Epo Ops

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. Description adds critical context: default model is free, Anthropic requires BYO key and direct billing, and return structure includes per-model fields and combined view. No contradiction with annotations.

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?

Three sentences: first captures core action, second details optional behavior and billing, third lists use cases. Front-loaded and no fluff. Every sentence adds essential information.

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?

Despite no output schema, description specifies return fields (score, confidence, signals, raw_response per model plus combined view). All four parameters are explained with examples and defaults. The tool's behavior (free default, optional Anthropic) is fully disclosed. Sufficient for an agent to use correctly.

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 100%, but description enriches semantics with examples (e.g., 'Pipeworx', 'OpenInvoice'), explains the default model and the role of `_apiKey` in enabling Anthropic, and clarifies the 'context' parameter helps disambiguate. This adds value beyond the schema alone.

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?

Description clearly states 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' It specifies default model, optional Anthropic probing, and return details. It distinguishes from sibling tools like 'entity_profile' or 'ask_pipeworx' by focusing on multi-model visibility scoring.

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?

Description gives explicit instructions on when to use `_apiKey` (to probe Anthropic) and notes the default model is free. It lists use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not explicitly exclude scenarios or name alternatives, but context implies when this tool is appropriate.

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 explains the routing behavior (picks tool, fills arguments, returns result) but lacks details on error handling, limitations, or what happens if the question cannot be answered.

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 efficient and front-loaded with the core purpose, including examples and a list of sources. Minor verbosity but overall concise and well-structured.

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 adequately covers the tool's purpose, usage, and behavior with examples. It does not explain return format but that is acceptable for this type of 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 only parameter 'question' has a description in the schema that mirrors the description. Since schema coverage is 100%, the description adds no extra semantic value beyond what the schema provides, earning a baseline 3.

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 answers natural-language questions by automatically routing to the right data source, listing many specific sources. It distinguishes itself from sibling tools like search_patents and compare_entities by being a general query tool.

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 explicitly says 'Use when a user asks "What is X?"...' and instructs the agent to avoid figuring out which tool to call. It does not explicitly state when not to use it, but the examples and context imply that for specific tasks, other siblings are more appropriate.

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?

Annotations already indicate read-only and non-destructive behavior. The description adds valuable context: it resolves markets, classifies bets, fans out to relevant packs, and returns a comparison. Minor lack of error handling details, but solid overall.

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 paragraph of five sentences, front-loading the core purpose. It covers inputs, process, output, use cases, and value proposition concisely 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?

Despite no output schema, the description explains the return format (evidence packet + market-vs-model comparison) clearly. It covers input options, depth behavior, and overall workflow, sufficient for a complex 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?

Schema already describes parameters, but the description adds meaning for 'depth' (quick=2-3 sources, thorough=full fan-out) and explains how 'market' input types work (slug, URL, question). Adds useful context beyond 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 the tool researches Polymarket bets by pulling Pipeworx data, specifying the action, resource, and method. It distinguishes itself from siblings by being the 'core demo product' for bet context, making its purpose unmistakable.

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 explicitly lists use cases like 'should I bet on X?' and 'what does the data say about this market?', and notes that agents using this tool convert better than those discovering packs themselves, providing clear guidance on when to use it.

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?

Given no annotations, description adds significant behavioral detail: data sources (SEC EDGAR/XBRL for company; FAERS, FDA, trials for drug), specific metrics pulled, and return format (paired data + citation URIs). Missing error handling or response structure details.

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?

Single paragraph of ~150 words, front-loaded with main purpose. Every sentence provides unique value. No redundant or filler content.

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?

Covers purpose, trigger phrases, data sources, and return format adequately for an agent to invoke correctly. Lacks explicit output structure details, but complexity is moderate and description is sufficient without output schema.

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 already covers enum and array with descriptions. Description enriches by explaining what each type retrieves (e.g., 'revenue, net income, cash, long-term debt' for company) and clarifies value formats (tickers/CIKs, drug names).

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?

Description clearly states 'Compare 2–5 companies (or drugs) side by side in one call' with specific verb and resource. It differentiates from siblings by noting it replaces 8–15 sequential calls.

