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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds significant context: it probes LLMs, returns per-model scores with confidence and signals, and notes that using the Anthropic API key incurs direct costs to the user. No contradictions with annotations.

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

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

The description is four sentences long, front-loading the core action and then efficiently covering models, API key, return format, and use cases. Every sentence adds value with no redundancy or filler.

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

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

Despite lacking an output schema, the description explicitly details the return structure (per-model score, confidence, signals, raw_response plus combined view). It also covers default behavior, optional parameters, and use-case context, making it complete for an AI agent to invoke correctly.

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

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

Schema coverage is 100%, but the description adds substantial meaning: it explains that `_apiKey` is for Anthropic and that using it triggers direct billing, that omitting `models` defaults to Workers AI, and that `context` helps disambiguate entities. These details go beyond the schema's property 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 that the tool probes LLMs for knowledge about an entity and scores visibility per model (0-100). It specifies the verb 'probe' and the resources (LLMs, entity), and distinguishes itself from siblings by focusing on AI visibility scoring for marketing audits, brand checks, and competitive monitoring.

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

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

The description provides explicit when-to-use scenarios (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains how to use the default free model vs. the paid Anthropic model with an API key. However, it does not explicitly name alternative tools or give when-not-to-use conditions, which would strengthen the 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?

Beyond annotations (readOnly, idempotent, openWorld), the description adds that it routes to one of 3,436 tools, fills arguments, and returns structured answers with stable citation URIs. No contradiction with annotations.

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

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

The description is concise, front-loaded with a clear recommendation, and well-structured with source categories and examples. Every sentence adds value.

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

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

Given no output schema, the description explains the return format (structured answer with citation URIs). Parameter count and schema coverage are sufficient. However, it lacks details on error handling or edge cases, but overall complete for its intended use.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the type of questions to ask and providing examples, which helps the agent understand parameter usage beyond the schema's property 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 answers factual questions about current/historical data from specific sources, with examples. It distinguishes itself from web search by emphasizing authoritative structured data and citations.

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 'PREFER OVER WEB SEARCH' and lists scenarios and example queries. Provides guidance on when to use ('what is', 'look up', etc.), though it does not explicitly state 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 already indicate readOnlyHint=true and destructiveHint=false. Description adds extensive behavioral details: low-confidence resolution short-circuit, closed market handling, wide-spread market warnings, resolver contract, parent event extraction, and news fallback behavior. No contradiction with annotations.

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

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

Description is very long (several paragraphs) and dense with details. While front-loaded with main purpose, it could be more concise. Some details like fan-out examples and response shapes could be shortened or moved to schema.

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 (3 parameters, no output schema, 21 siblings), the description covers all essential aspects: resolver contract, parent events, news fields, safety mechanisms, and response shapes. It provides sufficient context for an agent to use the tool correctly.

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

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

Schema coverage is 100%, baseline 3. Description adds value by giving concrete examples for market parameter (slug, URL, question text), explaining depth options in context, and illustrating fan-out examples for different bet types, enhancing understanding beyond schema descriptions.

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

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

The description clearly states it researches a Polymarket bet by pulling Pipeworx data, specifying input types (slug, URL, question) and what it returns (evidence packet + comparison). It also distinguishes from sibling tools like polymarket_edges by focusing on a single bet with deep data.

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

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

Explicitly states use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z'), provides examples of fan-out for different bet types, and explains depth parameter options. Does not explicitly exclude alternative tools but gives clear 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 indicate read-only, open-world, idempotent. Description adds critical behavioral details: single parallel call, correct off-calendar fiscal year handling, sorted results by primary metric, inclusion of pipeworx:// citation URIs, and that it replaces 8–15 sequential lookups. 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?

Description is front-loaded with the core purpose in the first line, followed by usage guidance and data details. Every sentence is necessary and concise, 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 2 required params, no output/sibling complexity, description fully covers what the tool does, what it returns, and when to use it. No missing aspects for an agent to select and invoke correctly.

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

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

Schema coverage is 100%, but description enriches both parameters: for 'type', it details what data is retrieved (income statement items for company, FAERS counts for drug); for 'values', it clarifies tickers vs. drug names and range (2–5). Adds meaning well beyond basic schema.

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

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

The description clearly states the tool performs side-by-side comparisons of 2–5 companies or drugs, with specific verbs like 'compare' and resources like 'entities'. It distinguishes itself from siblings by being the preferred method for comparisons vs. sequential single-site 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?

