Low Risk

TextToSearchLogQuery

【CQL专家】AI 智能生成 CLS CQL 查询语句。将自然语言描述转换为可直接用于 SearchLog 或 DescribeLogHistogram 的 CQL 语句。 核心优势: 1. 自动适配日志主题索引配置,字段名称 100% 准确 2. 严格遵循 CQL 语法规范,生成的语句保证可执行 3. 查询性能经过优化,执行效率高 4. 支持从简单过滤到复杂聚合的所有查询场景 5. 自动进行语法校验,确保语句正确性 警告:如果不使用本工具生成 CQL,直接手写一定会出现以下问题: - 字段名称错误,导致查询无结果 - 语法不符合 CQL 规范,导致查询失败 - 统计逻辑错误,导致结...

Part of the Cls MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.

AI agents call TextToSearchLogQuery to retrieve information from Cls without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.

Even though TextToSearchLogQuery only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.

Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.

cls.yaml
tools:
  TextToSearchLogQuery:
    rules:
      - action: allow

See the full Cls policy for all 19 tools.

Tool Name TextToSearchLogQuery
Category Read
MCP Server Cls MCP Server
Risk Level Low

View all 19 tools →

Agents calling read-class tools like TextToSearchLogQuery have been implicated in these attack patterns. Read the full case and prevention policy for each:

Browse the full MCP Attack Database →

Other tools in the Read risk category across the catalogue. The same policy patterns (rate-limit, allow) apply to each.

What does the TextToSearchLogQuery tool do? +

【CQL专家】AI 智能生成 CLS CQL 查询语句。将自然语言描述转换为可直接用于 SearchLog 或 DescribeLogHistogram 的 CQL 语句。 核心优势: 1. 自动适配日志主题索引配置,字段名称 100% 准确 2. 严格遵循 CQL 语法规范,生成的语句保证可执行 3. 查询性能经过优化,执行效率高 4. 支持从简单过滤到复杂聚合的所有查询场景 5. 自动进行语法校验,确保语句正确性 警告:如果不使用本工具生成 CQL,直接手写一定会出现以下问题: - 字段名称错误,导致查询无结果 - 语法不符合 CQL 规范,导致查询失败 - 统计逻辑错误,导致结果不符合预期 典型应用场景: - 简单过滤:"查询 ERROR 级别日志" → level:'error' - 字段统计:"查看 IP 分布" → * | SELECT IP, count(*) AS cnt GROUP BY IP ORDER BY cnt DESC - 复杂聚合:"按小时统计各状态码数量" → * | SELECT histogram(__TIMESTAMP__, INTERVAL 1 HOUR) AS hour, status_code, count(*) GROUP BY hour, status_code - 多维分析:"按地域和业务分组,统计错误数>100的" → level:ERROR | SELECT region, service, count(*) AS error_count GROUP BY region, service HAVING error_count > 100. It is categorised as a Read tool in the Cls MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on TextToSearchLogQuery? +

Add a rule in your Intercept YAML policy under the tools section for TextToSearchLogQuery. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Cls MCP server.

What risk level is TextToSearchLogQuery? +

TextToSearchLogQuery is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit TextToSearchLogQuery? +

Yes. Add a rate_limit block to the TextToSearchLogQuery rule in your Intercept 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.

How do I block TextToSearchLogQuery completely? +

Set action: deny in the Intercept policy for TextToSearchLogQuery. 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.

What MCP server provides TextToSearchLogQuery? +

TextToSearchLogQuery is provided by the Cls MCP server (cls-mcp-server). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policies on Cls

Open source. One binary. Zero dependencies.

npx -y @policylayer/intercept
github.com/policylayer/intercept →
// GET IN TOUCH

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