Obsidian MCP (pgvector + Ollama, self-hosted)

get_neighborhood

Retrieve the connected cluster of notes linked to a seed note, traversing links and backlinks up to a chosen depth. Use it to map a topic's local network and summarize everything connected to a project.

Instructions

The connected subgraph reachable from path via links or backlinks, up to depth hops (treated as undirected).

Use this when an agent needs the local cluster around a topic — e.g. "summarize everything connected to this project". Prefer this over find_related when explicit links are the signal you want; prefer find_related when the connection is conceptual rather than linked.

Args: path: Vault-relative path to the seed note. depth: Maximum BFS depth (default 1, capped at 5). limit: Maximum distinct neighbor notes (default 50, hard cap 200).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`path`	Yes
`depth`	No
`limit`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.8.2Sep 24, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```
First observedMay 17, 2026

TDQS

A4.8/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the BFS traversal, undirected treatment, depth and limit caps, and that it returns distinct neighbor notes. However, it does not mention error handling (e.g., missing path) or whether the seed note itself is included, which are minor gaps for a read-only query tool.

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 well-structured with a clear definition first, usage guidance, and a separate Args list. Every sentence adds value, and it is not overly verbose for a tool with three parameters.

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?

Given the tool has an output schema (which handles return format) and the description covers behavior and parameters, nothing essential is missing for an agent to call it correctly. It is complete for a graph-query tool.

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

Parameters5/5

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

The description includes an Args section that explains each parameter beyond the schema's type/default info: path is the seed note, depth is max BFS depth with default and cap, limit is max distinct neighbors with cap. This fully compensates for the 0% schema description coverage.

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 returns a connected subgraph reachable from a seed note via links/backlinks, with a specific traversal algorithm. It also names the sibling tool it is not (find_related) and explains the distinction, so an agent can differentiate without inspecting schemas.

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?

Explicitly states when to use this tool ('when an agent needs the local cluster around a topic') and provides a concrete example. It also contrasts with find_related, giving clear selection criteria based on whether links are explicit or conceptual.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.