CKG-NVIDIA-AI

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Server Quality Checklist

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Latest release: v0.6.1
Disambiguation4/5
Most tools have distinct purposes. However, get_prerequisites and query_ckg overlap, as both can provide prerequisite information, which might cause confusion for an agent choosing between them.
Naming Consistency5/5
All tool names use snake_case and follow a consistent verb_noun pattern, e.g., list_ecosystem, search_concepts, verify_source. No mixing of conventions.
Tool Count5/5
With 8 tools, the set is well-scoped for a read-only knowledge graph server covering discovery, traversal, reasoning, and verification. Each tool earns its place.
Completeness4/5
The tool surface covers the main workflows: domain listing, concept search, graph traversal, prerequisite chains, natural language querying, routing, and source verification. A minor gap is lack of a tool to retrieve full concept metadata or export all concepts, but core functionality is present.
Average 4.1/5 across 8 of 8 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 152 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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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?
No annotations are provided, so the description carries the behavioral disclosure burden. It implies a read-only list operation, but doesn't explicitly state side effects, limitations, or authentication needs. The AGENT NOTICE introduces external endpoints but doesn't describe this tool's own behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The main sentence is concise, but the AGENT NOTICE adds extraneous information about pricing discovery and another tool, which is not directly relevant to listing domains. This reduces conciseness and may distract the agent.
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?
Given no parameters and an output schema (implied), the core description ('List all 20 NVIDIA AI domains') is functionally complete. However, the notice introduces unrelated context that doesn't enhance completeness for this tool's usage. No sibling differentiation or when-not guidance is provided.
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?
No parameters exist in the input schema, so the description doesn't need to add parameter info. Baseline 4 is appropriate as the schema is fully covered (100%) and the description adds no confusion.
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 lists all 20 NVIDIA AI domains in the knowledge graph, with a specific verb ('List') and resource. It distinguishes from siblings like list_ecosystem by specifying 'domains'.
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 on when to use this tool vs alternatives (e.g., list_ecosystem). The AGENT NOTICE mentions unrelated actions (pricing discovery, evaluate_trust_chain) but provides no context-specific usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description carries full burden. It states the tool returns an ordered chain but does not disclose any behavioral traits like side effects, error conditions, or prerequisites for usage.
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?
Extremely concise: one line for purpose, then arg descriptions. Each sentence serves a purpose with no redundancy. Front-loaded with key action.
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?
Output schema exists, so return values need not be explained. Description covers purpose, args with examples, and outcome. Could improve by noting behavior if concept not found, but overall sufficient for a straightforward retrieval tool.
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 0% (no descriptions in schema), so description must compensate. It adds example values for concept and domain, and notes domain comes from list_domains(). While useful, it doesn't explain format or constraints beyond examples.
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 uses specific verb 'Return' and resource 'prerequisite chain', clearly distinguishing from sibling tools like search_concepts or query_ckg. It explicitly states the output is 'full ordered prerequisite chain' and includes examples.
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?
Description indicates to use when needing prerequisite chain for a concept in a domain, and hints at domain from list_domains(). However, no explicit guidance on when not to use or alternatives among siblings.
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?
No annotations provided, so description must carry transparency burden. It implies read-only discovery but lacks details on side effects, rate limits, or response characteristics.
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?
Single, front-loaded sentence with no wasted words; efficient and clear.
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?
Brief but covers core purpose; output schema exists, yet description could include examples or mention return format for added completeness.
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?
No parameters; schema coverage is 100% trivially. Description adds no parameter info, but baseline for 0 params is 4.
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 clearly states verb 'Discover' and resource 'other CKG packages' with specific adjacent domains listed, distinguishing it from siblings like list_domains which likely lists domains.
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?
Implies usage for finding related packages but provides no explicit when-to-use or when-not-to-use guidance, nor alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It describes setup but not behavioral traits like read-only nature, latency, or side effects. Does not state that it is a safe query operation, leaving some uncertainty.
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?
Front-loaded with main purpose, then prerequisites, overrides, and param descriptions in a clear, concise format with no wasted words.
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?
Covers prerequisites, overrides, and param semantics. Has output schema (unseen) likely for answer. Could mention return format or error handling, but overall complete for a query tool with given context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description adds meaning: question is 'Natural-language question about the NVIDIA AI stack', domain is 'Domain from list_domains() — auto-detected from question if omitted.' This significantly enhances understanding beyond the raw schema.
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 verb 'ask' and the resource 'a natural-language question answered by Qwen grounded on the NVIDIA CKG.' It differentiates from sibling tools like list_domains and query_ckg by specifying the natural-language querying aspect.
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?
