wikiviews

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive. The description adds behavioral context beyond annotations: the cost implication of using Anthropic (BYO key, pay directly) and the return structure (per-model fields + combined view).

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 4 sentences, front-loaded with the main purpose, then concise details on default behavior, parameter usage, return format, and use cases. 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 4 parameters and no output schema, the description sufficiently covers purpose, parameter usage, return format (per-model fields), and use cases. It lacks explanation of how score/confidence are computed, but that is acceptable for this level of detail.

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 adds meaning: explains default model, that _apiKey is only needed for Anthropic, and that context helps disambiguate. This provides 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 it probes LLMs for knowledge about a business/brand/product/topic and scores visibility (0-100). It distinguishes from sibling tools like 'scan_competitor_ai_presence' by specifying the exact output and use case (AI-marketing audits, brand 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 clear context on when to use the tool (AI-marketing audits, pre-launch brand checks, competitive monitoring) and how to choose models (default free model, Anthropic requires API key). However, it does not explicitly state when not to use or compare to alternatives.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds behavioral info: the tool routes to 3,775 tools, fills arguments, and returns structured answers with citation URIs. 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, well-structured paragraph that front-loads the key instruction. Every sentence adds value, covering purpose, scope, and examples 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 tool's complexity and lack of output schema, the description adequately covers purpose, use cases, and output format (citations). Minor gap: no mention of error handling or rate limits.

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 aliases fully documented. The description provides examples but does not add significant semantic value beyond the schema. 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 function: answering factual questions using authoritative structured data. It lists numerous domains (SEC, FDA, FRED, etc.) and provides concrete examples, distinguishing it from web 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?

The description explicitly instructs 'PREFER OVER WEB SEARCH' and specifies when to use (questions like 'what is', 'look up', 'find', etc.). It lacks explicit guidance on when not to use, but the positive criteria are 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?

Beyond annotations (readOnlyHint, idempotentHint), description details the extra LLM call cost, structured response with evidence and refusal reasons, and the extraction-only 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?

Single paragraph is dense but efficient; every sentence adds value. Could be slightly more structured but is concise given the amount of 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?

Covers return format, refusal reasons, cost trade-off, and use cases. No output schema exists, but description compensates adequately 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%, so baseline is 3. Description only mentions the question parameter implicitly; no added meaning beyond schema's alias 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?

Description clearly states the tool is a hallucination-resistant answer mode for high-stakes reads, explicitly distinguishing it from the sibling 'ask_pipeworx' by noting the extra extraction step and cost.

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 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.

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 beyond the annotations. It covers safety mechanisms (low-confidence resolution blocking, closed market handling), result structure (market_match_confidence, parent_event extraction), and limitations (wide-spread illiquidity, resolution-rule risks like refund_50_50). The annotations already indicate readOnlyHint and idempotentHint, and the description aligns with these while adding crucial operational details.

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

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

The description is very long and contains extensive detail, which is helpful but not concise. It is well-organized with labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, etc.), but the sheer volume of text means it is not front-loaded efficiently. While every sentence adds value, the length detracts from quick scanning. A more concise version could retain key points.

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 the tool (3 parameters, no output schema), the description is remarkably complete. It covers input formats, classifier categories, fan-out examples, response shape details, resolver contract (confidence, alternatives), parent_event extraction, news fields with fallback behavior, safety checks, and resolution-risk analysis. It leaves no major gaps for an agent to interpret the tool's behavior.

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 value by explaining the market parameter accepts three input formats (slug, URL, question text), which is not fully clear from the schema. It also details the depth parameter with default behavior ('Default thorough') and explains the trade-offs for include_raw ('keeps responses under ~20KB' vs. full payloads). This enrichment justifies a score 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 clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the action (research), the resource (Polymarket bet), and the method (pull Pipeworx data). The description provides examples of inputs (slug, URL, question text) and usage scenarios, making the purpose distinct from sibling tools like polymarket_edges or polymarket_arbitrage, even without explicit differentiation.

