Data Europa

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context: default free model, cost implication for Anthropic, and the return structure (per-model score, confidence, signals, raw_response + combined view). This enhances transparency 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 four sentences with no wasted words. It front-loads the core purpose, then adds key details about defaults, API key usage, and return format. Every sentence serves a purpose.

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 clearly states what the tool returns (per-model object with score, confidence, signals, raw_response + combined view). It also covers the optional API key and context parameter. For a tool of this complexity, the description fully prepares an 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 baseline is 3. The description adds meaning beyond schema by explaining the default model selection, the purpose of '_apiKey' (BYO key, direct payment), and how 'context' helps disambiguate. This provides practical usage guidance not present in 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 uses a specific verb 'probe' with clear resources: LLMs, business/brand/product/topic, and scores visibility per model. It explicitly distinguishes itself from sibling tools by focusing on multi-model probing and visibility scoring, which is not covered by other 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 explains when to use this tool: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also clarifies the default model and how to include Anthropic with an API key. However, it does not explicitly state when not to use it or compare it to siblings like 'scan_competitor_ai_presence', leaving a minor gap.

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, open-world, idempotent, non-destructive. Description adds valuable behavioral context: routes to the correct tool among 3,436, fills arguments, and returns structured answers with citation URIs. Does not contradict annotations. Minor omission of rate limits or error handling, but overall strong.

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 a clear directive but is somewhat lengthy due to many domain examples. While thorough, not every sentence is essential; it could be more concise without losing 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?

For a tool with 6 parameters (1 required) and no output schema, the description covers core functionality, usage context, and return format (structured answer with citation URIs). Missing details like pagination or rate limits, but given annotations and examples, it is mostly 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 clear descriptions for the 'question' parameter and its aliases. The description adds examples but does not provide additional semantic meaning beyond what the schema offers. 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 the tool's purpose: answering factual questions using authoritative structured data with citations. It distinguishes itself from web search by specifying preference and listing numerous data domains and examples.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises preference over web search for factual questions. Provides conditional triggers like 'what is', 'look up', and concrete examples, giving clear when-to-use guidance and implying when not to use (non-factual questions).

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 significant behavioral context beyond annotations: it discloses the extra LLM call cost, the refusal reasons ('not_in_source', 'no_tool_match', etc.), and the exact extraction logic. 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 (4 sentences) and well-structured, with the most critical information front-loaded. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (high-stakes, refusal handling, multiple sibling tools) and the lack of an output schema, the description fully satisfies the need for completeness by detailing the return structure and possible failure modes.

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 baseline is 3. The description adds minimal parameter-specific information beyond noting that the tool accepts a natural language question with aliases, which is already clear from the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as a 'hallucination-resistant answer mode for high-stakes reads' and explicitly distinguishes it from ask_pipeworx by use case and cost. The verb 'extracts' and resource 'answer' are specific, and the scope is well-defined.

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 an answer will be quoted, cited, or acted on' and 'prefer ask_pipeworx for casual lookups,' providing clear when-to-use and when-not-to-use guidance with a named alternative.

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., but the description adds extensive behavioral details: fan-out logic, resolver contract with confidence scores, parent_event extractor, safety checks (low-confidence, closed markets, illiquid spreads), and news fallback fields. 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 lengthy but well-structured: overview, classifiers, fan-out examples, response shapes, resolver, parent event, news, safety. Information is front-loaded and every paragraph adds value, though slight trimming could improve conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description fully documents return structure (market, analysis, evidence fields, resolver contract, parent_event, news) and edge cases (low-confidence, closed markets, illiquid spreads). It is complete and leaves no ambiguity.

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 description adds meaningful context: examples for market input, explanation of depth enum (quick vs thorough) with what each entails, and details about include_raw (response size trade-offs). This surpasses mere schema documentation.

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, with specific verb ('research'), resource ('Polymarket bet'), and examples of inputs (slug, URL, question text). It also lists use cases like 'should I bet on X', making the purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit use cases are given ('should I bet on X', 'what does the data say about Y', 'is there edge in Z'), which helps the agent decide when to invoke. However, it lacks explicit when-not-to-use or direct alternatives among siblings, so it is slightly below a perfect score.

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 the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral details: it pulls latest 10-K data for companies (with off-calendar fiscal year handling) and FAERS/FDA/trial data for drugs, and returns citation URIs. This goes beyond the annotations to inform the agent of the exact data behavior.

