Search Stobox's verified knowledge base on real-world-asset tokenization: canonical, source-linked answers, facts, and articles (GraphRAG: vector + relationship expansion). Use for any question about tokenization, RWA, security tokens, regulation, Compass, STBX/STBU, or Stobox itself.
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | Max results (default 8). | |
| query | Yes | The question or search query. |
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 carries the transparency burden. It discloses useful behavioral details (verified, source-linked, GraphRAG expansion), but it omits other behavioral aspects like return format, rate limits, or any limitations. This is adequate but not rich.
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 two sentences, front-loaded with the main action and scope, and every sentence adds value. There is no unnecessary padding 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?
For a search tool with two parameters and no output schema, the description covers the search scope, topics, and retrieval approach. It doesn't detail the response format, but that is not critical for a search tool. The minor gap prevents a 5.
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% for the two parameters (query and k), so the schema already documents them fully. The description adds no extra parameter semantics beyond restating the search scope, so the baseline 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 clearly states the tool searches Stobox's verified knowledge base on real-world-asset tokenization, listing specific topics covered. However, it does not explicitly differentiate from sibling tools like stobox_fact_check or stobox_term_lookup, so it stops short of a top score.
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?
Explicitly provides a 'Use for any question about...' clause with a comprehensive topic list, giving clear context for when to use the tool. It does not mention alternatives or when not to use it, so it lacks the full when/when-not guidance for a 5.
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.
