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Ravenroot Second Brain MCP

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by ravenroot-ai
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Python
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query_graph

Search a local knowledge graph via BFS or DFS to return relevant nodes and edges as text context for broad questions or specific path tracing.

Instructions

Search the knowledge graph using BFS or DFS. Returns relevant nodes and edges as text context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNobfs=broad context, dfs=trace a specific pathbfs
depthNoTraversal depth (1-6)
questionYesNatural language question or keyword search
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
context_filterNoOptional explicit edge-context filter, e.g. ['call', 'field']

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations provided, so the description must carry the burden. It states the tool returns 'relevant nodes and edges as text context' but does not explain output structure, side effects, or required permissions. The description adds limited behavioral context beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, which is concise but too brief for a 6-parameter tool. It front-loads the main purpose but omits important details, making it borderline underspecified.

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?

Given the tool's complexity (6 params, no output schema, no annotations) and multiple siblings, the description is incomplete. It does not explain what 'text context' looks like, how to interpret results, or provide usage examples. Sibling tools like graph_stats are not differentiated.

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%, so baseline is 3. The tool description adds no extra meaning over the schema; it repeats the mode choices but without enriching parameter semantics.

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 searches a knowledge graph using BFS or DFS, distinguishing it from siblings like get_node (single node retrieval) and shortest_path (path-specific). The verb 'Search' and resource 'knowledge graph' are specific.

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?

No explicit guidance on when to use BFS vs DFS, nor compared to alternative tools like get_neighbors or shortest_path. The description implies use for traversal but lacks contextual decision rules.

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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