Dane Gov Pl

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

Annotations already indicate readOnly=true, idempotent=true, destructive=false, so the tool is safe. The description adds valuable behavioral context: it returns per-model {score, confidence, signals, raw_response} + combined view, clarifies that Workers AI is free and Anthropic requires a BYO key (cost implication), and explains the context parameter for disambiguation. No contradictions 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?

The description is three sentences and front-loaded with the action (probe LLMs, score visibility). Every sentence provides essential information: what it does, default model, key parameter usage, return structure, and use cases. 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?

The description covers the purpose, parameters, return structure (per-model and combined view), and use cases. With no output schema, it does a good job detailing outputs. It could be slightly more explicit about the confidence range or signal details, but overall sufficient for the 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%, with each parameter described. The description adds meaning beyond schema by specifying default model (Workers AI Llama-3.3-70b), clarifying that '_apiKey' is passed directly to Anthropic, and explaining how 'context' helps disambiguate. This enriches the semantic understanding for the agent.

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 'probe' and defines the resource as 'LLMs for knowledge about a business/brand/product/topic', with a clear outcome (score 0-100). It distinguishes from sibling tools like ask_pipeworx or compare_entities by focusing on multi-model visibility scoring for AI marketing audits.

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 it's 'useful for AI-marketing audits, pre-launch brand checks, competitive monitoring', which provides clear usage context. It also explains when to pass '_apiKey' for Anthropic model probing. However, it does not explicitly mention when NOT to use this tool or point to alternatives for related tasks.

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 readonly, open-world, idempotent, non-destructive. Description adds value by mentioning return format (structured answer with citation URIs) and routing behavior, but could be more specific about performance or 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?

Well-structured with key preference statement upfront, but somewhat long. All sentences add value; minor trimming possible.

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 (6 params, many siblings, no output schema), the description is remarkably complete: purpose, usage, alternatives, examples, and return format are all covered.

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 all parameters described as aliases. Description adds context through examples and clarifies natural language input, justifying a slight above baseline.

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 tool's purpose: routing questions to a vast set of authoritative data sources with citations, with numerous examples differentiating from siblings.

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 guidance to prefer over web search, lists specific use cases, and explains when to use alternative tools (grounded, deep_research).

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 destructiveHint=false. The description adds valuable behavioral context: the source-preference ranking behavior, that it is a full working router, and that it has the same tools and response shape as the stable version. 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 three sentences long, front-loading the purpose and key behavioral changes. It is concise and contains no fluff, though the structure could be slightly improved by separating the behavioral detail from usage guidance.

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, the description adequately explains the tool's behavior (routing with improvements), arguments (identical to ask_pipeworx), and usage context (experimental version). It covers the essential aspects for an agent to decide when to use it.

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 6 parameters all documented as aliases for 'question'. The description does not add detail beyond saying 'same arguments' as ask_pipeworx, which is sufficient given full schema coverage. Baseline 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?

The description clearly states it is a beta version of ask_pipeworx with identical functionality but candidate routing improvements. It explicitly names the stable sibling 'ask_pipeworx' as the alternative, making its distinct purpose clear.

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 gives explicit guidance: use it exactly like ask_pipeworx when you want the newest routing, and notes that results are compared against the stable router to decide merges. It also clarifies that it is a full working router, not a fallback.

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, idempotentHint, and destructiveHint. Description adds significant behavioral detail: extraction only from tool result, explicit refusal reasons, and cost of one extra LLM call. 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?

Description is moderately long but information-dense with front-loaded purpose. Every sentence adds value (use case, mechanism, refusal reasons, cost tradeoff). Could be slightly tighter but still 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 complexity (refusal mechanism, multiple aliases, high-stakes), description is comprehensive. It explains success and failure responses, refusal reasons, and use cases. No output schema, so return structure details are slightly lacking, but otherwise complete for the 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 coverage is 100% with multiple aliases well-documented. Description adds no additional meaning beyond confirming 'question in natural language.' Baseline of 3 appropriate since schema already handles parameter 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 is a 'Hallucination-resistant answer mode for high-stakes reads' and distinguishes it from sibling ask_pipeworx by explaining it extracts answers only from tool results and provides explicit refusals. The verb 'extract' and resource 'answer from tool results' are specific and unique.

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 when to use: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete examples (financial verdicts, legal claims). Also provides alternative: 'prefer ask_pipeworx for casual lookups'. Complete 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?

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description goes far beyond by detailing fan-out patterns, response shapes, resolver contract (confidence levels, alternatives), parent event extraction, news fallbacks, safety short-circuits, and resolution-rule risks. 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?

The description is very long and information-dense, covering many edge cases and examples. While every sentence earns its place given the tool's complexity, it could be slightly more concise without losing essential details. Structure is logical but wordy.