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 trigger phrases ('compare X and Y', 'X vs Y', 'how do X, Y, Z stack up', 'which is bigger') and use cases (tables/rankings). Implicitly advises against using for single entities.

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?

Although no annotations exist, the description clearly indicates it is a read-only search tool that returns tool names and descriptions. It does not mention any side effects or destructive actions, which is appropriate for its function.

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 front-loaded with the core purpose and includes examples, but the list of domains (SEC filings, financials, etc.) is somewhat lengthy. However, every sentence adds useful context, and there is 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?

Given no output schema, the description adequately explains returns ('top-N most relevant tools with names + descriptions'). It could mention ordering or pagination, but for a simple discovery tool, the current detail is 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 100% description coverage, so the baseline is 3. The description adds value by clarifying that 'query' is a natural language description and 'limit' has default (20) and max (50) constraints, which the schema already specifies but the description reinforces.

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 uses a specific verb ('Find tools') and resource ('by describing the data or task'), and distinguishes from sibling tools by positioning itself as a discovery/browsing tool, not for direct task execution.

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 states 'Call this FIRST when you have many tools available and want to see the option set', and provides concrete examples of when to use it (SEC filings, FDA drugs, etc.), which contrasts with sibling tools like search_patents or resolve_entity.

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, but description details what data is returned (SEC filings, fundamentals, patents, news, LEI) and mentions citation URIs. Implies read-only 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?

Two sentences front-loaded with purpose and usage. No unnecessary words. Every sentence provides value.

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?

Despite lacking output schema, the description comprehensively covers return data, parameter usage, and context. No gaps for intended use.

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%, but description adds contextual meaning: explains type enum constraint, value formats (ticker or zero-padded CIK), and clarifies that names are not supported with example values.

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 'Get everything about a company in one call' and distinguishes from siblings by mentioning alternative approaches like calling 10+ pack tools. It also provides specific user query examples.

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 says when to use (when user asks for company profile) and implies limitations (names not supported, use resolve_entity first). Could be more explicit about when not to use.

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 must cover behavior. It states deletion but does not mention irreversibility or error handling (e.g., key not found). Adequate but not thorough.

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, front-loaded with purpose, then usage context. Every word is necessary; no filler.

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?

Tool is simple (one param, no output schema). Description covers purpose, usage context, and sibling relationship. Missing explicit error behavior but sufficient for typical use.

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% with parameter description 'Memory key to delete'. The description repeats 'by key' without adding new semantics beyond 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?

Description clearly states 'Delete a previously stored memory by key' with specific verb and resource. It distinguishes from siblings by mentioning 'pair with remember and recall'.

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 tells when to use: 'Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' Implies alternatives via sibling mention.

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive nature. The description adds value by detailing the process: fetches page, extracts title/description/links, emits markdown format. It does not cover edge cases like errors or rate limits, but the annotations provide a strong baseline.

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, front-loaded with main action and output, followed by use cases. No wasted words, perfectly sized for quick comprehension.

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 tool with 2 parameters and no output schema, the description provides a complete overview: input, process, output format, and practical applications. Minor absence of error handling mention does not significantly detract.

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 defines both parameters. The description does not add significant new meaning beyond what the schema provides (e.g., no explanation of data types or constraints beyond defaults). Baseline of 3 is appropriate.

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?

Description clearly states the tool generates an llms.txt file for AI crawlers, with specific verb ('Generate') and resource ('llms.txt file for any URL'). It distinguishes itself from siblings by focusing on AI crawler indexing, and no sibling tool appears to offer similar functionality.

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?

Description lists three concrete use cases (client indexing, own project drafting, competitor auditing), providing clear context for when to use. However, it does not explicitly mention when not to use or directly specify alternatives within the same server.

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. Description does not disclose behavioral details such as return format, limitations, or authentication needs.

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?

Extremely concise, but lacks completeness. Could be improved without losing conciseness.

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?

No output schema, yet description does not mention what is returned (e.g., raw text). Incomplete for a simple 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?

