Show what is connected to a term in the Stobox knowledge graph — the related products, standards, regulations, and concepts, 1–2 relationship hops out.
| Name | Required | Description | Default |
|---|---|---|---|
| hops | No | ||
| name | 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 discloses the hop range (1–2) and the nature of traversal, which adds useful behavioral context. However, it does not mention pagination, output format, or any side effects, though it clearly indicates a read-only query operation.
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 that communicates the core purpose and key constraint without wasted words. It is efficiently structured and easy to parse.
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 has no output schema and only two parameters. The description covers the main purpose and relationship scope, but does not mention what the result looks like (e.g., list of terms, types) or handle edge cases like no results or invalid names. It is adequate for a simple query but leaves some operational 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 0%, so the description must compensate. It partially explains the 'hops' parameter through '1–2 relationship hops out', but does not explicitly map the parameter to the number of hops or explain the 'name' parameter beyond implying it is the starting term. The description adds some meaning but is incomplete.
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 shows connected terms in the Stobox knowledge graph with specific relationship types (products, standards, regulations, concepts) and a defined traversal depth (1–2 hops). This distinguishes it from siblings like stobox_term_lookup, which likely returns a single term definition.
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 when exploring relationships in the knowledge graph, but it does not explicitly state when to use this tool versus alternatives such as knowledge_search or term_lookup. No exclusions or alternate tool references are given, but the purpose is contextually clear.
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.
Each tool has a distinct role: search, lookup, fact-check, related topics, context building, and starting tokenization. The descriptions clearly separate these purposes, so an agent can easily select the right tool without confusion.
All tools share the stobox_ prefix and use snake_case, but the action part mixes verb-first (answer_context, fact_check, start_tokenization) and noun-first (knowledge_search, related_topics, term_lookup) patterns. This inconsistency is minor since the names remain descriptive and readable.
With 6 tools, the server is well-scoped for its purpose: knowledge retrieval and tokenization initiation. Each tool has a clear function and there are no redundant or trivial additions.
The server covers the full knowledge lifecycle: searching, defining terms, exploring relations, verifying facts, building context, and acting on a user's request to start tokenization. No obvious gaps exist for the stated domain.