Description explicitly instructs to 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Provides concrete query examples (e.g., 'X vs Y', 'which is bigger') that help agents recognize when to invoke this tool.

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

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

Annotations already indicate read-only and idempotent behavior. The description adds detail about returning top-N tools with full schemas and curated examples, enhancing transparency beyond annotations.

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

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

Description is front-loaded with purpose and includes useful domain examples, though it is somewhat lengthy. Every sentence adds value; could be slightly more 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?

No output schema exists, but the description compensates by explaining the output format (top-N tools with schemas and examples) and mentions 'ready to call directly'. Sufficient for a discovery 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% and includes examples. The description adds minimal extra meaning beyond what the schema provides, such as context that the query is a 'natural language description'.

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

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

The description clearly states it 'finds tools by describing the data or task' and lists numerous domains (SEC filings, financials, etc.), distinguishing it from sibling tools which are specific data retrieval functions.

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

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

Explicitly advises 'Call this FIRST when you have many tools available and want to see the option set (not just one answer)', providing clear when-to-use guidance and implying direct tool usage as an alternative.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context: fans out across multiple sources, returns specific fields, notes USPTO API sunset and soft-fail behavior, and mentions the parallel call nature. No contradictions with annotations.

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

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

The description is information-dense but well-structured, starting with example queries and then detailing output components. Every sentence provides value. Could be slightly more concise, but overall efficient.

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

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

Given the tool's complexity (multi-source, no output schema), the description fairly completely lists all returned data types and includes caveats (patents sunset, name limitation). It provides sufficient context for an agent to assess if this tool fits the user's intent.

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

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

Schema coverage is 100% with descriptions for both parameters. The description reinforces that value accepts ticker or CIK (not names) and that type is currently limited to 'company'. This adds clarity beyond the schema's enum description.

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

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

The description clearly states the tool provides a 'full cross-source profile of a US public company' and lists concrete outputs (cik, filings, fundamentals, patents, news, LEI). It distinguishes from siblings like resolve_entity by noting names are not supported here.

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

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

Explicitly advises 'ALWAYS PREFER' this tool over chaining individual lookups for holistic requests. Also specifies when to use resolve_entity (if only a name is available), 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?

Description confirms destructive nature via 'Delete' and adds context about clearing sensitive data, complementing annotations (destructiveHint=true). However, it does not discuss idempotency or side effects beyond the annotations.

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

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

Two sentences, front-loaded with action and conditions. No 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 the tool's simplicity (one required param, no output schema), the description fully covers purpose, usage, and behavioral context.

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

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

Schema coverage is 100% with parameter 'key' described as 'Memory key to delete'. The tool description adds no further information about the parameter, meeting the baseline 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?

Clearly states verb 'Delete' and resource 'memory by key'. Differentiates from siblings 'remember' and 'recall' by explicitly naming 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?

Explicitly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. Also suggests pairing with 'remember' and 'recall'.

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 the process: fetches the page, extracts title/description/key links, and emits standard markdown format. Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds value by explaining the extraction and output format, 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?

The description is concise: three sentences covering purpose, process, output, and use cases. No fluff or redundancy; every sentence adds value.

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

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

Given the tool has two well-described parameters, complete annotations, no output schema needed (output is a text blob), the description fully covers functionality, behavior, and application scenarios. Contextually complete.

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

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

Schema coverage is 100%, with descriptions for both parameters. The description adds minimal extra meaning, just reiterating the URL format and default/max for max_links already in 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 generates a production-ready llms.txt file for any URL, specifying the verb ('generate'), resource ('llms.txt file'), and context (AI crawlers). It distinguishes itself from siblings by focusing on this specific output format, unlike other tools like scan_competitor_ai_presence or ai_visibility_check.

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

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

The description provides explicit use cases: indexing a client's site, drafting your own llms.txt, or auditing a competitor. It doesn't explicitly mention when not to use or alternatives, but the use cases are clear and context-specific.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds return structure with fields like value, timePeriodStart, geoAreaName, source, footnotes, offering more detail than annotations alone.

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 wasted words. Front-loaded with purpose and includes essential guidance.

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

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

Without an output schema, the description describes the return object sufficiently. Mentions source tools for parameters. Adequate for a data retrieval tool.

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

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

All 5 parameters have schema descriptions (100% coverage). The description adds value by telling where to obtain seriesCode and areaCode, which the schema doesn't cover.

