Ygoprodeck

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds valuable behavioral context: cost implications for Anthropic ('you pay Anthropic directly'), and output structure (per-model fields plus combined view). No contradictions with annotations.

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

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

The description is a single, well-structured paragraph with the main purpose front-loaded, followed by default behavior, optional key usage, output summary, and use cases. Every sentence adds value with no superfluous 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?

Given the tool has 4 parameters, full schema coverage, and no output schema, the description adequately explains the output format and primary use cases. Minor missing details like error handling or response size are not critical for core functionality.

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

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

Schema coverage is 100% with descriptions for all 4 parameters. The description adds semantic value by explaining the default model behavior, when to use _apiKey, and how the 'context' parameter aids disambiguation. This goes beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: probing LLMs for knowledge about a brand/product and scoring visibility. It includes a specific verb ('probe'), resource ('LLMs'), and output ('score 0-100'). The description effectively distinguishes this tool from siblings like 'scan_competitor_ai_presence' by focusing on visibility scoring rather than general AI presence scanning.

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

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

The description provides clear usage context: AI-marketing audits, pre-launch brand checks, and competitive monitoring. It explains the default model and how to opt into Anthropic with a BYO key. However, it does not explicitly state when NOT to use this tool or mention alternatives among siblings.

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

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

Despite rich annotations (readOnly, openWorld, idempotent), the description adds meaningful context: it routes questions to 3,538 tools across 815 sources, fills arguments, and returns structured answers with citation URIs. This internal behavior is not implied by annotations.

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

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

The description is front-loaded with a strong usage directive, well-organized with domain list, mechanism, and examples. While slightly lengthy, every sentence adds value; could be marginally tighter but is effective.

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

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

The description explains the tool's orchestration role and output format (structured answer with citation URIs) but does not detail the exact return structure or error behavior. Given the complexity and lack of output schema, some gaps remain, though the context is adequate for basic 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 100%, so baseline is 3. The description does not add substantive meaning beyond schema for parameters; it merely reiterates the question parameter's role via examples. No extra details about parameter constraints 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 clearly states the tool's purpose: routing factual questions to a vast set of verified sources and returning structured answers with citations. It explicitly distinguishes itself from web search and lists specific domains (SEC filings, FDA drugs, etc.), making the purpose unmistakable.

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

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

The description provides explicit guidance: 'PREFER OVER WEB SEARCH' for factual questions about real-world entities, events, or numbers. It lists example queries and specifies trigger phrases, effectively telling the agent when to use this tool over alternatives.

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

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

Annotations declare readOnlyHint true and destructiveHint false. The description adds extensive behavioral details: resolver contract with match confidence, parent event extraction, news error handling, safety mechanisms for low-confidence/closed/illiquid markets. This goes well beyond annotations, providing transparency 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 very long and verbose, with multiple paragraphs of detailed examples and response shapes. While informative, it lacks conciseness and front-loading. The length could be reduced, and a structured format (e.g., bullets) would improve scanability.

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

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

Given the tool's complexity (3 params, no output schema) and the need to describe return structures and edge cases, the description is extremely thorough. It covers resolution, fan-out, safety, and error handling, compensating for lack of output schema.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds minor context (e.g., include_raw effect on response size) but does not significantly enhance understanding beyond the schema. Baseline 3 applies.

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

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

The description clearly states the verb and resource ('Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call') and provides usage examples. However, it does not explicitly differentiate from sibling tools like polymarket_edges or polymarket_arbitrage, which may have overlapping functionality.

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 use cases such as 'should I bet on X' and 'is there edge in Z', which imply when to use it. However, it lacks explicit guidance on when not to use or how it compares to alternatives like polymarket_edges for edge analysis. No alternatives 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 indicate readOnly, openWorld, idempotent, non-destructive. The description adds behavioral traits: sorting by primary metric, handling off-calendar fiscal years, data sources (SEC EDGAR, FAERS), and citation URIs. No contradictions. Some details like rate limits are omitted.

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. The first sentence front-loads the purpose with examples. Each subsequent sentence adds meaningful context without redundancy. Every line earns its place.

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

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

Given the tool's complexity (two entity types, different data sources, sorting, off-calendar fiscal years), the description is quite complete. It explains return value (paired data + citations) and replaces 8–15 sequential lookups. Minor missing details (e.g., explanation of pipeworx:// URIs) but overall sufficient.