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 exists, so the description fully explains return values: result.market fields, result.analysis with model probability and edge warning, result.evidence keyed by source, resolver contract with confidence and alternatives, parent event extraction, news fallback fields, and safety mechanisms. Covers edge cases thoroughly.

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 all three parameters documented. The description adds significant value beyond schema: examples for market (slug, URL, question), explanation of depth with 'quick' vs 'thorough' fan-out, and include_raw with size implications (~20KB vs 50-500KB). This enriches understanding of parameter behavior.

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 a Polymarket bet by pulling Pipeworx data. It specifies the input types (slug, URL, or question text) and lists classifiers, which distinguishes it from sibling tools like validate_claim or deep_research. The purpose is specific and actionable.

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 use cases: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. It provides context on when to use the tool but does not explicitly describe when not to use it or name alternatives. This is clear guidance but lacks exclusions.

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 readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds valuable behavioral details: it uses SEC EDGAR/XBRL for companies (handling off-calendar fiscal years) and FAERS for drugs, sorts results by primary metric, and returns citation URIs. 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?

The description is somewhat long but every sentence serves a purpose: example queries, usage guidance, data details, and output format. It is front-loaded with the most critical usage patterns. Could be slightly tightened but remains effective.

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 only 2 parameters and no output schema, the description covers the behavior comprehensively: what each type retrieves, handling of fiscal years, sorting, and output format (paired data + citation URIs). It also quantifies the efficiency gain (replaces 8–15 lookups). Highly 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?

Schema coverage is 100% with descriptions for both parameters. The description adds context beyond the schema, such as specifying tickers/CIKs for companies and drug names, and explaining the metric source. It enhances parameter understanding without being redundant.

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 performs side-by-side comparisons of 2–5 entities (companies or drugs) in one call. It lists example queries and specifies the data sources for each type, making the purpose unmistakable and distinguishing it from single-entity lookup 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?

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing clear guidance on when to use this tool and what to avoid. This is a strong directive for the AI agent.

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, idempotentHint, etc. The description adds value by specifying the return format (JSON:API dataset object) and listing example fields (title, notes, license, keywords, regions, institution relationship, resource count). 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?

Two sentences, no redundancy. The first sentence states the core purpose and prerequisite, the second describes the return value. Every word is useful.

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 two-parameter tool with full schema coverage and no output schema, the description provides sufficient detail about input (numeric id) and output (fields of dataset object). 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 coverage is 100%, so the description does not need to explain parameters in depth. It adds context by stating that the id is numeric and comes from search_datasets, which aids understanding beyond schema 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 gets full metadata for a single dataset by numeric id, specifies the source (from search_datasets), and lists the returned fields (title, notes, license, etc.). This distinguishes it from sibling tools like search_datasets which likely return lists.

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 indicates the id should come from search_datasets, implying a prior search step. It does not explicitly exclude other sources or state when not to use, but the context is clear enough for a simple lookup tool.

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 decomposition of questions, parallel routing to 5008 tools, returning findings packet with verbatim evidence, confidence, source, citation, gaps array, contradictions, and hop field. Mentions second-hop iteration for different depths and that large records are semantically excerpted. Adds significant context beyond 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?

Well-structured with critical info front-loaded (account requirement, core functionality, usage guidance). However, it is verbose; several technical details could be condensed without losing 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?

No output schema exists, but the description thoroughly explains the return structure (findings packet with specific fields) and covers edge cases like breaking news returning empty gaps and handling of large records.

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 meaningful detail: explains the depth enum with number of facets and behaviors (gap recovery, contradictions scan). Also clarifies that broad/multi-part questions are fine.

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: grounded multi-source research across 1318 structured data sources. It distinguishes itself from ask_pipeworx by specifying it is not open-web search and is best for broad/multi-part 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?

Explicitly tells when to use (broad/multi-part questions over structured data) and when not to (single lookups, breaking news). Provides alternatives like ask_pipeworx and notes account/paid plan requirements.

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 and idempotentHint. Description adds that results are ready to call directly with schemas and examples, which is useful behavioral context.

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 informative and front-loaded with purpose. Slightly long but every sentence adds value; could be tightened 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, description explains return format (tool names, descriptions, schemas). Covers usage, parameters, and domains comprehensively.

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. Description adds value by noting alias parameters (task, q, description, search) and providing natural language examples, enhancing usability.

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 discovers tools by describing data or task. Lists many domains and distinguishes from sibling search tools by focusing on tool discovery rather than data search.

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 'Use when you need to browse...' and 'Call this FIRST...' providing clear context. Could add when not to use, but the guidance is strong.

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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds critical behavioral context: it fans out across multiple data sources (SEC, XBRL, USPTO, news, GLEIF), details the return structure, notes the USPTO API sunset and soft-fail, and clarifies it does a parallel call. 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?

