Create or update rows in a knowledge list. Maximum 500 rows per call — paginate for larger datasets. Resolution order per row: - rows with id → update existing row by id - rows with userKey → O(1) upsert (same userKey in the same list always maps to the same row, across calls and runs — ideal for...
Part of the Agentled server.
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AI agents use upsert_knowledge_rows to create or modify resources in Agentled. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call upsert_knowledge_rows repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Agentled.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"upsert_knowledge_rows": {
"limits": [
{
"counter": "upsert_knowledge_rows_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Agentled policy for all 119 tools.
These attack patterns abuse exactly the kind of access upsert_knowledge_rows gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Create or update rows in a knowledge list. Maximum 500 rows per call — paginate for larger datasets. Resolution order per row: - rows with id → update existing row by id - rows with userKey → O(1) upsert (same userKey in the same list always maps to the same row, across calls and runs — ideal for idempotent sourcing/enrichment workflows) - rows with neither → plain insert with a fresh UUID (no dedup) Pick one stable key per row. userKey is caller-defined: a candidateId, a normalized URL, a domain, etc. Whatever string uniquely identifies the entity within the list for your use case. mergeStrategy controls how updates are applied to existing rows: - "overwrite" (default): replace existing rowData entirely with the new values - "merge": shallow-merge new values into existing rowData (preserves downstream-added fields like scores or notes) Returns: { inserted, updated, errors[] } — errors are per-row and do not abort the batch.. It is categorised as a Write tool in the Agentled MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Agentled MCP server in PolicyLayer and add a rule for upsert_knowledge_rows: 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 Agentled. Nothing to install.
upsert_knowledge_rows is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the upsert_knowledge_rows 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 upsert_knowledge_rows. 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.
upsert_knowledge_rows is provided by the Agentled MCP server (@agentled/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 119 Agentled tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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