Steam

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

Annotations already indicate safe, non-destructive, idempotent behavior. The description adds valuable context: it probes external LLMs, requires a BYO API key for Anthropic (with cost implications), returns structured results per model, and explains default free model. Could mention potential latency or error handling, but overall strong.

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

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

The description is three sentences: first sentence states main action and default, second covers the optional API key, third summarizes return values and use cases. It is front-loaded, concise, and every sentence adds essential information 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 no output schema, the description adequately describes the return format (per-model {score, confidence, signals, raw_response} + combined view). It covers key behavioral aspects (default model, optional anthropic) and use cases. Minor gaps: error handling, behavior when Anthropic key is missing but requested are not addressed, but overall satisfactory 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%, so baseline is 3. The description adds value by giving concrete examples for 'entity' (e.g., 'Pipeworx'), clarifying default behavior for 'models' (omit for just workers-ai), and explaining the role of '_apiKey' and 'context'. This goes beyond the schema descriptions.

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

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

The description uses a specific verb ('probe') and resource ('visibility of business/brand/product/topic') and clearly distinguishes the tool's purpose from sibling tools by detailing the per-model scoring and default model. It is not a tautology and provides a clear, actionable purpose.

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

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

The description states usage contexts ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains how to use with/without an Anthropic API key. However, it does not explicitly mention alternatives or when not to use it, though the provided use cases are clear.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive, so the safety profile is clear. The description adds minimal behavioral information beyond naming the API source (Steam Store API). It does not disclose rate limits, error handling, or data freshness, but given the annotations, the baseline is acceptable.

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

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

The description is a single sentence that efficiently conveys the tool's purpose and source. It is appropriately front-loaded and contains no redundant text. However, it could be slightly expanded to include parameter context without sacrificing 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?

Given the presence of an output schema (covers return values) and comprehensive annotations (covers behavior), the description is adequate but incomplete. The main gap is the lack of parameter documentation, which is not compensated by the schema's 0% coverage. The tool's complexity is low, so the description meets minimal expectations.

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 0% description coverage for its three parameters. The description does not explain what 'l', 'cc', or 'appids' represent or how they affect results. Examples in the schema imply usage (e.g., 'english', 'US') but the description itself adds no semantic value. The agent must infer parameter meaning from the examples 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 that the tool retrieves store page details from the Steam Store API. It implicitly identifies the resource (store page details) and the action (getting details), which distinguishes it from siblings like 'app_list' or 'app_news'. However, it could be more explicit about what constitutes 'store page detail'.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention context, prerequisites, or exclusions. For example, it does not compare to 'app_list' or 'player_summaries' to help the agent decide.

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 false, so the agent knows it's safe and idempotent. The description only adds 'large', hinting at a potentially large response. No behavioral drawbacks are disclosed beyond annotations.

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

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

The description is extremely concise at four words, front-loading the essential purpose and scope. Every word earns its place with no waste.

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

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

With no parameters and an output schema existing, the description could be more complete by mentioning that the result is a large list possibly requiring pagination or being slow. It provides the core information but lacks nuance for a 'large' output.

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, so schema coverage is 100%. The description does not need to add parameter details. Baseline score of 4 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 'Full app list (large)', specifying the verb 'list' and resource 'apps', and distinguishes from sibling tools like app_details (single app) and app_news (news). The word 'large' adds useful scope context.

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

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

No explicit guidance on when to use this tool versus alternatives like app_details or search tools. The description does not mention prerequisites, limitations, or alternative contexts.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. Description adds no additional context such as rate limits, output format, or data freshness.

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 words is extreme brevity. While concise, it is under-specified and fails to convey sufficient 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 having an output schema and three parameters requiring explanation, the description provides none. It is inadequate for an agent to use effectively.

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

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

Schema has 0% description coverage for parameters. Description does not explain the meaning of appid, count, or maxlength, leaving the agent without guidance.

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 is a vague noun phrase 'App news feed.' It lacks a verb to indicate the action (e.g., retrieve, fetch) and does not distinguish from sibling tools like app_details or app_list.

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

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

No guidance on when or when not to use this tool, no mention of alternatives or prerequisites.

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, not destructive. The description adds valuable context: it routes questions, fills arguments, returns structured answers with stable pipeworx:// citation URIs, and references a vast set of sources. This goes beyond the annotations by explaining the internal mechanism and output format.