Input schema completely describes the single parameter (number, epodoc format). Description adds no further meaning 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?

Clear verb+resource: 'Abstract text for a patent.' Indicates the tool retrieves the abstract. However, does not differentiate from siblings like get_claims or get_biblio beyond the name.

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 guidance on when to use this tool vs alternatives. No context on prerequisites or when not to use it.

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 exist, so the description must disclose behavioral traits. It only lists what data is returned, but does not mention read-only nature, authorization needs, or any side effects. Limited transparency.

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?

Single sentence that front-loads the purpose and lists key fields. No wasted words.

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 retrieval tool with one parameter and no output schema, the description explains what data is returned (title, inventors, etc.). It could mention that it returns multiple fields or that it is a read operation, but it is largely complete.

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?

Input schema has 100% coverage, so baseline is 3. Description does not add meaning beyond the schema; the parameter 'number' is sufficiently described in the schema. No extra context 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?

Description clearly states 'bibliographic data for a patent' and lists specific fields (title, inventors, applicants, dates, classifications). This distinguishes it from siblings like get_abstract, get_claims, and get_family.

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 guidance on when to use this tool versus alternatives. Usage is implied by the description, but no when-not-to or alternative suggestions are provided.

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 but only states 'Claims text for a patent.' It does not disclose whether the tool requires specific authentication, whether it handles different patent number formats beyond epodoc, or what the response looks like (e.g., plain text, JSON).

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 sentence, which is efficient. However, it is so terse that it sacrifices critical context; a slightly longer description clarifying the output format would be more helpful without being verbose.

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 absence of an output schema, the description should indicate what the tool returns (e.g., a string, a list of claims). It only says 'Claims text', leaving the agent uncertain about the structure or whether it includes dependent claims, formatting, etc.

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 100% since the single parameter 'number' is documented as 'Patent number, epodoc format'. Baseline is 3, and the description adds no additional meaning beyond the schema, so it remains at 3.

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 'Claims text for a patent.' clearly indicates the tool retrieves the claims of a patent. However, it does not specify the format (e.g., raw text, structured claims) or whether it returns all claims or a summary, which would further clarify its purpose versus siblings like get_abstract.

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 guidance is provided on when to use this tool versus alternatives. For example, it does not suggest using this when claims text is needed, as opposed to get_abstract for abstract or get_biblio for bibliographic data.

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?

Annotations empty, so description carries the burden. It states a read operation but lacks details on what fields are returned, pagination, or limitations (e.g., number of results). Adequates only the basic concept.

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?

Single sentence is concise and front-loads the core purpose. No redundancy, but slightly too terse for full value.

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?

With one parameter and no output schema, description adequately conveys the tool's function but lacks information about output format or behavior. Could be more complete with examples or return structure.

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?

Parameter 'number' is fully described in the schema. Description adds no extra meaning beyond schema. Baseline 3 applies due to 100% schema coverage.

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?

Description clearly states it retrieves INPADOC family, which is a well-known concept of related patent applications. The verb 'get' and resource 'family' are specific, and it distinguishes from siblings like get_abstract by focusing on family relationships.

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 guidance on when to use this tool vs siblings like search_patents or get_biblio. No prerequisites or exclusions mentioned.

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?

Despite no annotations, the description thoroughly discloses behavioral traits: rate-limited to 5 per identifier per day, free usage, and that feedback is reviewed daily and influences the roadmap. It does not contradict any annotations (none provided).

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 and well-structured. It front-loads the purpose, then provides detailed usage guidelines and behavioral notes. Every sentence adds value without unnecessary filler, making it efficient for an agent 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?

Given the tool's complexity (3 parameters with nested objects and enums) and no output schema, the description covers all necessary information: parameter meanings, usage contexts, rate limits, and behavioral effects. An agent can fully understand how to use the tool without needing additional context.

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 100% description coverage, so baseline is 3. The description adds further meaning by explaining the enum values, the optional context fields, and the message format (be specific, 1-2 sentences, 2000 chars max), providing clarity 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 the tool's purpose: to tell the Pipeworx team about bugs, feature requests, data gaps, praise, or other issues. It uses a specific verb ('tell') and distinguishes itself from sibling tools like search and retrieval tools by focusing on feedback.

