Agent tools

These are the tools a Glood agent calls while it works, in a scheduled run or in a chat. Use this page when you write an agent’s instructions or a custom skill: name a tool exactly as it appears here, for example “Run get_changelog for the hour before the drop”.

These are not the AI assistant’s tools. Those are what Claude, ChatGPT or another assistant calls through the MCP connector, with your role, across the organizations you shared. The tools on this page are what an agent calls inside Glood, on the one store its deployment runs on.

How they are scoped

One store. Every data tool answers for the store the deployment watches. No tool takes a store, so an agent cannot read another store.
Itself. Tools that change an agent change the agent making the call: its own instructions, schedules, deployments and memory. No tool names another agent, and an agent cannot list or read its siblings.
Your role, in chat. In a scheduled run an agent may use every tool. In chat it may use only the tools your own role allows, by the permission named under each group below.
Act is a person’s decision. Setting a deployment to Act, by deploy_agent or set_mode, works only in chat, asked by someone whose role has agent:mode.

Budget per run

Each run, and each chat message, has a budget. An agent working through a long procedure can reach it, so write instructions that fit.

Limit	Per run or chat message
Turns	12. With 2 turns left, the agent is told to record its findings and finish
Tool calls	40, counting every tool except the two below
Skill calls (`list_skills`, `read_skill`)	15, a separate allowance
`record_finding` calls	10, a separate allowance
Time for one tool call	10 seconds. `run_checks` gets 60 seconds and `apply_order_actions` 90

A run that uses all 12 turns and is still calling tools ends with Error, “turn budget reached”. A call past the tool limit is refused with “tool budget exhausted”, and the agent has to answer with what it has. If a check needs more than one run’s worth of reading, split it into two checks, or ask for fewer, wider queries rather than many narrow ones.

Store data

In chat these need analytics:read, except list_insights (insight:read) and the last three (agent:read).

Tool	What it answers	Inputs	When to use it
`describe_reports`	The reports this store can answer, what each returns, and which extra input it needs	none	Before `run_report`, unless the exact report name is already known
`run_report`	One named report over this store, optionally against a comparison window, with the exact change per column	`report`, `dateGte`, `dateLte`; optional `compare` (none, previous, previousYear, previousYearDow, custom), `compareGte`, `compareLte`, `granularity` (session, user), `model` (session, journey), `collection`, `limit`	For any figure a named report covers. Quote the report, the window and its origin with every number
`describe_query`	Every measure and dimension this store’s data can answer, what each means, and which may be grouped together	none	Before composing a `query` it is not certain of, and before reporting money
`query`	A breakdown no report covers: measures by dimensions, filtered, grouped and bucketed by time	`select`, `since`, `until`; optional `by`, `over` (hour, day, week, month), `where`, `having`, `orderBy`, `limit` (up to 500, default 50), `compare` (none, previousPeriod, previousYear, previousYearDow), `includeBots`	For a question a notch off a named report. The query-language skill teaches it
`get_changelog`	What the merchant changed on the store in a window: prices, stock, product status, collections, discounts, themes and market settings. Dense bursts come back as one episode	`since`, `until` (ISO 8601 with an offset); optional `kind`, `subjectType`, `subjectId`, `field`, `limit` (up to 200)	After pinning a metric break to a window, to find what could have caused it. It is the only record of changes made in the store
`list_insights`	What Glood’s checks found about this store, with each insight’s evidence figures	optional `status`, `severity`, `category`, `limit` (up to 50)	Before working out a condition itself, so it does not file a second record of an insight that already exists
`get_store`	The store’s name, domain, platform, status and time zone	none	To know which time zone query buckets are in. Change-log times are UTC
`get_product`	One product’s title, handle, vendor, type, status, whether it is in stock, and its price range	`productId`	To name a product a report or change refers to. In stock is yes or no, not a quantity
`list_integrations`	What the organization has connected, and the health of each connection	none	To reason about what data exists, for example to say Slack is not connected

Orders and fraud

In chat, run_checks, apply_order_actions and revert_changes need agent:write; list_changes needs agent:read.