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

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

The description is front-loaded with the key preference instruction, then explains what the tool does, followed by usage cues and examples. Every sentence serves a purpose, and it is concise given the amount of information conveyed (routing, sources, output type, examples). No filler or repetition.

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

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

Even without an output schema, the description explains that the tool returns a structured answer with citation URIs. It covers scope, input format, and behavior comprehensively. For a general-purpose query tool, this is sufficiently complete to guide correct invocation.

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

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

Input schema has 6 aliases for 'question' with 100% coverage. The description supplements by providing concrete examples (e.g., 'current US unemployment rate', 'Apple's latest 10-K') that clarify the expected natural language input. The examples are directly relevant and help an agent formulate proper queries.

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

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

The description clearly states it answers factual questions by routing to 3,547 tools across 819 sources, returning structured answers with citations. It explicitly says 'PREFER OVER WEB SEARCH' and lists many domains, distinguishing it from sibling tools like 'recall' or 'entity_profile' which are more narrow.

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 strong usage guidance: 'PREFER OVER WEB SEARCH' for specific data types, and lists trigger phrases like 'what is', 'look up', etc. It includes multiple examples. It does not explicitly state when not to use, but the positive guidance is clear and effective.

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 extensive behavioral context beyond annotations: resolver contract, parent event extractor, news fields with fallback handling, safety on low-confidence matches, closed/dead market handling, and wide-spread illiquidity notes. Annotations (readOnlyHint, openWorldHint, idempotentHint) 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?

The description is long but well-structured with labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.) and front-loaded with the main purpose. Some redundancy exists but overall efficient given the 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 fully explains response fields (result.market, result.analysis, result.evidence) and edge cases (low confidence, closed markets, wide spreads). Complete coverage for a 3-parameter tool with no output schema.

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

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

All three parameters are described in detail: market explains slug/URL/question text, depth explains quick vs thorough, include_raw explains when to use true/false and size implications. Schema coverage is 100%, and description adds significant 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 researches a Polymarket bet, specifies input types (slug, URL, question text), and lists classifiers and fan-out examples. It distinguishes from sibling tools like polymarket_edges by focusing on per-bet research.

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

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

Explicitly says 'Use for' scenarios like 'should I bet on X', providing clear context. Does not explicitly state when not to use, but the purpose is well-defined, and safety behaviors (low confidence, closed markets) guide appropriate 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=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral details: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/trials for drugs), handling of off-calendar fiscal years, sorting by primary metric, and return format (paired data + citation URIs). This goes beyond annotations, though the description could briefly note that results are sorted for easy interpretation.

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 yet well-structured. It front-loads with natural language trigger phrases and a clear statement of purpose, then provides detailed breakdowns for each entity type. Every sentence adds value with no redundancy 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?

With two simple parameters and no output schema, the description fully compensates by explaining what data is returned for each type, including citation URIs. It also mentions sorting and entity type differences, making the tool self-explanatory 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% with descriptions, but the description adds significant meaning: for type='company' it specifies revenue, net income, cash, long-term debt; for type='drug' it specifies adverse-event counts, approval counts, trial counts. It also clarifies that values should be tickers/CIKs for companies and drug names for drugs, which is essential for correct usage.

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: side-by-side comparison of 2–5 companies or drugs in one parallel call. It uses specific verbs like 'compare', 'rank', 'head to head' and explicitly distinguishes from sequential single-pack lookups, which is a key differentiator from siblings like entity_profile.

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

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

The description provides explicit guidance on when to use this tool: for comparing entities (e.g., 'which is bigger / better') and when not to use sequential single-pack lookups. It also includes example queries and replaces 8–15 sequential lookups, giving clear usage context.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false, so the safety profile is clear. The description adds minimal behavioral context beyond stating the tool returns a current count, but does not elaborate on rate limits, time granularity, or interpretation of the count. Given annotations cover the core traits, a score of 3 is appropriate.

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

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

The description is extremely concise (4 words) and front-loaded. It is appropriately sized for a simple tool with one parameter and annotations, but could benefit from slight elaboration on the time scope of 'current' to earn a perfect score.

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 1 parameter, annotations, and an output schema, the description lacks completeness. It fails to clarify the data source, time granularity, or any limits on the count. Schema description coverage is 0%, and the description does not compensate, leaving ambiguity about the exact meaning of 'current' and the return format.