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 that efficiently packs example queries, usage guidance, data details, and behavioral notes. It is front-loaded with examples and the most important guidance. While slightly dense, it avoids unnecessary words and each 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?

With no output schema, the description fully explains the return structure: paired data with citation URIs, sorted by primary metric. It also handles edge cases like off-calendar fiscal years and specifies min/max items (2-5). This gives the agent complete context 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% with both parameters described. The description adds semantic value by providing examples ('AAPL', 'MSFT' for company; 'ozempic', 'mounjaro' for drug) and explaining the data pulled for each type. This clarifies usage beyond the enum and array specifications.

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 performing side-by-side comparisons of 2-5 entities (companies or drugs) in a single call, with specific data sources. It lists example queries ('compare X and Y', 'which is bigger') that distinguish it from single-entity lookups, and explicitly states it replaces 8-15 sequential lookups, setting it apart from sibling tools 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 explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing strong guidance on when to use. It also explains that results are sorted by primary metric to answer 'largest/most/biggest' queries. However, it does not explicitly mention when not to use (e.g., for a single entity), but the context makes it 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 indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context beyond annotations: 'Returns the top-N most relevant tools with names, descriptions, and full input schemas... each result is ready to call directly, no second schema lookup needed.' 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 two sentences, front-loaded with purpose and use cases, with no redundancy. Every sentence adds value, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (discovery) and rich schema and annotations, the description is complete enough. It explains the output format and encourages first use. Could be more explicit about when not to use, but overall adequate.

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% with good descriptions for each parameter (e.g., 'Natural language description of what you want to do...'). The description does not add significant semantic value beyond what the schema already 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?

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It provides specific domain examples (SEC filings, financials, FDA drugs, etc.) and distinguishes from sibling tools by advising 'Call this FIRST...' which implies it is for discovery, not direct data retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available...'. It provides clear usage context, though it lacks explicit when-not-to-use 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?

The description adds significant behavioral context beyond the annotations: it details the tool fans out across SEC EDGAR, XBRL, USPTO, news, and GLEIF; specifies that the USPTO API sunset May 2025 and will soft-fail; and notes that only 'company' type is supported. This informs the agent of potential failures and limitations, which annotations alone do not cover.

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 structured logically, but it is somewhat lengthy. Each sentence provides valuable information, though it could be slightly more concise without losing meaning.

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 data sources, fallback behavior, specific output fields), the description covers all essential aspects: what it returns, limitations (names, USPTO sunset), and input constraints. The absence of an output schema is compensated by a thorough listing of returned data.

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 no major gaps. The description clarifies that 'type' is limited to 'company' and that 'value' accepts tickers or zero-padded CIKs, and reiterates that names are not supported. This adds useful nuance beyond the schema's enum and string descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with concrete examples like 'Tell me about X' and 'company profile for Microsoft', clearly indicating the tool's purpose of providing a holistic cross-source profile for US public companies. It distinguishes itself from sibling tools by explicitly stating to prefer this tool over chaining single-pack lookups and mentions that names are unsupported, guiding users to resolve_entity instead.

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 ('when the user asks for a holistic view') and when not to use ('names not supported — use resolve_entity first if you only have a name'). It also provides guidance to prefer this tool over chaining multiple lookups, making usage boundaries clear.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true. The description confirms destructive behavior ('Delete') and adds context about clearing sensitive data. No contradiction; could mention idempotency but not required.

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, efficient, front-loaded with the action 'Delete'. No unnecessary words or fluff.

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 simple tool with one parameter, high schema coverage, and annotations provided, the description is complete. It explains purpose, usage, and sibling relationships effectively.

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 a description for the key parameter. The description mentions 'by key' but does not add new information about key format or naming beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it deletes a memory by key, specifying the verb 'Delete' and the resource 'memory'. It distinguishes from siblings (remember, recall) by focusing on deletion for stale or sensitive data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly provides when to use: when context is stale, task done, or to clear sensitive data. Also advises pairing with remember and recall, giving clear usage 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 read-only, idempotent, non-destructive behavior. The description adds that it fetches the page, extracts title/description/key links, and outputs standard llms.txt markdown format, providing 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?