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 for "should I bet on X", "what does the data say about Y", or "is there edge in Z"', providing clear use cases. It also gives examples of fan-out for different bet types (e.g., BTC bet, Fed bet). However, it does not explicitly state when NOT to use this tool or mention alternative tools like polymarket_edges or deep_research. This omission prevents 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 readOnly, openWorld, idempotent. Description adds: pulls from SEC EDGAR/XBRL, handles off-calendar fiscal years, FAERS data for drugs, returns paired data and citation URIs. Adds value 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?

Information is front-loaded with key phrases like 'side-by-side comparison' and 'ALWAYS PREFER'. Uses hyphenation for structure. Could be slightly more structured but still 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?

Given the complexity of two entity types and different data sources, the description is thorough. No output schema, but mentions returned data and citation URIs. Covers tool purpose and usage completely.

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%. Description enriches: explains type enum (company vs drug) with data source details, and values parameter with formatting examples (tickers/CIKs for company, names for drug). Adds meaning 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 it does side-by-side comparison of 2-5 companies or drugs in one call, and specifies the data pulled for each type. It distinguishes from siblings like entity_profile which likely does single 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 says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Provides example queries. Could add when not to use, but it's clear it's for comparison.

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, but description adds significant behavioral details: decomposition into sub-questions, parallel routing, pre-verified findings with citations, gaps[] for unanswered facets, and 15-60s expected time. 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 informative but slightly long. However, it is well-structured with key details front-loaded. Every sentence adds value; could be more concise but acceptable 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 explains return format: structured findings with evidence, confidence, source, citation, and gaps[]. Lacks exact output structure but compensates with detailed behavioral context for a complex research 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%, but description adds meaning: for 'question', it notes natural language and multi-part acceptance; for 'depth', it explains levels (quick=3, standard=5, thorough=8) and links thorough to paid plan. Adds value beyond schema enums.

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 in ONE call' with decomposition and parallel routing. It distinguishes from sibling ask_pipeworx by noting single lookups vs multi-part 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?

Explicitly states when to use (broad/multi-part questions) and when not (use ask_pipeworx for single lookups). Includes prerequisites: Pipeworx account, paid plan for 'thorough' depth.

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, destructiveHint=false. The description adds that results are ready to call directly without a second schema lookup. No contradictions, but no deeper behavioral disclosure beyond what annotations provide.

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 the purpose. The list of domains is verbose but provides useful context. No redundant sentences; it earns its length.

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 annotations and full schema coverage, the description is complete. It explains the return format (top-N tools with names, descriptions, schemas) and the 'ready to call' aspect. No output schema needed.

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

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

Schema coverage is 100% with all parameters described. The description mentions the 'query' parameter and aliases, but does not add significant new meaning beyond the schema's own descriptions.

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

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

The description clearly states the tool's purpose: finding tools by describing data or task, and specifies it returns top-N relevant tools with full schemas. It distinguishes from sibling tools like 'search_within' or 'compare_entities' by being a meta-tool for tool discovery.

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, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available'. It provides clear context but does not explicitly exclude cases where direct tool invocation is better.

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 hints. The description adds behavioral details: parallel fan-out across multiple sources, specific return fields (CIK, filings, fundamentals, patents, news, LEI), and a note about patents soft-failing. 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 relatively long but front-loaded with example queries and a clear usage directive. Every sentence serves a purpose (examples, behavior, limitations). It is as concise as possible given the tool's 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?

Given the absence of an output schema, the description thoroughly enumerates all returned fields (CIK, filings with URIs, fundamentals, patents, news, LEI). It also covers limitations (names, patents sunset). This provides sufficient context for an agent to understand the tool's capabilities and constraints.

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%, and the description reinforces the value parameter by specifying ticker or zero-padded CIK, and explicitly that names are not supported. It adds context about the type parameter being limited to 'company'. This goes 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?

The description opens with concrete example queries and explicitly states 'full cross-source profile of a US public company in ONE parallel call'. It clearly distinguishes from chaining single-pack lookups, making the tool's purpose highly specific and immediate.

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 clearly indicates when to use (holistic view) and provides a strong preference rule over chaining. It also warns that names are not supported and directs to resolve_entity. However, it does not mention cases where the tool should not be used (e.g., for a single data point).

