markdown-vault-mcp

Graph Neighborhood

vault_graph_neighborhood

Read-onlyIdempotent

Retrieves the link graph around a note, returning connected notes as nodes and edges. Supports adjustable depth, node limits, and optional semantic edges.

Instructions

Return the link neighborhood of a note as a node/edge graph (app-only).

Called by the SPA graph view via app.callServerTool(). Not visible to the LLM.

Input Schema

TableJSON Schema

Name	Required	Description
`path`	Yes	Center note path.
`depth`	No	How many hops to traverse (default 1).
`max_nodes`	No	Soft cap on returned node count (default 200). BFS and any semantic expansion both stop once the cap is hit; the response sets ``truncated=True``. Bounds dense-vault depth=2 traversals that would otherwise bog down vis-network.
`include_semantic`	No	When True, add dashed semantic-similarity edges for each interior node (requires embeddings to be configured; silently omitted when unavailable).

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv5.0.0Sep 15, 2026

TDQS

A3.8/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false; the description adds the node/edge graph return shape and internal caller context. It does not contradict annotations, but it contributes little behavioral detail beyond what the schema and annotations already encode.

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 only three short sentences, with the core purpose front-loaded before caller context. It is efficient and scannable, with only slight redundancy between 'app-only' and 'Not visible to the LLM.'

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?

An output schema exists, the input schema is fully documented, and annotations cover safety, so the description need not repeat those details. It sufficiently conveys the tool's restricted visibility and purpose for its intended caller, though it could improve by pointing to graph-related siblings.

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 100%, with rich per-parameter details such as the default depth, soft node cap, truncated flag, and conditional semantic-edge behavior. The description itself adds no parameter-level meaning, so the baseline of 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 first sentence identifies a specific action and resource: 'Return the link neighborhood of a note as a node/edge graph (app-only).' The purpose is unmistakable, but it does not differentiate this from related graph siblings such as vault_graph_hubs or get_connection_path.

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 explicitly scopes the tool as 'app-only,' says it is 'Called by the SPA graph view via app.callServerTool(),' and adds 'Not visible to the LLM,' giving clear invocation context and an exclusion. It stops short of naming alternatives or selection conditions versus other graph tools.

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