The description is a single paragraph with three concise sentences. It front-loads the core action and efficiently includes how it works and use cases without superfluous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the main aspects: purpose, process, output format, and use cases. It lacks details on edge cases (e.g., site unreachable) but given the tool's simplicity and rich annotations, it is sufficiently 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%, so baseline is 3. The description does not add additional meaning beyond the schema for url or max_links; it merely restates 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 generates an llms.txt file for any URL, specifying the verb 'generate', resource 'llms.txt file', and the process of fetching, extracting, and emitting. It also lists the target AI crawlers, making the purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases: getting a client site indexed, drafting for own projects, or auditing competitors. It does not explicitly state when not to use or provide alternatives among siblings, but given the unique functionality, this is adequate.

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, idempotentHint=true, and destructiveHint=false. The description adds no additional behavioral context, such as pagination or result variability.

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 extremely concise (3 words) but omits essential context. It is under-specified for a tool with sibling ambiguity.

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 having an output schema, the description does not explain what 'themes/groups' are or how the optional limit parameter affects results, leaving significant gaps in context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description does not mention the only parameter 'limit'. With 0% schema description coverage, the description fails to add any meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (List) and the resource (themes/groups). However, it lacks context to differentiate from sibling tools like 'tags' or 'organizations'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description does not mention any context for usage.

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, destructiveHint. Description adds return field details but no extra behavioral context. Adequate but not enhanced 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 sentences, front-loaded with purpose, no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool is simple with one optional param and no output schema. Description covers purpose, usage, and return fields adequately.

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 100% of parameter description. Description adds no extra meaning beyond schema. Baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'List the caller's active subscriptions' and specifies returned fields, distinguishing it from subscribe/unsubscribe 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?

Explicitly says to use for reviewing monitoring before adding more or to find IDs for cancellation, providing clear context. No explicit when-not-to, but adequate.

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, idempotentHint, and non-destructive behavior, so the description adds no further behavioral context beyond listing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, front-loaded sentence with no waste; appropriately concise for a simple list operation.

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?

Adequate for a read-only list tool with an output schema, but missing details like default limit, ordering, or what 'publishing' means in this context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description does not explain the 'limit' parameter despite 0% schema description coverage, leaving the agent to infer from the example only.

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 'List publishing organizations' clearly states the verb 'List' and the resource 'publishing organizations', distinguishing it from sibling tools like 'groups' and '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?

No guidance on when to use this tool versus alternatives, e.g., 'Use this to get a list of all publishing organizations; for detailed info on a specific organization, use entity_profile.'

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 no behavioral context beyond what is in the annotations. It does not explain what 'dataset' implies about data scope or availability.

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 extremely concise at one short sentence. While this avoids fluff, it does not effectively front-load critical information. The brevity comes at the cost of completeness, making this an underspecified rather than concise description.

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 complexity of sibling tools and the presence of an output schema, the description still fails to provide adequate context for agent decision-making. It does not explain what a 'package' is, how it relates to other entities, or what the output represents.

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 0%, and the tool description does not explain the 'id' parameter. There is no indication of the expected format, scope, or how to obtain a valid id. The parameter's meaning is left entirely to the agent to infer.

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 states 'Single dataset by id.' which indicates a retrieval operation by identifier. However, it does not clarify what constitutes a 'dataset' or how this tool differs from other retrieval tools like 'entity_profile' or 'resolve_entity'. The purpose is clear in a basic sense but lacks specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, limitations, or when not to use it. The agent receives no help in choosing this tool over 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?

Discloses rate limit (5 per identifier per day), free usage, and that it doesn't count against tool-call quota. Annotations only provide basic hints, so description adds valuable behavioral context beyond structured fields.

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?

Concise and front-loaded: first sentence states purpose, followed by usage conditions, then behavioral details. Each sentence earns its place 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?

Given the simple tool with full schema coverage and no output schema, the description is complete. It covers when to use, what to include, rate limits, and cost implications. No gaps for an agent to misinterpret.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for each parameter. Description reinforces the meaning of each type enum and adds context like message length limit, though it doesn't add much beyond schema. Slightly above baseline due to helpful enumeration explanation.

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 is for telling the Pipeworx team about bugs, feature gaps, data gaps, or praise. Uses specific verb 'Tell' and resource 'Pipeworx team', distinguishing it from sibling tools which are all different functionalities.

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 lists when to use (bug, feature/data_gap, praise) and provides negative guidance: 'don't paste the end-user's prompt'. 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?

