NVIDIA AI knowledge graphs — 20 domains. 4x F1, 11x fewer tokens, SHA-256 provenance. MCP-native.
Ask a natural-language question answered by Qwen grounded on the NVIDIA CKG.
Requires Ollama running locally with a Qwen model pulled:
ollama pull qwen2.5:14b
Override model: NVIDIA_CKG_MODEL env var (default: qwen2.5:14b)
Override host: NVIDIA_CKG_OLLAMA env var (default: http://localhost:11434)
Args:
question: Natural-language question about the NVIDIA AI stack.
domain: Domain from list_domains() — auto-detected from question if omitted.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | No | ||
| question | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full disclosure burden. It does this well by revealing an external runtime dependency on Ollama, the required model, default host, and environment-variable overrides, plus domain auto-detection. It stops short of describing failure modes or latency, but the core behavioral traits are meaningfully disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The core purpose is front-loaded, followed by compact, well-organized dependency and argument sections. The code block and argument list are efficient, and every line adds operational value, including defaults for the model and host overrides.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the essential invocation path: prerequisites, environment configuration, and both parameters. Since an output schema exists, return-value documentation is not required here. The only notable gap is lack of sibling-routing guidance among the eight related tools, but the tool is still safely callable with this description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description fully compensates. It defines question as a natural-language question about the NVIDIA AI stack and explains that domain comes from list_domains(), is auto-detected if omitted, and only needed for disambiguation. This gives the agent real semantics beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The first sentence clearly states a specific verb-resource pair: 'Ask a natural-language question answered by Qwen grounded on the NVIDIA CKG.' This is unambiguous about what the tool does and is distinct from the sibling names, though it does not explicitly name an alternative to differentiate against.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a concrete prerequisite ('Requires Ollama running locally with a Qwen model pulled') and explains that domain is auto-detected when omitted. It does not, however, say when to prefer ask_nvidia over siblings like query_ckg or route_query, nor does it provide explicit when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the full ordered prerequisite chain for a concept — everything to learn first.
Args:
concept: Target concept — e.g. 'Speculative Decoding', 'Isaac Lab', 'NeMo Guardrails'.
domain: Domain name from list_domains().
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | ||
| concept | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden, and it does disclose the core behavior: it returns a full, ordered chain. 'Return' implies a read-only operation. It does not detail error handling, determinism, or whether the data is fetched versus local, but for a retrieval tool this is adequate disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact: one purpose sentence plus a short Args block. Every element earns its place, and the key behavior is front-loaded. No filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description should indicate what the tool returns — and it does: the full ordered prerequisite chain. It also handles parameter sourcing via list_domains(). It doesn't specify the exact return format or error behavior, but everything needed to call it correctly is present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 — and it does thoroughly. It provides concrete examples for 'concept' and tells the agent that 'domain' must come from list_domains(), which prevents invalid input. Both parameters are meaningfully explained beyond their bare schema definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Return the full ordered prerequisite chain for a concept.' It also adds scope qualifiers ('full', 'ordered', 'everything to learn first') that make the tool's role immediately clear. This is more specific than a generic 'get prerequisites' phrasing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives clear context for when to use the tool — when you need the ordered prerequisite chain for a concept. It also instructs the agent to source the domain from list_domains(), which is useful operational guidance. However, it does not name alternatives or explicitly state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all 20 NVIDIA AI domains available in this knowledge graph.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the disclosure burden. It communicates the operation is a read-only listing, specifies the exact scope ('all 20'), and names the data source ('this knowledge graph'). This is sufficient behavioral transparency for a simple zero-parameter enumeration, though it does not mention ordering or freshness.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It provides the essential facts: what is listed, how many, and where.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with an output schema, the description is complete. It states the exact resource, scope, and source, leaving no ambiguity about what the call will do.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so there is nothing for the description to add beyond the schema. The baseline of 4 applies because no parameter documentation is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('List') and resource ('NVIDIA AI domains') and scopes it to 'all 20' in the knowledge graph. It is clear, though it does not explicitly contrast itself with the similarly named sibling 'list_ecosystem'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives like 'list_ecosystem' or the other query-style siblings. The intended usage is somewhat implied by the tool name and zero-parameter signature, but exclusions or selection criteria are absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Discover other CKG packages for adjacent domains — finance, healthcare, legal, and more.