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 explicitly specifies when to use the tool: for bug reports, feature/data gap requests, or praise. It provides clear usage instructions (describe issues in terms of Pipeworx tools/packs, avoid pasting end-user prompts) and mentions rate limits and quota-free usage, making it easy for an agent to decide when to invoke it.

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?

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context: no PII, self-aggregating from CF analytics, caching behavior (5min-1h). No contradictions.

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 concise sentences plus a bullet list. Front-loaded with the core purpose. Every sentence adds value without 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?

Despite no output schema, the description adequately explains the return (top tools, packs, call volume) and provides details on caching, data source, and privacy. No missing information for correct use.

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% (baseline 3). The description adds meaningful guidance for the 'window' parameter, explaining that shorter windows show what's hot and longer windows show steady-state demand.

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 action ('returns') and the resource ('top tools, top packs, and total call volume over a recent window'). It is specific and distinct from sibling tools like 'discover_tools' or 'ask_pipeworx'.

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 lists three clear use cases (discovering hot sources, confirming canonical choice, checking alignment) and explains window options. It does not explicitly mention when not to use, but the context is clear.

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?

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds valuable behavioral details: explains the mechanism (monotonicity checking), the two modes and their inputs, what results to expect (ranked opportunities with reasoning). No contradiction.

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?

Description is well-structured: starts with purpose, then enumerates two modes with clear explanations. No unnecessary sentences; each sentence adds value. Concise yet informative.

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?

Despite no output schema, the description explains the return value (ranked opportunities with trade direction and reasoning). Covers both modes, their inputs, and the logic behind the tool. No missing context for its intended use.

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% with descriptions for 'event' and 'topic'. Description adds meaning by explaining each parameter's role in context (e.g., 'event' is a slug or URL for single-event mode, 'topic' is a seed question for cross-event mode) and provides examples.

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?

Description clearly states the tool's purpose: finding arbitrage opportunities by checking monotonicity violations on Polymarket. It distinguishes two modes ('event' and 'topic') and explains their use cases, providing a very clear purpose.

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 on when each mode is appropriate: event mode for a single event's child markets, topic mode for cross-event searches. Explains why cross-event mode is necessary (covers cases where events are listed separately). No explicit when-not, but the context is clear.

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?

Describes the process: scans top markets, groups by asset, fetches price history once, computes model probability, ranks by edge. Annotations readOnlyHint=true are consistent, and description adds value by detailing steps and data sources (lognormal model, FRED, coinpaprika).

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?

Three sentences: first states core purpose, second provides technical details, third gives use case. No wasted words, front-loaded with key action.

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?

Covers return values (top N with edge magnitude and trade direction), process steps, and model scope (crypto-price bets). No output schema, but description sufficiently explains output. Annotations provide safety context.

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 has 100% coverage with parameter descriptions. The description adds context beyond schema, e.g., 'V1 covers crypto-price bets', which explains the model domain. Baseline 3 elevated due to added context.

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?

Clearly states it scans highest-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price, distinguishing it from sibling polymarket_arbitrage. Specific verb 'scan' and resource 'Polymarket markets'.

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 built for the 'what should I bet on today' question, helping discover opportunities without manual paging. Implicit usage context, but no direct mention of when not to use or 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?

Annotations declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds behavioral context: it returns leg-by-leg prices in raw probability and spread in percentage points, and explains the meaning of the delta. No contradictions.

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 well-structured with clear enumeration of modes and outputs. It is slightly verbose but front-loads the purpose effectively. Every sentence adds value, though it could be trimmed slightly.

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 no output schema, the description adequately explains the return values: leg-by-leg prices and spread. It also provides context about why the spread matters. However, it does not cover error cases or explain the behavior when no topic is provided.

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% with descriptions for each parameter. The description adds significant value by explaining the topic parameter as pre-mapped shortcuts and the explicit parameters for custom pairings, including how they interact. It also clarifies the output structure related to parameters.