Beyond annotations (readOnly, idempotent, non-destructive), the description adds details: data source (CF analytics-engine), no PII, cached 5min-1h, and derived nature. 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 concise and front-loaded, though the bullet-like list could be more streamlined. Still, every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one optional param, no output schema), the description provides adequate context about return content, caching, and purpose. Missing explicit return format but 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?

Although schema coverage is 100%, the description adds semantic guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand', which helps agents 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 verb 'returns' and the resource 'top tools, top packs, and total call volume' over a recent window, distinguishing this tool from siblings like discover_tools or 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?

It provides three specific use cases (discovering hot data sources, confirming canonical choice, checking use case alignment) but no explicit when-not-to-use or alternatives.

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

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

Annotations already indicate read-only, open-world, and idempotent behavior. The description adds significant behavioral details: semantic anchor (Jaccard ≥0.30), partition filter (placeholder >20% → null), and response structure. 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 detailed but well-organized, using bold for key terms and separating modes. Each sentence adds value, though some technical details (Jaccard, placeholders) could be streamlined for brevity without losing 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?

No output schema is provided, so the description must cover response structure—it does, explaining opportunities[] and partition_check fields. All key aspects (modes, filters, constraints) are addressed, making the tool fully understandable.

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 meaningful context beyond parameter names and short descriptions: explains modes, gives examples of slugs and seed questions, and describes the underlying logic (monotonicity, partition check). This enriches understanding.

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 two modes (event and topic) with specific examples, making it distinct from sibling tools like polymarket_edges or polymarket_kalshi_spread.

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 that one of 'event' or 'topic' is required and explains when to use each mode. Recommends 'event' for specific markets and 'topic' for cross-event scanning. Does not explicitly list exclusions but provides clear context for selection.

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 a safe, read-only, idempotent tool. The description adds extensive detail beyond annotations: the three model families, caching strategy, Fed bet exclusion reasoning, and output diagnostics. 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 extremely verbose, containing dense technical details and full model formulas. While structured, it exceeds what an AI agent can quickly parse, making it less concise and user-friendly.

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 fully explains the response structure (by_segment, fed_candidates, diagnostics), caching, and all nine parameters. It is contextually complete for a complex tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, providing a baseline of 3. The description adds value by explaining nuanced behaviors like min_partition_leg_kelly's role over min_kelly and slippage context. This extra context raises the score to 4.

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's purpose: scanning top Polymarket markets for opportunities where Pipeworx data disagrees with market price. It is distinct from sibling tools like polymarket_arbitrage (inter-platform arbitrage) and search, as it focuses on proprietary edge detection.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the tool is for 'what should I bet on today' and mentions Fed bets are included but excluded from ranking. However, it does not explicitly state when to prefer this tool over alternatives like polymarket_arbitrage 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?

The description discloses behavioral traits beyond annotations: it details the compatibility_warning conditions, skipped_cross_type counters, temporal alignment checks, and the fact that spreads may be meaningless despite matched tokens. This adds significant context to the annotations' readOnlyHint and idempotentHint, and there is 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 well-structured with sections for purpose, modes, response, and safety fields. It is slightly verbose but every sentence adds value. It could be trimmed slightly while retaining clarity, but overall it maintains good conciseness and front-loads critical information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with multiple modes and safety checks, the description is highly complete. It explains the response structure (raw probabilities, top_spreads_pp, compatibility_warning, temporal_alignment, skipped counters), and covers edge cases where spreads are not tradeable. No output schema exists, but the description adequately covers return fields.

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 description adds meaning by explaining the role of each parameter: topic as a shortcut for 10 pre-mapped macros, kalshi_event_ticker and polymarket_event_slug as explicit overrides. It provides a list of topics and example values, enhancing the schema's 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 calculates 'Cross-venue spread between Kalshi and Polymarket for the same resolving question,' identifies two distinct modes (topic shortcuts and explicit pairing), and distinguishes itself from sibling tools like polymarket_arbitrage by focusing specifically on cross-venue spread across different platforms.

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 use the tool: it explains that spreads are meaningful only when bet shapes are equivalent and when temporal alignment is true. It also warns that most pre-mapped topics return compatibility warnings, indicating when not to rely on the spread. Alternatives are implied by the two modes.