The description is front-loaded with examples and uses imperative statements. However, it is somewhat dense with implementation details (e.g., 'fans out across...') which could be condensed. Still, every sentence adds value and the overall length is appropriate.

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 comprehensively lists all returned fields (cik, company_name, recent_filings with URIs, fundamentals sorted, patents with caveat, news via GDELT→GNews fallback, LEI). It covers failure modes (soft-fail) and limitations (only company type). This is complete for a multi-source profile 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 coverage is 100% with descriptions for both parameters. The description enhances this by explaining the specific format for 'value' (ticker or zero-padded CIK), reiterating that names are unsupported, and directing to resolve_entity for names. This goes beyond the schema 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 specifies the tool as a 'full cross-source profile of a US public company', supported by concrete examples like 'Tell me about X' and 'company profile for Microsoft'. It distinguishes itself from sibling tools by stating 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups'.

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 when to use: 'when the user asks for a holistic view'. It provides explicit exclusions: 'names not supported (use resolve_entity first if you only have a name)' and describes fallback behavior for patents. This gives clear guidance on 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 already indicate destructiveHint=true and idempotentHint=true. Description adds context about clearing sensitive data, but doesn't describe side effects like inability to recover data. 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?

Two concise sentences front-loading the action and usage. 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?

Given a simple single-parameter tool with annotations, the description covers purpose, usage, and context completely. 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 has 100% coverage with description 'Memory key to delete'. Description does not add additional meaning beyond what schema provides, so 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?

Clearly states 'Delete a previously stored memory by key', which is a specific verb and resource. Distinguishes from sibling tools like 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 says when to use: when context is stale, task is done, or clearing sensitive data. Also mentions pairing with remember and recall for context.

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 a safe, read-only, idempotent operation. The description adds that the tool fetches an external page and outputs a standard markdown format. It lacks disclosure of potential rate limits or timeout behaviors but is consistent 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?

The description is well-structured, front-loading the purpose and process, followed by use cases. It is slightly verbose but every sentence adds value, earning a 4.

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, the description fully explains inputs (URL, optional max_links), process (fetching, extracting), and output (text blob ready for site-root). Annotations cover safety. No gaps remain.

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?

Both parameters are fully described in the input schema (100% coverage). The description does not add extra semantic meaning beyond restating the schema, resulting in a baseline score of 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 identifies the tool as generating an llms.txt file for a given URL, explaining the process (fetch, extract, emit) and the output format. It differentiates from siblings by focusing specifically on llms.txt generation, not general AI visibility or scanning.

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 concrete use cases (client indexing, own project drafting, competitor auditing) but does not explicitly state when not to use this tool or mention alternatives like scan_competitor_ai_presence. The guidance is clear but not exhaustive.

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 readOnly, idempotent, openWorld, non-destructive. The description adds value by detailing the return format (JSON:API resource objects with specific fields) and noting the tabular_data relationship for further reading. 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 sentences that concisely convey purpose, output format, and a related tool hint. Every sentence earns its place; no redundancy or 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?

The description explains the output fields adequately for a listing tool, especially since there is no output schema. Annotations cover safety. It does not mention pagination or effect of parameters, but those are in the schema. Overall fairly complete given the tool's simplicity.

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 all 4 parameters. The description does not add any additional meaning beyond what the schema provides, so it meets the baseline but does not exceed 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 action (list) and resource (resources attached to a dataset). It mentions the output format and hints at the relationship to resource_data, which helps distinguish it from that sibling. However, it does not explicitly differentiate from other siblings like dataset_details or search_within.

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?

Usage context is implied: use to list resources for a dataset. The description hints that if you need row-by-row data, use resource_data, but does not provide explicit when-to-use or when-not-to-use guidance compared to other siblings. No 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?

Annotations indicate readOnlyHint and idempotentHint, and the description adds value by listing the exact return fields (id, type, params, created_at, last_fired_at, fire_count). It does not contradict 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?

The description is two sentences with no wasted words. The first sentence states purpose and return fields, the second gives use cases. This is 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?

For a simple listing tool with one optional parameter and no output schema, the description adequately covers what the tool does, what it returns, and when to use it. No missing critical information.

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% coverage, describing the include_inactive parameter. The description does not add additional semantic detail about the parameter beyond the schema, so a baseline score 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?

The description clearly states the verb 'List' and the resource 'subscriptions', and specifies that it returns active subscriptions. This distinguishes it from sibling tools like subscribe and unsubscribe.

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 when to use the tool: 'to review what you're monitoring before adding more or to find an id to cancel.' It does not mention when not to use it, but the context from siblings provides adequate differentiation.

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 are present but don't cover rate limits or quota behavior. The description adds this context, including that feedback is read daily, affects roadmap, and doesn't count against tool-call quota. No contradictions 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?