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. The description adds only 'Delete,' which is redundant. No additional behavioral context beyond what annotations provide.

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 primary purpose. No wasted words. Highly concise and clear.

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

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

For a simple tool with one parameter and no output schema, the description covers purpose, usage, and pairing. Could mention success/failure outcomes, but generally 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 key described as 'Memory key to delete.' The description adds 'by key' but no extra meaning beyond the schema. Baseline 3 applies.

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 'Delete a previously stored memory by key,' specifying the action (delete) and resource (memory by key), distinguishing it 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 states when to use: 'when context is stale, the task is done, or you want to clear sensitive data.' Also mentions pairing with remember and recall, providing alternative 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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral details: fetches the page, extracts specific elements (title, description, key links), and outputs a single text blob ready for deployment. 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 with no wasted words. The main action is front-loaded, and every sentence adds value (purpose, process, output, use cases).

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 the output format (single text blob ready for site-root/llms.txt). It covers the simple input-output model fully, given only 2 parameters and no nested 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% with both parameters described. The description does not add new meaning beyond the schema; it only implicitly mentions URL. Baseline of 3 is appropriate as the schema already handles parameter 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 uses a specific verb (Generate) and clearly identifies the resource (llms.txt file for any URL). It distinguishes from sibling tools like ai_visibility_check or scan_competitor_ai_presence by focusing on producing the standard llms.txt markdown format.

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

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

The description explicitly lists three use cases (client site indexing, personal drafting, competitor auditing), which guides when to invoke. It does not mention when not to use or provide alternatives, but the provided contexts are clear and sufficient.

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 false, signaling a safe, read-only operation. The description adds the important constraint that dates must be in YYYYMMDD format. No further behavioral details (e.g., rate limits, data source) are needed given the strong annotation support.

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 highly concise: two sentences that front-load the core purpose and immediately state the critical date format constraint. Every element is necessary and informative, with no redundant or extraneous 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?

Given that an output schema exists, the description does not need to explain return values. It covers the main purpose, date format requirement, and the tool's scope. It does not mention URL-encoding for titles, but that is detailed in the schema. Overall, it provides sufficient context for an AI 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 has 100% description coverage, with each parameter (title, start, end) well-documented. The description only adds the date format note, which is already in the schema's parameter descriptions. Thus, the description adds minimal extra semantics beyond what the schema provides, earning 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 states the tool's purpose: retrieving daily pageview counts for a specific Wikipedia article over a date range. It uses a specific verb ('Get') and resource ('a specific Wikipedia article'), and its sibling tools like 'get_project_views' and 'get_top_articles' have distinct purposes, so there is no confusion.

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

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

The description provides clear context for when to use the tool (to get pageviews for a Wikipedia article) and specifies the required date format. However, it does not explicitly mention when not to use it or suggest alternative tools for similar tasks, such as 'get_project_views' for project statistics or 'get_top_articles' for top articles.

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 contextual details (date format, scope) beyond annotations, with 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, no fluff. The purpose is stated first, followed by a critical usage detail (date format). Every word earns its place.

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

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

Given the tool's simplicity (2 required parameters, no nested objects), rich annotations, and presence of an output schema, the description fully covers the necessary context: what it does, scope, and format constraints.

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 both parameters with descriptions and examples (100% coverage). The description merely reiterates the date format, adding no new semantic meaning beyond what the schema provides.

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 ('Get') and resource ('aggregate daily pageview totals for all of English Wikipedia'), clearly distinguishing it from sibling tools like 'get_article_views' which focus on individual articles.

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 specifies date format requirements (YYYYMMDD) and scope (all of English Wikipedia), but does not explicitly contrast with siblings or state when not to use this tool. However, the distinction from similar tools is implicit.

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 and idempotentHint=true, indicating safe, repeatable reads. The description adds that it returns up to 1000 articles ranked by view count, which is useful behavioral detail beyond annotations. 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 with no wasted words. Purpose and key details (scope, limit, ranking) are front-loaded.

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

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

For a simple tool with 3 parameters, an output schema, and rich annotations, the description is fully adequate. It explains the action, input constraints, and output nature.