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

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

Description clearly states verb 'Get observations' and resource 'SDG series' with geographic and time constraints. It references sibling tools for input codes, distinguishing from list_indicators/list_series which only return metadata.

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 mentions that seriesCode comes from list_indicators/list_series and areaCode from list_geoareas, providing prerequisites. Does not explicitly state 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?

Annotations already cover safety (readOnlyHint, idempotentHint). The description adds value by noting the M49 code format and linkage to another tool, 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 concise sentences, front-loaded with the action and key details. Every word serves a purpose, with no redundancy.

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

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

The description fully covers what the tool does, what it returns (M49 codes), and how to use the output. No output schema is needed for such a simple reference 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?

No parameters exist, and schema coverage is 100%. The baseline for zero-param tools is 4; the description adds no extra param info because none is needed.

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: listing geographic areas with M49 area codes. It distinguishes from siblings by showing its role as a reference for get_series_data, using specific examples like '1 = World, 4 = Afghanistan'.

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 tells the agent when to use the output: pass the M49 code as areaCode in get_series_data. While it doesn't list exclusions, the context is clear for a parameterless lookup tool.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description is consistent with these (e.g., 'list' implies read-only). However, it adds no additional behavioral context such as authentication needs, rate limits, or error handling for invalid goal codes, so a score of 3 is appropriate given the annotation coverage.

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 long, no fluff. The first sentence states the primary purpose, and the second adds the optional drill-down behavior. Every sentence 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 tool with one optional parameter, the description covers both usage scenarios completely. There is no output schema, but the description adequately implies the return format (goals with code, title, description; optionally with targets and indicators). 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?

The input schema has 100% coverage with a description for the 'goal' parameter that is identical to the part of the tool description describing behavior. The tool description essentially repeats this, so it adds no new meaning beyond what the schema already provides. Baseline 3 is correct.

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 lists the 17 UN Sustainable Development Goals with optional drill-down into one goal for targets and indicators. It uses a specific verb ('list') and resource ('goals'), and the optionality distinguishes it from sibling tools like 'list_indicators' and 'list_geoareas'.

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 the optional parameter behavior: if 'goal' is set, it returns that goal with details; otherwise returns all goals. This provides clear context for when to use the parameter, though it does not explicitly exclude other use cases or mention alternatives from 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 already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by detailing that the tool nests data series under indicators, which is behavioral context beyond the annotations.

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

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

The description is two sentences, each purposeful: the first states the tool's role, the second provides a concrete example and links to a sibling. No 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 no parameters and no output schema, the description sufficiently explains what the tool returns (list of indicators with nested series) and how it relates to get_series_data. It is complete for a simple catalog listing tool.

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

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

The tool has no parameters, and schema coverage is 100% (vacuously). Per guidelines, baseline is 4, and the description adds no parameter information since none exist.

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

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

The description clearly states the tool provides a queryable catalog of SDG indicators, with an example of an indicator and its nested series code. It distinguishes itself from siblings like get_series_data by indicating that this tool yields the series codes to be used elsewhere.

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 implicitly guides when to use it (before get_series_data) by stating 'Use a series code as seriesCode in get_series_data.' However, it lacks explicit exclusions or alternatives to other sibling tools like list_series or list_goals.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds that series codes are passed to get_series_data, offering useful context beyond annotations.

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

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

Two sentences, front-loaded with purpose and usage. No wasted words.

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

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

With zero parameters, no output schema, and annotations covering behavioral aspects, the description fully addresses what the tool returns and when to use it.

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

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

No parameters; schema coverage is 100% trivially. Baseline for 0 parameters is 4, and description does not add parameter info since none exist.

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 returns a flat list of all SDG data series with code, description, and associated goal/target/indicator. It distinguishes from sibling list_indicators by specifying raw series list vs indicator-nested view.

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

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

Explicitly says to use this when you want the raw series list rather than the nested view from list_indicators. Provides clear context and alternative.

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

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

The description discloses rate limiting (5 per identifier per day), that it's free and doesn't count against tool-call quota, and the team's reading habits. Annotations are all false, which is consistent with a write operation. 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 4 sentences, each earning its place. It is front-loaded with purpose, followed by usage guidance, then behavioral context. No redundant or vague language.

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 covers all necessary aspects: purpose, usage, parameter semantics, rate limits, and behavioral notes. It is complete for a feedback tool and leaves no major gaps.

