store_finding
[MAINTENANCE] PARALLEL COORDINATION (c35370cc): persist a per-task intermediate result to the session_findings table so it survives session boundaries. Parallel reader agents write findings; an orchestrator or writer agent reads them via get_findings. Unlike save_finding (which creates a research...
This record as markdown: /tools/io-github-ajc3xc-meridian/store-finding.md
What store_finding does on Meridian
AI agents call store_finding to permanently remove resources in Meridian, typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
| Parameter | Type | Required | Description |
|---|---|---|---|
key | string | — | Optional bucket/topic for scoped retrieval (e.g. a subsystem name). |
title | string | — | Optional short title. |
content | string | Yes | The finding body. |
task_id | string | — | Optional task this finding belongs to. |
project_id | string | — | |
session_id | string | — | Optional writing session. |
project_name | string | — | Project name — an alternative to project_id. |
Parameters from the server's own tool schema.
Why store_finding is rated Critical
An AI agent that decides to call store_finding doesn't hesitate, doesn't double-check, and doesn't stop at one. Whatever it removes from Meridian is gone. There is no undo for destructive operations.
Risk signalsAccepts raw HTML/template content (content)
Attacks that exploit this kind of access
The rule that runs store_finding safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Meridian, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For store_finding, this is the rule to start with:
store_finding is removed from the agent's tool list entirely, so the agent never calls it. The rest of the server keeps working.
The button opens the PolicyLayer dashboard: create your workspace, connect Meridian, apply this rule, and every store_finding call is checked against it from then on.
Questions about store_finding
[MAINTENANCE] PARALLEL COORDINATION (c35370cc): persist a per-task intermediate result to the session_findings table so it survives session boundaries. Parallel reader agents write findings; an orchestrator or writer agent reads them via get_findings. Unlike save_finding (which creates a research note), this is a lightweight key→content store for agent-to-agent handoff of intermediate work. Persistent-state disclosure: on hosted Meridian, supplied text and project/session metadata -- including task log entries, pinned decisions, sprint items, notes, handoff/goal state, and HITL queue items -- are sent to and stored in Meridian's service, in an isolated per-tenant Postgres database (Neon); self-hosted deployments keep the same categories in the configured local SQLite/Postgres database. This data is visible in the dashboard and API, and may resurface in later project context or handoffs. Notes and pinned decisions can be deleted individually; task log entries and sprint items can be deleted via the dashboard/API (not exposed as an agent-facing tool); HITL queue items and handoff state have no per-record delete. Full removal of any of this data is available via project or account deletion, using the documented controls. Do not include secrets. It is categorised as a Destructive tool in the Meridian MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
store_finding accepts 7 parameters: key, title, content, task_id, project_id, session_id, project_name. Required: content. The full parameter table on this page comes from the server's own tool schema.
Register the Meridian MCP server in PolicyLayer and add a rule for store_finding: 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 Meridian. Nothing to install.
store_finding is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the store_finding 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_finding. 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_finding is provided by the Meridian MCP server (@meridianmcp/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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