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

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

Schema coverage is 100% with each parameter having a description (e.g., 'Year as 4-digit string'). The description adds no new meaning beyond tying parameters to a 'specific day', so 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 verb 'Get' and the resource 'most viewed Wikipedia articles for a specific day', with additional details on return limit and ranking. This purpose is distinct from sibling tools like get_article_views (for a single article) and 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 specifies usage for 'a specific day', which is clear context. It does not explicitly mention when not to use or list alternatives, but the tool name and sibling set imply differentiation (e.g., get_article_views for a single article).

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 and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by specifying return fields and scope ('caller's active').

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 action and output, no superfluous text; every part earns its place.

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

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

For a simple list tool with one optional parameter and no output schema, the description fully covers what the tool does, what it returns, and when to use it. Annotations further ensure completeness.

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

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

Schema description coverage is 100% for the single parameter. The description does not mention the parameter, so it adds no extra semantics beyond the schema, meeting the 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 the verb 'List' and the resource 'the caller's active subscriptions', and distinguishes from sibling tools like subscribe/unsubscribe by focusing on read-only listing.

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 when to use it: 'to review what you're monitoring before adding more or to find an id to cancel', providing clear context and implying 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?

Annotations provide limited info (all false), but the description adds key behavioral context: rate-limited, free, not counted against quota, and that feedback directly affects roadmap. 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 and well-structured: purpose first, then usage guidelines, then constraints. Every sentence adds value, 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 the tool's simplicity (feedback submission), the description covers all necessary aspects: purpose, when to use, parameter details, constraints, and behavioral notes. No output schema needed; return values are implicit. Complete and 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?

With 100% schema coverage, baseline is 3. However, the description adds significant value by explaining each enum value in detail, describing the context object's usage, and providing constraints on message length and content. This exceeds the 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 clearly states it's for sending feedback about broken/missing features, distinguishing between bug, feature, data_gap, praise, and other. It also tells what not to do (don't paste end-user prompt), making it highly specific and distinct from any 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?

Explicitly states when to use (wrong/stale data, missing tool, etc.), provides rate-limit info (5 per identifier per day), and instructs to describe issues in terms of Pipeworx tools/packs. Clear and actionable 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 indicate read-only, idempotent, non-destructive. Description adds crucial details: data source (CF analytics-engine), no PII, caching duration. 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?

Three sentences, each purposeful. Front-loaded with output, then use cases, 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?

For a simple tool with one optional param and no output schema, the description covers output format, data source, caching, and use cases. 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 single parameter window is fully described in schema with enum and description. The description adds extra meaning about choosing window length for hot vs steady-state demand. Schema coverage 100% so baseline 3, plus extra 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?

The description clearly states what the tool does: returns top tools, top packs, and total call volume over a window. It is distinct from sibling tools like discover_tools or get_top_articles.

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?

Three explicit use cases are given (discovering hot data, confirming canonical tool, seeing alignment). The window parameter choice is explained (hot vs steady-state). No explicit when-not-to-use, but 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?

The description discloses behavioral traits beyond annotations: the need for one parameter, the semantic anchor for cross-event mode, partition filter, and the fill check which warns about realizable edge. Annotations (readOnlyHint, idempotentHint, etc.) are consistent and complemented by these details.

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

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

The description is long but well-structured with clear sections (REQUIRES, event mode, topic mode, SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). Information is front-loaded with the requirement. Every sentence adds value, though the length could be slightly trimmed for maximum 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?

Despite no output schema, the description details the response structure (opportunities[], partition_check, fill_check outputs) and covers edge cases (no args failure, placeholder filtering, fill check). It is complete for a tool of this 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%, and the description adds significant meaning: it explains the slug format for 'event' (e.g., 'fed-decision-may-2026'), the use of seed questions for 'topic', and that full URLs are accepted. The description also clarifies the behavioral differences between the two parameters.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes two modes (event and topic) and explains the specific use cases for each, making the purpose highly specific and distinct 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?