The description is concise (3 sentences) and front-loaded with the most important info. Every sentence serves a purpose: purpose, usage guidelines, behavioral context, and constraints. 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?

Given no output schema, the description adequately explains what happens after submission (digests, roadmap impact). It covers all necessary context: rate limits, quota, and the fact it's free. Complete for a feedback 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 input schema already has 100% coverage with descriptions for each parameter. The description adds further guidance: describe the issue in terms of tools/packs and not to paste the user's prompt. This adds significant 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 explicitly states the tool sends feedback to the Pipeworx team for bugs, features, data gaps, or praise. It uses specific verbs ('tell', 'describe') and clearly distinguishes from sibling tools by being the only one that accepts 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 provides explicit when-to-use scenarios for each type (bug, feature, data_gap, praise) and what not to do (don't paste the end-user's prompt). It also mentions rate limits and that it's free, helping the agent decide when to invoke.

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 destructiveHint. The description adds valuable behavioral context: data is 'self-aggregating signal' from 'CF analytics-engine', 'no PII', and 'cached 5min-1h'. This clarifies privacy and freshness expectations beyond 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?

The description is concise (67 words) and well-structured: main output first, then numbered use cases in plain English, then technical details. Every sentence provides essential information without redundancy. It is front-loaded and easy to scan.

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 aggregation tool with no output schema, the description sufficiently explains what is returned (top tools, packs, call volume), data source, privacy, and caching. The tool's purpose is complete given its low complexity.

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% coverage with a single parameter (window) and enum values. The description adds semantic guidance: 'shorter windows surface what's hot right now; longer windows show steady-state demand'. This enriches the enum descriptions and helps the agent choose.

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 returns 'top tools, top packs, and total call volume' over a specified window. It provides specific use cases (discovering hot data sources, confirming canonical tools, aligning use cases). The verb 'returns' and resource 'Pipeworx trending' are explicit, and the purpose is easily distinguishable from sibling tools like ask_pipeworx or discover_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?

The description provides three explicit use cases for when to use the tool. While it does not explicitly state when not to use it or name alternatives, the context of sibling tools and the specific use cases strongly imply the tool's niche. A minor improvement would be to add a note on when to avoid this tool (e.g., for individual data queries).

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 (true), idempotentHint (true), and destructiveHint (false). The description goes beyond by detailing behavioral traits: scanning top 200 markets, walking child markets, computing partition_check, applying semantic anchor (Jaccard similarity), partition filter (placeholder detection), and fill check against live CLOB depth. 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?

The description is long and detailed, but well-structured with clear sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). Every sentence adds value given the tool's complexity, though it could be slightly more concise. The front-loading of core purpose and usage is effective.

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 (multiple modes, partition checks, fill checks, similarity filters, placeholder handling) and the absence of an output schema, the description fully covers the response structure (opportunities[], partition_check, fill check details) and behavior. It references a sibling tool for custom sizing, making it 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?

Both parameters are described in the input schema (100% coverage), but the description adds substantial context: explains the difference between event and topic modes, gives example slugs and seed questions, and describes what each mode does (e.g., walks child markets, searches related events). This adds significant meaning beyond the schema 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 finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes between three modes (trending_scan, event, topic) and explains their specific use cases, differentiating it from sibling tools like polymarket_edges or polymarket_fill_risk.

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 guidance on when to call with no args (trending_scan), when to use event mode (specific market slug), and when to use topic mode (cross-event scanning). It also advises when not to trade (if realizable_edge_pp ≤ 0) and references fill risk tool for custom sizing.

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?

The description extensively discloses behavioral traits: caching (1h KV-level), model families, knobs, diagnostic output for empty segments, and edge calculation details. It aligns perfectly with annotations (readOnlyHint, idempotentHint, etc.) and adds significant context beyond them.

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 densely packed with technical details and spans multiple paragraphs. While front-loaded with a clear purpose, it could be more concise by summarizing or referencing external doc. The level of detail is appropriate for the tool's complexity but sacrifices brevity.

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?

Without an output schema, the description fully explains the response structure (by_segment, fed_candidates, _diagnostics), edge computation, and edge cases (empty segments, filtering). It is comprehensive enough for an agent to invoke and interpret results 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 coverage is 100%, but the description adds meaningful context for parameters like slippage_pp (rationale for default), min_kelly (half-Kelly clarification), and min_partition_leg_kelly (explanation of per-leg filter). This goes beyond the schema 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 it scans Polymarket markets for edges where Pipeworx data disagrees with market price, explicitly framing it as 'what should I bet on today'. It distinguishes from siblings like polymarket_arbitrage by focusing on multi-model edge detection rather than pure 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?

While the description implies usage for daily opportunity scanning, it does not explicitly compare with sibling tools like polymarket_arbitrage or bet_research. No direct guidance on when to use this vs alternatives is 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?