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

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

The input schema has 100% coverage with descriptions for each parameter. The description adds extra context beyond the schema, such as advising to describe issues in terms of Pipeworx tools/packs and not paste end-user prompts, which enhances parameter understanding.

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

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

The description clearly states the tool's purpose: to report bugs, feature requests, data gaps, or praise to the Pipeworx team. It uses specific verbs like 'tell', 'is broken, missing, or needs to exist', and distinguishes itself from sibling tools (e.g., data retrieval tools) by being a feedback mechanism.

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

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

The description provides explicit guidance on when to use each feedback type (bug, feature, data_gap, praise), includes a note to describe issues in terms of Pipeworx tools/packs, and states rate limits and quota exemption. This helps the agent decide 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?

Annotations declare readOnlyHint, openWorldHint, idempotentHint true. The description adds that data is self-aggregating from CF analytics-engine, no PII, and cached 5min-1h, providing behavioral context beyond annotations.

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

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

Three well-structured sentences: main purpose, then bullet-pointed use cases. Front-loaded with main action, no fluff. Exceptionally concise for the information conveyed.

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

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

For a simple tool with one parameter and no output schema, the description explains return values (top tools, packs, call volume), data source, caching, and privacy. Annotations cover safety. Complete context for an agent.

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

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

The only parameter 'window' is fully described in schema (enum, description). The description adds value by explaining that shorter windows surface current trends and longer windows show steady-state demand, enhancing parameter semantics.

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

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

The description clearly states what the tool does: returns top tools, packs, and call volume over a recent window, using the verb 'returns'. It distinguishes from siblings by focusing on trending usage data, which is unique among sibling tools like 'discover_tools' and 'ask_pipeworx'.

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

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

The description provides three explicit use cases (discovering hot data sources, confirming canonical choice, seeing alignment) and implies guidance on window selection. It does not specify exclusions but the use cases give sufficient context.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and not destructive. The description adds critical behavior: walks child markets, computes partition_check, applies Jaccard similarity threshold (0.30), filters placeholders, and describes response structure. 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 comprehensive but somewhat verbose (over 200 words). It is front-loaded with the core requirement and uses clear sections (CAPS for key terms). Some sentences could be shortened without losing information, but overall it is well-structured.

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

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

Given no output schema, the description thoroughly explains response fields (opportunities[], partition_check) and failure modes (skipped_low_similarity, partition filter). It covers complex behavior like cross-event mode and placeholder filtering, making the tool self-contained for an AI agent.

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

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

Schema coverage is 100%, but the description provides rich added context: example slugs (e.g., 'fed-decision-may-2026'), URL acceptance, mode meanings, and detailed side effects like the partition check and semantic anchor. This goes well beyond the schema descriptions.

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

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

The description clearly states it finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It specifies two modes (event and topic) with concrete examples (e.g., 'fed-decision-may-2026') and explains what each does. This distinguishes it from sibling tools like polymarket_edges and polymarket_kalshi_spread.

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

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

The description explicitly requires one of 'event' or 'topic' and warns that call with no args fails. It recommends event mode for a specific market and explains when to use topic mode. However, it does not provide explicit guidance on when NOT to use the tool or compare directly with sibling 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 provides extensive behavioral details beyond annotations: explains three segments, methodology (e.g., 'lognormal barrier from 90d FRED log-returns'), edge calculations, tradeable-edge knobs, 24h-move warnings, and caching (1h KV-level). 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 lengthy but well-organized with clear sections (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, etc.) and front-loaded purpose. Every sentence adds value, though some details (e.g., per-sport α values) could be streamlined without losing essential information.

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

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

Given the absence of an output schema, the description thoroughly explains response structure (by_segment, _diagnostics), edge components, and why segments might be empty. It covers caching, knobs, and edge cases (Fed bets) to fully inform an agent.

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

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

Schema coverage is 100%, but the description adds significant value: explains knobs like min_partition_leg_kelly with context ('Partition arbs always return kelly_fraction_half=0'), default values, and practical usage tips (e.g., 'Polymarket has zero trading fees...'). This compensates for the high schema coverage 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 starts with a clear verb ('Scan top Polymarket markets and return opportunities') and resource ('Polymarket markets where Pipeworx data disagrees with market price'). It explicitly states the agent's goal ('what should I bet on today') and distinguishes from siblings like polymarket_arbitrage by focusing on disagreement with Pipeworx data.

