Stobox Intelligence & Tokenization

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must disclose behavior. It describes the output as a briefing with passages, facts, and URLs, but does not mention if the tool is read-only, has side effects, or requires authentication. The behavior is partially transparent.

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 two sentences, front-loading the main purpose. However, it lacks structure like bullet points and could benefit from a brief parameter explanation.

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 0% schema parameter coverage, no output schema, and no annotations, the description is insufficient. It does not explain the output format, how to use parameters, or when to prefer this over siblings.

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% description coverage and the description does not explain the parameters. 'query' is implied but not defined, and 'max_chars' is not mentioned at all, leaving the agent to guess their semantics.

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 it builds a 'source-linked briefing' with specific content (passages, entity facts, source URLs) for answering tokenization questions, distinguishing it from siblings like stobox_knowledge_search which likely returns raw search results.

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 says 'Use it to ground an answer' implying use before responding, but it does not explicitly state when to avoid it or compare with siblings like stobox_fact_check or stobox_start_tokenization.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 return format: 'verdict (contradicted / supported / no_contradiction_detected / unverifiable), any canon violations with corrections, and the canonical evidence.' It also indicates the source is canonical. The description is transparent about behavior, though it could mention read-only nature or any limitations.

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?

Two sentences: first covers purpose and output, second covers usage guidance. No redundancy, front-loaded, every word earns its place.

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 the single parameter, no output schema, and no annotations, the description is largely complete. It explains the verdict types, evidence, and when to use. It could mention that it is a read operation, but overall sufficient for an agent to understand the tool's role.

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 single parameter 'claim' has a schema description ('The factual claim to verify') and the tool description adds context with examples of valid claims ('about STBX, ERC-7943, Compass chains, Gene Deyev's title'). Schema coverage is 100%, so baseline is 3; the extra guidance elevates it.

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's purpose: 'Verify a claim about Stobox or tokenization against the CANONICAL Stobox knowledge graph.' It uses a specific verb ('verify') and resource ('claim against knowledge graph'), and distinguishes from siblings by emphasizing fact-checking and canonical evidence, differentiating from search or context tools.

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?

Explicit guidance: 'Call this on every factual claim about STBX, ERC-7943, Compass chains, Gene Deyev's title, etc.' This tells when to use the tool. While it doesn't explicitly state when not to use, the context implies other tools for non-factual queries. The guidance is clear and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description discloses the type of results (canonical, source-linked answers) and the GraphRAG technique. It does not cover limitations, error handling, or response format.

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, dense sentence that efficiently conveys purpose and usage. It is well front-loaded, though the parenthetical could be simplified.

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 lacks an output schema, and the description does not explain what the search results contain or how they are structured. Given the simplicity and sibling tools, the description is adequate but not fully self-contained.

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%, so the schema already explains both parameters. The description merely reiterates the purpose, adding no extra semantic 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 clearly identifies the tool as searching a knowledge base for tokenization topics, specifying the domain and content types. It is distinct from siblings like fact_check or answer_context, though not explicitly differentiating.

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 states to use for any question about specified topics (tokenization, RWA, etc.), providing clear usage context. However, it does not mention when not to use or suggest alternative siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It states that the tool shows connections at 1-2 hops, implying a read-only query, but does not disclose any side effects, authorization requirements, or return format. The behavior is predictable but not fully transparent.

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 with no wasted words. It efficiently conveys the core purpose and scope.

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 the tool's simplicity (2 params, no output schema), the description covers the main purpose and hop range, but lacks parameter definitions and output expectations. It is minimally complete but could better compensate for missing annotations and schema coverage.

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 mentions '1-2 relationship hops' which relates to the 'hops' parameter, but does not explicitly define it or the 'name' parameter. The agent may infer 'name' as the term, but no syntax or format guidance is given.

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 verb 'Show' and specifies the resource 'related topics in the Stobox knowledge graph', including the types of connections and the hop range. It distinguishes the tool from siblings like stobox_term_lookup (for exact term info) and stobox_knowledge_search (for search), although not explicitly.

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?

No explicit when-to-use or when-not-to-use guidance is provided. The description implies usage for exploring related concepts, but does not mention alternatives or exclusions. The context is clear from the purpose but lacks explicit usage boundaries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses that email is shared with Stobox, requires consent, and returns confirmation+next steps. Could mention rate limits or auth but sufficient for a contact form tool.

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?

A single paragraph that is well-structured: action, prerequisites, exclusions. Every sentence adds value, front-loaded with the key function.

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 the lack of output schema and annotations, the description is remarkably complete: covers purpose, prerequisites, consent, email requirement, and return format. Leaves no major 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 description coverage is high (83%), and description adds context like 'Never invent an email' and 'No sensitive IDs or financials' for notes. However, 'company' and 'jurisdiction' lack descriptions, but overall parameter guidance is adequate.

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 action ('Send the user's request to start tokenizing'), the resource ('real-world asset'), and the context (sends to Stobox, shares email with consent). It distinguishes from sibling tools by specifying it's for initiating tokenization, not general questions.

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?

Provides explicit when-to-call conditions: only when user explicitly wants to begin, has stated asset, and consents. Also says 'Do NOT call for general questions — use the search/lookup tools', naming alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It explicitly states the output includes authoritative description, aliases, relationships, and source documents, which is informative for a read-only lookup. However, it does not mention idempotency, error behavior, or access requirements.

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-loading the purpose and examples, then specifying return values. Every word adds value with no redundancy.

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 lookup tool with one parameter and no output schema, the description covers what the tool does, what input to provide, and what output to expect. It could mention behavior when the term is not found, but the provided information is largely sufficient.

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 input schema has 0% description coverage (name parameter has no description). The description compensates by giving concrete examples of valid values (STBX, Compass, etc.) and clarifying the parameter refers to a term from the knowledge graph, adding essential context 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 clearly states it explains one term from the Stobox knowledge graph, providing examples (STBX, Compass, ERC-7943, MiCA) and listing return values (description, aliases, relationships, source documents). This is a specific verb+resource and distinguishes from sibling tools like stobox_knowledge_search or stobox_fact_check.

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 for detailed term explanations but does not explicitly state when to avoid using it or compare with alternative tools. The examples and nature of the tool give some context, but lack definitive guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.