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 calculates cross-venue spreads between Kalshi and Polymarket for the same resolving question. It specifics two modes (topic and explicit pairings), distinguishing its function from sibling tools like polymarket_arbitrage.

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 explains what the tool does but does not provide explicit guidance on when to use it versus alternatives like polymarket_arbitrage. It mentions that the delta is an arb signal, hinting at usage, but lacks when-not-to-use or comparative guidance.

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 must disclose behaviors. It states scoping (by identifier) and implies a read-only, idempotent operation. It lacks details on missing key behavior or rate limits, but the tool is simple.

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?

Three efficient sentences with no redundancy. Action is front-loaded, and every word adds value.

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 single-parameter retrieval tool, the description covers purpose, usage, scoping, and alternative actions (remember/forget). No output schema needed.

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% with a clear description. The tool description adds value by providing usage examples and scoping 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 retrieves a saved value by key or lists all keys, with concrete examples (ticker, address, notes). It distinguishes itself from siblings 'remember' (save) and 'forget' (delete).

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?

Describes when to use (look up stored context without re-deriving) and how to list keys by omitting the argument. It pairs with remember/forget, but does not explicitly state when not to use.

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 parallel fan-out to three sources, return structure (structured changes, count, URIs), and since parameter formats. It lacks details on limitations, auth, or error conditions, but is fairly transparent for a simple 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?

The description is a single, well-structured paragraph. It front-loads the purpose and includes concise examples, with no unnecessary words.

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 tool with 3 parameters and no output schema, the description adequately covers functionality, return values, and usage. It does not mention error handling or limitations, but overall it is complete for its complexity level.

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%, but the description adds value by explaining type is limited to 'company', since accepts ISO or relative formats with examples, and value is ticker or CIK. This complements the schema well without redundancy.

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: 'What's new with a company in the last N days/months?' It specifies the verb 'fans out' and the resources (SEC EDGAR, GDELT, USPTO), making it distinct from siblings like entity_profile or search_patents.

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 explicit usage examples ('when a user asks...') and tips for the since parameter. However, it does not explicitly state when not to use this tool or mention alternatives among 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?

No annotations provided, so description carries full burden. It discloses scoping by identifier, persistence differences (authenticated vs anonymous, 24-hour retention), and pairing with recall/forget. This is sufficient for a simple save 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?

Four sentences, each serving a clear purpose: main action, when to use, scoping/retention, and pairing. No fluff, 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 the tool's simplicity (2 required params, no output schema), the description covers purpose, usage context, scoping, retention, and relationship to siblings. No 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 100%, but description adds value with usage examples for key (e.g., 'subject_property', 'target_ticker') and explains value as 'any text — findings, addresses, preferences, notes'. This goes beyond 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?

Description clearly states verb 'Save', resource 'data', and context 'across this conversation or across sessions'. It distinguishes from sibling tools 'recall' and 'forget' by explicitly naming them as partners.

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 context: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' Also references paired tools, though no explicit when-not-to-use.

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 full behavioral disclosure burden. It mentions that the tool returns IDs plus citation URIs, but does not cover error behaviors, authentication requirements, rate limits, or what happens if the entity is not found. This is adequate but not comprehensive.

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 front-loaded with the core purpose and includes examples and usage guidance. It is efficient but could be slightly tightened; every sentence adds value.

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 absence of an output schema, the description adequately explains return values (IDs plus citation URIs). It covers both company and drug cases, but could mention handling of ambiguous or missing inputs for higher completeness.

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 baseline is 3. The description adds significant value by explaining the 'type' and 'value' parameters with concrete examples (e.g., 'Apple' → AAPL, 'Ozempic' → RxCUI), clarifying acceptable formats beyond the schema's enum and string descriptions.

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: looking up canonical identifiers for companies or drugs. It lists specific identifier types (CIK, ticker, RxCUI, LEI) and provides concrete examples, distinguishing it from siblings by noting it replaces 2–3 lookup calls.

