filter_data
Filter array items by a condition expression.
This record as markdown: /tools/io-github-scotia1973-bot-api-hub/filter-data.md
What filter_data does on GadgetHumans API Hub
AI agents invoke filter_data to trigger actions in GadgetHumans API Hub. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why filter_data is rated High
The tool evaluates a 'condition expression' against array items, which implies executing or evaluating arbitrary expressions/predicates. This goes beyond a simple read operation — it involves running logic (expression evaluation) whose behavior depends on the supplied arguments. If the expression language is expressive or unsandboxed, it could be misused to execute unintended logic.
From the tool's definition Filter array items by a condition expression
Attacks that exploit this kind of access
The rule that runs filter_data safely
PolicyLayer is an MCP gateway: it sits between your AI agents and GadgetHumans API Hub, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For filter_data, this is the rule to start with:
filter_data stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect GadgetHumans API Hub, apply this rule, and every filter_data call is checked against it from then on.
Questions about filter_data
Filter array items by a condition expression. It is categorised as a Execute tool in the GadgetHumans API Hub MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the GadgetHumans API Hub MCP server in PolicyLayer and add a rule for filter_data: 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 GadgetHumans API Hub. Nothing to install.
filter_data is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the filter_data 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 filter_data. 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.
filter_data is provided by the GadgetHumans API Hub MCP server (pypi:gadgethumans-api-hub-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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