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

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

Schema coverage is 100%, but the description adds value by providing examples of values (tickers/CIKs for company, drug names for drug), explaining what each type returns, and clarifying constraints (2–5 items). This goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: side-by-side comparison of 2–5 companies or drugs. It provides specific user query examples ('Compare X and Y', 'which is bigger') and distinguishes from alternatives like sequential lookups. The verb+resource is specific and actionable.

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

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

Explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' giving clear context for when to use. However, it does not explicitly state when not to use (e.g., comparing more than 5 entities) or provide alternative sibling tool names.

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 behavioral details beyond annotations: it returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples)' and ensures results are 'ready to call directly, no second schema lookup needed.' No contradiction with annotations (readOnlyHint, idempotentHint).

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

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

Two sentences, front-loaded with purpose, no filler. Every sentence adds value: first sentence defines purpose and examples, second sentence explains output and usage advice.

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 role as a discovery tool with no output schema, the description adequately explains what it returns (relevant tools with schemas) and how to use it first. It could mention output format more explicitly, but it's sufficient for the complexity level.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description provides overall context and examples but does not add significant meaning beyond what the schema already states for individual parameters.

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

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

The description uses a specific verb-resource pair ('Find tools') and lists concrete use cases (SEC filings, financials, etc.). It clearly distinguishes the tool as a discovery mechanism for the sibling toolset, stating it returns top-N relevant tools ready to call.

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 advises to call this tool 'FIRST when you have many tools available' and clarifies it's for browsing/searching/discovering options. It does not explicitly name alternatives but the context of sibling tools and the phrase 'not just one answer' implies when not to use it.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds significant context: fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), returns specific fields with URIs, mentions a sunset and soft-fail for patents, and explains the parallel call nature. No contradiction with annotations.

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

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

The description is a single, fairly long paragraph but front-loads with example queries and key action. Every sentence adds value—usage guidance, return fields, fallback, limitations. While slightly verbose, it is well-structured and easily scannable. A minor improvement would be bullet points for clarity, but it remains effective.

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

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

Given the absence of an output schema, the description thoroughly explains return fields (cik, company_name, recent_filings with URIs, fundamentals with specific metrics sorted by period_end, patents with sunset note, news fallback, LEI). It covers the parallel fan-out, source list, and limitations. This is remarkably complete for a complex tool, leaving no major gaps.

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

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

Schema coverage is 100% and descriptions already exist for both parameters (type with enum, value with format). The description adds value by reinforcing that names are not supported and that value can be ticker or zero-padded CIK, providing concrete examples. It also implies the relationship between parameters (type must be 'company' for now). This extra context raises it above baseline 3.

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

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

The description starts with example queries, explicitly states the tool provides a 'full cross-source profile of a US public company in ONE parallel call,' and distinguishes it from chaining single-pack lookups. It clearly links action ('tell me about', 'research', 'brief me') to resource (US public company) and differentiates from siblings like compare_entities or resolve_entity.

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

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

It explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' provides specific when-to-use guidance, and clearly states when not to use (names not supported, pointing to resolve_entity instead). It also mentions fallback behavior (GDELT→GNews), giving clear guidance on limitations.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true; description adds context about clearing sensitive data, but no contradiction.

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

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

Two concise sentences, front-loaded with the action and purpose, no filler.

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

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

Adequate for a simple 1-param tool with annotations; could mention return value but not necessary.

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

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

Single parameter 'key' has 100% schema coverage. Description repeats schema description without adding new information 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?

Clearly states verb 'Delete' and resource 'previously stored memory by key', distinguishing it from sibling tools like remember and recall.

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

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

Explicitly states when to use (context stale, task done, clear sensitive data) and suggests pairing with remember and recall.

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

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

The description details the tool's behavior: it fetches the page, extracts title/description/key links, and outputs a markdown blob. This adds value beyond the annotations (which indicate read-only, idempotent, non-destructive) by explaining the exact extraction 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 three sentences, each delivering key information: purpose, process, and use cases. No wasted words, front-loaded with the most important information.

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

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

For a simple tool with two parameters and no output schema, the description covers all necessary aspects: what it does, how it works, and when to use it. No gaps remain.

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

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

Schema coverage is 100%, so baseline is 3. The description mentions 'Full URL' and 'max_links' in context but does not add significant semantics beyond the schema's descriptions.

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

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

The description clearly states the tool generates a production-ready llms.txt file, specifying the verb and resource. It distinguishes from sibling tools by focusing on llms.txt generation, different from scanning AI presence or visibility checks.