The description explicitly requires one of the two parameters and warns that calling with no args fails. It provides guidance on when to use each mode (event for specific markets, topic for cross-event scanning). It also references a sibling tool (polymarket_fill_risk) for custom sizing. However, it does not explicitly compare against other sibling tools like polymarket_edges.

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 extensive behavioral traits beyond annotations: internal model families (crypto_price, news_momentum, partition_overround, concentrated_longshot), caching (1h at KV level), filtering logic (placeholder-slug filter, segment-specific gates), and diagnostics for empty segments. 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 dense but well-structured, using clear segmentation (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, CONCENTRATED_LONGSHOT) and inline explanations. While every sentence adds value, it could be slightly more concise without losing information.

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

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

Despite lacking an output schema, the description thoroughly explains the response structure (by_segment, fed_candidates, _diagnostics) and the content of each segment. It covers edge calculations, filters, and diagnostics, making the tool comprehensible and actionable.

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 explaining parameter usage (e.g., max_spread_pp: 'Set to 2 to require tight books') and clarifying edge cases (min_partition_leg_kelly vs min_kelly). This meaningfully enhances 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 explicitly states it scans top Polymarket markets for edge opportunities using Pipeworx data, with a clear purpose: 'what should I bet on today'. It distinguishes from sibling tools like polymarket_arbitrage by focusing on model-driven, structural arbitrage, and concentrated longshot opportunities.

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—discovering opportunities without paging hundreds of markets—and details tradeable-edge knobs for filtering. However, it does not explicitly state when not to use the tool or contrast with specific sibling tools.

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 read-only, idempotent, non-destructive. The description adds behavioral details: output structure (tracked, expired, snapshot_dates), computation from daily closes, historical bounds, and snapshot triggering on cache-miss. This enriches beyond 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?

Description is moderately long but each sentence adds value. It front-loads the core question answered and organizes output fields clearly. Could be slightly more concise, but structure is good.

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

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

No output schema, yet description fully explains return structure: tracked[] with time-series, trend, decay; expired[] with lifespan; snapshot_dates. Also covers limits and computation details. This is highly 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% for both parameters. Description repeats parameter info (days lookback, window family) without adding new meaning or examples. Baseline score of 3 applies since schema is sufficient.

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

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

Description clearly states the tool's purpose: telemetry on edge persistence and decay from polymarket_edges snapshots. It answers 'how long has this edge existed and is it shrinking?' and distinguishes between different edge ages. It contrasts with sibling tools like polymarket_edges by focusing on historical tracking.

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 describes when to use: to understand edge longevity and decay. Provides limits (TTL, snapshot start) and context that a fresh wide edge differs from an old one. Does not explicitly state when not to use or list alternatives, but the sibling context implies its specialized role.

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, idempotentHint, destructiveHint=false. Description adds behavioral details: walks the order book, returns verdicts (clean/degraded/cannot_fill), warns about forced directional risk in basket mode. 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?

Well-structured with bold headers (SINGLE-MARKET, BASKET) and clear return listings. Slightly verbose but all sentences are informative. No wasted words, but could be more terse.

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 comprehensively lists all return fields and explains their significance (top_of_book, vwap_fill_price, slippage_pp, capture_ratio, thin_legs, max_clean_notional_usd, forced_directional_risk). Covers both modes fully for the agent to understand behavior.

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

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

Schema coverage is 100%, but description adds context: explains side defaults and auto-selection in basket mode, distinguishes market vs event parameters, clarifies size_usd interpretation (single-market: spend/proceeds; basket: notional per leg). Adds 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?

The description clearly states it checks 'realizable-vs-theoretical edge against live CLOB order-book depth', specifying two modes (single-market and basket). It distinguishes from siblings like polymarket_arbitrage and polymarket_edges by being a pre-trade risk assessment tool.

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

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

Explicitly states when to use: 'before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Also explains risks of not using it (partial fills, unhedged directional positions).

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 details behavior beyond annotations, including response structure, safety fields (compatibility_warning, temporal_alignment), and conditions that make spreads meaningless. No contradiction with readOnlyHint, idempotentHint, or openWorldHint 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 with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS). It front-loads the purpose but could be more concise; some detail like skipped_cross_type counters could be shortened.

