nebula-mcp

Server Quality Checklist

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Latest release: v0.1.3
Disambiguation5/5
Each tool targets a distinct action and resource: connection testing, graph listing, GQL validation, schema retrieval, read queries, and mutations. There is no meaningful overlap, even between validate and execute, since the former never executes and the latter is read-only or mutation-specific.
Naming Consistency5/5
All tools follow the same nebula_verb_noun pattern, with clear verbs like test, list, validate, get, and execute. The naming is consistent, predictable, and hierarchical.
Tool Count5/5
Six tools is well-scoped for a graph database server covering connection, discovery, validation, schema, and execution. Each tool has a clear purpose without unnecessary bloat.
Completeness4/5
The tool surface covers the core workflow: connect, discover graphs, validate, inspect schema, execute reads, and execute mutations. Minor gaps exist, such as no explicit tool for listing all schemas or managing graphs, but these are likely outside the server's intended scope.
Average 3.7/5 across 6 of 6 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- Last stable release on August 31, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, lowering the burden on the description. The description adds the useful detail that DDL output can be optionally included and is UTF-8 byte-bounded, but does not explain what happens when max_ddl_bytes is exceeded or how the DDL string appears in the output.
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?
The description is a single, compact sentence with no filler or repeated schema information. It front-loads the core operation and puts the optional behavior second, though it is perhaps too sparse to fully carry the 0% schema coverage.
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 tool with two required parameters and no schema-level descriptions, the description is not complete enough to guide correct invocation. The presence of an output schema helps with return values, but the opaque 'schema' and 'graph_type' parameters and lack of sibling differentiation leave meaningful gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the missing parameter documentation. It provides a hint about include_ddl and max_ddl_bytes through the 'UTF-8 byte-bounded DDL string' phrase, but the required parameters 'schema' and 'graph_type' are left completely unexplained.
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 names a specific verb and resource: 'Read a Graph Type schema'. This clearly distinguishes it from mutation/execution siblings and signals it is a retrieval operation. However, 'Graph Type' is domain-specific and not expanded, so a less knowledgeable agent could still be unsure what object is being read.
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 given about when to use this tool over siblings like nebula_list_graphs or nebula_execute_query. The description implies a read operation but does not state prerequisites, when it is appropriate, or when an alternative should be chosen.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare destructiveHint=true, so the description's 'This tool is destructive' reinforces rather than adds. It does add useful context by stating 'returns no charts' and 'one mutation only', but it does not disclose other behavioral traits such as whether changes are reversible or what happens on failed validation.
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?
Two compact sentences with no filler. The critical usage condition is front-loaded, and the destructive/no-charts warning is placed prominently. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is adequate for a simple mutation tool with an output schema: it states the mutation role, safety gate, destructive nature, and output expectation. However, it lacks guidance on how 'statement' and 'graph' relate, and it could benefit from explicitly routing non-mutating users to nebula_execute_query.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate by explaining the parameters. It mentions confirm_mutation as a safety gate, which is useful, but it gives no semantics for the required 'statement' parameter or the optional 'graph' parameter, leaving the agent to infer their roles.
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 clearly identifies a specific verb ('Execute'), a specific resource ('mutation'), and adds a scope constraint ('one mutation only'). It is distinguishable from sibling tools by the term 'mutation', though it does not explicitly name the sibling it contrasts with, such as nebula_execute_query.
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 provides explicit gating conditions: 'only when NEBULA_ALLOW_MUTATIONS=true and confirm_mutation=true'. This is strong usage guidance for a destructive tool. However, it does not explicitly state when to prefer a sibling like nebula_execute_query for non-mutating work.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds the qualifier 'persistent graphs', which implies a distinction from temporary graphs. It does not mention pagination behavior (limit/offset) or the optional schema filter, but since annotations carry the main behavioral load, a baseline 3 is reasonable. It doesn't contradict 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?
The description is a single, front-loaded sentence with no wasted words. It states the core action and its usage context efficiently. Every word earns its place, making it highly concise and well-structured.
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?
While the tool is simple and has an output schema (reducing the need to describe return values), the description is incomplete due to the lack of parameter explanations and limited behavioral detail. It does not address the optional schema filter, which is a common source of confusion. The description provides only minimal context, which is insufficient for an agent to use the tool optimally, even with annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention any of the three parameters (limit, offset, schema). With low coverage, the description must compensate, but it completely fails to explain what these parameters control. An agent would have to rely solely on the schema, which may be insufficient for understanding the schema parameter's purpose. This is a critical gap.