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

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

The description lists three specific use cases (client indexing, own project, competitor audit), providing clear context for when to use. It does not explicitly state when not to use it, but the use cases imply 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 readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds valuable detail about the output fields (type, ATK/DEF, banlist status, prices, etc.), enriching the agent's understanding without contradicting annotations.

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

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

The description is two sentences: the first clearly states the primary action and constraints, the second enumerates returned data. No wasted words; 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 tool's simplicity (one required parameter, no output schema), the description fully explains the input requirement and all output fields. It is complete for the tool's complexity.

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

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

Schema coverage is 100% with a clear description of the 'name' parameter. The tool description does not add additional meaning beyond what the schema already provides, so baseline score of 3 is appropriate.

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

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

The description clearly states the tool looks up a single Yu-Gi-Oh! TCG card by exact name and lists the full set of returned details (type, ATK/DEF, etc.). This is specific and distinguishes it from sibling 'search_cards' which likely returns multiple results.

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

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

The description specifies that an exact name is required but does not explicitly mention when to use this tool versus alternatives like 'search_cards' or 'random_card'. It provides adequate context for correct invocation but lacks exclusionary 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 significant behavioral context beyond annotations: rate-limited to 5 per identifier per day, free, doesn't count against quota, and team reads digests daily. This informs agent of constraints and effects.

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

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

The description is concise and front-loaded with purpose, but could be slightly more structured (e.g., bullet points). Still, every sentence adds value, and it avoids unnecessary verbosity.

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 complete schema coverage, the description provides sufficient context for correct usage. It lacks an explicit description of the return value, but feedback tools typically don't need one. Nearly complete.

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

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

With 100% schema coverage, the description adds meaning by explaining each enum value for 'type' and the purpose of 'context' and 'message'. It goes beyond schema definitions to clarify usage semantics.

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

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

The description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It enumerates specific feedback types (bug, feature/data_gap, praise) and distinguishes from sibling tools, none of which are for feedback.

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

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

Explicit guidance on when to use each feedback type (e.g., 'wrong/stale data (bug)') and what not to do ('don't paste the end-user's prompt'). Also mentions rate limits and cost, providing comprehensive 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 and idempotentHint. The description adds behavioral details: data source (CF analytics-engine), no PII, caching 5min-1h, which goes beyond annotations.

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

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

The description is a compact paragraph with bullet points, each sentence adds value. No redundancy or filler.

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

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

Given the simple schema (one optional parameter) and no output schema, the description covers purpose, usage, and behavior adequately. It lacks explicit output format but is sufficient for a trend tool.

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

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

Schema description coverage is 100% and already provides good meaning (window values and their implications). The description reinforces this but does not add new information beyond the default.

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 trending tools, packs, and call volume over a window, distinguishing it from siblings like 'discover_tools' by specifying it shows what other AI agents are calling right now.

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

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

The description provides explicit use cases (discovering hot data sources, confirming popular tools, aligning with agent demand) but does not mention when not to use or compare with alternatives like 'discover_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, including parameter requirements, semantic anchor (Jaccard similarity threshold), partition filter logic, and response structure. No contradictions with annotations.

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

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

The description is well-structured with clear sections and front-loaded requirements, but it is verbose. Some details could be streamlined without losing clarity.

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

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

The description thoroughly covers the tool's behavior, constraints, and output format despite lacking an output schema. It explains the algorithm, filtering, and response fields, providing complete guidance 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 description coverage is 100% with detailed descriptions for each parameter. The description supplements these with usage context, examples, and behavioral details for both `event` and `topic` modes, adding substantial 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?

Clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks, with two distinct modes (event and topic). The description uses specific verbs and resources, and the purpose is unambiguous.

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

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

Explicitly states that one of `event` or `topic` is required, and calling with no args fails. It provides recommendations for when to use each mode and explains the limitations of single-event vs cross-event scanning, giving clear guidance on 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?

Beyond readOnlyHint annotations, the description exposes algorithmic details (Kelly caps, partition filters, Fed exclusion), caching behavior, and diagnostic fields. No contradiction with annotations.

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

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

The description is lengthy but well-organized: overview, segments, per-opportunity fields, knobs, response structure. Every section adds value, though some extraneous detail (e.g., exact α values) could be streamlined.