Annotations already indicate read-only, idempotent, non-destructive. The description adds valuable behavioral details: response structure (tracked, expired, snapshot_dates), limits (60-day TTL, decay computation from daily closes), and clarifies no intraday data.

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 relatively long but well-structured: purpose first, then response format, then limits. Could be slightly more concise, but no redundant sentences.

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 thoroughly explains the return structure (tracked with fields like edge_pp_net time-series, first_seen, trend, decay; expired with lifespan; snapshot_dates). Combined with schema coverage and annotations, the tool is fully comprehensible.

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 both parameters with descriptions. The description adds default values (days=14, window='1wk') and clamp info (2-30), providing marginal additional 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 states the tool provides 'edge persistence and decay telemetry' and answers a specific question about edge existence over time. It distinguishes from sibling tools like 'polymarket_edges' by focusing on the temporal dimension.

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 when to use (to understand edge persistence), but lacks explicit guidance on when not to use or comparisons with sibling tools like polymarket_edges or polymarket_arbitrage.

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 convey readOnly, openWorld, idempotent, non-destructive. Description adds operational details like walking the ladder, returning verdict, and warning about thin books and partial fills. 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 long but well-structured: summary, mode details, usage guidance. Front-loaded with purpose. Could be slightly more concise, but the detail is justified by tool complexity.

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, but description details return fields for both modes, including verdict and risk indicators. Lacks explicit statement that it's a simulation/check, but annotations imply non-destructive read. Very complete 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%, and description goes beyond by explaining mode-specific behavior, default interpretations, and the meaning of size_usd in each mode. Adds significant clarity.

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 defines the tool as a realizable-vs-theoretical edge check against live order book depth, and distinguishes between single-market and basket modes. It contrasts with siblings like polymarket_arbitrage and polymarket_edges by specifying when to use.

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 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500' and explains risks of partial fills. Provides clear context and 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?

The description goes far beyond the annotations (readOnlyHint, idempotentHint, etc.) by explaining the conditions under which the spread signal is real, the response structure (leg prices, top_spreads_pp, compatibility_warning, temporal_alignment), and detailed counters for skipped comparisons. This level of detail fully discloses the tool's behavior and 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?

The description is well-structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS) and front-loads the core purpose. It is thorough but somewhat verbose; a few sentences could be trimmed without losing essential information. Overall, it balances detail and clarity.

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 and absence of an output schema, the description fully covers return values, safety fields, edge cases (e.g., compatibility_warning conditions), and interpretation guidance. It leaves no significant gaps for an agent to misuse the 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?

With 100% schema coverage, the schema already describes parameters. The description adds value by explaining the two modes, providing examples, and clarifying override behavior. However, it does not explicitly state that topic and explicit parameters are mutually exclusive or how conflicts are resolved, leaving a slight gap.

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 computes cross-venue spreads between Kalshi and Polymarket for the same resolving question. It distinguishes between two modes (topic shortcuts and explicit event pairs) and explains the concept of compatibility warnings, setting it apart from sibling tools like polymarket_arbitrage and bet_research.

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 details when to use the tool (to compare spreads between the two venues), the two operation modes with examples, and important caveats such as compatibility warnings indicating no arbitrage exists. It warns that pre-mapped topics often return warnings, guiding the agent to use explicit parameters for real opportunities.

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?

The description aligns with annotations (readOnlyHint, idempotentHint, destructiveHint) and adds beyond them: explains scoping to identifier and the pairing with other tools. 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: first states the primary functionality, second provides context and usage guidance. Every sentence 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 read tool with no output schema, the description covers the return behavior (value or list), scoping, and pairing. It is complete for the tool's complexity.

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 clarifying that omitting the key lists all keys, which is not explicit in the schema. This enhances understanding of the optional parameter.

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 (retrieve/list) and the resource (saved values/keys). It differentiates between retrieving a specific key and listing all keys. It also mentions pairing with remember and forget, distinguishing it from sibling 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?

The description provides clear usage context (look up stored context like ticker, address, notes) and states scoping to an identifier. It advises pairing with remember and forget, but does not explicitly state when not to use the tool or mention alternatives beyond the implied 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?

Annotations already declare readOnlyHint, idempotentHint, etc., so the bar is lower. The description adds value by detailing return fields (source, citation_uri, raw payload) and the mark_read behavior, without contradicting 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?

The description is concise (4 sentences) and well-structured: purpose, return fields, filtering, mark_read behavior, alternative access. 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?

Covers key aspects like return fields, filtering, and mark_read, but misses the unread_only parameter and does not explain return format beyond listed fields. Overall good for a read tool with strong annotations and no 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 coverage is 100% with good descriptions, but the description adds meaning by explaining the effect of mark_read and providing a concrete example for type filtering, going 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 'Pull' and the resource 'fired events from your subscription feed,' specifying exact return fields and filtering options, which distinguishes it from siblings like 'subscribe' or 'list_subscriptions.'