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. 'Discover' implies a read-only exploration action and the absence of parameters suggests a simple listing, but it does not explicitly state that the tool returns a list, is non-mutating, or requires no auth. For a zero-parameter listed operation this is minimally adequate, though not richly transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. It states the action, the resource, the scope, and gives concrete examples of adjacent domains. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple zero-parameter tool with an output schema, the description is mostly complete: it communicates the purpose and the domain focus. However, it does not clarify what 'other' is relative to, and it gives no hint about when to prefer this tool over sibling tools. The presence of an output schema reduces the need to explain return values, but the missing usage guidance keeps this from being fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 100%, so the schema already fully describes all inputs. The description does not need to add parameter semantics. The baseline of 4 for a zero-parameter tool is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb, 'Discover', and identifies the resource as 'other CKG packages' with a clear scope: adjacent domains such as finance, healthcare, and legal. This makes the tool's purpose reasonably clear and helps distinguish it from siblings like list_domains, which likely lists domains rather than packages. It could be slightly more direct by saying 'List', but it is not vague or tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives such as list_domains, query_ckg, or search_concepts. It does not mention any exclusions, prerequisites, or conditions that would route an agent to this tool over a sibling. Usage context is only implied by the tool's name and the phrase 'adjacent domains'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Traverse the NVIDIA knowledge graph from a concept — prerequisites and dependents.
Args:
concept: Concept name (partial match supported) — e.g. 'TensorRT', 'NIM', 'Isaac Lab'.
domain: Domain name from list_domains() — e.g. 'nvidia-tensorrt-triton', 'nvidia-isaac'.
depth: Traversal depth 1–5 (default 3).
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | ||
| domain | Yes | ||
| concept | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the behavioral burden. It discloses useful non-schema behaviors: partial match support for concept, depth range 1–5, default depth 3, and the requirement that domain comes from list_domains(). It does not mention result format or traversal edge semantics, but for a read-style graph traversal tool the described behaviors are meaningful and not misleading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is succinct and well-organized: a one-sentence purpose statement followed by three focused parameter annotations. There is no filler, and the most important behavioral detail (traversal from a concept) is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The input side is complete: all parameters, ranges, defaults, and domain sourcing are documented. However, because there is no output schema and no description of the return shape, an agent still has some uncertainty about what a traversal result looks like. This is a minor gap given the otherwise strong parameter coverage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It does: each parameter is explained with concrete examples, a valid range for depth, a default value, and a source command for domain. This is exactly the kind of parameter-level detail an agent needs beyond the raw JSON schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Traverse') and resource ('NVIDIA knowledge graph') and identifies the exact relationship type being explored ('prerequisites and dependents'). This clearly distinguishes what the tool does from generic alternatives like search_concepts or ask_nvidia, and the coverage is specific enough to separate it from get_prerequisites.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied through the traversal semantics and the instruction that domain should come from list_domains(). However, the description does not explicitly say when to choose this tool over siblings like get_prerequisites or query_intersect, nor does it mention when not to use it. The context is helpful but not fully decision-ready.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Answer a conjunctive query: concepts reachable from EVERY anchor at once (A AND B).
query_ckg walks outward from one concept. This intersects the reachable sets of two or
more, which is the shape of most real questions — "the component that satisfies A AND
applies to B". Neither anchor alone answers it; the answer lives in the overlap.
Every branch is an exact set of declared edges, so the intersection is exact. A concept
appears only if a declared path reaches it from each anchor. A relation missing from the
graph produces an empty result, never a guess.
Args:
branches: Two or more branches. Either a bare anchor ("TensorRT-LLM"), which takes
everything within `depth` hops, or an anchor plus an explicit relation path using
'>' ("TensorRT-LLM > REQUIRES > ENABLES"), where each relation replaces the
frontier. '*' matches any relation. Mix both forms freely.
domain: Domain name from list_domains().
depth: Hops for bare-anchor branches, 1-5 (default 2). Ignored for explicit paths.
direction: 'out' follows dependencies, 'in' follows them backwards, 'both' (default).
mode: 'AND' (default) intersects branches; 'OR' unions them.
limit: Max concepts listed, 1-200 (default 40). The true count is always shown.