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

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

Given the complexity (3 segments, 9 parameters, no output schema), it fully covers return values, filtering logic, and diagnostic information. Agents have everything needed to decide when and how to call.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaningful context for most parameters (e.g., min_edge_pp being net of slippage, min_partition_leg_kelly explaining why partition arbs bypass min_kelly).

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

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

The description explicitly states it scans Polymarket markets for opportunities where Pipeworx data disagrees with market price, with a clear scope ('what should I bet on today'). It distinguishes from siblings by detailing three specific model families and segments.

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 strong usage context ('what should I bet on today') and notes caching. It lacks explicit when-not-to-use or direct sibling comparisons, but the clear segment filters and knobs implicitly guide use cases.

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

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

Annotations confirm read-only, idempotent, non-destructive behavior; the description adds rich behavioral context: it explains that the tool reports when bet shapes are non-equivalent, provides safety fields, and describes when spreads are meaningless.

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 thorough and well-structured, front-loading the core purpose and modes. While slightly verbose, every sentence adds value; could be slightly tightened but remains clear.

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

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

With no output schema, the description comprehensively explains the response structure (leg prices, spreads, compatibility_warning, temporal_alignment) and safety fields, fully covering what the tool returns and when results are meaningful.

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

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

Schema coverage is 100%; the description adds meaning beyond schema by listing the topic shortcuts, explaining override behavior, and clarifying that explicit parameters override topic-mapped sides.

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

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

The description clearly states it is a 'Cross-venue spread between Kalshi and Polymarket for the same resolving question' and distinguishes itself from sibling tools like 'polymarket_arbitrage' by focusing on cross-venue comparison and providing explicit modes.

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

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

The description explicitly details two modes ('topic' for pre-mapped shortcuts and explicit ticker/slug for custom pairings) and warns when usage may be invalid (compatibility warning, temporal misalignment), guiding when not to use.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by specifying the fields returned (type, description, ATK/DEF, attribute, etc.), which is useful behavioral context beyond the annotations.

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

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

The description is a single, well-structured sentence that front-loads the main action and then lists the return details. No unnecessary words.

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

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

Given no output schema and simple tool with no parameters, the description fully explains what the tool does and what it returns (full details including specific fields). It is complete for the tool's purpose.

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?

There are no parameters, so schema description coverage is 100%. The description does not need to add parameter information, and the baseline for 0 parameters is 4. No additional detail required.

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 a random Yu-Gi-Oh! TCG card with full details. It uses a specific verb ('Get') and resource ('random card'), and is distinct from siblings like 'get_card' (specific card) and 'search_cards' (query-based).

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 directly states when to use the tool: to get a random card. However, it does not explicitly mention alternatives or when not to use it. Given the tool's simplicity and the context provided by sibling names, this is still 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 readOnlyHint, idempotentHint, destructiveHint. Description adds scope (anonymous IP, BYO key hash, account ID) and listing behavior when key omitted. No contradictions.

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

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

Description is 4 sentences, well-structured, front-loaded with purpose. Could be slightly more concise but 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?

Even without output schema, description explains return behavior (value vs list of keys). Covers scope, pairing, and usage. Fully adequate for a simple param 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 parameter description. Description adds context about usage with remember and examples, but doesn't significantly enhance meaning beyond schema. Baseline 3 met.

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

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

The description clearly states the tool retrieves values saved via remember or lists all keys when key is omitted. It provides specific use cases (ticker, address, research notes) and distinguishes it from sibling tools like remember and forget.

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

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

Explicitly states when to use: to look up stored context without re-deriving. Mentions scoping and pairing with remember/forget, but doesn't explicitly state when not to use. Still provides clear context.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and no destructiveness. The description adds valuable behavioral details: parallel fan-out fallback (GDELT preferred, GNews on error), USPTO soft-fail status, and window-based filtering via `since`. No contradiction.

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

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

The description is front-loaded with user-facing example queries, then efficiently covers sources, parameter details, and caveats. Every sentence earns its place, and the structure is logical. No redundancy.

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

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

Without an output schema, the description sufficiently describes the return format (structured changes grouped by source, total_changes count, pipeworx:// URIs). It also notes error behavior for USPTO. For a 3-parameter tool with no nested objects, this is complete and actionable 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 good descriptions for all three parameters. The description enhances this by explaining `since`'s ISO and relative shorthand formats with examples ('7d', '30d', '1y'), and clarifying `value` accepts ticker or CIK. Only minor gap: no further semantics on `type` beyond 'company only'.

