and identifies components with " + + "pay-per-use pricing (Bedrock LLM, Textract, S3, SQS, SNS, etc.). " + + "Returns per-execution cost estimate and monthly projection at a given throughput. " + + "Cost data is approximate based on published AWS pricing.") + public CostEstimateResult camel_route_cost_estimate( + @ToolArg(description = "The Camel route definition (YAML or XML)") String route, + @ToolArg(description = "Expected messages per hour for cost projection (default: 100)") Integer messagesPerHour, + @ToolArg(description = "Average document pages per message for Textract/Docling (default: 5)") Integer avgPages, + @ToolArg(description = "Average LLM input tokens per request (default: 1000)") Integer avgInputTokens, + @ToolArg(description = "Average LLM output tokens per request (default: 500)") Integer avgOutputTokens) { + + if (route == null || route.isBlank()) { + throw new ToolCallException("Route content is required", null); + } + + int throughput = messagesPerHour != null && messagesPerHour > 0 ? messagesPerHour : 100; + int pages = avgPages != null && avgPages > 0 ? avgPages : 5; + int inTokens = avgInputTokens != null && avgInputTokens > 0 ? avgInputTokens : 1000; + int outTokens = avgOutputTokens != null && avgOutputTokens > 0 ? avgOutputTokens : 500; + + List<String> detectedSchemes = extractSchemes(route); + + List<ComponentCostBreakdown> breakdown = new ArrayList<>(); + double totalPerExecution = 0; + + for (String scheme : detectedSchemes) { + ComponentCostProfile profile = COST_PROFILES.get(scheme); + if (profile == null) { + continue; + } + + double costPerExec = profile.estimateCostPerExecution(pages, inTokens, outTokens); + totalPerExecution += costPerExec; + breakdown.add(new ComponentCostBreakdown( + scheme, profile.displayName(), profile.pricingModel(), + costPerExec, profile.pricingNote())); + } + + breakdown.sort(Comparator.comparingDouble(ComponentCostBreakdown::estimatedCostPerExecution).reversed()); + + double hourly = totalPerExecution * throughput; + double daily = hourly * 24; + double monthly = daily * 30; + + String mostExpensive = breakdown.isEmpty() ? null : breakdown.get(0).scheme(); + + List<String> optimizationTips = buildOptimizationTips(detectedSchemes, breakdown); + + CostProjection projection = new CostProjection( + throughput, + formatCost(hourly), formatCost(daily), formatCost(monthly)); + + CostSummary summary = new CostSummary( + formatCost(totalPerExecution), + breakdown.size(), + detectedSchemes.size(), + mostExpensive, + breakdown.isEmpty() ? "No pay-per-use components detected in this route" : null); + + return new CostEstimateResult( + breakdown.isEmpty() ? null : breakdown, + projection, summary, + optimizationTips.isEmpty() ? null : optimizationTips, + "Cost estimates are approximate based on published AWS pricing (us-east-1). " + + "Actual costs vary by region, volume tier, and model."); + } + + List<String> extractSchemes(String route) { + List<String> schemes = new ArrayList<>(); + Matcher m = SCHEME_PATTERN.matcher(route); + while (m.find()) { + String scheme = m.group(1); + if (!schemes.contains(scheme)) { + schemes.add(scheme); + } + } + return schemes; + } + + private List<String> buildOptimizationTips(List<String> schemes, List<ComponentCostBreakdown> breakdown) { + List<String> tips = new ArrayList<>(); + + if (schemes.contains("aws-bedrock")) { + tips.add("Consider using a smaller/cheaper Bedrock model for simpler tasks " + + "(e.g., Nova Lite instead of Claude for classification)"); + tips.add("Use streaming to reduce perceived latency without affecting token costs"); + } + if (schemes.contains("aws2-textract") && schemes.contains("docling")) { + tips.add("Using both Textract and Docling — consider using only Docling (free, open-source) " + + "for text extraction and reserving Textract for table/form extraction"); + } + if (schemes.contains("aws2-s3")) { + tips.add("S3 GET costs are minimal but add up at scale — consider caching frequently accessed documents"); + } + if (breakdown.size() > 1) { + tips.add("Most expensive component: " + breakdown.get(0).displayName() + + " — focus optimization here for maximum savings"); + } + return tips; + } + + private