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DataForge Semantic MCP Server

DataForge Semantic MCP Server

by SGromych
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

df_get_git_connection

Read-onlyIdempotent

Retrieve a saved Git connection by its unique ID to access DataForge metadata for AI-driven project analysis.

Instructions

Get one saved Git connection.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "connection_id": "<connection_id>"
    },
    "name": "df_get_git_connection"
  }
}

Input Schema

NameRequiredDescriptionDefault
use_cacheNo
connection_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

B3.1/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds no behavioral context beyond the purpose, such as error behavior, return format, or cache semantics. It doesn't contradict the annotations but also contributes nothing beyond them.

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 a single, front-loaded sentence with zero redundancy. It states the core purpose immediately and wastes no words, which is ideal for a simple get-by-id tool.

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

Completeness2/5

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

Given the tool's complexity (only 2 parameters, no output schema), a rich description is not required, but the description leaves critical gaps: use_cache is unexplained, and there is no guidance on how this relates to the git connection lifecycle tools. The agent cannot fully understand invocation semantics without additional inference.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not explain connection_id or use_cache. The agent can guess connection_id identifies the target, but use_cache's purpose is completely unspecified. The description fails to compensate for the empty schema descriptions.

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 states a specific verb ('Get') and resource ('one saved Git connection'), clearly distinguishing it from df_list_git_connections (plural listing) and df_get_connection (non-Git connection). An agent can immediately identify the tool's job without opening the schema.

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?

There is no explicit guidance on when to use this tool versus df_list_git_connections or df_get_connection. The phrase 'one saved Git connection' strongly implies single-item retrieval by ID, but the description does not state exclusions or alternatives.

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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