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

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

Schema description coverage is 0%, and the description does not explain the `appid` parameter. The tool name and description imply it correlates with a game identifier, but no explicit meaning is added. The agent must rely on external knowledge about what appid represents (e.g., Steam app ID). This is insufficient for 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 'Current concurrent players' clearly indicates the tool retrieves the current number of concurrent players for a given appid. This verb+resource pattern is specific and distinguishes from sibling tools like player_achievements, player_bans, player_stats, etc., which focus on individual player data rather than aggregate counts.

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

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

No guidance is provided on when to use this tool versus alternatives. The description does not mention context, prerequisites, or exclusions. It leaves the agent to infer when a player count is needed without any comparative information.

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. Description adds that it returns 'names, descriptions, and full input schemas (with curated examples)' and that results are 'ready to call directly, no second schema lookup needed.' This provides useful behavioral context beyond annotations.

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

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

Two sentences with no wasted words. First sentence defines purpose with examples, second sentence describes output and usage advice. Front-loaded and 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 complexity (meta-tool, multiple aliases, no output schema), the description is complete: it explains purpose, when to use, output format, and actionability. No gaps.

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

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

Schema description coverage is 100% with clear descriptions for each parameter including aliases. Description adds examples of query topics but does not provide additional semantic meaning beyond what the schema already offers. Baseline 3 is appropriate.

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

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

Description clearly states 'Find tools by describing the data or task' and lists many specific domains (SEC filings, FDA drugs, etc.), distinguishing it from sibling tools which are specific tools. The tool is a discovery meta-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 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 and want to see the option set (not just one answer).' Provides clear context but does not explicitly state when not to use or suggest alternatives; however, sibling list provides implicit 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, and openWorldHint=true. The description adds substantial behavioral context: it fans out across multiple APIs, soft-fails for patents, and uses fallback for news. It details return fields (cik, filings with URIs, fundamentals, patents, news, LEI) and notes limitations, which goes well 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 about 100 words, densely packed with useful information. It is front-loaded with example queries and purpose. Every sentence adds value, though the list of return fields could be slightly more concise. Overall, it is well-structured and efficient.

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

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

For a complex tool with no output schema, the description explains the return format (up to 5 filings with URIs, fundamentals sorted, patents soft-fail) and limitations. It covers most relevant aspects, but does not explicitly state 'US public companies only' (though implied by CIK/ticker). This is a minor gap.

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

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

The input schema has 100% description coverage, so baseline is 3. The description adds value by giving concrete examples ('Pass ticker "AAPL" or zero-padded CIK "0000320193"') and clarifying that names are not supported. This provides practical guidance beyond the schema basics.

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 'full cross-source profile of a US public company', using specific verbs like 'Tell me about X' and 'brief me on Tesla'. It clearly distinguishes from sibling tools like 'resolve_entity' and 'compare_entities' by focusing on a single company's holistic 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?

The description explicitly states 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view', and provides a clear exclusion: 'names not supported (use resolve_entity first if you only have a name)'. This gives explicit when-to-use, when-not-to-use, and alternatives.

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

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

The description adds value beyond annotations by explaining the use cases (stale context, task done, clear sensitive data). Annotations already indicate destructiveHint=true and idempotentHint=true, so the description provides behavioral context 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?

Two sentences with no wasted words. The purpose is front-loaded in the first sentence, and the second sentence provides usage guidance. Highly 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?

For a simple tool with one parameter and no output schema, the description covers purpose, usage, and context comprehensively. No missing information.

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

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

Schema coverage is 100% with the only parameter 'key' described as 'Memory key to delete'. The description does not add extra semantics beyond the schema, which is adequate for this simple parameter.

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

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

The description clearly states 'Delete a previously stored memory by key', using a specific verb (delete) and resource (memory by key). It effectively distinguishes from sibling tools '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?

The description explicitly states when to use the tool: 'when context is stale, the task is done, or you want to clear sensitive data'. It also pairs it with 'remember' and 'recall' for 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 adds no behavioral info beyond annotations. Annotations indicate safe, read-only, idempotent operation, but the description does not leverage or extend this.

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-word description is under-specific. While concise, it fails to earn its place by providing any actionable information.

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

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

With 2 parameters, no param descriptions, and an output schema not shown, the description is completely inadequate for an AI agent to correctly select and invoke this tool.

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

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

Schema has 0% description coverage and description offers no explanation of parameters (steamid, relationship). Agent cannot infer meaning or constraints from description alone.