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 lack of output schema, the description covers important aspects like temporal alignment and compatibility warnings. It addresses edge cases well, though error handling or rate limits are not mentioned.

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 3. Description lists the topic shortcuts and modes but adds no additional semantic information beyond what the schema already provides through parameter descriptions and examples.

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

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

The description opens with 'Cross-venue spread between Kalshi and Polymarket for the same resolving question,' clearly identifying the specific function. It distinguishes itself from siblings like polymarket_arbitrage (likely intra-venue) by focusing on cross-venue spreads.

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 two modes and warns that pre-mapped topics often yield compatibility warnings, setting expectations. However, it does not explicitly state when to use this tool versus 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?

Annotations indicate readOnly, idempotent, and non-destructive. The description adds scoping to identifier and pairing with remember/forget, which goes beyond 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 concise (two sentences) and front-loaded with the primary function. Every sentence adds value; 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 schema, no output schema, and annotations, the description fully covers usage, scope, and pairing. It is complete for a read-only lookup 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 covers the single parameter fully; description adds crucial behavior: omitting key lists all keys. This meaningfully extends schema information.

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 retrieves a value saved via 'remember' or lists all keys if omitted. It distinguishes from sibling tools 'remember' and 'forget' by specifying its role as 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 provides explicit when-to-use guidance (e.g., 'look up context the agent stored earlier') and mentions scoping. However, it does not explicitly state when not to use or list alternatives, but the context is clear enough.

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 set readOnlyHint, idempotentHint, destructiveHint to safe values. Description adds crucial behavioral context: mark_read:true will flag events as read (a state change but non-destructive), polling behavior, and the availability of the same feed via GET endpoint.

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 concise sentences front-loaded with purpose, then returned fields, filtering/mark_read, and alternative access. Every sentence adds value with no redundancy 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 no output schema, the description adequately mentions returned fields. With 5 optional params and good annotations, the description covers key aspects. Could add more detail about pagination or ordering, but sufficient for typical use.

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

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

Schema coverage is 100%, but description adds significant value: gives example for type ('sec_8k'), explains mark_read's effect, states defaults (limit 50, mark_read false), and describes unread_only and since parameters beyond the schema's basic 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 'Pull fired events from your subscription feed' with specific verb and resource. It distinguishes by listing returned fields (source, citation_uri, raw payload) and notes alternative access via GET endpoint, setting it apart from sibling tools like subscribe, unsubscribe, etc.

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 usage context: filter by type/since, mark_read for tracking read state, and mentions polling works fine. It also points to an alternative GET endpoint for scripts/dashboards. However, it does not explicitly state when not to use this tool versus 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 multi-source fan-out (SEC, GDELT/GNews, USPTO), fallback behavior (GDELT preferred, GNews on rate-limit/5xx), relative date syntax, and return structure. Does not contradict annotations (all safe/idempotent hints are 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?

Description is dense but well-organized with front-loaded examples. Every sentence adds value. Slightly long, but not wasteful. Could be more structured but still 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?

For a complex tool with multiple sources, fallback, date formats, and no output schema, the description is remarkably complete. Explains return structure, limitations (USPTO soft-fail), and provides alternative tool reference.

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

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

Schema coverage is 100% but description adds meaningful context: examples for 'since' (ISO and relative), explains 'value' accepts ticker or CIK, and notes 'type' only supports 'company'. This goes beyond the schema's bare 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?

Description clearly states it provides a 'change feed for a company in the last N days/weeks/months' using specific verb+resource+scope. It distinguishes from sibling 'entity_profile' by explicitly contrasting dynamic vs static use cases.

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: 'Use entity_profile instead when you want the static profile...' and includes example queries like 'what happened to Z this week'. Clearly states when to use this tool vs 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?

Description adds useful behavioral context beyond annotations: scoped by identifier, persistent for authenticated users, 24-hour TTL for anonymous sessions, and key-value store nature. Annotations already indicate write operation (readOnlyHint=false) and idempotency, and description aligns 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, front-loaded with purpose, and every sentence adds value. No extraneous 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 could mention return value but the tool is simple and context is largely complete. Minor gap but 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 coverage is 100% with clear descriptions, so description adds minimal extra meaning beyond schema examples. 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 'Save data the agent will need to reuse later' which is a specific verb ('Save') and resource ('data'), and distinguishes from siblings like 'recall' and 'forget' by mentioning them.

