GrowSurf MCP Server

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growsurf_get_campaign

Read-onlyIdempotent

Retrieve detailed campaign information and reward settings from GrowSurf by passing a campaign ID or using the default. Helps review program setup without confirming individual rewards.

Instructions

Fetch your GrowSurf campaign (program) details via REST. Embedded reward settings do not establish that an individual reward was earned, approved, or delivered; read the affected participant for earned reward records. Targets campaignId if you pass it, otherwise GROWSURF_CAMPAIGN_ID.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`campaignId`	No	Target program (campaign) id for this call. Defaults to GROWSURF_CAMPAIGN_ID when omitted. Pass the `id` returned by growsurf_create_campaign to configure or operate a program you just created, without restarting the server.

Output Schema

TableJSON Schema

Name	Required	Description
`id`	No	The program's unique id.
`name`	No	The program name (internal only, never shown to participants).
`type`	No	The program type.
`status`	No	The program status.
`rewards`	No	The program's reward configs (`CampaignReward`). Item shape is documented on the `growsurf_list_campaign_rewards` tool.
`currencyISO`	No	The program currency as an ISO 4217 code (e.g. `USD`).
`inviteCount`	No	Total invites sent by participants.
`winnerCount`	No	Participants with at least one approved reward.
`referralCount`	No	Total referrals.
`rewardEvidence`	No	What this response establishes about rewards. Combine with other reads; unknown here does not override evidence elsewhere.
`impressionCount`	No	Total referral-link views across participants.
`participantCount`	No	Total participants.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.14.0Sep 10, 2026

addedOutput schema / properties / rewardEvidence

Added value: +{
+  "description": "What this response establishes about rewards. Combine with other reads; unknown here does not override evidence elsewhere.",
+  "properties": {
+    "approvalPolicy": {
+      "description": "Referral reward approval policy from requireManualRewardApproval, not affiliate commission approval or an individual reward state.",
+      "enum": [
+        "manual",
+        "automatic",
+        "unknown"
+      ],
+      "type": "string"
+    },
+    "automaticFulfillmentMarking": {
+      "description": "The autoFulfillRewards setting, when returned by an options read. Null means unknown; this controls marking, not delivery.",
+      "type": [
+        "boolean",
+        "null"
+      ]
+    },
+    "basis": {
+      "enum": [
+        "this_response_only"
+      ],
+      "type": "string"
+    },
+    "conclusion": {
+      "type": "string"
+    },
+    "deliveryStatus": {
+      "enum": [
+        "unknown"
+      ],
+      "type": "string"
+    },
+    "integrationConnection": {
+      "enum": [
+        "not_established"
+      ],
+      "type": "string"
+    },
+    "nextStep": {
+      "type": "string"
+    },
+    "programReferralTrigger": {
+      "enum": [
+        "not_established"
+      ],
+      "type": "string"
+    }
+  },
+  "type": "object"
+}

First observedv0.12.2Aug 25, 2026

TDQS

A4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds a non-obvious semantic caveat about reward settings and clarifies target selection behavior, both of which help the agent interpret the response correctly without contradicting the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with its purpose, followed by a valuable caveat and a parameter-targeting note. It contains minimal filler, though the REST qualifier and default mention overlap with schema details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter, read-only tool with full schema coverage and an output schema, the description covers the important traps: target selection and misinterpretation of reward settings. No critical information for correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the campaignId schema entry already explains the default and how to use an id from growsurf_create_campaign. The description re-states the default behavior but does not add meaningful parameter semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the action ('Fetch') and resource ('campaign (program) details'), making the core purpose easy to grasp. It distinguishes from create/update/delete/list siblings, though it does not explicitly differentiate itself from the more specialized get_campaign_* sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives useful context: it is the campaign-level fetch and falls back to GROWSURF_CAMPAIGN_ID when campaignId is omitted. It also steers agents away from treating embedded reward settings as proof of earned rewards and points them to the participant record for that data.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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