Find AgentForce concepts by keyword.
Args: query: Search term — e.g. 'resolution', 'trust', 'grounding', 'action', 'NIM'.
| Name | Required | Description | Default |
|---|---|---|---|
| query | 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?
No annotations are provided, so the description must bear the full burden of behavioral transparency. It only states the operation, but does not disclose whether it is read-only, any side effects, rate limits, or pagination behavior. For a search tool, this lack of safety information is a significant gap.
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 concise, with only two sentences and a single example list. However, the 'Args:' section is somewhat redundant for a single parameter. The key 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, the description does not need to explain return values. It covers the core purpose and input sufficiently. Missing details like error handling or pagination are minor for a simple search tool.
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 parameter 'query' has a schema description coverage of 0%, meaning the schema provides no description. The description adds meaningful context by specifying the parameter as a search term and providing examples (e.g., 'resolution'). 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb 'Find', the resource 'AgentForce concepts', and the method 'by keyword'. It distinguishes from the sibling tool 'list_concepts' which likely lists all concepts, making the purpose unambiguous.
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 examples of search terms (e.g., 'resolution', 'trust') implying when to use this tool. It does not explicitly mention when not to use it or alternatives, but the sibling context suggests that 'list_concepts' is for retrieving all concepts. The guidance is clear but lacks explicit exclusions.
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.