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DataForge Semantic MCP Server

DataForge Semantic MCP Server

by SGromych
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

df_list_git_connections

Read-onlyIdempotent

List a company's saved Git connections without exposing credentials. Requires an administrator API key to review integration settings.

Instructions

List the company's saved Git connections. Credentials are never returned. Requires a company administrator API key.

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": "df_list_git_connections"
  }
}

Input Schema

NameRequiredDescriptionDefault
pageNo
page_sizeNo
use_cacheNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: credentials are never returned, and a company administrator API key is required. This goes beyond 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.

Conciseness5/5

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

Two sentences with no waste. The core purpose is front-loaded, and the security/credential note is a single additional sentence. Every word earns its place.

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?

For a simple list tool with no output schema and only three optional parameters, the description covers the essential context: what it lists, the credential safety guarantee, and the auth requirement. It doesn't mention pagination behavior or cache semantics, but those are minor for a read-only list operation with self-explanatory parameter names.

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 0%, so the description carries the burden for parameter semantics. However, the description doesn't explain the page, page_size, or use_cache parameters. The parameter names are fairly self-explanatory, and defaults are in the schema, but the description adds no meaning beyond what the schema provides. Baseline 3 is appropriate.

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 tool lists the company's saved Git connections, with a specific verb and resource. It also distinguishes itself from related tools like df_get_git_connection (singular) and df_list_connections (general connections) by specifying 'Git connections'.

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 implies when to use this tool: when you need to list Git connections. It doesn't explicitly name alternatives or exclusions, but the sibling list shows df_get_git_connection for a single connection and df_list_connections for non-Git connections, providing enough context for an agent to differentiate.

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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