Tool	What it answers	Inputs	When to use it
`run_checks`	A fraud and delivery risk assessment, by fixed rules rather than judgment: per order, its risk level, verdicts (RTO, returns, prank order, chargeback, stolen card, post-delivery fraud and more), reasons, and the actions the default policy calls for in this mode. With `discount` or `carrier`, one assessment per discount code or courier and pincode	`resource` (order, discount, carrier); optional `orderId` (an order id or the number a person uses, like #1009), `hours` (1 to 168, default 1), `page`	On each new order, return or refund, and once a day for discounts and couriers. Act on its verdicts as the fraud-policy skill says; never re-derive one. 25 orders a page; call again while more remain
`apply_order_actions`	Tags orders in Shopify and adds Shopify risk assessments, citing the `run_checks` assessment for each. Returns what happened per order	`orders`: 1 to 25, each `orderId`, `assessmentId`, optional `tags`, `riskLevel` (medium, high), `facts`	Act mode only. After `run_checks`, on the orders its policy flags. Every write is listed on the store’s Changes tab
`list_changes`	What this deployment changed in Shopify, newest first, with each change’s status	optional `runId`, `orderId`, `status`, `from`, `to`, `page`	To answer “what did you change” or find a change to undo
`revert_changes`	Undoes changes: tags it added are removed, a risk assessment is answered with a new one at no risk	exactly one of `changeId`, `runId`, `assessmentId`, `orderId`, or `from` and `to`; optional `reason`	When a person asks to undo something. It only asks: the revert happens moments later

Findings, runs and memory

In chat, record_finding and memory_write need agent:write; the rest need agent:read.

Tool	What it answers	Inputs	When to use it
`record_finding`	Records that a watched condition is true now, with evidence. The same fingerprint while the finding is open adds to it instead of duplicating it	`fingerprint`, `title`, `because`, `severity` (critical, warning, info); optional `recommend`, `impactMinor`, `evidence` (`insightIds`, `facts`, `ruledOut`)	Once per cause. Keep the fingerprint stable across runs, with no dates or figures in it. A finding raises its own alert; the agent never announces one itself
`list_findings`	This deployment’s findings, most recently active first: titles and counts	optional `state` (open, applied, dismissed, resolved), `limit` (up to 50)	To find finding ids. Never conclude there are none without calling it
`get_finding`	One finding: what was found, its evidence, severity, state and how often it recurred	`findingId`	To read the evidence behind a finding from `list_findings`
`list_runs`	This deployment’s recent runs: what woke each, what happened, and how many findings each raised	optional `limit` (up to 50)	To answer “what happened last night”. It hands out run ids
`get_run`	One past run: status, when, what started it and what it raised	`runId`	To answer about a past run from its record, never by working the numbers out again
`memory_search`	What the agent currently believes, agent-wide and for this store	`query`; optional `limit` (up to 50)	Before relying on something it was told earlier
`memory_write`	Records something durable it learned or was told	`content`, `scope` (agent: true wherever it runs; instance: true of this store only), `because`	When a person tells it something to remember, or it learns a lasting fact about the store

Skills

In chat, list_skills and read_skill need agent:read, list_org_skills needs skill:read, attach_skill and detach_skill need skill:attach, and author_skill needs skill:write.

Tool	What it answers	Inputs	When to use it
`list_skills`	The skills attached to this agent for this store: each slug and its Use when line	none	Before acting on anything specific to the organization
`read_skill`	The full text of one to five skills	`slugs` (1 to 5)	When a skill’s Use when matches. A loaded skill outranks the agent’s general knowledge
`list_org_skills`	Every skill in the organization’s library, Glood’s and your own	none	To find a skill that is not attached yet
`attach_skill`	Gives this agent a skill, on every store or on one deployment	`slug`; optional `instanceId`	When asked to start using a skill
`detach_skill`	Takes a skill away, or switches it off on one deployment only	`slug`; optional `instanceId`, `suppress`	When asked to stop using a skill
`author_skill`	Writes or revises one of the organization’s own skills. Glood’s skills cannot be edited this way, only copied	`slug`, `body` (up to 32,768 characters); `title` and `useWhen` for a new skill	When asked to write down a procedure

The agent itself

In chat, reads need agent:read and changes need agent:write.

