Chunk inline text with one strategy + params (no embeddings).
AI agents call preview_chunks to retrieve information from ChunkTuner without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool takes text input and applies a chunking strategy to preview results. It explicitly states 'no embeddings' and implies no storage or side effects — it is a pure read/transform operation on provided data.
From the tool's definition 'preview_chunks' and 'no embeddings' — the tool processes inline text transiently to show chunking output without persisting data or triggering external operations
Attacks that exploit this kind of access
Chunk inline text with one strategy + params (no embeddings). It is categorised as a Read tool in the ChunkTuner MCP Server, which means it retrieves data without modifying state.
Register the ChunkTuner MCP server in PolicyLayer and add a rule for preview_chunks: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches ChunkTuner. Nothing to install.
preview_chunks is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the preview_chunks rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for preview_chunks. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
preview_chunks is provided by the ChunkTuner MCP server (shantanu-deshmukh/chunktuner). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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