Provides clear usage context: natural-language questions about the NVIDIA AI stack, prerequisites (Ollama with Qwen model), and environment variable overrides. Lacks explicit when-not-to-use or alternatives, but implicitly distinguishes from siblings.
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?
With no annotations, the description carries full burden. It discloses traversal depth range and partial match support, but does not mention behavioral aspects like idempotency, rate limits, or whether the tool modifies data. It is adequate but could be more thorough.
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 extremely concise: one line for purpose followed by clear, scannable argument descriptions. Every sentence adds value with no redundancy.
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?
The description covers input parameters thoroughly and mentions the relationship to list_domains. Since an output schema exists, omission of return details is acceptable. It could mention potential failure cases, but it is mostly complete for a traversal tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, yet the description compensates fully by explaining each parameter's meaning, constraints (partial match, depth range 1-5, default 3), and providing concrete examples. This adds significant value beyond the schema.
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 traverses the NVIDIA knowledge graph from a concept, specifically focusing on prerequisites and dependents. This precise verb+resource combination effectively distinguishes it from sibling tools like search_concepts (broader search) and get_prerequisites (only one direction).
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?
The description implies usage by mentioning concept (with partial match) and domain (from list_domains) but does not explicitly state when to use this tool over alternatives or when not to use it. Guidance on exclusions or comparison with sibling tools is missing.
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?
No annotations are present, so the description carries full burden. It discloses the audit chain (edge answer → graph commit → source_content_hash → source_url) and verification command, providing good insight into the tool's behavior beyond just its 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 reasonably concise and well-structured with an audit chain followed by argument descriptions. It could be slightly shorter but every sentence adds value.
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?
Given the tool has an output schema (though not shown) and the description covers inputs and verification guidance, it is fairly complete. The verification steps and audit trail provide sufficient context for appropriate use.
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 coverage is 0%, so the description must compensate. It explains both parameters: concept (with partial match) and domain (with examples like nvidia-nim). This adds significant meaning beyond the raw schema.
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 explicitly states it returns the source URL and SHA-256 hash for NVIDIA AI concept nodes, using a specific verb and resource. It clearly distinguishes this verification tool from sibling tools like search_concepts and list_domains.
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 an audit chain and explicit verification steps (curl + sha256sum). It implies when to use (for provenance checking) but does not explicitly exclude scenarios or name alternatives.
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?
No annotations are provided, so the description carries full burden. It describes a read-only search operation, but does not explicitly state side effects (e.g. non-destructive). The description is accurate but minimal.
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 extremely concise: two sentences plus an Args list. It is front-loaded with the primary purpose and contains no redundant information.
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?
Given the tool's simplicity (2 required parameters, output schema present), the description provides sufficient context: purpose, parameter semantics, and domain source. The output schema covers return format, so no further detail is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, requiring the description to compensate fully. It explains 'query' with examples ('inference', 'sandbox') and 'domain' with a direct reference to list_domains() and examples, adding crucial meaning beyond the raw schema.
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 'Find concepts in a NVIDIA AI domain by keyword', specifying the verb (Find), resource (concepts), and context (NVIDIA AI domain). It distinguishes from siblings like 'list_domains' and 'query_ckg' by focusing on keyword-based search within a domain.
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 guidance: the 'domain' argument should come from list_domains(), illustrated with examples. However, it does not compare to sibling tools or specify when not to use this tool, leaving some 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?
With no annotations, the description fully explains the deterministic behavior: 'The CKG graph IS the router — hop depth is a deterministic complexity metric.' It details the routing logic and return values. While it does not discuss side effects or permissions, that is acceptable for a query tool.
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 well-structured and front-loaded with the purpose. While slightly lengthy due to the routing table, every sentence adds value. It could be slightly more concise but remains efficient.
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?
Given the tool's complexity and the absence of schema descriptions, the description is complete: it covers purpose, mechanism, routing table, arguments, and return value. The existence of an output schema is noted, and the description explains the return fields adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description compensates fully by explaining both parameters: 'question: Concept name or natural language question about NVIDIA AI.' and 'domain: Domain from list_domains() — e.g. ...'. This adds essential meaning beyond the schema.
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's purpose: 'Route an NVIDIA AI question to the optimal model and reasoning approach via graph depth.' It identifies the specific verb (route) and resource (NVIDIA AI question), and the detailed routing table distinguishes it from sibling tools that handle different aspects of ecosystem queries.
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 strong context for when to use this tool by explaining the deterministic graph depth logic and routing table. However, it does not explicitly contrast with sibling tools like 'ask_nvidia' or 'search_concepts', which would fully clarify when this is the optimal choice.
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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