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 usage context such as filtering by type and since, and mentions that polls work fine, but lacks explicit guidance on when to use this tool versus sibling tools 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?

Discloses multi-source fan-out (SEC, GDELT→GNews fallback, USPTO), fallback behavior due to rate limits/errors, and USPTO soft-fail due to API sunset. Adds context beyond annotations (readOnly, openWorld, idempotent, non-destructive). 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?

Single paragraph front-loads with example queries. Each sentence adds value (sources, fallback, format, sibling reference). Could be slightly tighter (e.g., combine fallback explanation), but overall efficient 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?

Despite no output schema, description fully describes return structure (changes[] grouped by source, total_changes count, citation URIs). Covers source behavior, fallbacks, and date formats. Comprehensive for a complex tool with multiple backends and edge cases.

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 covers all 3 parameters with descriptions, but the description adds value: explains relative shorthand syntax for `since`, gives typical monitoring values, and clarifies `value` accepts ticker or CIK. This enriches understanding 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 this is a change feed for a company over a recent window, listing specific sources (SEC EDGAR, GDELT, USPTO) and what they provide. It distinguishes from sibling tool 'entity_profile' by contrasting static vs. dynamic data. Example queries in the first line make the purpose instantly recognizable.

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 entity_profile instead ('Use entity_profile instead when you want the static profile...'). Provides typical monitoring suggestion for `since` ('Use "30d" or "1m"'). Could add more exclusionary guidance (e.g., when not to use), but overall 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 provide idempotentHint and destructiveHint. Description adds useful lifecycle details (persistent vs 24-hour retention, scoped by identifier) beyond 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?

Front-loaded with purpose, then usage, then technical details. Each sentence contributes meaningfully with 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?

No output schema, but description adequately covers storage behavior, lifecycle, and pairing with siblings. Complete for a write-memory 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 coverage is 100% with clear descriptions. Description enhances with naming conventions and purpose, adding value 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?

Clear verb 'Save' and resource 'data' with specific use case. Distinguishes from sibling tools recall and forget by pairing with them 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?

Provides explicit when-to-use ('when you discover something worth carrying forward') and how to use with siblings ('Pair with recall to retrieve later, forget to delete').

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 provide readOnlyHint=true, idempotentHint=true, and openWorldHint=true. The description adds valuable context: internal cascading through multiple endpoints, auto-disambiguation for companies, and specific return formats (ticker, CIK, RxCUI) with citation URIs. There is no contradiction, and the description enhances transparency beyond the 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?

The description is front-loaded with common query patterns and quickly states the core purpose. It then efficiently covers details per supported type. While dense, every sentence adds value. Slight wordiness (e.g., 'Each call cascades through...') could be trimmed, but overall it is well-structured and concise.

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 an output schema, the description thoroughly explains return values for each entity type, including citation URIs. It covers input variations, internal processing, and usage contexts. It is missing explicit error handling (e.g., if name not found) or rate limits, but for a lookup tool with rich annotations, it provides sufficient 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?

With 100% schema coverage, the description still adds significant meaning. It details valid input formats for each type (ticker, CIK, name for company; brand/generic for drug), explains what each returns (e.g., ticker + 10-digit CIK + company_name + citation URI), and mentions auto-disambiguation—far exceeding the schema's minimal 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 uses specific verbs ('resolve', 'look up', 'find') and explicitly states the tool's purpose: converting a name to a canonical/official identifier needed by other tools. It provides concrete example queries and distinguishes the tool as the 'FIRST' step when an ID is needed, setting it apart from siblings like entity_profile.

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 gives clear guidance: 'Use FIRST whenever you have a name but need an ID.' This indicates when to use the tool. It also mentions it replaces 2-3 manual lookups, which helps the agent understand efficiency. However, it does not explicitly state when NOT to use it or suggest specific sibling alternatives for cases where more than an ID is needed.

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. The description adds valuable behavioral context: returns rows with columns mapped to {repr, val} pairs, supports paging and full-text row filtering via q. 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?

Three sentences: first states purpose and output format, second adds features (paging, filtering), third provides usage guidance. No wasted words, front-loaded, 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 4 parameters with good schema descriptions, clear annotations, and no output schema, the description covers return format, filtering, paging, and prerequisite. An agent has enough information to use the tool 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 coverage is 100%, baseline 3. The description adds meaning beyond schema: explains q as 'Full-text filter across row cells (Polish or English)' and gives example 'AUSTRIA'. Also mentions paging generally. This adds value.

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 'Read row-level data from a tabular resource', specifying verb and resource. It distinguishes from siblings by mentioning prerequisite 'Use list_resources first' and focusing on row data with paging and filtering, unlike search_within which might search across resources.