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

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

Description 'Friends.' is a tautology that restates the tool name without specifying a verb or resource. It fails to indicate what action the tool performs (e.g., list friends, search friends).

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

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

No guidance on when to use this tool vs alternatives. The description provides no context for tool selection among many 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 indicate read-only, idempotent, non-destructive hints. The description adds valuable behavioral details: fetches the page, extracts title/description/links, and emits standard markdown format. This complements 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 three sentences, each adding value: first states main purpose, second explains process, third lists use cases. No redundant information, perfectly 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?

Despite no output schema, the description fully explains what the tool returns (a single text blob in standard llms.txt markdown format). Combined with annotations and schema, it provides complete context for an agent to invoke and interpret the tool correctly.

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

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

The input schema covers both parameters (url, max_links) with descriptions. The description adds meaning by explaining the output (single text blob in standard format) and usage context, going beyond the schema's property definitions.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the action (generate), resource (llms.txt file), and context (for AI crawlers). It distinguishes itself from sibling tools by focusing on a specific output format and use case.

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

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

The description explicitly lists use cases: indexing a client's site, drafting for own project, or auditing competitors. While it does not explicitly state when not to use, the context is clear and sufficient for an agent to determine appropriate scenarios.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds no additional behavioral context (e.g., input requirements, error handling). Since annotations cover the safety profile, a score of 3 is appropriate.

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

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

The description is extremely short (two words) but severely under-specified. It sacrifices essential information, making it inadequate for understanding tool behavior.

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 a required parameter and an output schema, the description is far too minimal. It lacks any detail about return values, parameter semantics, or use cases, leaving significant gaps for the 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 description coverage is 0%, and the tool description does not explain any of the three parameters (steamid, include_appinfo, include_played_free). The agent receives no guidance on parameter meanings or usage.

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 'Owned games' merely restates the tool name without providing a clear verb or action context. It does not specify what operation is performed (e.g., retrieve, list), and does not differentiate the tool from siblings like 'app_details' or 'player_achievements'.

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

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

No guidance is given on when to use this tool versus alternatives. The sibling list includes many game-related tools, but the description offers no context for appropriate usage or 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 are all false, so the description fully shoulders the burden. It discloses critical behavioral traits: rate-limited to 5 per identifier per day, free, doesn't count against quota, and that the team reads digests daily. 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 with front-loaded purpose. Every sentence adds value—usage guidelines, behavioral notes, and boundaries. 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 tool's simplicity (3 params, no output schema), the description covers purpose, guidelines, transparency, and parameter context. It does not mention return values, but that is acceptable for a feedback submission tool. Missing confirmation of success/failure, but minor.

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

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

Schema coverage is 100%, so the schema already documents all three parameters well. The description adds context on how to use the 'type' enum and emphasizes not to paste prompts, but does not add significant meaning beyond the schema. Baseline 3, minor improvement for usage tips.

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 is for providing feedback to the Pipeworx team about bugs, features, data gaps, or praise. It uses specific verbs and resources, and the purpose is distinct from siblings like 'discover_tools' or 'ask_pipeworx'.

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

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

The description explicitly lists when to use each feedback type (bug, feature, data_gap, praise) and provides guidance on what to avoid (e.g., not pasting end-user prompts). It also mentions rate limits and that the tool is free, helping agents decide when to invoke it.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds: self-aggregating from CF analytics-engine, no PII, cached 5min-1h. No contradiction; 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?

Description is concise with two sentences and a bulleted list of use cases. Front-loaded with purpose, then usage guidance. No wasted text.

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 returns (top tools, packs, volume) and data source (CF analytics, no PII, caching). Complete for a read-only aggregation tool.

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

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

Schema coverage is 100% with one parameter. Description adds meaning: '24h (default) | 7d | 30d. Shorter windows surface what's hot; longer windows show steady-state demand.' This enhances the schema 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 returns top tools, top packs, and call volume over a window. It uses specific verbs ('returns') and resources, and distinguishes from siblings by focusing on popularity/trending 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?

Explicit use cases are given: discovering hot data sources, confirming canonical choices, aligning with majority use. Window guidance is provided (shorter for hot, longer for steady-state). No explicit when-not-to-use, but the purpose 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 indicate readOnlyHint=true and destructiveHint=false, but the description adds no behavioral detail (e.g., whether appid/steamid are required, what happens on invalid input). It adds no value 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 extremely short (4 words), which is concise, but it omits essential information. It fails to use the space to communicate purpose, parameters, or behavior.