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 explicitly tells when to use the tool ('when a user mentions a name and you need the CIK...') and advises using it before other tools that need official identifiers. It does not explicitly state when not to use it, but 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the tool is safe and idempotent. The description adds process-level detail (probes each entity, ranks by score, returns score/confidence/signal density) and clarifies that the first entity is the subject. No contradictions with annotations; the description complements them well.

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?

Three sentences, front-loaded with the primary function, followed by method and example. Every sentence provides essential information: what it does, how it works, and a practical use case. No unnecessary words or repetition. Highly efficient.

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 has 4 parameters, no nested objects, no output schema, and sibling tools are many, the description adequately covers purpose, process, and output format. It could potentially mention the sibling 'compare_entities' as an alternative, but the description is self-contained for the task at hand. The lack of output schema is compensated by describing the ranked list with score, confidence, and signal density.

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% (all 4 parameters described). The description adds modest value by noting that the first entity in the array is treated as the 'subject' for narrative, which is not in the schema. However, this is a single additional insight, and the description does not deepen understanding of other parameters. Baseline of 3 is appropriate given high schema coverage.

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?

Description clearly states the tool compares AI visibility across multiple entities side-by-side, uses ai_visibility_check per entity, ranks results, and surfaces the most/least recognized. It includes a concrete example question ('does Claude know about us as well as our competitors?'), which distinguishes it from the sibling 'ai_visibility_check' that likely handles single entities.

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 explicitly marks the tool as useful for 'competitive AI-marketing audits' and gives a specific use case. It implies comparison when you need to benchmark against competitors, and the existence of sibling 'ai_visibility_check' suggests the single-entity alternative. However, it does not explicitly state when not to use this tool or name the alternative, so it scores a 4.

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 full burden. It only describes the basic operation as a search returning a list, but fails to disclose any behavioral traits like read-only nature, authentication needs, or rate limits.

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 plus examples, front-loaded with purpose, and every sentence is informative without any wasted words.

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 has only 2 parameters and no output schema, the description adequately covers what the tool does and how to use it, though it could mention prerequisites or the response structure.

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 baseline is 3. The description adds value by providing example CQL queries and clarifying the range parameter format (1-indexed, max 100 per page), which aids understanding 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 the verb 'Search', the resource 'published patents', and the method 'CQL'. It effectively distinguishes from sibling tools like get_abstract or get_claims which retrieve specific documents.

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 clear context for searching patents with CQL and returns a paginated list, but does not explicitly state when not to use this tool or directly mention alternatives among 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?

No annotations are provided, so the description carries the full burden. It discloses the return types (verdict values: confirmed, approximately_correct, refuted, inconclusive, unsupported), extracted structured form, actual value with citation, and percent delta. It also notes that the tool replaces 4-6 sequential calls, indicating efficiency benefits. No destructive or mutating behavior is implied.

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 appropriately sized at about 4-5 sentences, front-loading the main purpose and usage guidance. Each sentence adds value: purpose, usage triggers, supported domain, and output summary. There is no fluff, and the structure is logical.

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 an output schema, the description adequately explains the return value (verdict, extracted data, citation, delta). It covers the domain (company-financial claims) and mentions the capability of replacing multiple sequential calls. However, it does not address potential error conditions or rate limits, which would be helpful for completeness.

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 100% schema description coverage, but the description adds value by providing illustrative examples like 'Apple's FY2024 revenue was $400 billion' and clarifying that it should be a natural-language factual claim. This helps the agent understand the expected format 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 the tool's purpose: fact-checking, verifying, validating, or confirming/refuting a natural-language factual claim against authoritative sources. It provides specific examples of trigger phrases like 'Is it true that…?' and distinguishes the tool from siblings such as 'ask_pipeworx' or 'compare_entities' by emphasizing its unique role in validation.

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 explicitly states when to use the tool: when an agent needs to check the truth of a user's statement and provides trigger phrases. It also clarifies the supported domain (company-financial claims via SEC EDGAR + XBRL) and notes the version scope, implying limitations for non-financial claims. However, it does not explicitly name alternative tools or when not to use it beyond the domain scope.

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