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 list_resources first to find a tabular resource id.' This provides clear prerequisite context. However, it does not explicitly mention when not to use or alternatives, but the instruction is sufficient for an agent.

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, and idempotentHint as true, so the tool is safe. The description adds value by explaining it probes via 'ai_visibility_check' and returns a ranked list with score, confidence, and signal density, giving agents a clear behavioral model.

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 (4 sentences) and front-loaded with the main purpose. Every sentence adds value: purpose, method, use case, and return format.

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 return format (ranked list with score, confidence, signal density). It also mentions constraints (2-8 entities). Annotations further cover safety. A minor gap is not explicitly stating the knowledge source, but overall complete for a read-only 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 coverage is 100% with detailed parameter descriptions. The description repeats some information (e.g., entities format) but does not add new meaning beyond the schema, so a 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?

The description uses specific verbs like 'compare', 'probes', 'ranks', and 'surfaces', clearly stating the tool compares AI visibility across multiple entities. It distinguishes itself from sibling tool 'ai_visibility_check' by focusing on side-by-side comparison rather than single entity checks.

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 a clear use case ('competitive AI-marketing audits') and implies when to use it (when comparing multiple entities). However, it does not explicitly state when not to use it or directly mention alternatives beyond the sibling list.

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?

Beyond annotations (readOnlyHint, idempotentHint, etc.), description details compositing behavior (fans out across two APIs), graceful degradation (partial failures allowed, sources_failed field), and temporal behavior (bundlephobia first measurement can take 5-30 seconds, timeout leads to degraded response). This adds significant behavioral context.

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 relatively long but each sentence earns its place. It starts with the core purpose, then usage, then return format, then limitations. Could be slightly tighter (e.g., 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly' is a bit wordy), but overall well-organized and 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?

Given no output schema, the description thoroughly details the return summary fields, per-advisory detail, links, and alternative versions list. It also covers edge cases like scoped packages, default version, partial failures, and bundlephobia timeouts. This is comprehensive for a complex, multi-source 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 covers both parameters with solid descriptions. Description adds extra nuance: scoped packages accepted, version defaults to latest, and mentions bundlephobia measurement delay relevant to version parameter. Schema coverage is 100%, so description adds meaningful but not essential extras.

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's a composite check for evaluating npm packages, listing specific data sources (deps.dev, bundlephobia) and criteria (license, advisories, version history, bundle size). Distinguishes from alternatives by specifying NPM-only in v1 and mentioning fallback to deps.dev:version for other ecosystems.

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 gives use cases: 'when an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also explains limitations (NPM only, partial failure behavior, bundlephobia first measurement delay of 5-30s) and cross-references sibling tools for other ecosystems.

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, destructiveHint. The description adds that the tool returns a JSON:API list and that content is mostly Polish, which provides useful behavioral context beyond 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?

Two sentences, front-loaded with the main purpose, no wasted words. Efficient and to the point.

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 structure (JSON:API list with specific fields) and how to use the results with sibling tools. All five parameters are covered, and pagination/language hints are provided. Complete for a search/list 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?

All parameters have descriptions in the input schema (100% coverage), so the baseline is 3. The description adds minor extra context (e.g., q accepts Polish/English) but does not significantly enhance understanding 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 searches/lists datasets on Poland's national open data portal, specifying the returned fields (id, title, notes, slug, institution, resource count). It distinguishes itself from sibling tools like dataset_details and list_resources.

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 clear guidance on language usage (q accepts Polish/English, content mostly Polish) and directs the agent to use the returned dataset id with dataset_details/list_resources. While it doesn't explicitly exclude alternatives, 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 provide safety profile (readOnly, idempotent, non-destructive). Description adds return format details (JSON:API fields) and language behavior, which is useful beyond 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?

Two concise, front-loaded sentences. No redundancy; 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?

No output schema, but description lists return fields, compensating. Given low complexity and good annotations, coverage 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?

Schema coverage is 100%, so description adds minimal value. Only minor addition: q accepts Polish or English. Baseline 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?

Clearly states the action ('search/list') and resource ('data-publishing institutions'). Distinguishes from siblings like 'search_datasets' by specifying institution focus.

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 vs alternatives. Only mentions language behavior ('Mostly Polish content; q accepts Polish or English') but no when-not or context for switching.

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`, `idempotentHint`, and `destructiveHint`, making safety clear. The description adds useful behavioral details: uses BGE-base-en embeddings + cosine, 500-char overlapping windows, 200K char cap with truncation flag, and that each passage includes offset for verification. This is sufficient context beyond 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?

The description is a single paragraph with front-loaded purpose, no redundant sentences, and each sentence adds value. It clearly structures the information from purpose to usage to technical details.