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 3 parameters (2 required), an output schema, and no parameter descriptions, the single sentence is grossly inadequate. The agent cannot determine how to invoke or interpret results from this description alone.

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 0%, meaning the input schema has no descriptions. The description does not explain any parameters (e.g., steamid, appid, l). The agent receives no assistance in understanding parameter purpose or format.

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 'Achievements for a game' is a noun phrase that vaguely suggests the tool provides achievements, but it lacks a verb (e.g., 'retrieve', 'list') and does not clearly distinguish from sibling tools like player_stats or owned_games.

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

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

No guidance is given on when to use this tool versus alternatives (e.g., player_stats for stats, owned_games for games). The description provides no context for selection.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds no behavioral context beyond the name, such as whether bans are returned as list, count, or 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?

Extremely concise at 3 words, but this sacrifices clarity. The phrase is front-loaded but fails to be informative. Could trade brevity for more substance.

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

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

With one simple parameter and output schema present, description still fails to explain what the output is or how to interpret results. Lacks completeness for an agent to reliably use the tool.

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

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

Schema description coverage is 0%, placing burden on description. Description does not explain the 'steamids' parameter format (e.g., comma-separated, single ID, max length). The example in schema provides some hint but description adds zero value.

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

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

Description is a noun phrase 'VAC/community bans' lacking a verb. It does not specify what the tool does (e.g., check, list, get). Compared to siblings like 'player_summaries', it is vague and fails to convey the action.

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

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

No guidance on when to use this tool instead of siblings such as 'player_summary' or 'player_stats'. No context on prerequisites or alternatives, leaving the agent to infer usage.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds no behavioral context beyond what annotations convey. It does not contradict annotations but fails to add value.

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

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

Extremely concise but under-specified. A single word or phrase is not sufficient; it sacrifices clarity for brevity.

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

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

With one required parameter, no schema descriptions, and multiple related sibling tools, the description is completely inadequate for understanding the tool's purpose and usage.

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

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

Schema description coverage is 0%, and the description provides no meaning for the 'steamid' parameter. The description does not compensate for missing 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 'Steam level.' is a mere noun phrase that does not state what the tool does. It lacks a verb and fails to distinguish from sibling tools like 'player_summaries' which also provide level information.

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

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

No guidance is provided on when to use this tool versus alternatives. Given siblings like 'player_summaries', the description gives no basis for choosing 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 declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds no additional behavioral context (e.g., rate limits, auth needs, data pagination) 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 extremely short (one sentence) but is not informative enough to be useful. While concise, it lacks the necessary detail for an agent to understand the tool's use.

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 and the presence of an output schema, the description could be minimal but still fails to provide essential details like expected parameter values or usage caveats. The agent is left without enough context 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 description coverage is 0%, and the description does not explain the meaning or format of 'appid' or 'steamid'. With no parameter documentation in the description, the agent must rely solely on the schema, which is insufficient for correct invocation.

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 'User stats for a game.' clearly indicates the tool retrieves stats for a specific game, but it lacks specificity and does not differentiate from sibling tools like player_achievements or player_bans. The verb is implied but not explicit.

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

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

No guidance on when to use this tool versus alternatives such as player_summaries or player_achievements. The description provides no context for selecting this tool over others.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds no additional behavioral context beyond annotations.

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

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

Two words is under-specification, not conciseness. The description is too short to convey necessary 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 single parameter and presence of output schema, the description should explain bulk behavior, input format, and expected output. It is completely inadequate.

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

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

Schema description coverage is 0%. The 'steamids' parameter is not explained in description. Only an example in schema shows comma-separated IDs, but no format, limits, or meaning is provided.

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 is 'Bulk profile.' It vaguely suggests fetching multiple profiles but lacks a specific verb or resource. Does not distinguish from sibling 'player_summary'.

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

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

No guidance on when to use this tool versus alternatives like 'player_summary' or other profile tools. No context provided.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds no additional behavioral context, such as what the summary contains or any side effects. It does not contradict annotations but adds negligible value.

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

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

The description is extremely short (two words), but this brevity comes at the cost of clarity. It does not earn its place as it provides insufficient information for an agent to understand the tool.

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 presence of an output schema and multiple sibling tools, the description is completely inadequate. It fails to explain the tool's purpose, behavior, or usage context, leaving the agent without critical information.

