Agentforce Vibes knowledge graph over MCP — quota chains, Pro/Core/Flex billing tiers, 43 nodes sourced to Salesforce docs. Works in claude.ai in 30 seconds.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.5/5 across 4 of 4 tools scored.
Each tool has a distinct purpose: listing domains, searching concepts, getting prerequisites, and querying subgraphs. There is no overlap or ambiguity between them.
All tool names follow a consistent verb_noun pattern in snake_case (get_prerequisites, list_domains, query_ckg, search_concepts), making them predictable and easy to understand.
With only 4 tools, the server is well-scoped for exploring a knowledge graph. Each tool serves a clear need without redundancy or excessive specialization.
The set covers key discovery and traversal operations, but lacks a tool to retrieve detailed properties of a single concept without its graph neighbors. This is a minor gap for a complete read-only surface.
get_prerequisitesBInspectGet the full upstream prerequisite chain for a concept — everything it depends on.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain | |
| concept | Yes | Concept label |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full responsibility for behavioral disclosure. It states the tool retrieves a 'full upstream prerequisite chain' but does not clarify whether this includes transitive dependencies, depth limits, or side effects. The read-only nature is implied but not explicit.
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?
A single sentence that immediately conveys the tool's purpose. No unnecessary words. Well front-loaded with the key action and resource.
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 no annotations, no output schema, and two parameters with full schema coverage, the description is minimally adequate. However, it lacks details on return format or whether the chain is exhaustive, which would be helpful for an agent using the output.
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 coverage is 100% with both parameters described (Domain enum and Concept label). The description adds no additional meaning beyond the schema, so baseline of 3 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 clearly states the verb 'Get' and the resource 'full upstream prerequisite chain for a concept', with the clarifying phrase 'everything it depends on'. This distinguishes it from sibling tools like query_ckg or search_concepts, which serve different purposes.
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 provides no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, limitations, or when not to use it. The agent must infer usage solely from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_domainsAInspectList available AgentForce CKG domains with node counts and descriptions.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It describes the output (domains with node counts and descriptions) but does not disclose permissions, freshness, or side effects. It is adequate but not detailed.
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 redundant information. Every word adds value.
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 list operation with no parameters and no output schema, the description sufficiently conveys the tool's purpose and output. No additional information is necessary.
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% (empty properties). The description adds value by specifying the content returned (domains with node counts and descriptions), which is not captured in the 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 clearly states the tool lists AgentForce CKG domains and specifies that it includes node counts and descriptions. This distinguishes it from siblings like query_ckg or 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?
No explicit guidance on when to use this tool versus alternatives is provided. Usage context is implied but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_ckgAInspectRetrieve a concept's subgraph (prerequisites + dependents) up to N hops. Use this to traverse the knowledge graph.
| Name | Required | Description | Default |
|---|---|---|---|
| hops | No | Traversal depth (1-5, default 3) | |
| domain | Yes | Domain to query | |
| concept | Yes | Exact concept label (e.g. 'Flex Credit', 'Lifetime Token Allocation') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry full burden. It only says 'retrieve', implying read-only, but does not disclose potential side effects (e.g., large result sets, performance impact), authentication needs, or any constraints beyond what the schema covers.
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?
Two concise sentences: first defines the action, second states the use case. No redundant phrases, 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?
Tool has three parameters, no output schema, and no annotations. The description does not explain the output format (e.g., nodes, edges), error conditions, or domain selection guidance. Adequate but leaves gaps.
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 coverage is 100% with clear descriptions for all three parameters. The description adds only 'up to N hops' which echoes the hops parameter. Minimal additional meaning beyond the schema, so baseline score of 3.
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?
Clearly states the tool retrieves a concept's subgraph (prerequisites and dependents) up to N hops. Distinguishes from siblings like get_prerequisites (which likely only gets prerequisites) and search_concepts (which searches, not traverses).
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?
Mentions 'Use this to traverse the knowledge graph' but provides no explicit guidance on when to use versus alternatives like get_prerequisites, no when-not-to-use conditions, and no exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_conceptsBInspectSearch for a concept by keyword across AgentForce CKG domains.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Keyword to search for (e.g. 'Flex Credit', 'Trust Gate') | |
| domain | No | Optional: limit search to one domain |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states what the tool does. It omits behavioral traits like pagination, read-only nature, or domain boundaries beyond the enum.
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?
A single, clear sentence with no unnecessary words. It is front-loaded and efficient.
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 tool is simple (2 params, no nested objects), and the schema covers all parameters. However, the lack of output schema and minimal description limits completeness for an agent to understand what is returned.
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 coverage is 100% with good descriptions for both parameters. The overall description adds no additional meaning beyond what's in the schema, so baseline score of 3 applies.
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 ('Search') and resource ('concept across AgentForce CKG domains'). While it doesn't explicitly differentiate from siblings like 'query_ckg', the keyword search focus is clear.
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 on when to use this tool versus alternatives like 'query_ckg' or 'list_domains'. No when-not or prerequisite information provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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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
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