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 ('when you discover something worth carrying forward') and hints at paired tools ('Pair with recall to retrieve later, forget to delete'), but 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?

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable context about internal cascading ('Each call cascades through several lookup endpoints internally') and efficiency ('replaces 2-3 manual lookups'). 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 yet comprehensive. It front-loads with example queries, efficiently uses bullet-like structure for types, and every sentence contributes value. No redundant or vague phrases.

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 return values for both types, including citation URIs. It covers input variations and cascading behavior. Minor gaps: no mention of error handling or what happens if entity not found, but overall it is fairly complete for a lookup 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 description coverage is 100%, baseline is 3. The description significantly enhances meaning by detailing what each type returns: for company (ticker, CIK, company_name, citation URI) and for drug (RxCUI, ingredient, brand, citation URI), and explains flexible input formats (ticker, CIK, name for company; brand or generic for drug).

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 resolves a user-spoken name to a canonical/official identifier, with examples like ticker, CIK, RxCUI. It distinguishes itself by saying 'Use FIRST whenever you have a name but need an ID', but does not explicitly mention alternatives like entity_profile or 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?

The description provides clear context on when to use: 'Use FIRST whenever you have a name but need an ID.' It gives supported types and inputs, but does not list when not to use or name specific 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 value beyond annotations by explaining the process (probes each entity with ai_visibility_check, ranks by score) and return details (ranked list with score, confidence, signal density). Annotations already indicate safe, idempotent behavior, and the description does not contradict 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?

Three sentences, each earning its place: first defines the action, second explains the method, third provides use case and output. No unnecessary words, front-loaded with the core 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?

For a tool with no output schema, the description sufficiently describes the return format (ranked list with score, confidence, signal density). It covers all crucial behavioral aspects and works well with the rich annotations, making it fully informative.

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 documents parameters well. The description adds extra meaning: that the first entity is treated as 'subject' for narrative and that 'context' disambiguates common names, enhancing understanding beyond the schema.

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

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

The description uses specific verbs (Compare, Probes, ranks, surfaces) and clearly identifies the resource (AI visibility across multiple entities). It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison), making the tool's unique purpose unambiguous.

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

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

Provides clear context for usage ('competitive AI-marketing audits') and an example question. Though it does not explicitly state when not to use or name alternatives, the sibling list implies ai_visibility_check as a single-entity alternative, giving implicit 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?

Adds significant behavioral context beyond annotations: partial failure handling, potential delays from bundlephobia first measurement, and sources_failed reporting. No contradiction with readOnlyHint, openWorldHint, etc.

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?

Every sentence adds value. Front-loaded with core purpose, followed by usage, output summary, scope, and edge cases. 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?

Despite no output schema, the description fully explains return structure (summary fields, advisories, links, versions) and error handling. 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%, so baseline is 3. Description adds value by clarifying scoped package acceptance and default version behavior, raising 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?

Clearly states the composite check for evaluating npm packages, specifying integration with deps.dev and bundlephobia. Distinguishes from siblings by being the only tool focused on dependency 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?

Explicitly describes when to use: for questions about safety, popularity, size, or cost of adding a package. Also notes ecosystem limitation (NPM only) and directs to alternative 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 indicate read-only, idempotent, open-world, and non-destructive. The description adds technical details beyond annotations: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K character cap with truncation and flagging, and that outputs include character offsets for verification.

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 but front-loaded with key information. It is concise for the amount of detail but could be improved with bullet points or clearer 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?

Despite no output schema, the description explains return format (passages with offsets and similarity scores), constraints (200K char cap, truncation), and underlying implementation (embedding model, chunking strategy). All essential context is provided.

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

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

With 100% schema coverage, baseline is 3. The description adds value by explaining the 200K char limit for 'text', providing example queries for 'query', and mentioning the default value for 'limit'.