Returns:
Markdown with the query plan and its per-step set sizes, then the answer set with
taxonomy tags. Reports which branch was empty when the intersection is empty.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | AND | |
| depth | No | ||
| limit | No | ||
| domain | Yes | ||
| branches | Yes | ||
| direction | No | both |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden, and it does so thoroughly. It discloses that the intersection is exact, that missing relations yield an empty result rather than a guess, that limit shows the true count, and that the return value is Markdown with per-step set sizes and taxonomy tags. It also reports which branch was empty when the intersection is empty, which is valuable runtime behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized with a clear purpose statement, a short rationale distinguishing it from query_ckg, an Args block mapping to parameters, and a Returns block. Each sentence adds information about semantics, edge cases, or output behavior. It is longer than average, but the tool is complex and the length is proportional to what an agent needs to invoke it correctly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given six parameters, zero schema descriptions, no output schema, and no annotations, the description is complete enough for correct invocation. It covers all parameters, explains return format, describes the empty-result behavior, and references list_domains() for obtaining the domain value. No critical operational detail is left to inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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, and it does comprehensively. It explains branches with bare anchors versus explicit relation paths, the meaning of '>' and '*', that depth applies only to bare-anchor branches, the direction options, mode AND/OR semantics, and that limit caps listed concepts while still showing the true count. This goes well beyond the raw schema fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a precise definition: 'Answer a conjunctive query: concepts reachable from EVERY anchor at once (A AND B).' It names the specific operation, the resource (concepts in the graph), and explicitly contrasts itself with sibling query_ckg, which walks outward from one concept. An agent can readily distinguish this tool from its siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly identifies the alternative query_ckg and the condition that selects between them: query_ckg handles one concept, while query_intersect handles two or more anchors and finds the overlap. It also explains why this shape fits 'most real questions' and notes that neither anchor alone answers the query. This gives clear when-to-use versus when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Route an NVIDIA AI question to the optimal model and reasoning approach via graph depth.
The CKG graph IS the router — hop depth is a deterministic complexity metric.
Deeper NVIDIA prerequisite chains (CUDA → TensorRT → TensorRT-LLM → NIM) require
more capable models. No heuristic: the graph decides.
Routing table:
hop_depth 1 → haiku · direct (simple lookup)
hop_depth 2 → sonnet · generic_cot (moderate chain)
hop_depth 3+ → opus · sparql_cot (deep dependency, structured reasoning)
Args:
question: Concept name or natural language question about NVIDIA AI.
domain: Domain from list_domains() — e.g. "nvidia-tensorrt-triton", "nvidia-nim".
Returns:
model_tier + reasoning_approach + why + context subgraph to inject before LLM call.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | No | ||
| question | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
……
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
……
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
……
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
……
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
……
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
……
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find concepts in a NVIDIA AI domain by keyword.
Args:
query: Search term — e.g. 'inference', 'sandbox', 'quantization', 'guardrails'.
domain: Domain name from list_domains() — e.g. 'nvidia-nim', 'nvidia-openshell'.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| domain | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool finds concepts but discloses nothing about return format, pagination, matching behavior, rate limits, or whether the operation is purely read-only. The examples given are helpful for parameters but not for behavioral expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is tight and well-organized: a one-sentence purpose statement followed by a compact Args block with illustrative examples. Every line adds value and there is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only two required parameters, both are documented with examples, and an output schema exists so return values do not need explanation, the description is largely complete. It lacks richer context like matching semantics or relationship to sibling query tools, but an agent has enough to invoke it correctly in most cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 bare schema. It does a good job by giving concrete examples for query ('inference', 'sandbox') and explicitly sourcing domain from list_domains(), adding meaningful context that the schema lacks. It stops just short of specifying accepted formats or validation rules.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Find concepts') and a specific resource ('in a NVIDIA AI domain by keyword'). It is easy to understand what the tool does, but it does not explicitly differentiate itself from sibling tools like query_ckg or query_intersect, so it misses the highest bar for sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through the argument examples and instructs that domain should come from list_domains(), but it gives no explicit guidance about when to use this tool versus alternatives such as query_ckg, query_intersect, or route_query. There are no exclusions or conditions stated, leaving the agent to infer the right selection context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the source URL and SHA-256 content hash for any NVIDIA AI concept node.
