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ravenroot-ai

Ravenroot Second Brain MCP

Official
by ravenroot-ai

get_neighbors

Retrieve all direct neighbors of a graph node with edge details. Filter by relation type, set token budget, or specify a project path to explore connections.

Instructions

Get all direct neighbors of a node with edge details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelYes
project_pathNoAbsolute path to a project directory containing graphify-out/graph.json. Optional — defaults to the graph this server was started with.
token_budgetNoMax output tokens
relation_filterNoOptional: filter by relation type

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full disclosure burden and largely fails: it does not state that the operation is read-only, whether traversal is directed or undirected, how token_budget truncation affects results, or whether neighbors are paginated. Only the vague phrase 'with edge details' hints at the response shape.

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?

A single front-loaded sentence with zero filler, which is appropriately sized for a tool whose parameters are mostly self-describing. It is arguably over-terse, but nothing is wasted or buried.

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?

For a graph-traversal tool with no annotations and no output schema, the definition omits essentials: traversal direction, output shape beyond 'edge details', and how token_budget bounds the result. An agent could call it, but not confidently predict or handle the response.

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 coverage is 75%, so most parameters (project_path, token_budget, relation_filter) are already documented in the schema, establishing a baseline of 3. The description adds nothing about how label matching works or what values relation_filter accepts, so it does not exceed that baseline.

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 gives a specific verb and resource (get all direct neighbors of a node) plus a scope qualifier (with edge details), so an agent can immediately tell what the tool returns. It does not, however, differentiate itself from siblings like get_node or query_graph, which could plausibly also surface adjacency information.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to reach for get_neighbors versus query_graph, get_node, or shortest_path, and no mention of prerequisites or typical contexts. The agent must infer usage entirely from the name.

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