Return the full ordered prerequisite chain for an AgentForce concept.
Shows everything the concept depends on — the complete upstream path.
Args: concept: Target concept — e.g. 'Autonomous Resolution', 'Multi-LoRA Serving', 'Custom Actions', 'Semantic Retrieval'.
| Name | Required | Description | Default |
|---|---|---|---|
| concept | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It indicates the output is an ordered chain and is read-only by nature, but it does not disclose error handling, performance, or what happens if the concept does not exist.
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 very concise with three sentences plus the Args block, all front-loaded. Every sentence adds value without repetition or waste.
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 one-parameter tool with an output schema, the description adequately covers what it does and what the parameter is. It could mention the ordering or structure of the returned chain, but it is largely 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 schema has 0% coverage, but the description compensates with an Args section that explains the 'concept' parameter with concrete examples, adding significant meaning 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 description clearly states the tool returns the 'full ordered prerequisite chain' for an AgentForce concept, using a specific verb and resource. It distinguishes from siblings like 'list_concepts' and 'search_concepts' by focusing on dependencies.
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 explains the tool shows the upstream path of dependencies, which implies when to use it, but it does not explicitly compare to siblings or mention when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools serve clearly distinct roles: querying, searching, routing, and verification are separate concerns. However, query_ckg and get_prerequisites both return prerequisite information, which could cause an agent to select the wrong tool when a simple prerequisite list is needed. The descriptions do clarify the difference (generic traversal vs. full ordered chain), but some ambiguity remains.
The vast majority of tools follow a consistent snake_case verb_noun pattern, such as evaluate_trust_chain, list_concepts, and route_query. The only exception is resolution_path, which is a noun phrase rather than a verb_noun, making it slightly less predictable. This is a minor deviation from an otherwise strong pattern.
With 10 tools, the server has a well-scoped surface area for a knowledge graph that requires discovery, traversal, routing, and audit capabilities. Each tool represents a distinct operation, and there are no redundant or unnecessary entries. This falls comfortably within the ideal range for a domain-specific server.
The toolset covers the full read-side lifecycle of the knowledge graph: discovery, traversal, path analysis, routing, and trust verification. It also includes specialized tools for benchmarking and source verification, which are unusual but valuable additions. No obvious critical gaps exist, such as missing search or traversal capabilities.