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MuYiYong

nebula-mcp

by MuYiYong

nebula_render_graph

Read-onlyIdempotent

Render a non-empty query graph with its table, charts, explanation, and exact executed GQL. Explain findings, meaning, and limits; counts alone are insufficient.

Instructions

Legacy compatibility entry; prefer nebula_render_result. Render a non-empty query graph together with its table, charts, explanation, and exact executed GQL. Explain specific findings, their meaning and limitations; counts alone are insufficient.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
explanationYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
explanationYesExplain result meaning and insights with concrete entities, directions, values and comparisons. Distinguish facts from hypotheses and state sampling/missing-data limits. Do not merely repeat row or path counts.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.4

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds non-trivial behavioral context beyond that: the graph must be non-empty, and the explanation must address meaning and limitations rather than bare counts. No contradiction with annotations.

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?

Very short and front-loaded: the legacy/deprecation status leads, then the scope of what is rendered, then the explanation requirement. Every clause carries information; nothing is filler.

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?

For a rendering tool whose output schema is rich, the description covers the essential contract: non-empty graph, all artifacts rendered, and explanation expectations. It is complete enough to invoke correctly, though it leaves the legacy fallback condition unstated.

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

Parameters4/5

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

Schema description coverage is 0% for two parameters, so the description must compensate. It does meaningfully for 'explanation' ('Explain specific findings, their meaning and limitations; counts alone are insufficient'), which tells the agent what content to supply. It gives no guidance for the 'result' payload beyond restating that it carries table/charts/graph, so the coverage is good but not complete.

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?

States a specific verb (render) and resource (query graph plus table, charts, explanation, executed GQL), and explicitly distinguishes itself from the sibling nebula_render_result as a legacy compatibility entry. An agent can tell what this does and why it might be a second choice without opening the schema.

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 clearly routes agents to nebula_render_result with 'prefer nebula_render_result', giving a comparative context. However, it never states the condition under which the legacy entry should still be used, so the when-to-use guidance is incomplete.

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