Tool	What it answers	Inputs	When to use it
`get_agent`	The agent in full: instructions, the version in force, its settings, every deployment and every schedule	none	Before proposing any change to itself
`edit_instructions`	Replaces its instructions as a new version. Earlier versions stay readable	`instructions` (up to 20,000 characters)	When asked to change what it does
`update_agent`	Changes its name, model or default mode. Not versioned	optional `name`, `model`, `defaultMode` (observe, recommend, act)	When asked to rename it. Use `edit_instructions` for what it does
`configure_agent`	Changes its Rules, section by section. Only agents with Rules, the Fraud agent today	`config`	When asked to change a threshold, block list or allowed tag. Read the current settings with `get_agent` first
`list_versions`	Every version of its instructions: number, when, how and by whom	none	To find the version to roll back to
`get_version`	The instructions one version carried	`version`	Before rolling back, so the change can be described
`rollback_version`	Puts an earlier version’s instructions back, as a new version	`version`	When asked to undo an instructions change
`list_schedules`	Its checks: key, name, cron or event, and whether each is on	none	Before adding or changing a check
`put_schedule`	Creates or replaces a check, on a cron (in each store’s time zone) or an event such as `order.created`, never both. An event check may add conditions the event must match	`key`, `label`, `instructions`, and `cron` or `onEvent`; optional `eventTriggerRule`	When asked to add or change a check, including event triggers the dashboard cannot set
`remove_schedule`	Switches a check off on every deployment. The check is kept	`key`	When asked to stop a check

Deployments, runs and memory

In chat, reads need agent:read, list_connections needs integration:read, set_mode needs agent:mode, and the other changes need agent:write.

Tool	What it answers	Inputs	When to use it
`list_deployments`	Where it runs: deployment id, store, mode, status and last run	none	To find a deployment id
`get_deployment`	One deployment in full: mode, status, who paused it and when, missing integrations and recent runs	`instanceId`	Before changing a deployment
`deploy_agent`	Starts it on a store, with its schedules	`storeId`; optional `mode` (default recommend)	When asked to run on another store. Find the store with `list_stores`
`set_mode`	Sets how far one deployment may go: observe, recommend or act	`instanceId`, `mode`	When asked to change a deployment’s mode. Act needs a person with `agent:mode`
`pause_deployment`	Pauses one deployment: its schedules stop until resumed	`instanceId`, `reason`	When asked to stop on one store for now
`resume_deployment`	Resumes a paused deployment	`instanceId`	When asked to start again
`stop_deployment`	Takes a deployment out of service for good. Its history is kept	`instanceId`, `reason`	When asked to stop on a store permanently
`run_now`	Starts a run on one deployment immediately, optionally with a task for that run	`instanceId`; optional `task`	When asked to do something now rather than at the next check
`cancel_run`	Stops a run in progress at its next turn. What it already recorded stays	`runId`	When asked to stop a run
`list_memory`	What it believes about one store, with who wrote each entry and when	`instanceId`; optional `query`	To review what it remembers
`forget_memory`	Retires one belief, with a reason. The entry stays readable	`memoryId`, `reason`	When told something it remembers is wrong
`list_connections`	Which connected accounts one deployment may use, and whether each is on	`instanceId`	Before switching a connection
`toggle_connection`	Turns one connected account on or off for one deployment	`instanceId`, `connectionId`, `enabled`	When asked to stop or start reading an account on a store

The organization

Read only. In chat each needs the permission named.

Tool	What it answers	Inputs	When to use it
`list_stores`	The organization’s stores: id, name, domain, platform and status (`store:read`)	none	To find a store id for `deploy_agent`
`list_members`	Who is in the organization, their role, and whether they are active (`member:read`)	none	To answer who could act on something
`list_roles`	The roles and what each may do (`role:read`)	none	Before suggesting who should hold what
`list_org_connections`	Every account the organization has connected, with its health (`integration:read`)	none	To see what is connected across the organization

No tool returns credentials, and no tool can invite people, create roles or change integrations.

Dashboard

Agents

Skills library

AI Assistant

Agent tools

How they are scoped

Budget per run

Store data

Orders and fraud

Findings, runs and memory

Skills

The agent itself

Deployments, runs and memory

The organization

​How they are scoped

​Budget per run

​Store data

​Orders and fraud

​Findings, runs and memory

​Skills

​The agent itself

​Deployments, runs and memory

​The organization

How they are scoped

Budget per run

Store data

Orders and fraud

Findings, runs and memory

Skills

The agent itself

Deployments, runs and memory

The organization