Skip to main content
Glama
zcsabbagh

Knowledge Graph MCP Server

by zcsabbagh

query_graph

Identify learning gaps, misconceptions, and optimal next steps by querying a knowledge graph with spaced repetition scheduling.

Instructions

Query the knowledge graph for learning insights.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
query_typeYesType of query to execute. One of: - "prerequisites": All prerequisites for a concept (requires node_id) - "ready_to_learn": Concepts where all prerequisites are mastered - "due_for_review": Nodes where scheduled review date has passed - "struggling": High difficulty + low mastery concepts - "stalled": Multiple reviews but mastery not improving - "misconceptions": Nodes with detected misconceptions - "knowledge_gaps": Low mastery nodes blocking other concepts - "next_recommended": Smart recommendation for what to study next - "all_nodes": All nodes in the graph
node_idNoFocus node for some queries (required for "prerequisites")
domainNoFilter results by domain (e.g., "mathematics")
limitNoMaximum number of results to return. Default 10.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations exist, and the description does not disclose behavioral traits such as read-only nature, side effects, or performance implications. The query operation implies reading, but this is not explicitly stated.

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 single-sentence description is concise but under-specified. It could be front-loaded with a brief summary of query types to improve usefulness without adding length.

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?

Despite an output schema, the description does not mention it or summarize the various query types listed in the schema. The tool is complex, yet the description lacks completeness.

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 100%, so the description adds no extra meaning beyond the schema. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Query the knowledge graph for learning insights' vaguely states the tool's purpose but does not differentiate it from sibling tools like get_learning_path or read_subgraph, which also query the graph for learning data.

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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention conditions, prerequisites, or exclusions.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/zcsabbagh/knowledge-graph-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server