Audit chain: edge answer → graph commit → source_content_hash → source_url (fetch hint).
Verification: curl -s <source_url> | sha256sum # compare to source_hash
Args:
concept: Concept label (partial match supported).
domain: Domain from list_domains() — e.g. 'nvidia-nim', 'nvidia-tensorrt-triton'.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | ||
| concept | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden and performs well: it exposes the audit chain, states that concept supports partial matches, and provides a concrete curl command for manual verification. It does not disclose failure modes, but the core behavior and expected outputs are clearly specified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured: a one-sentence purpose, the audit chain, a verification command, and an Args block. Every line adds distinct value with no redundancy, and the most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists, return values are already covered elsewhere. The description supplies the audit chain, verification method, and parameter sourcing, making it quite complete for a two-parameter tool. It lacks explicit when-not-to-use guidance and error behavior, but those are minor gaps here.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the Args section compensates fully by defining both parameters. It adds partial-match behavior for concept and directs the agent to list_domains() for valid domain values, including concrete examples like 'nvidia-nim' and 'nvidia-tensorrt-triton'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a precise statement: 'Return the source URL and SHA-256 content hash for any NVIDIA AI concept node.' This clearly identifies the verb, resource, and scope, and distinguishes verify_source from sibling query/search tools by focusing on provenance verification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The audit chain and verification command provide clear operational context, and the Args section explicitly instructs that domain should come from list_domains(), giving a concrete usage prerequisite. It does not explicitly name alternatives or exclusion criteria, but the verification purpose is evident enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
query_intersectget_prerequisites1 field changedOutput schema / (root)Previous value: -{
- "properties": {
- "result": {
- "title": "Result",
- "type": "string"
- }
- },
- "required": [
- "result"
- ],
- "title": "get_prerequisitesOutput",
- "type": "object"
-}New value: +nullquery_ckg1 field changedOutput schema / (root)Previous value: -{
- "properties": {
- "result": {
- "title": "Result",
- "type": "string"
- }
- },
- "required": [
- "result"
- ],
- "title": "query_ckgOutput",
- "type": "object"
-}New value: +nullask_nvidiaget_prerequisiteslist_domainslist_ecosystemquery_ckgroute_querysearch_conceptsverify_sourceClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
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After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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NVIDIA NemoClaw knowledge graph — 55 nodes, F1 0.576 (+269% vs RAG), 11x fewer tokens. MCP-native.
Salesforce AgentForce knowledge graph — 40 nodes, Einstein Trust Layer, 11x fewer tokens than RAG.
Art provenance intelligence — 282K-node knowledge graph with cited answers and honest gaps.
Graph-native persistent memory for AI agents — 33 MCP tools, zero-LLM writes.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes: listing, searching, traversing, intersecting, routing, and verifying are all separated. The main potential confusion is between get_prerequisites and query_ckg, since both traverse prerequisite relationships, though the former is explicitly ordered and full-chain while the latter is general graph traversal.
All tool names follow a consistent verb_noun pattern: ask, get, list, query, route, search, verify. Even the query_* pair is consistent, and the one brand-tinged name (ask_nvidia) still follows the same verb_noun structure.
Nine tools is a well-scoped set for an NVIDIA knowledge-graph server. There are enough tools to cover discovery, querying, specialized traversal, routing, and verification without feeling bloated or thin.
The surface covers domain listing, concept search, graph traversal, prerequisite chains, conjunctive queries, routing, NL question answering, and source verification—strong coverage for a knowledge-graph query server. A minor gap is the lack of a direct get-concept-details tool, though query_ckg and ask_nvidia partially compensate.