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

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

The description explicitly frames the tool for opportunity discovery ('what should I bet on today') and mentions when Fed bets are surfaced but excluded. However, it does not explicitly state when to use alternatives like polymarket_arbitrage or bet_research, leaving some ambiguity for the agent.

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

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

The description adds substantial detail beyond annotations: it explains temporal alignment, compatibility warnings, skipped cross-type/subtype counters, and the conditions under which matched_pairs=0 indicates no arbitrage vs semantic mismatch. This fully discloses behavioral traits like non-equivalent bet shapes and temporal misalignment, which the annotations (readOnlyHint, openWorldHint) do not cover.

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

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

The description is well-structured with sections for modes, response fields, safety checks, and temporal alignment. It is front-loaded with the main purpose and each sentence adds value. Despite length (~200 words), it is concise for the complexity covered.

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 cross-venue spreads with multiple edge cases (compatibility, alignment, skipped pairs), the description is complete. It explains the response structure, safety fields, and temporal alignment in sufficient detail for an agent to understand the output. No output schema exists, but the description compensates by detailing expected fields.

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

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

Schema coverage is 100% with descriptions for each parameter, so baseline is 3. The description adds value by explaining the two modes and the effect of explicit overrides, but the examples in the schema already provide some context. Thus a 4 is appropriate for moderate added meaning.

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 computing cross-venue spreads between Kalshi and Polymarket for the same question. It uses a specific verb ('cross-venue spread') and resource ('same resolving question'), distinguishing it from siblings like polymarket_arbitrage which likely focus on Polymarket internal arbitrage.

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

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

The description explicitly states two usage modes (topic macro shortcuts vs explicit tickers) and warns that most pre-mapped topics return compatibility warnings, advising that 'pre-mapped ≠ tradeable'. It also explains when spreads are meaningful (via compatibility_warning fields) and when they are not, 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?

Annotations already indicate read-only and idempotent. Description adds valuable context about scoping (anonymous IP, key hash, account ID) and non-destructiveness, which aligns 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?

Concise, well-structured, each sentence adds value: functionality, use cases, scoping, and pairing with related tools.

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 parameter and no output schema, the description fully covers purpose, usage, scoping, and relationships, leaving 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%. The description adds crucial semantics: omitting the key lists all keys, which is not obvious from 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 clearly states the tool retrieves a saved value or lists all keys, using specific verbs like 'retrieve' and 'list', and distinguishes it from sibling tools 'remember' and 'forget'.

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

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

It provides clear examples of when to use (look up target ticker, address, research notes) and mentions pairing with remember/forget, but does not explicitly state when not to use.

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

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

Annotations already declare readOnlyHint and idempotentHint; description adds behavioral details including fan-out to multiple sources, fallback from GDELT to GNews, soft-fail for USPTO, and return structure.

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 well-organized: opens with use cases, then sources, parameter details, and alternative tool. Could be slightly more structured but remains efficient.

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

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

Given no output schema, description sufficiently explains return format (changes[], total_changes, citation URIs). Covers all parameters and source behavior, fulfilling context needs.

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

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

Schema coverage is 100%; description adds usage tips like recommended 'since' values ('30d' or '1m') and explains value can be ticker or CIK, enhancing clarity.

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

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

The description explicitly states the tool provides a change feed for a company over a time window, uses clear examples like 'what's new with X', and distinguishes from sibling tool 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?

Provides explicit when-to-use examples and identifies entity_profile as an alternative for static profiles. Could be improved by referencing other siblings like compare_entities, but current guidance is strong.

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

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

Annotations indicate idempotentHint=true, which matches a store operation. Description adds valuable context: scoping by identifier, persistence differences between authenticated/anonymous, and 24-hour retention. No contradictions, but could mention behavior on overwrite.

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?

Five sentences with no waste. Front-loaded with purpose and usage, then adds behavioral details and related tools.

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 all essential aspects: purpose, usage, behavioral traits, and parameters. Lacks mention of return value or confirmation of storage, but for a simple save operation this is acceptable. No output schema is provided, so description carries full burden.

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

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

Schema covers both parameters with descriptions. Description adds naming convention examples (e.g., 'subject_property', 'target_ticker') and clarifies that value is any text, which is helpful beyond the schema's brief 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 'Save data the agent will need to reuse later' with a specific verb and resource. It distinguishes itself from siblings like 'recall' and 'forget'.

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

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

Explicitly tells when to use: 'when you discover something worth carrying forward' and mentions pairing with recall and forget for retrieval and deletion. Provides excellent usage context.

