Explain one term, product, standard, or regulation from the Stobox knowledge graph — e.g. 'STBX', 'Compass', 'ERC-7943', 'MiCA'. Returns its authoritative description, aliases, relationships, and source documents.
| Name | Required | Description | Default |
|---|---|---|---|
| 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?
No annotations are provided, so the description carries the burden. It discloses that the tool returns authoritative descriptions, aliases, relationships, and source documents, indicating a read-only lookup. However, it does not mention behavior for unknown terms, case sensitivity, or potential errors, which limits full transparency for an agent.
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, well-structured sentence with no unnecessary words. It front-loads the action ('Explain one term'), includes examples, and states the output, making it efficient 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?
For a simple one-parameter lookup tool with no output schema, the description provides sufficient information: input examples and expected output content. It does not cover error handling or integration with sibling tools, but these are not critical for a straightforward lookup of this nature.
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 one 'name' parameter with no description (0% schema coverage), so the description compensates by providing concrete examples ('STBX', 'Compass', 'ERC-7943', 'MiCA') and explaining that the parameter is the term to look up. This adds meaningful value beyond the schema's bare type definition.
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 explains a single term, product, standard, or regulation from the Stobox knowledge graph, with specific examples. It distinguishes this from sibling tools by emphasizing 'one term' and listing output types (description, aliases, relationships, source documents), making it specific and 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 implies it is used for looking up a specific term, but it does not explicitly state when to use this tool versus alternatives like stobox_knowledge_search or stobox_related_topics. No exclusions or conditions are provided, leaving the usage context implicit rather than explicit.
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.