static String formatCost(double cost) { + if (cost < 0.01) { + return String.format("$%.6f", cost); + } + return String.format("$%.4f", cost); + } + + private static Map<String, ComponentCostProfile> buildCostProfiles() { + Map<String, ComponentCostProfile> profiles = new LinkedHashMap<>(); + + profiles.put("aws-bedrock", new ComponentCostProfile( + "AWS Bedrock (Claude Sonnet 4)", "per-token", + "Input: ~$3/MTok, Output: ~$15/MTok (Claude Sonnet 4 pricing)") { + @Override + double estimateCostPerExecution(int pages, int inputTokens, int outputTokens) { + return (inputTokens * 3.0 / 1_000_000) + (outputTokens * 15.0 / 1_000_000); + } Review Comment: This hardcodes Claude Sonnet 4 pricing ($3/$15 per MTok), but users choose their Bedrock model — Nova Lite, Haiku, Titan, Mistral, etc. all have very different pricing. A route using Nova Lite would be dramatically overestimated. Since the model isn't detectable from the route definition, at minimum the display name and pricing note should make clear this is a **specific model estimate**, not a Bedrock-wide one. Ideally the tool would accept a model parameter or default to a mid-range estimate rather than the most expensive model. ########## dsl/camel-jbang/camel-jbang-mcp/src/main/java/org/apache/camel/dsl/jbang/core/commands/mcp/RouteCostEstimateTools.java: ########## @@ -0,0 +1,317 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.camel.dsl.jbang.core.commands.mcp; + +import java.util.ArrayList; +import java.util.Comparator; +import java.util.LinkedHashMap; +import java.util.List; +import java.util.Map; +import java.util.regex.Matcher; +import java.util.regex.Pattern; + +import jakarta.enterprise.context.ApplicationScoped; + +import io.quarkiverse.mcp.server.Tool; +import io.quarkiverse.mcp.server.ToolArg; +import io.quarkiverse.mcp.server.ToolCallException; + +/** + * MCP Tool for estimating API costs of a Camel route based on its component usage. + * <p> + * Particularly useful for AI pipelines where Bedrock, Textract, and S3 calls have per-unit pricing. Provides + * per-execution cost estimates and projections at a given throughput. + * <p> + * Cost data is approximate and based on published AWS pricing as of 2025. Actual costs depend on model, region, and + * volume tiers. + */ +@ApplicationScoped +public class RouteCostEstimateTools { + + private static final Pattern SCHEME_PATTERN = Pattern.compile( + "(?:uri:\\s*[\"']?|from:\\s+[\"']?|to:\\s+[\"']?|toD:\\s+[\"']?)([a-zA-Z][a-zA-Z0-9+.-]*):(?://)?", + Pattern.MULTILINE); + + private static final Map<String, ComponentCostProfile> COST_PROFILES = buildCostProfiles(); + + @Tool(annotations = @Tool.Annotations(readOnlyHint = true, destructiveHint = false, openWorldHint = false), + description = "Estimate API costs for a Camel route based on its component usage. " + + "Analyzes a YAML or XML route definition and identifies components with " + + "pay-per-use pricing (Bedrock LLM, Textract, S3, SQS, SNS, etc.). " + + "Returns per-execution cost estimate and monthly projection at a given throughput. " + + "Cost data is approximate based on published AWS pricing.") + public CostEstimateResult camel_route_cost_estimate( + @ToolArg(description = "The Camel route definition (YAML or XML)") String route, + @ToolArg(description = "Expected messages per hour for cost projection (default: 100)") Integer messagesPerHour, + @ToolArg(description = "Average document pages per message for Textract/Docling (default: 5)") Integer avgPages, + @ToolArg(description = "Average LLM input tokens per request (default: 1000)") Integer avgInputTokens, + @ToolArg(description = "Average LLM output tokens per request (default: 500)") Integer avgOutputTokens) { + + if (route == null || route.isBlank()) { + throw new ToolCallException("Route content is required", null); + } + + int throughput = messagesPerHour != null && messagesPerHour > 0 ? messagesPerHour : 100; + int pages = avgPages != null && avgPages > 0 ? avgPages : 5; + int inTokens = avgInputTokens != null && avgInputTokens > 0 ? avgInputTokens : 1000; + int outTokens = avgOutputTokens != null && avgOutputTokens > 0 ? avgOutputTokens : 500; + + List<String> detectedSchemes = extractSchemes(route); + + List<ComponentCostBreakdown> breakdown = new ArrayList<>(); + double totalPerExecution = 0; + + for (String scheme : detectedSchemes) { + ComponentCostProfile profile = COST_PROFILES.get(scheme); + if (profile == null) { + continue; + } + + double costPerExec = profile.estimateCostPerExecution(pages, inTokens, outTokens); + totalPerExecution += costPerExec; + breakdown.add(new ComponentCostBreakdown( + scheme, profile.displayName(), profile.pricingModel(), + costPerExec, profile.pricingNote())); + } + + breakdown.sort(Comparator.comparingDouble(ComponentCostBreakdown::estimatedCostPerExecution).reversed()); + + double hourly = totalPerExecution * throughput; + double daily = hourly * 24; + double monthly = daily * 30; + + String mostExpensive = breakdown.isEmpty() ? null : breakdown.get(0).scheme(); + + List<String> optimizationTips = buildOptimizationTips(detectedSchemes, breakdown); + + CostProjection projection = new CostProjection( + throughput, + formatCost(hourly), formatCost(daily), formatCost(monthly)); + + CostSummary summary = new CostSummary( + formatCost(totalPerExecution), + breakdown.size(), + detectedSchemes.size(), + mostExpensive, + breakdown.isEmpty() ? "No pay-per-use components detected in this route" : null); + + return new CostEstimateResult( + breakdown.isEmpty() ? null : breakdown, + projection, summary, + optimizationTips.isEmpty() ? null : optimizationTips, + "Cost estimates are approximate based on published AWS pricing (us-east-1). " + + "Actual costs vary by region, volume tier, and model."); + } + + List<String> extractSchemes(String route) { + List<String> schemes = new ArrayList<>(); + Matcher m = SCHEME_PATTERN.matcher(route); + while (m.find()) { + String scheme = m.group(1); + if (!schemes.contains(scheme)) { + schemes.add(scheme); + } + } + return schemes; + } + + private List<String> buildOptimizationTips(List<String> schemes, List<ComponentCostBreakdown> breakdown) { + List<String> tips = new ArrayList<>(); + + if (schemes.contains("aws-bedrock")) { + tips.add("Consider using a smaller/cheaper Bedrock model for simpler tasks " + + "(e.g., Nova Lite instead of Claude for classification)"); + tips.add("Use streaming to reduce perceived latency without affecting token costs"); + } + if (schemes.contains("aws2-textract") && schemes.contains("docling")) { + tips.add("Using both Textract and Docling — consider using only Docling (free, open-source) " + + "for text extraction and reserving Textract for table/form extraction"); + } + if (schemes.contains("aws2-s3")) { + tips.add("S3 GET costs are minimal but add up at scale — consider caching frequently accessed documents"); + } + if (breakdown.size() > 1) { + tips.add("Most expensive component: " + breakdown.get(0).displayName() + + " — focus optimization here for maximum savings"); + } + return tips; + } + + private static String formatCost(double cost) { + if (cost < 0.01) { + return String.format("$%.6f", cost); + } + return String.format("$%.4f", cost); + } + + private static Map<String, ComponentCostProfile> buildCostProfiles() { + Map<String, ComponentCostProfile> profiles = new LinkedHashMap<>(); + + profiles.put("aws-bedrock", new ComponentCostProfile( + "AWS Bedrock (Claude Sonnet 4)", "per-token", + "Input: ~$3/MTok, Output: ~$15/MTok (Claude Sonnet 4 pricing)") { + @Override + double estimateCostPerExecution(int pages, int inputTokens, int outputTokens) { + return (inputTokens * 3.0 / 1_000_000) + (outputTokens * 15.0 / 1_000_000); + } + }); + + profiles.put("aws2-textract", new ComponentCostProfile( + "AWS Textract", "per-page", + "DetectDocumentText: ~$1.50/1000 pages, AnalyzeDocument: ~$15/1000 pages") { + @Override + double estimateCostPerExecution(int pages, int inputTokens, int outputTokens) { + return pages * 1.50 / 1000; + } + }); + + profiles.put("docling", new ComponentCostProfile( + "Docling (self-hosted)", "free", + "Open-source, self-hosted. Cost is only infrastructure (compute/memory)") { + @Override + double estimateCostPerExecution(int pages, int inputTokens, int outputTokens) { + return 0; + } + }); + + profiles.put("aws2-s3", new ComponentCostProfile( + "AWS S3", "per-request", + "GET: ~$0.40/1M requests, PUT: ~$5/1M requests") { + @Override + double estimateCostPerExecution(int pages, int inputTokens, int outputTokens) { + return 0.40 / 1_000_000; + } + }); + + profiles.put("aws2-sqs", new ComponentCostProfile( + "AWS SQS", "per-request", + "~$0.40 per 1M requests (Standard)") { + @Override + double estimateCostPerExecution(int pages, int inputTokens, int outputTokens) { + return 0.40 / 1_000_000; + } + }); + + profiles.put("aws2-sns", new ComponentCostProfile( + "AWS SNS", "per-publish", + "~$0.50 per 1M publishes") { + @Override + double estimateCostPerExecution(int pages, int inputTokens, int outputTokens) { + return 0.50 / 1_000_000; + } + }); + + profiles.put("aws2-kinesis", new ComponentCostProfile( + "AWS Kinesis", "per-shard-hour + per-PUT", + "~$0.015/shard/hour + $0.014/1M PUT units") { + @Override + double estimateCostPerExecution(int pages, int inputTokens, int outputTokens) { + return 0.014 / 1_000_000; + } + }); Review Comment: This uses the same Claude Sonnet 4 pricing ($3/$15 per MTok) as the Bedrock profile, but `langchain4j-chat` is provider-agnostic — it works with OpenAI, Ollama (free), Anthropic, Mistral, local models, etc. The display name says "model-dependent" but the calculation is fixed to one specific (expensive) model. A user running langchain4j with Ollama would see significant phantom costs. At minimum, the pricing note should warn that the estimate assumes a specific cloud-hosted model and may be zero for self-hosted providers. ########## dsl/camel-jbang/camel-jbang-mcp/src/main/java/org/apache/camel/dsl/jbang/core/commands/mcp/RouteCostEstimateTools.java: ########## @@ -0,0 +1,317 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.camel.dsl.jbang.core.commands.mcp; + +import java.util.ArrayList; +import java.util.Comparator; +import java.util.LinkedHashMap; +import java.util.List; +import java.util.Map; +import java.util.regex.Matcher; +import java.util.regex.Pattern; + +import jakarta.enterprise.context.ApplicationScoped; + +import io.quarkiverse.mcp.server.Tool; +import io.quarkiverse.mcp.server.ToolArg; +import io.quarkiverse.mcp.server.ToolCallException; + +/** + * MCP Tool for estimating API costs of a Camel route based on its component usage. + * <p> + * Particularly useful for AI pipelines where Bedrock, Textract, and S3 calls have per-unit pricing. Provides + * per-execution cost estimates and projections at a given throughput. + * <p> + * Cost data is approximate and based on published AWS pricing as of 2025. Actual costs depend on model, region, and + * volume tiers. + */ +@ApplicationScoped +public class RouteCostEstimateTools { + + private static final Pattern SCHEME_PATTERN = Pattern.compile( + "(?:uri:\\s*[\"']?|from:\\s+[\"']?|to:\\s+[\"']?|toD:\\s+[\"']?)([a-zA-Z][a-zA-Z0-9+.-]*):(?://)?", + Pattern.MULTILINE); + + private static final Map<String, ComponentCostProfile> COST_PROFILES = buildCostProfiles(); + + @Tool(annotations = @Tool.Annotations(readOnlyHint = true, destructiveHint = false, openWorldHint = false), + description = "Estimate API costs for a Camel route based on its component usage. " + + "Analyzes a YAML or XML route definition and identifies components with " + + "pay-per-use pricing (Bedrock LLM, Textract, S3, SQS, SNS, etc.). " + + "Returns per-execution cost estimate and monthly projection at a given throughput. " + + "Cost data is approximate based on published AWS pricing.") + public CostEstimateResult camel_route_cost_estimate( + @ToolArg(description = "The Camel route definition (YAML or XML)") String route, + @ToolArg(description = "Expected messages per hour for cost projection (default: 100)") Integer messagesPerHour, + @ToolArg(description = "Average document pages per message for Textract/Docling (default: 5)") Integer avgPages, + @ToolArg(description = "Average LLM input tokens per request (default: 1000)") Integer avgInputTokens, + @ToolArg(description = "Average LLM output tokens per request (default: 500)") Integer avgOutputTokens) { + + if (route == null || route.isBlank()) { + throw new ToolCallException("Route content is required", null); + } + + int throughput = messagesPerHour != null && messagesPerHour > 0 ? messagesPerHour : 100; + int pages = avgPages != null && avgPages > 0 ? avgPages : 5; + int inTokens = avgInputTokens != null && avgInputTokens > 0 ? avgInputTokens : 1000; + int outTokens = avgOutputTokens != null && avgOutputTokens > 0 ? avgOutputTokens : 500; + + List<String> detectedSchemes = extractSchemes(route); + + List<ComponentCostBreakdown> breakdown = new ArrayList<>(); + double totalPerExecution = 0; + + for (String scheme : detectedSchemes) { + ComponentCostProfile profile = COST_PROFILES.get(scheme); + if (profile == null) { + continue; + } + + double costPerExec = profile.estimateCostPerExecution(pages, inTokens, outTokens); + totalPerExecution += costPerExec; + breakdown.add(new ComponentCostBreakdown( + scheme, profile.displayName(), profile.pricingModel(), + costPerExec, profile.pricingNote())); + } + + breakdown.sort(Comparator.comparingDouble(ComponentCostBreakdown::estimatedCostPerExecution).reversed()); + + double hourly = totalPerExecution * throughput; + double daily = hourly * 24; + double monthly = daily * 30; + + String mostExpensive = breakdown.isEmpty() ? null : breakdown.get(0).scheme(); + + List<String> optimizationTips = buildOptimizationTips(detectedSchemes, breakdown); + + CostProjection projection = new CostProjection( + throughput, + formatCost(hourly), formatCost(daily), formatCost(monthly)); + + CostSummary summary = new CostSummary( + formatCost(totalPerExecution), + breakdown.size(), + detectedSchemes.size(), + mostExpensive, + breakdown.isEmpty() ? "No pay-per-use components detected in this route" : null); + + return new CostEstimateResult( + breakdown.isEmpty() ? null : breakdown, + projection, summary, + optimizationTips.isEmpty() ? null : optimizationTips, + "Cost estimates are approximate based on published AWS pricing (us-east-1). " + + "Actual costs vary by region, volume tier, and model."); + } + + List<String> extractSchemes(String route) { + List<String> schemes = new ArrayList<>(); + Matcher m = SCHEME_PATTERN.matcher(route); + while (m.find()) { + String scheme = m.group(1); + if (!schemes.contains(scheme)) { + schemes.add(scheme); + } + } + return schemes; + } + + private List<String> buildOptimizationTips(List<String> schemes, List<ComponentCostBreakdown> breakdown) { + List<String> tips = new ArrayList<>(); + + if (schemes.contains("aws-bedrock")) { + tips.add("Consider using a smaller/cheaper Bedrock model for simpler tasks " + + "(e.g., Nova Lite instead of Claude for classification)"); + tips.add("Use streaming to reduce perceived latency without affecting token costs"); + } + if (schemes.contains("aws2-textract") && schemes.contains("docling")) { + tips.add("Using both Textract and Docling — consider using only Docling (free, open-source) " + + "for text extraction and reserving Textract for table/form extraction"); + } + if (schemes.contains("aws2-s3")) { + tips.add("S3 GET costs are minimal but add up at scale — consider caching frequently accessed documents"); + } + if (breakdown.size() > 1) { + tips.add("Most expensive component: " + breakdown.get(0).displayName() + + " — focus optimization here for maximum savings"); + } + return tips; + } + + private static String formatCost(double cost) { + if (cost < 0.01) { + return String.format("$%.6f", cost); + } + return String.format("$%.4f", cost); + } + + private static Map<String, ComponentCostProfile> buildCostProfiles() { + Map<String, ComponentCostProfile> profiles = new LinkedHashMap<>(); + + profiles.put("aws-bedrock", new ComponentCostProfile( + "AWS Bedrock (Claude Sonnet 4)", "per-token", Review Comment: All pricing data is hardcoded in Java source with no mechanism to update it without a code change, rebuild, and release. Cloud providers change pricing regularly (AWS has changed Bedrock pricing multiple times since launch). Users running an older Camel version will silently get increasingly inaccurate estimates. Consider externalizing the cost profiles to a JSON resource file (e.g., `cost-profiles.json` on the classpath) that could be updated independently, or at least add a clear note in the tool output stating when the pricing data was last updated. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