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 and idempotentHint. Description adds context about scoping to user identifier and pairing with remember/forget, which is valuable 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?

Three sentences, no fluff. Front-loaded with the core action. Every sentence earns its place with specific 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 only one optional parameter, no output schema, and clear annotations, the description fully covers purpose, usage, and scope.

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 one parameter. Description adds that omitting the key lists all keys, providing useful behavioral nuance not in schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it retrieves a value saved via remember or lists all keys if no key is given. It distinguishes from sibling tools remember and forget, specifying the exact verb and resource.

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 (look up context stored earlier) and provides alternatives (remember to save, forget to delete). Guides the agent on use cases like user's target ticker or address.

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, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond annotations: the mark_read flag's effect on future calls, that polling is suitable, and the availability of an alternate endpoint. 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 concise at three sentences, front-loaded with the core purpose. Every sentence adds distinct value: purpose, return fields, parameter usage, and polling/alternate access. No wasted words; excellent structure.

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 5 optional parameters and no output schema, the description explains return fields (source, citation_uri, payload) and key behaviors (mark_read effect, polling). It does not explicitly mention the 'limit' parameter or pagination, but the schema covers those. Overall, it provides sufficient context for an agent 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?

The input schema covers all 5 parameters with descriptions, achieving 100% schema coverage. The description adds extra context: an example type ('sec_8k'), explicit mention of ISO timestamp format for 'since,' and explanation of 'mark_read' behavior. This goes beyond the schema's basic descriptions, enhancing 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 the tool's purpose: 'Pull fired events from your subscription feed.' It specifies the resource (subscription feed alerts) and verb (pull/return), and distinguishes from sibling tools by mentioning the specific nature of 'most recent alerts the evaluator has written to your persisted feed.'

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, such as filtering by type and since, and the mark_read parameter. It also mentions that polling works fine and offers an alternative API endpoint for scripts/dashboards. However, it does not explicitly compare to sibling tools like 'search' or 'recall' to guide when to use this tool versus 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 mark readOnlyHint, idempotentHint, and openWorldHint. The description adds substantial behavioral context: parallel fan-out to multiple APIs, fallback behavior, USPTO sunset handling, date format flexibility, and return structure. 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 a single paragraph that front-loads key use patterns, then systematically covers fallbacks, parameter details, and return structure. It is concise yet comprehensive, earning a high score for efficiency.

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 API sources, fallbacks, date parsing) and no output schema, the description covers all necessary aspects: purpose, parameters, behavior, return format (structured changes, total_changes, URIs), and alternative tools. It is fully self-contained.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% so baseline is 3, but the description adds meaningful context beyond the schema: explains the 'since' parameter's ISO and relative shorthand formats, suggests typical values ('30d' or '1m'), and clarifies that 'value' can be ticker or CIK. This elevates the score.

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 function as a change feed for a company over a time window, specifying sources (SEC, GDELT/GNews, USPTO) and explicitly distinguishing from the sibling tool entity_profile. The verb 'get/summarize' and resource 'company changes' are well-defined.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage examples ('what's new with X', 'latest on Y') and clear guidance on when not to use it ('Use entity_profile instead when you want the static profile...'). It also explains fallback logic (GDELT→GNews) and soft-fail scenarios.

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?

Adds key behavioral details: key-value scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous. 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?

Four sentences well-structured, front-loaded with purpose, every sentence adds value without fluff.

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 simplicity (2 params, no output schema) and excellent annotations, description is fully complete for correct usage.

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 both parameters fully; description adds helpful examples like 'subject_property', 'target_ticker', and clarifies value is any text.

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 tool saves data for later reuse across conversations/sessions. Distinguishes from sibling tools recall and forget.

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 (discover something worth carrying forward) and pairs with recall and forget as 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 read-only, idempotent, non-destructive behavior. The description adds valuable context: internal cascading lookups, citation URIs, and auto-disambiguation for company. This complements the annotations without contradicting 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 well-structured, starting with examples, then usage, then details. Each sentence adds value, though it could be slightly more concise. Front-loaded with the most important information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description fully explains return values for each type, lists fields and citation URIs, and mentions internal cascading. It covers all necessary information for the agent to understand what the tool returns and how it works.

