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sprout-mcp

sprout-mcp

by jmeserve
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

list_customers

Retrieve customer IDs and names from Sprout Social to enable subsequent API operations for social media management.

Instructions

List all customers/accounts accessible with the current API token.

Returns customer IDs and names needed for other API calls.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_customers"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool lists items 'accessible with the current API token', which hints at authentication scope, and mentions the return format ('customer IDs and names'). However, it doesn't cover potential behaviors like pagination, rate limits, or error handling, leaving gaps in transparency for a read operation.

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

Conciseness5/5

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

The description is two sentences, front-loaded with the core purpose and followed by a brief note on returns. Every sentence adds value without waste, making it highly efficient and well-structured for quick comprehension.

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

Completeness4/5

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

Given the tool's low complexity (0 parameters, read-only operation) and the presence of an output schema (which handles return values), the description is mostly complete. It covers the purpose and basic usage context. However, it could be more robust by addressing sibling tool differentiation or behavioral aspects like data scope, slightly reducing completeness.

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

Parameters4/5

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

The input schema has 0 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is applied as it compensates by not introducing unnecessary information, aligning with the zero-parameter context.

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 states the verb ('List') and resource ('customers/accounts'), making the purpose understandable. It specifies 'accessible with the current API token' which adds useful context, but it doesn't explicitly differentiate from sibling tools like 'list_users' or 'list_groups', preventing a perfect score.

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

Usage Guidelines3/5

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

The description implies usage by mentioning that returns are 'needed for other API calls', suggesting a prerequisite or preparatory role. However, it lacks explicit guidance on when to use this tool versus alternatives like 'list_users' or 'list_groups', and doesn't state any exclusions or specific contexts for application.

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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