# rag-chunk-boundary-optimizer MCP for AI Agents AI Agent Connect > rag-chunk-boundary-optimizer MCP analyzes how text is split into pieces for RAG pipelines. It checks for character overlap, ensures sentences aren't cut in half, and measures semantic continuity to make sure your retrieval stays accurate and meaningful. ## Overview - **Category:** data-analysis - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_f3mbb934ODmYj40cpYR2BbY8BQOqdDe7gJEWXZfQ/ai-agent-connect - **Tags:** rag, chunking, nlp, text-analysis, llm-optimization ## Description Building a RAG pipeline often feels like a guessing game. You pick a chunk size, set an overlap, and hope your agent finds the right context. But when chunks break mid-sentence or lose their meaning because the split happened at a bad spot, your retrieval quality tanks. This MCP takes the guesswork out of that process by letting your agent inspect the structural integrity of your text segments. Instead of manually inspecting thousands of lines of text, you can have your agent evaluate the quality of your chunking strategy. It looks for gaps where text might be missing, checks if your overlap is actually consistent, and flags segments that break semantic flow. It's about making sure the context your agent retrieves is actually coherent and useful. By connecting this to your workflow through the Vinkius catalog, you can quickly iterate on your data preparation and stop fighting with broken context windows. ## Tools ### analyze_chunk_boundaries Evaluates a sequence of text chunks to determine their structural and semantic quality. It helps you see if your splits make sense linguistically. ### get_chunking_summary Provides high-level statistical insights into the quality of an entire chunking strategy. Use this to get a bird's-eye view of your dataset's health. ### validate_overlap_integrity Checks if the character overlap is consistent or if there are gaps between chunks. It prevents data loss during the splitting process. ## Prompt Examples **Prompt:** ``` Check these chunks to see if the boundaries are okay. ``` **Response:** ``` I've analyzed the provided text segments. Here is the structural report: | Metric | Value | | :--- | :--- | | **Overlap Count** | 12 characters | | **Mid-Sentence Breaks** | 0 (All sentences intact) | | **Continuity Score** | 0.94 (High) | The boundaries look solid. There are no broken sentences and the semantic flow is preserved. ``` **Prompt:** ``` Give me a summary of how my current chunking strategy is performing. ``` **Response:** ``` Based on the dataset provided, here is the statistical summary of your chunking strategy: * **Total Boundaries Analyzed:** 150 * **Average Overlap:** 22.4 characters * **Mid-Sentence Rate:** 2.1% * **Average Continuity Score:** 0.88 **Observation:** Your mid-sentence rate is slightly high. You might want to adjust your splitting logic to favor sentence boundaries. ``` **Prompt:** ``` Are there any gaps in my text chunks? ``` **Response:** ``` I performed an integrity check on your segments. I found a discrepancy: **Gap Detected:** * **Location:** Between Chunk 4 and Chunk 5 * **Gap Length:** 8 characters It looks like some text was lost during the split at this junction. You should check your slicing logic for this specific index. ``` ## Capabilities ### Inspect chunk junctions Check exactly how text segments meet to ensure they flow logically. ### Audit chunking statistics Get a high-level view of how your entire dataset is being partitioned. ### Detect text gaps Find missing characters or lost information between adjacent chunks. ### Verify sentence integrity Ensure your splitting logic isn't cutting sentences in half. ### Measure semantic continuity Use linguistic markers to see if chunks retain their original meaning. ## Use Cases ### Fixing broken context in long documents An engineer notices their agent is missing facts from long PDFs. They use analyze_chunk_boundaries to find where sentences are being sliced incorrectly. ### Validating new chunking parameters A developer changes the chunk size from 512 to 256 tokens. They use get_chunking_summary to see if the semantic continuity score dropped. ### Detecting data loss in pipelines A data scientist suspects their preprocessing script is dropping characters. They run validate_overlap_integrity to catch gaps between chunks. ### Benchmarking RAG performance A team needs to compare two different splitting methods. They use the statistical tools to decide which strategy preserves more meaning. ## Benefits - Stop losing context by using validate_overlap_integrity to find gaps between text segments. - Improve retrieval precision by using analyze_chunk_boundaries to prevent mid-sentence breaks. - Get instant feedback on your chunking logic with get_chunking_summary for rapid iteration. - Ensure semantic flow remains intact across your entire vector database. - Reduce agent hallucinations by providing cleaner, more coherent context windows. ## How It Works The bottom line is you get a mathematical way to prove your RAG data is actually ready for retrieval. 1. Connect the MCP to your preferred AI client via Vinkius. 2. Provide your text segments or chunking configuration to your agent. 3. Receive detailed reports on overlap, sentence breaks, and semantic scores. ## Frequently Asked Questions **How can I use rag-chunk-boundary-optimizer to improve my RAG accuracy?** You use it to identify where your text splitting is breaking sentences or losing semantic meaning. Fixing these boundary issues ensures your agent retrieves complete, coherent context, which directly reduces hallucinations. **Can rag-chunk-boundary-optimizer find missing text in my data?** Yes. It can detect gaps between your text segments, helping you ensure that your chunking process isn't accidentally dropping characters or words during the split. **Is rag-chunk-boundary-optimizer useful for large datasets?** Absolutely. Instead of manually checking chunks, you can get aggregate statistical summaries of your entire chunking strategy to see how it performs across thousands of segments. **Does rag-chunk-boundary-optimizer work with any text splitter?** Yes. It is designed to analyze the output of your existing chunking logic, regardless of whether you use character-based, token-based, or recursive splitting. **How does rag-chunk-boundary-optimizer help with agent hallucinations?** Hallucinations often happen when an agent receives fragmented or incomplete context. By ensuring your chunks don't break mid-sentence and maintain semantic continuity, you provide much cleaner data to your agent. **How can I check if my chunks are breaking sentences?** You can use the `analyze_chunk_boundaries` tool, which returns an `isMidSentence` boolean for every boundary evaluated. **What is the purpose of the continuity score?** The continuity score is a proxy for semantic integrity. It measures the density of pronouns and conjunctions at the boundary to identify if a logical connection is being severed. **How do I get a high-level overview of my chunking strategy?** Use the `get_chunking_summary` tool to receive aggregate metrics like average overlap and mid-sentence rates for your entire set of chunks.