complete_research_run
[SUPPORT] a5343387 — finalize a research run with a compact, byte-bounded receipt (16KB cap; exceeding it is REJECTED, never silently truncated). disposition is explicit and REQUIRED (keep|discard|promote) — never inferred from the run's outcome. Idempotent on an already-terminal run (completed/f...
This record as markdown: /tools/io-github-ajc3xc-meridian/complete-research-run.md
What complete_research_run does on Meridian
AI agents use complete_research_run to create or update resources in Meridian, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Meridian environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
run_id | string | Yes | |
receipt | object | — | Compact receipt (16KB total cap): files_touched (list of project-relative paths), commands_run (list, truncated if long), result_summary (bounded string), artif |
project_id | string | — | |
session_id | string | Yes | |
disposition | string | Yes | Explicit, never inferred. 'promote' makes this run eligible for promote_research_run. |
project_name | string | — | Project name — an alternative to project_id; resolved to the id internally. project_id wins if both are given. |
Parameters from the server's own tool schema.
Why complete_research_run is rated Medium
An AI agent can call complete_research_run faster than any human can review: one bad instruction and it creates or modifies resources in Meridian by the hundred, each call as confident as the last.
Risk signalsHigh parameter count (11 properties)
Attacks that exploit this kind of access
The rule that runs complete_research_run 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 complete_research_run, this is the rule to start with:
complete_research_run 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Meridian, apply this rule, and every complete_research_run call is checked against it from then on.
Questions about complete_research_run
[SUPPORT] a5343387 — finalize a research run with a compact, byte-bounded receipt (16KB cap; exceeding it is REJECTED, never silently truncated). disposition is explicit and REQUIRED (keep|discard|promote) — never inferred from the run's outcome. Idempotent on an already-terminal run (completed/failed/abandoned/expired): a duplicate call returns the existing terminal state unchanged, never an error. Only disposition='promote' runs are eligible for promote_research_run, and only disposition in (keep, promote) runs are ever embedded into a handoff's research_run_receipts (see generate_handoff/build_continuation_manifest) — discard means exactly that. 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 Write tool in the Meridian MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
complete_research_run accepts 6 parameters: run_id, receipt, project_id, session_id, disposition, project_name. Required: run_id, session_id, disposition. 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 complete_research_run: 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.
complete_research_run 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 complete_research_run 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 complete_research_run. 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.
complete_research_run 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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