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

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

Annotations already indicate it is read-only, open-world, idempotent, and non-destructive. The description adds context: internal cascading through multiple endpoints, replacing manual lookups, and providing citation URIs. It does not contradict annotations.

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

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

The description is a single paragraph that is well-structured: starts with examples, then usage guidance, then detailed type-specific info. It is concise and front-loaded, with every sentence adding value.

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

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

Given no output schema, the description explains return fields for each type (ticker, CIK, company name, RxCUI, ingredient, brand, citation URIs). It also mentions auto-disambiguation. This is fairly complete for a lookup tool with two parameters.

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?

Despite 100% schema coverage, the description adds meaning: explains that 'value' can be ticker, CIK, or name for companies, and brand/generic name for drugs. It also clarifies the 'type' enum values further. This goes beyond the schema.

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

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

The description clearly states the tool resolves names to canonical IDs, with concrete examples (ticker, CIK, RxCUI). It specifies supported entity types (company, drug) and what each returns. However, it does not explicitly differentiate from sibling tools like compare_entities or entity_profile, though it advises 'Use FIRST when you have a name but need an ID,' which implies a unique role.

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

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

The description explicitly states when to use the tool: 'Use FIRST whenever you have a name but need an ID.' It does not list when not to use it or provide alternative tools, but the guidance is clear and actionable.

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

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

Annotations already provide readOnlyHint, idempotentHint, and openWorldHint, confirming safe, read-only behavior. The description adds that it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list, which is transparent and consistent with 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?

The description is concise (three sentences), front-loaded with the main purpose, and every sentence adds value: purpose, internal mechanism, use case, and output. 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?

The description covers purpose, usage, important parameter nuance (first entity as subject), internal operation (calls ai_visibility_check), and output (ranked list with score, confidence, signal density). Given the complexity and lack of output schema, this is complete and sufficient for an agent.

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

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

Input schema has 100% description coverage for all 4 parameters. The description adds value by noting that the first entity in the 'entities' array is treated as the 'subject' for narrative purposes, which is not in the schema. Otherwise, schema already explains remaining parameters adequately.

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 compares AI visibility across multiple entities side-by-side, using ai_visibility_check probes. It distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison). The verb 'compare' and resource 'AI visibility across multiple entities' are specific, and an example use case is given.

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 notes it's 'useful for competitive AI-marketing audits' and implies the tool is for comparing multiple entities. However, it does not explicitly state when not to use it versus alternatives like ai_visibility_check. The context is clear but lacks exclusions.

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

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

Annotations confirm read-only, idempotent, non-destructive. Description adds: fans out across services, partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, sources_failed field. Excellent disclosure beyond annotations.

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

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

Well-structured: starts with core purpose, then details on services, return fields, and limitations. Slightly long but every sentence adds value. Could be slightly tighter.

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 lists return fields (summary block, advisories, links, alternatives). Covers ecosystem restrictions, graceful degradation, and timeouts. Complete for a composite tool.

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

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

Schema covers both parameters with descriptions (100% coverage). Description adds: package accepts scoped names, version defaults to latest. Provides meaningful 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 articulates the tool's composite nature: it checks npm packages across deps.dev and bundlephobia, answering 'should I add this package'. It distinguishes itself from siblings by being the only scan_ tool and specifying the 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?

Explicit usage guidance: 'Use whenever an agent asks is X safe / popular / small or what does adding lodash cost me'. Also notes NPM-only in v1 and alternatives for other ecosystems (deps.dev:version).

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 safe, read-only behavior. Description adds value by detailing the return verdict types, structured form extraction, and citation format. 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?

Front-loaded with trigger phrases, then provides clear scope and behavior. Slightly long but each sentence adds useful information with minimal redundancy.

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

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

For a single-parameter tool without output schema, the description covers everything needed: purpose, scope, input format, and return types. The lack of output schema is compensated by listing the verdict types.

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 is well-described in the schema with examples. The description adds natural-language context and clarifies the claim format beyond the schema definition.

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

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

The description provides specific trigger phrases and explicitly states it performs natural-language claim verification against authoritative sources for company-financial claims. It distinguishes from siblings by noting it replaces 4-6 sequential calls.

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

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

Clearly instructs to use whenever checking factual correctness of a user statement. Specifies supported domain (company-financial) and mentions the return types. Lacks explicit when-not-to-use but is sufficient given the context.

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