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 output format (passages with offsets and similarity scores, top-N), truncation behavior, and pairing with other tools. Given the tool's complexity (semantic search, 3 parameters), it covers all essential aspects for an AI agent to use it 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%, so the baseline is 3. The description adds context for `query` (examples) and `limit` (default 5), but these are already in the schema. The textual description doesn't significantly enhance beyond the schema; thus a 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?

The description clearly defines the tool as 'Semantic search INSIDE a fetched record,' specifying the verb (search within), resource (text), and use case (when records are too large for prompts). It distinguishes from sibling tools like `ask_pipeworx_grounded` by noting it returns passages with offsets for verification.

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: 'Use when the record is too big to cram into the prompt.' It also provides guidance on pairing with `ask_pipeworx_grounded` and mentions alternatives indirectly by contrasting with full-document approaches.

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 indicate write operation and idempotency. Description adds behavioral details: returns subscription id, requires OAuth, delivery constraints (SMS cap, webhook auto-disable), and security (HMAC signing). No contradictions 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?

Though lengthy, every sentence adds value. The description is front-loaded with the main action. Could be structured with bullet points for readability, but the prose is efficient and comprehensive.

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 params, nested objects, multiple types), the description covers all essential aspects: types, parameters, delivery, prerequisites, return value, and behavioral constraints. No output schema exists, but description mentions return of subscription id and webhook secret. Complete for effective 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). Description adds significant meaning: explains each enum value with real-world examples, details param structure for each type, and clarifies delivery subfields (email, SMS, webhook) with constraints and security notes. Greatly enhances 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 action: 'Create a proactive monitoring subscription to a live-data event stream.' It distinguishes from siblings like 'unsubscribe' and 'list_subscriptions' by focusing on creation, and specifies the resource (subscription to event stream).

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 clear context on when to use (proactive monitoring) and prerequisites (requires Pipeworx OAuth account). Lists supported types with examples and delivery options. Does not explicitly mention when not to use or compare to alternatives, but the context is sufficient for most cases.

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?

The annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint comprehensively. The description adds context about returning category-bucketed questions from a live catalog, but the added behavioral info beyond annotations is moderate.

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 common user queries and efficiently packed with information. It is somewhat lengthy but every sentence contributes meaning. The structure flows from common triggers to behavior to usage.

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 format: category-bucketed example questions with exact tool+argument shapes. It covers what the tool does, when to call it, and what to expect, making it complete for an agent.

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 listing example values for topic ('finance', 'pharma', etc.) and clarifying that omitting the parameter gives a cross-category spread, which goes beyond the schema's description.

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: an onboarding entry point that returns categorized example questions with exact tool+argument shapes. It distinguishes itself from sibling tools by positioning as the first tool to use when unsure of Pipeworx's capabilities.

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 this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains when to pass a topic vs. omit for full spread. However, it does not explicitly state when NOT to use it, leaving some ambiguity.

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 provide destructiveHint=false and idempotentHint=true; the description adds that the row is deactivated (not deleted) and historical events persist, aligning with annotations and adding valuable behavioral context.

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, front-loaded with the main action, no wasted words, and structured to convey key constraints and effects efficiently.

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 annotations, the description fully covers behavior, ownership, and the deactivation policy, making it complete for the tool's simplicity.

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% and the schema already describes the 'id' parameter well. The description adds 'by id' which is generic, and ownership enforcement is more behavioral than param-specific, so minimal added value.

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 'Cancel' and the resource 'subscription by id', and distinguishes from the sibling 'subscribe' by its opposite action, making purpose immediately clear.

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 ownership enforcement ('you can only cancel your own subscriptions') and mentions that historical events remain available via recent_alerts, providing clear when-to-use context without explicit 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?

The description adds substantial behavioral details beyond the annotations, including the dual-pipeline approach (structured vs grounded), exact percent-delta math, fallback logic, and the return of verbatim evidence with citations. This aligns with the readOnlyHint and openWorldHint annotations without 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?

The description is somewhat lengthy (around 150 words) but highly informative and well-structured, front-loading examples and purpose. Each sentence contributes meaningful detail, though a slightly tighter version might be possible.

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, the description covers purpose, usage, internal logic, return values (including specific verdicts), and efficiency benefits over multiple tools. It is complete enough for an agent to select and invoke correctly, even without an 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?

With 100% schema coverage, the description still adds value by providing examples for the 'claim' parameter and detailed guidance for 'tolerance_pct' (overriding implied tolerance, use cases like hallucination detection, default behavior). This enriches the schema definitions.

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 starts with concrete user intents and clearly states it performs natural-language claim verification against authoritative sources. It distinguishes between company-financial claims (using SEC EDGAR + XBRL) and other claims, making the tool's purpose highly specific and differentiating it from sibling 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?

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear usage context. However, it does not mention when NOT to use this tool or suggest alternative tools, which would enhance guidance.

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