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

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

The input schema has 0% description coverage for parameters. The description does not explain the required 'steamid' parameter beyond its name and an example. It fails to provide meaning or constraints 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?

Description 'Profile summary' is vague and essentially restates the tool name 'player_summary'. It does not specify what kind of profile or what data is summarized, failing to distinguish it from sibling tools like player_stats or player_level.

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

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

No guidance on when to use this tool versus alternatives such as player_achievements, player_bans, or player_summaries. The context does not help an agent choose correctly among similar 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?

The description adds significant behavioral context beyond annotations: it details the monotonicity check, partition-sum check, semantic anchor (Jaccard similarity threshold), partition filter for placeholders, and response structure. Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint; the description extends transparency on internal logic and outputs.

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 and well-structured with visual cues (bold, uppercase important terms). However, it is somewhat lengthy and could be more concise while retaining key details. It front-loads the requirement and mode distinction, which is good for an agent.

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 tool lacks an output schema, so the description fully explains the response structure (opportunities array with fields, partition_check object in event mode). It covers edge cases (low similarity, placeholder filter) and integrates with annotations for a complete picture.

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 valuable semantics: example values for event slug, explanation that event is recommended for specific markets and topic for scanning, and what happens in each mode. This meaningfully supplements 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 finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks, and distinguishes between single-event and cross-event modes. The verb 'find' and specific resource ('arbitrage opportunities on Polymarket') are precise, and the explanation of the two modes differentiates it from related tools like polymarket_edges.

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 begins with 'REQUIRES one of `event` (single-event mode) OR `topic` (cross-event mode) — call with no args fails', providing explicit usage conditions and examples for each mode. It explains when cross-event mode is preferable, but does not compare to sibling tools or state when not to use this tool.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds extensive behavioral details: caching at KV level keyed on knobs, response structure with diagnostics for empty segments, edge components including slippage and kelly fractions, and market move warnings. 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 very verbose (over 500 words) and contains extensive technical details about model families, segments, and diagnostics. While well-structured, it is not concise; many details could be moved to output schema or parameter descriptions. Every sentence earns its place but the length reduces readability.

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

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

Given no output schema, the description thoroughly explains the return value structure, including the by_segment organization, diagnostics for debugging empty segments, and the fed_candidates/fed_note section. It also covers caching behavior and knob effects, making the tool's behavior complete for an agent.

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

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

Schema coverage is 100% with all parameters described. The description adds significant value beyond the schema by explaining defaults, the purpose of tradeable-edge knobs (like min_liquidity, max_spread_pp), and the reasoning behind min_partition_leg_kelly vs min_kelly. This helps an agent understand parameter interactions.

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

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

The description clearly states it scans top Polymarket markets and returns opportunities where Pipeworx data disagrees with market price, with a specific use case ('what should I bet on today'). However, it does not explicitly distinguish from sibling tools like polymarket_arbitrage or polymarket_kalshi_spread, which would help an agent differentiate.

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

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

The description provides usage context ('Built for what should I bet on today') and mentions agents can discover opportunities without paging hundreds of markets. However, it does not specify when NOT to use this tool or suggest alternatives among siblings, leaving the agent to infer usage boundaries.

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 extensive behavioral context beyond the annotations (readOnlyHint, idempotentHint, etc.). It explains safety fields like compatibility_warning with two specific cases, temporal_alignment, and skipped_cross counters, which are critical for understanding the tool's limitations and edge cases.

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 purpose and modes, but it includes very detailed explanations of response fields (e.g., skipped_cross_type details) that could be shortened. It is organized but slightly verbose for a tool description.

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

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

Despite lacking an output schema, the description covers the response structure (prices, spreads, safety fields, temporal alignment) in enough detail for an agent to interpret the result. It is thorough given 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?

With 100% schema coverage, the baseline is 3. The description adds value by listing the exact pre-mapped topics and explaining how the explicit parameters override the topic mapping, which helps the agent construct correct inputs.

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

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

The description clearly states the tool finds cross-venue spreads between Kalshi and Polymarket for the same resolving question. It specifies two modes (topic and explicit pairings) and distinguishes itself from siblings that focus on single-venue arbitrage or edges.

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: topic mode for pre-mapped macro shortcuts, and explicit mode for custom pairings. It warns that most pre-mapped topics currently return compatibility warnings and that spreads are meaningless when temporal_alignment is false, effectively telling the agent when not to rely on the output.