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 semantic search inside a fetched record, with concrete examples (SEC 10-K, article). It distinguishes from sibling tools by explaining that it saves context and works with 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?

Explicitly states use case: when the record is too large for the prompt. Mentions pairing with ask_pipeworx_grounded and that passages come with offsets for verification. Does not explicitly say when not to use, but 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?

The description adds behavioral details beyond annotations: account requirements, delivery channels, phone verification, SMS cap, webhook auto-disable. Annotations already indicate non-read-only, non-destructive, and idempotent, so description complements 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 succinct and front-loaded with the purpose. It covers many details without redundancy, though it is fairly dense and could be broken into sections for easier scanning.

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, no output schema), the description covers purpose, requirements, types, param details, and delivery options. It mentions the return of a subscription id, but could be more explicit about the full return structure.

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

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

Schema coverage is 100%, but the description enriches with examples and constraints for each type and delivery channel, adding meaning beyond the schema descriptions (e.g., param structure for sec_8k, phone verification requirement).

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 opens with a clear verb+resource statement: 'Create a proactive monitoring subscription to a live-data event stream.' It distinguishes from siblings 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?

The description provides context on when to use: requires Pipeworx OAuth account, cannot be used by anonymous/BYO. It also lists supported types and delivery constraints, but does not explicitly state when not to use or provide alternatives among siblings.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so safety profile is clear. The description adds valuable behavioral context: returns 'category-bucketed example questions' with exact tool+argument shapes, drawn from live catalog. 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?

The description is front-loaded with user-question synonyms and provides compact details. It is slightly long but every sentence adds value: purpose, output structure, parameter usage, and use case context.

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, no nested objects), the description is complete. It explains purpose, when to use, behavior, return format, and parameter semantics. 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% (one parameter with description). The description adds examples of topic values (e.g., 'finance', 'pharma') and explains that omitting gives full spread, but this is marginal beyond the schema's description. 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 uses specific verbs and resources: 'what is Pipeworx good for', 'returns category-bucketed example questions'. It clearly distinguishes from sibling tools by explicitly stating it's the onboarding entry point to discover capabilities, unlike other tools like ask_pipeworx or 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?

Explicitly states 'Use this FIRST when you do not yet know what Pipeworx can do' and explains when to use the optional topic parameter. No explicit when-not-to-use or alternatives, but the context strongly implies this is for initial exploration.

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 significant context beyond annotations: explains that the row is deactivated (not deleted) and that historical events remain accessible via recent_alerts. Also mentions ownership enforcement. 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 well-structured sentences, front-loaded with the main action. No unnecessary 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 simple one-parameter tool with annotations, the description covers purpose, behavioral details, and parameter context completely, including the deactivation behavior and historical event availability.

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

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

Schema coverage is 100% with a clear description for the 'id' parameter. The description does not add further parameter details beyond mentioning 'by id', which is already implied by 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 'cancel' and the resource 'subscription by id', making the tool's purpose immediately clear. It distinguishes from siblings like 'subscribe' and 'list_subscriptions' by specifying cancellation.

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 ownership enforcement ('you can only cancel your own subscriptions'), giving a clear constraint. It does not mention when not to use or alternative tools, but the constraint 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?

Annotations already declare read-only, open-world, idempotent, non-destructive. Description adds behavioral context: replaces 4–6 sequential calls, returns verdict with citation and delta. 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 front-load the purpose with examples and scope. No unnecessary words; 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?

For a simple tool with one parameter and no output schema, the description covers return values (verdict, structured form, actual value, citation, delta) and limitations (v1 financial claims). Complete for agent decision-making.

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 for 'claim' already provides definition. Description adds examples and domain restriction (company-financial), enhancing 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?

Clearly states it performs natural-language claim verification against authoritative sources for company-financial claims. Distinguishes from siblings by being a single-call replacement for 4–6 sequential calls. Provides example queries and domain 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 says 'Use whenever the agent needs to check factual correctness' and lists trigger phrases. Mentions limitation to company-financial claims (v1). Does not explicitly list alternatives for non-financial claims, but the context is clear.

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