Summarise long text locally. Use this instead of having Opus process large blocks of text when you only need a summary. Feed in file contents, docs, logs, or any bulk text and get a concise summary back. Particularly valuable for: large file contents, documentation, log output, meeting notes, lon...
AI agents call local_summarize to retrieve information from Mcp Ollama without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and processes existing data (text summarization) without side effects. It does not create, modify, delete, execute external operations, or move money. It is a straightforward Read operation with minimal blast radius if misused—an AI agent could only waste compute cycles or return an inaccurate summary of content already accessible to it.
From the tool's definition Tool performs summarization of text — 'Summarise long text locally' — accepting input like 'file contents, docs, logs, or any bulk text' and returning 'a concise summary back'. No modification, deletion, execution of commands, or financial operations occur.
Attacks that exploit this kind of access
Summarise long text locally. Use this instead of having Opus process large blocks of text when you only need a summary. Feed in file contents, docs, logs, or any bulk text and get a concise summary back. Particularly valuable for: large file contents, documentation, log output, meeting notes, long git diffs, error traces. It is categorised as a Read tool in the Mcp Ollama MCP Server, which means it retrieves data without modifying state.
Register the Mcp Ollama MCP server in PolicyLayer and add a rule for local_summarize: 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 Mcp Ollama. Nothing to install.
local_summarize 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 local_summarize 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 local_summarize. 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.
local_summarize is provided by the Mcp Ollama MCP server (true-alter/mcp-ollama). 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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