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 scope (per identifier) and pairing with remember/forget. 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, front-loaded with main action. Every sentence adds value. No 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?

With one optional param, no output schema, and thorough annotations, the description covers scope, related tools, and behavior 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% with parameter description. Description reiterates that omitting key lists all keys, adding minor 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 retrieves a value saved via 'remember' or lists all keys when omitted. It distinguishes from siblings like 'forget' and 'remember'.

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

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

Provides clear guidance on when to use: to look up stored context, and mentions scope and pairing with related tools. No explicit when-not-to-use, but context is 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?

The description adds important behavioral details beyond annotations: fan-out to multiple sources, fallback behavior from GDELT to GNews, soft-fail for USPTO due to API sunset, and return structure with citation URIs. All annotations 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?

The description is efficiently packed with information and front-loaded with common user queries. While slightly lengthy, every sentence adds value, making it appropriate for 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?

Despite no output schema, the description clearly states the return format (structured changes grouped by source, total_changes count, citation URIs). It also covers edge cases like fallback, soft-fail, and date format options, providing a complete picture.

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 the 'since' parameter with examples and recommended defaults, clarifies that 'type' only supports 'company', and gives examples for 'value'. This aids correct parameter usage.

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: 'change feed for a company' and lists concrete examples. It clearly distinguishes from sibling tool 'entity_profile' by stating when to use that instead.

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

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

It explicitly advises using 'entity_profile' for static profiles, providing clear when-not-to-use guidance. It does not comprehensively cover all alternative contexts, but the key differentiation is present.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds no behavioral context beyond the annotations, so it neither helps nor contradicts.

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

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

The description is extremely short, but it is under-specified. It sacrifices necessary detail for brevity, making it less useful than a more informative concise description.

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

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

The description is severely incomplete. It does not explain what the tool returns, how the parameters affect results, or any usage context. Even with good annotations, the agent lacks critical information.

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

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

With 0% schema description coverage, the description 'Recent games.' fails to explain what 'steamid' or 'count' represent. The agent must infer meaning from parameter names and examples, which is insufficient.

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 'Recent games.' provides only a vague hint of the tool's purpose. It does not specify the verb (e.g., 'list' or 'get'), nor does it differentiate from sibling tools like 'owned_games' or 'player_stats'.

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

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

No guidance is given on when to use this tool versus alternatives. For instance, there is no comparison to 'owned_games' or suggestion that this tool is for recent activity.

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: key-value scope by identifier, persistence duration (24 hours for anonymous, permanent for authenticated). Annotations only provide hints; description enriches with concrete behavior 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?

Four sentences, each serving a distinct purpose: purpose, usage guideline, storage behavior, paired tools. No wasted words; front-loaded with key 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 only two simple string parameters and no output schema, the description covers purpose, usage, storage, scope, persistence, and related tools. Complete for a straightforward memory tool.

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

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

Schema coverage is 100% with descriptions for both 'key' and 'value'. The description does not add new parameter-level semantics beyond the schema; it reinforces purpose but provides no extra syntax or constraints.

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

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

The description clearly states the action ('Save data') and the resource ('data the agent will need to reuse later'), using specific examples like 'resolved ticker', 'target address'. It distinguishes from sibling tools 'recall' and 'forget' by naming them directly.

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 you discover something worth carrying forward'. Provides specific scenarios (e.g., 'user preference') and directs to pair with 'recall' to retrieve and 'forget' to delete, offering clear alternatives.

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

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

Annotations already indicate read-only, idempotent behavior. The description adds useful context: internal cascading through multiple endpoints and replacing 2-3 manual lookups, 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?

Well-structured with front-loaded examples, clear bullet points for supported types, and no extraneous text. Every sentence contributes to understanding.

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 and two entity types with distinct returns, the description thoroughly covers inputs, outputs, and internal mechanics. Minor gap: no mention of error handling for unresolved names.

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 rich detail: examples for 'value' (e.g., AAPL, ozempic) and explains return fields per type (ticker, CIK, RxCUI, citation URIs), significantly augmenting schema 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 states the tool resolves names to canonical identifiers, with specific examples like 'ticker for...' and 'CIK for...'. It distinguishes from sibling tools by being the go-to resolver for entity identification.

