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SLayer

SLayer

by MotleyAI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

list_datasources

List all configured database connections, showing names and types while hiding credentials. Use this tool to identify available data sources before running queries.

Instructions

List all configured database connections (names and types only, credentials are not shown). Use describe_datasource for connection details and status.

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_datasources"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.10.0

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations, the description carries the burden of disclosing behavior. It states that credentials are not shown, which is an important privacy/security behavior beyond the basic 'list' semantics. The read-only nature is implied by 'List' but not explicitly stated; a small gap, but the redaction disclosure adds meaningful transparency.

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?

Two concise sentences with no filler. The main purpose is front-loaded, and the second sentence adds routing guidance. Every word earns its place.

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 parameterless list tool with an output schema present, the description fully covers what the agent needs to decide whether to call it and what to expect. It also gives a pointer to the appropriate sibling for more detail, making the context complete.

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 tool has zero parameters, so there is no parameter documentation needed. The description focuses on the output content instead, clarifying that only names and types are returned, which is the relevant semantic information for an agent.

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

Purpose5/5

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

The description clearly states the action ('List all configured database connections') and the scope of the result ('names and types only'), immediately distinguishing it from connection detail operations. It also explicitly differentiates from describe_datasource.

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

Usage Guidelines5/5

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

It explicitly tells the agent when to use this tool versus an alternative: use describe_datasource for connection details and status. This is clear, direct routing guidance with no ambiguity.

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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