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aleksakarac

Obsidian MCP Extended

by aleksakarac

get_note_connections_tool

Discover how notes are linked by exploring direct and indirect connections up to three levels deep. Returns both incoming and outgoing links with depth annotations.

Instructions

Get connection graph for a specific note (filesystem-native, offline).

Explores connections from a note up to specified depth:

  • Depth 1: Direct connections (notes linked from target)

  • Depth 2: Second-degree connections (notes linked from direct connections)

  • Depth 3: Third-degree connections

Provides both inlinks (backlinks) and outlinks with depth information.

When to use:

  • Understanding note relationships

  • Exploring local note neighborhoods

  • Finding related content

  • Building connection visualizations

Performance:

  • Depth 1: < 1 second

  • Depth 2: < 5 seconds

  • Depth 3: < 30 seconds

Returns: Connection graph with multi-level links and depth annotations

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
note_nameYesNote name to analyze (with or without .md)
depthNoConnection depth to explore (1=direct, 2=second-degree, etc.)
vault_pathNoPath to vault (optional, uses OBSIDIAN_VAULT_PATH env if not provided)
ctxNo
Behavior5/5

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

No annotations provided, so description carries full burden. It discloses performance estimates for each depth level, mentions 'filesystem-native, offline', describes the return type (connection graph with multi-level links and depth annotations), and explains that it provides both inlinks and outlinks.

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?

Well-structured with clear sections: depth explanation, usage scenarios, performance expectations, and return description. Every sentence adds value and is front-loaded with the most important information (depth behavior).

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?

Despite no output schema, the description adequately explains what the tool returns (connection graph with depth annotations). Given the tool's complexity (4 parameters, multi-level graph building), the description provides sufficient context for an AI agent to understand and use it correctly.

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?

Input schema covers 75% of parameters with descriptions (note_name, depth, vault_path). The description adds semantic context beyond schema by explaining depth levels and the relationship between parameters (e.g., note_name is the starting point). ctx parameter is undocumented but appears optional and not critical.

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 it gets a connection graph for a specific note, with explicit depth levels (1=direct, 2=second-degree, 3=third-degree). It distinguishes from sibling tools like get_backlinks_tool and get_outgoing_links_tool by focusing on multi-level 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?

Provides a dedicated 'When to use' section listing four clear use cases (understanding relationships, exploring neighborhoods, finding related content, building visualizations). Missing explicit 'when not to use' or alternatives, but the context is sufficient.

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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