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 states a specific action ('List persistent graphs') on a clear resource, and adds the context of being a preliminary step ('before choosing schema and graph context'). This clearly distinguishes it from siblings like nebula_get_graph_schema (which retrieves schema) and nebula_execute_query (which executes queries). The verb-resource pairing is unambiguous.
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 gives a clear 'when to use' signal ('before choosing schema and graph context'), which is helpful. However, it does not explicitly mention when not to use it or alternative tools, only implying its role as a precursor. A 4 is appropriate because it provides clear context but lacks explicit exclusions or alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description adds value by disclosing the optional return artifacts (graph, analysis, charts) and reinforcing the read-only nature ('approved read-only'). This aligns with annotations and provides additional context about output variety.
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?
A single, well-structured sentence that front-loads the core action and then lists the optional outputs. There is no fluff or redundancy; every word contributes to agent comprehension.
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 having an output schema, the description is far too sparse for a 7-parameter tool. It omits critical context like what 'approved' means, the meaning of graph/max_rows/render_mode, and how the include_* flags map to return types. An agent would need to inspect the schema and still lack semantic guidance, making the tool risky to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 7 parameters and 0% schema description coverage, the burden falls entirely on the description, but it fails to explain any parameter. It vaguely hints at include_graph/include_charts/include_analysis through the mention of return types, but gives no guidance on the 'statement', 'graph', 'max_rows', or 'render_mode' fields. This is inadequate for a tool with a required statement parameter.
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 uses a specific verb ('Execute') and a clear resource ('one approved read-only GQL statement') and enumerates the possible return types (table, graph, analysis, charts). This distinguishes it from siblings like nebula_execute_mutation (which is write-oriented) and nebula_validate_gql (which validates rather than executes).
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 implies this tool is for executing approved read-only queries, which clearly separates it from mutation and validation tools. However, it does not explicitly state when to prefer this over nebula_validate_gql or reference sibling alternatives by name, so there's minor room for ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly discloses that the original statement is never executed, which goes beyond the readOnlyHint annotation by ruling out even read-side execution. It also surfaces optional EXPLAIN behavior, which helps set expectations for the run_explain parameter. 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?
The description is a single, front-loaded sentence with no filler. Every phrase adds meaning, and the most important safety behavior is placed at the end for emphasis.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is strong on safety and purpose, and the output schema plus annotations reduce the need for return-value detail. However, for a 3-parameter tool with zero schema coverage, it leaves the 'graph' parameter unexplained and does not explicitly establish when to use validation versus execution.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must carry the parameter-meaning burden. It does convey that 'statement' is the GQL being validated and hints at 'optional EXPLAIN' for run_explain, but it never explains the 'graph' parameter or specifies expected values for the validation dimensions.
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?
Description opens with a specific verb, 'Validate,' and identifies the precise resource, 'YueShu 5.3 GQL,' along with concrete validation dimensions: placeholders, dialect residuals, policy, and optional EXPLAIN. This clearly distinguishes it from sibling execution-oriented tools such as nebula_execute_query.
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 statement 'This never executes the original statement' clearly implies a safe validation use case and separates this tool from execution tools. However, it does not explicitly name alternatives or state when validation should be preferred over execution.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, open-world, and non-destructive behavior. The description adds value by disclosing that the response is redacted, which is a meaningful behavioral trait beyond the annotations. It does not contradict any annotation.
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 a single sentence with no filler, front-loading the action ('Test') before the output detail ('return only redacted settings'). Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no parameters, an output schema present, and a clear one-sentence description, nothing essential is missing for an agent to invoke this tool correctly. The redaction note and connection-testing purpose fully cover the low-complexity context.
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?
The tool has zero parameters and the schema coverage is effectively complete, so there is no parameter burden for the description to carry. The baseline of 4 applies here because no parameter documentation is needed.
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 uses a specific verb ('Test') and identifies the resource ('configured YueShu connection'), then clarifies the scope of the response ('return only redacted settings'). This makes the tool's purpose immediately distinguishable from sibling tools like nebula_execute_query or nebula_get_graph_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 phrase 'Test the configured YueShu connection' clearly implies use as a connectivity/health check before graph operations, and the sibling list reinforces that it is not for querying or schema work. It lacks explicit when-not-to-use statements or named alternatives, but the context is clear enough to guide selection.
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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