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 describes both parameters well. The description reinforces acceptable inputs for each type, adds context like auto-disambiguation, and explains what outputs correspond to each input. This adds meaningful clarity beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states that the tool resolves names to canonical identifiers, provides example queries, and lists supported entity types with clear output descriptions. It distinguishes itself from siblings like entity_profile and compare_entities which need identifiers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description says 'Use FIRST whenever you have a name but need an ID', giving clear when-to-use guidance. It also highlights efficiency by noting it replaces 2-3 manual lookups, implying it should be preferred over alternative 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?

Discloses that it probes each entity with ai_visibility_check and returns a ranked list with score, confidence, signal density. Annotations already declare readOnly, openWorld, idempotent, and non-destructive, and description adds valuable 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?

About 80 words, front-loaded with purpose, every sentence adds value without redundancy. Extremely efficient for the information conveyed.

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 (4 params, 1 required, no output schema) and rich annotations, the description fully explains behavior, use case, and output format, making it complete for an audit 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?

Adds significant meaning beyond the 100% schema coverage: explains first entity as subject, models supported with API key condition, context for disambiguation, all adding practical guidance not in 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 uses a specific verb 'Compare' and resource 'AI visibility' across multiple entities, clearly distinguishing it from the single-entity sibling ai_visibility_check and general compare_entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Describes a clear use case ('competitive AI-marketing audits') and implies alternatives (single checks use ai_visibility_check), but does not explicitly state when not to use this 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?

Adds crucial behavior beyond annotations: partial graceful degradation, bundlephobia first-measurement latency (5-30s), sources_failed listing. All annotations (readOnly, idempotent, etc.) consistent.

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?

Somewhat long but every sentence adds value. Front-loaded with purpose. Could be slightly more concise but appropriate given 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 (summary block, per-advisory, links, versions). Covers timing, failures, ecosystem limits. Complete for a composite 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 has 100% coverage, description adds context: scoped packages accepted, version defaults to latest. Provides semantic 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?

Description clearly states it's a composite check for npm packages covering license, advisories, bundle size, etc. Specific verb 'scan_dependency' with clear resource. Distinguishes from siblings (none similar) and ecosystem scope.

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 defines when to use: queries about safety, popularity, size cost of npm packages. Mentions 'NPM ecosystem only in v1' and directs to deps.dev:version 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description does not need to restate safety. However, it fails to add any behavioral context such as pagination behavior (start, rows), sorting, or filter capabilities, which are present in the schema but not explained.

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 extremely short (2 words), but this is underspecification, not efficient conciseness. It fails to convey necessary information, earning a low score despite 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?

Given the tool has 5 parameters, a required query, and pagination/filtering options, the description is grossly incomplete. Even with an output schema, it misses basic information about the search scope, behavior, and usage, making it nearly useless 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?

With only 40% schema description coverage, the description should compensate but does not. It adds no parameter information, leaving the agent without clarity on parameters like 'sort', 'start', or 'query' beyond their raw names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Search datasets' is essentially a tautology of the tool name 'search'. It provides no specifics on what datasets are searched or how this differs from sibling tools like 'compare_entities' or 'discover_tools', making it minimally informative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. There is no mention of context, prerequisites, or exclusions, leaving the agent to guess based solely on the name.

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?

Adds details beyond annotations: embedding model (BGE-base-en), window size (500-char overlapping), char cap (200K), truncation behavior, and offset tracking. 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 well-structured sentences: purpose, then usage, then technical details. 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 explains return format (passages with offsets, scores) and technical internals. Fully covers what an agent needs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description enriches with examples (SEC 10-K, article) and clarifies limit range (1-20, default 5). Adds practical context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states 'Semantic search INSIDE a fetched record' with specific verb and resource. Distinguishes from siblings like 'search' and 'ask_pipeworx_grounded'.

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 ('record too big to cram into prompt') and pairs with 'ask_pipeworx_grounded'. Offers clear alternatives and 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?

Description implies each call creates a new subscription, contradicting the idempotentHint=true annotation. This is a serious inconsistency. Additionally, it adds some behavioral detail beyond annotations (account requirement, delivery constraints) but the contradiction overrides.

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: front-loaded with main purpose, then requirements, types, delivery. Slightly verbose but each sentence adds value. Could be tightened but 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 complexity (3 parameters, nested objects, multiple types, no output schema), description covers account requirements, type-specific filters, delivery options with constraints. Missing error handling, but acceptable.