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 advises 'Use FIRST whenever you have a name but need an ID,' providing clear when-to-use guidance. It lacks explicit when-not-to-use or alternatives, but the context strongly implies its primary 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 already declare readOnlyHint=true and idempotentHint=true, so the agent knows it's safe. The description adds no behavioral context beyond what annotations provide, but 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?

The description is extremely concise (4 words) and front-loaded. Every word is meaningful with no 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?

With an output schema present (not shown but referenced), the description need not explain return values. The tool is simple with one parameter, and the description covers the core purpose. Minor gap: no mention of error handling for invalid vanity URLs.

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 0% description coverage for the single parameter 'vanityurl'. However, the description 'Vanity URL → SteamID64' implies that the parameter is a vanity URL, adding meaning beyond the schema. It partially compensates for the lack of schema 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 'Vanity URL → SteamID64' clearly states the verb (resolve) and the specific output (SteamID64). It distinguishes from sibling tools like 'resolve_entity' by specifying the exact transformation.

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

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

The description implies usage for converting vanity URLs to SteamID64, but does not explicitly state when to use this tool versus alternatives (e.g., 'resolve_entity', 'player_summary'). No exclusions or prerequisites are mentioned.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context: it probes each entity with 'ai_visibility_check,' ranks by score, and returns a ranked list with score, confidence, signal density. It also notes that the first entity is treated as 'subject' for narrative. This 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?

The description is three sentences, front-loaded with the main action. Every sentence adds value: purpose, method, use case, and return format. No unnecessary words or repetition.

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 4 parameters, 100% schema coverage, and no output schema, the description explains what the tool returns (ranked list with score/confidence/signal density). It references a sibling tool ('ai_visibility_check') for the probing method, which is acceptable. The optional API key is explained. The description is complete for an agent to understand and invoke 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 description coverage is 100% with detailed parameter descriptions. The description does not add significant extra meaning beyond what the schema provides. It contextualizes the 'entities' parameter as 'your brand + N competitors,' but the schema already mentions the first entry as 'subject.' Thus, baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies the verb (compare), resource (AI visibility), and scope (multiple entities). It distinguishes from siblings like 'ai_visibility_check' (single entity) and provides a concrete use case.

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

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

The description gives a clear use case: 'Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?"' It implies when to use but does not explicitly state when not to use or list alternatives. However, the sibling 'ai_visibility_check' is implied for single-entity checks.

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. The description adds crucial details: bundlephobia first measurement can take 5-30s, partial failures are handled gracefully with sources_failed field.

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 packs substantial information efficiently. Could be slightly more scannable but 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?

Despite no output schema, the description thoroughly enumerates return fields, explains partial failure behavior, and covers ecosystem limitations. 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% with clear descriptions for both parameters. The description reinforces but does not add meaningful meaning beyond what's already in the schema.

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

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

The description clearly states it's a composite check for adding an npm package, combining deps.dev and bundlephobia data. It is distinct from sibling tools like 'app_details' 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 says when to use: 'is X safe / popular / small' or 'what does adding lodash cost me'. Also notes limitations: NPM only in v1, 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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds value by detailing the output format (verdict types, extracted structure, citation, percent delta) and explaining that it replaces multiple sequential calls, which helps the agent understand the tool's behavior beyond annotations. No contradictions found.

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 the primary purpose, then usage guidance, scope, and output. Every sentence contributes distinct information without redundancy or verbosity. It is well-structured for quick comprehension by an AI agent.

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 with rich annotations, the description offers substantial context: purpose, when to use, scope (v1 company-financial claims), and output details. It lacks explicit mention of prerequisites or failure modes, but the annotations and schema nearly cover the essentials. The missing output schema is compensated by the description's explanation of return values.

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 single parameter 'claim' that has a clear description. The description reinforces the parameter's purpose with examples (e.g., "Apple's FY2024 revenue was $400 billion"), adding practical context. However, since the schema already specifies the parameter well, the description's added value is moderate, justifying a 4.

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

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

The description clearly states the tool's purpose with multiple natural-language phrasings ("fact check", "verify the claim", etc.) and specifies it performs claim verification against authoritative sources. It distinguishes itself from sibling tools like compare_entities and entity_profile by focusing on verifying factual claims rather than comparing or profiling entities.

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

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

The description explicitly says to use the tool when the agent needs to check factual correctness of a user's statement. It also scopes usage to company-financial claims (revenue, net income, cash position) for public US companies, providing clear context. While it doesn't list alternative tools for non-financial claims, the limitation is stated, making usage guidance good but not perfect.

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