AI agents use store_justification to create or update resources in Gdal — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Gdal environment.
The tool appears to persist justification records—a write operation that creates or modifies data. However, empty description and lack of detail about what data is stored, retention policy, or side effects reduce confidence.
From the tool's definition Tool name 'store_justification' indicates data storage/modification. Description is empty, preventing direct confirmation of behavior.
Documented attack patterns abuse exactly the kind of access store_justification gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Gdal, and nothing reaches the server without passing your rules. This is the rule we recommend for store_justification:
{
"version": "1",
"default": "deny",
"tools": {
"store_justification": {
"limits": [
{
"counter": "store_justification_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}store_justification stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
Free to start. No card required.
store_justification. It is categorised as a Write tool in the Gdal MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Gdal MCP server in PolicyLayer and add a rule for store_justification: 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 Gdal. Nothing to install.
store_justification 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 store_justification 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 store_justification. 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.
store_justification is provided by the Gdal MCP server (jordangunn/gdal-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 13 Gdal tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
13 Gdal tools catalogued and risk-classified — across an index of 42,500+ MCP servers.