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mcp-git-ingest

mcp-git-ingest

by adhikasp
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Git Directory Structure

git_directory_structure

Clone a Git repository and return its directory structure in tree format so you can inspect repository layout directly from a URL.

Instructions

Clone a Git repository and return its directory structure in a tree format.

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": {
      "repo_url": "<repo_url>"
    },
    "name": "git_directory_structure"
  }
}

Input Schema

NameRequiredDescriptionDefault
repo_urlYesThe URL of the Git repository

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden. It does disclose the core operation (a clone, implying a network fetch and latency) and the return format (tree), which is more than a bare restatement. It says nothing about authentication for private repos, repository size or timeout limits, or whether the clone is persisted anywhere.

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?

One sentence with no filler, and the operation plus return format are front-loaded. The trade-off is that the terseness leaves no room for the usage or behavioral context that would earn a 5.

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

Completeness3/5

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

An output schema exists, so the description need not explain the returned tree. However, for a network-dependent tool with zero annotations and no sibling routing, the definition should say more about auth requirements and constraints on repository size before an agent commits to calling it.

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?

There is a single parameter with 100% schema description coverage, so the schema already establishes that repo_url is a Git repository URL. The description adds no format detail such as HTTPS vs SSH, credentials embedding, or branch specification, so the baseline 3 is appropriate.

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 pairs a specific verb ('Clone') with a specific resource ('Git repository') and states the output shape ('directory structure in a tree format'). An agent can infer that this is for surveying repo layout rather than reading file contents, which implicitly separates it from the sibling git_read_important_files, though the sibling is never named.

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?

Usage is only implied: the mention of a tree format suggests 'use when you need an overview of a repository's layout.' There is no explicit when-to-use, no when-not, and no pointer to git_read_important_files for content-level inspection.

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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