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 description coverage, baseline is 3. Description adds significant value with type-specific examples, parameter constraints, and delivery channel details, justifying a score of 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it creates a proactive monitoring subscription to a live-data event stream and returns a subscription ID. It distinguishes from sibling tools like list_subscriptions and unsubscribe by focusing on creation.

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 context: requires Pipeworx OAuth account, supported types with examples. Does not explicitly state when not to use (e.g., if just checking existing subscriptions), but clear enough for typical usage.

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 readOnly, openWorld, idempotent, non-destructive. The description adds that it returns dynamic example questions drawn from the live catalog, and that it can be called with or without a topic. This extends understanding beyond the structured 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 trigger phrases and a clear purpose statement. It provides a lot of information in a compact form, though it is slightly long. Each sentence earns its place 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?

Given no output schema, the description thoroughly explains the return value (category-bucketed example questions with tool+argument shapes). It covers usage with and without topic, states that it's dynamic, and integrates well with the tool's onboarding role. For a simple one-parameter tool with rich context, this is fully 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?

The schema describes the topic parameter minimally. The description adds concrete example values (e.g., 'finance', 'pharma', 'betting'), explains the behavior difference between omitting and including the parameter, and lists all possible focus areas. This significantly enhances 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 it is the 'onboarding entry point' that 'returns category-bucketed example questions' with exact tool+argument shapes. It clearly distinguishes from sibling tools like ask_pipeworx, discover_tools, and entity_profile by specifying its role as a discovery tool for new users.

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?

It 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 references alternative tools by name, providing clear guidance on when to use this tool versus others.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds no behavioral context beyond 'List or search'. It does not explain the openWorldHint implications (e.g., varying output), pagination, or any side effects.

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 extremely concise at two words, but it omits essential information that would help the agent. Being concise should not sacrifice clarity; this is under-specified rather than optimally succinct.

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?

Even with an output schema present, the description lacks completeness. It does not specify whether the search is case-sensitive, supports wildcards, or any constraints like rate limits. The tool likely needs more context for correct invocation.

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 0%, so the description must compensate by explaining the parameters. It does not explain what 'query' filters or what 'limit' controls. The description adds no value beyond the parameter names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses specific verbs 'List or search' on resource 'tags', making it clear what the tool does. However, it lacks context on what kind of tags (e.g., user-defined or system) and the scope of the list/search. Since there are no sibling tag tools, the description does not need to differentiate but could be more specific.

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 no guidance on when to use list vs. search, or any context for selecting this tool over alternatives. No prerequisites, exclusions, or scenarios are 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?

The description adds valuable context beyond annotations: the row is deactivated (not deleted), historical events remain available via recent_alerts, and ownership is enforced. This aligns with and supplements the annotations (idempotentHint, destructiveHint, etc.) 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 concise, with two sentences that front-load the purpose and then provide essential behavioral nuance. Every word serves a purpose.

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 tool's behavior, effect, and constraints well. However, it does not specify the return value format (no output schema), which could leave ambiguity. Overall, it is fairly complete for the given 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 already provides 100% coverage with a description for the id parameter. The description does not add new semantic information beyond what the schema states.

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 (cancel a subscription by id) and the resource (subscription). It distinguishes itself from sibling tools like subscribe and list_subscriptions through opposite purpose and effect.

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?

It explains ownership enforcement (only own subscriptions can be canceled) and the deactivation behavior, which guides when to use this tool versus alternatives. It lacks explicit when-not-to-use scenarios but effectively provides context through the behavioral description.

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. Description adds return format (verdict, structured form, citation, delta) and domain constraints, enhancing transparency without 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 conveys all necessary information clearly and efficiently. Front-loaded with example phrasings; could be slightly more concise but not overly verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a single parameter, comprehensive annotations, and no output schema, the description fully covers purpose, scope, return values, and rationale (replaces 4-6 calls). No gaps identified.

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?

Single 'claim' parameter is well-described in schema. Tool description adds contextual examples and domain specificity (company-financial claims), enriching parameter understanding beyond 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 identifies the tool as natural-language claim verification with example phrasings, specifies domain (company-financial claims for public US companies), and distinguishes from siblings by noting it replaces multiple sequential calls.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states to use 'whenever the agent needs to check whether something a user said is factually correct' and specifies supported claim types. Lacks explicit 'when not to use' or alternatives, but domain restriction provides implicit guidance.

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