updateImprovement

SERVERStable Baseline SOURCEhttps://api.stablebaseline.io/functions/v1/cloud-serve/mcp

Medium RISK CLASS

Category Write

Parameters 120 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-stablebaseline-sb/updateimprovement.md

WHAT IT DOES

What updateImprovement does on Stable Baseline

AI agents use updateImprovement to create or update resources in Stable Baseline, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Stable Baseline environment.

Parameter	Type	Required	Description
`type`	string	—	One of: feature (New capability for users); enhancement (An improvement to something that already exists); bug (Something that does not work as it should); tech
`items`	array	—	Optional bulk form: update up to 200 improvements in one call, each entry { improvementId, versionTimestamp, ...the fields to change } with the same fields as a
`title`	string	—
`fields`	array	—	Optional. Answer with only these fields instead of the whole improvement, for example ["id", "versionTimestamp"]. Any of the improvement's own fields (snake_cas
`planId`	string	—	Optional. Narrows a friendly-id lookup (such as IMP-42) to one plan, given as the plan's UUID or friendly id (such as PLN-3): the item must be in that plan. It
`status`	string	—
`details`	object	—	The type's own fields. On update they MERGE key by key: keys you leave out are kept and null removes one. epic: success_measures. user_story: as_a, i_want, so_t
`is_task`	boolean	—	Mark as task. Prefer setting type='task' instead: is_task follows the type automatically.
`plan_id`	string	—	Link to plan. Null to unlink.
`urgency`	string	—
`why_now`	string	—
`end_date`	string	—	YYYY-MM-DD.

Parameters from the server's own tool schema.

RISK

Why updateImprovement is rated Medium

An AI agent can call updateImprovement faster than any human can review: one bad instruction and it creates or modifies resources in Stable Baseline by the hundred, each call as confident as the last.

Risk signalsAccepts freeform code/query input (details.statement) · High parameter count (186 properties) · Bulk/mass operation — affects multiple targets

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs updateImprovement safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Stable Baseline, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For updateImprovement, this is the rule to start with:

updateImprovement Rate-limited

updateImprovement 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "updateImprovement": {
      "limits": [
        {
          "counter": "updateimprovement_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Stable Baseline, apply this rule, and every updateImprovement call is checked against it from then on.

FAQ

Questions about updateImprovement

What does the updateImprovement tool do? +

Update an improvement, or many in one call (or a task: tasks share this row, but prefer the symmetric updateTask alias when working from getTask). Supports the full field set including checklist (tick-boxes with due dates + completion attribution), acceptance_criteria (objects with per-row updated_by/at attribution), parentItemId (the epic or story it belongs to in the work hierarchy; null removes it) and linkedRecords (the database rows it links: a list replaces them, { add, remove } changes some). versionTimestamp (optimistic locking) is the item's updated_at in epoch milliseconds, and you rarely need a getImprovement call for it: listImprovements returns it for every improvement in a project (fields ["id", "friendlyId", "versionTimestamp"] keeps that answer short), getPlan (items[].versionTimestamp) for every item in a plan, and every write returns the new one. Pass fields (for example ["id", "versionTimestamp"]) for a short answer instead of the whole record. To change many improvements, send items: up to 200 entries of { improvementId, versionTimestamp, ...changes } per call (more than 200: split them into several calls of up to 200), each checked and written on its own and answered with one row per entry plus a summary. Statuses are captured / in_progress / blocked / done / rejected / deferred, and FOUR of those are CLOSED: blocked, done, rejected, deferred. Entering one needs its comment (blocked needs blocked_comment, rejected needs rejection_comment, done needs completion_comment); leaving one for an open status needs reopened_comment, including blocked -> in_progress, which surprises people because blocked does not sound terminal. metadata MERGES into what is stored (null on a key deletes it); pass metadata_replace:true to overwrite the object wholesale. Assignment: pass owner_id=<uuid> to assign to a user, owner_team_id=<uuid> to assign to a team (mutually exclusive: an item has one owner, a user or a team, never both). To unassign, pass owner_id=null AND owner_team_id=null. To switch from a user owner to a team owner, send owner_id=null, owner_team_id=<uuid> in the SAME call (sending only one side leaves the stale value and triggers the XOR check). Use listAssignablePrincipals or listTeams to discover valid IDs. It is categorised as a Write tool in the Stable Baseline MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

What parameters does updateImprovement accept? +

updateImprovement accepts 12 parameters: type, items, title, fields, planId, status, details, is_task, plan_id, urgency, why_now, end_date. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on updateImprovement? +

Register the Stable Baseline MCP server in PolicyLayer and add a rule for updateImprovement: 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 Stable Baseline. Nothing to install.

What risk level is updateImprovement? +

updateImprovement is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit updateImprovement? +

Yes. Add a rate_limit block to the updateImprovement 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.

How do I block updateImprovement completely? +

Set action: deny in the PolicyLayer policy for updateImprovement. 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.

What MCP server provides updateImprovement? +

updateImprovement is provided by the Stable Baseline MCP server (https://api.stablebaseline.io/functions/v1/cloud-serve/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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