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

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

The description clearly states the tool provides a change feed for a company combining multiple sources (SEC, GDELT/GNews, USPTO) in one parallel call, with example queries like 'What's new with X' and a direct contrast to sibling entity_profile. This makes the purpose unambiguous.

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

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

The description includes explicit guidance to use entity_profile for static profiles instead, effectively telling agents when not to use this tool. However, it lacks explicit exclusions for other alternative tools like search_cards or validate_claim, which could have slightly overlapping use cases.

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

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

Discloses key-value scoping by identifier, persistence differences (authenticated vs anonymous 24hr), and pairing instructions. These details go beyond annotations (idempotentHint, readOnlyHint, destructiveHint).

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

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

Front-loaded with purpose, then usage, then behavior. 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?

Covers all necessary aspects: what, when, how, pairing, persistence. No output schema needed for a store operation; transparency sufficient.

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

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

Schemas already describe key and value well (100% coverage), but description adds naming conventions and acceptable value types, enhancing clarity for agents.

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 'Save data the agent will need to reuse later' with specific examples like ticker, address, preference. Distinct from sibling tools recall and forget.

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

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

Explicitly says 'Use when you discover something worth carrying forward' with concrete scenarios, and instructs to pair with recall and forget for lifecycle management.

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 safe, read-only, idempotent behavior. The description adds internal cascading details ('replaces 2-3 manual lookups') and specific return fields (ticker, CIK, URI for company; RxCUI, ingredient, URI for drug), which is valuable beyond annotations.

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

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

The description is a single paragraph packed with examples, supported types, return details, and usage guidance. It is front-loaded with examples, making it efficient, though it could be slightly more structured with bullet points.

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

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

Despite no output schema, the description details return values for each entity type and internal cascading. It covers the tool's purpose, inputs, and outputs comprehensively. Minor gaps exist (e.g., error handling), but overall it is complete for a lookup tool.

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

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

Schema covers 100% of parameters. The description enriches both parameters with examples (e.g., 'AAPL', '0000320193', 'ozempic') and notes auto-disambiguation for company. This adds clarity beyond the schema's generic descriptions.

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

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

The description uses specific verb-resource pairs ('resolve a user-spoken NAME to the canonical/official identifier') and provides concrete examples ('What's the ticker for…'). It clearly distinguishes from sibling tools by positioning itself as the first step when needing an ID.

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 FIRST whenever you have a name but need an ID.' It describes when to use each type (company vs drug) and what returns to expect, though it doesn't explicitly state when not to use it.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, indicating a safe, repeatable read operation. The description adds context beyond annotations: it explains the process (probes with ai_visibility_check, ranks by score) and the output format (ranked list with score, confidence, signal density per entity). It also notes that the first entity is treated as the 'subject' for narrative purposes. No contradictions.

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

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

The description is three sentences with no wasted words. The first sentence states the main function, the second details the process, and the third provides a use case and output summary. Information is front-loaded and each sentence earns its place.

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

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

Given the tool's complexity (4 parameters, 1 required, no output schema), the description covers purpose, process, output format (ranked list with score, confidence, signal density), and a usage scenario. It provides enough information for an agent to understand what the tool does and what it returns.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining that the first entity in the 'entities' array is treated as the 'subject' for narrative and the rest as competitors. It also clarifies the default model behavior (workers-ai) and the role of 'context' for disambiguation. These details are not present in the schema descriptions.

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

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

The description clearly states the tool's purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies the verb (compare), resource (AI visibility across multiple entities), and provides a concrete use case example ('does Claude know about us as well as our competitors?'). It distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison) by mentioning it probes each entity with ai_visibility_check and ranks results.

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

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

The description explicitly states the tool is 'useful for competitive AI-marketing audits,' giving clear context for when to use it. It implies the alternative ai_visibility_check for single entity checks by stating it probes each entity with that tool. However, it does not explicitly mention when not to use this tool (e.g., for non-competitive single-entity analysis).

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds important behavioral details: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed lists timeouts.

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 informative but slightly long; it front-loads the core purpose and all sentences are valuable. It could be tightened slightly but remains efficient.

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

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

Despite no output schema, the description comprehensively details the return structure (summary block, per-advisory detail, links, alternative versions) and error handling (partial failures). Covers all key aspects for a complex, multi-source tool.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by noting scoped packages are accepted for 'package' and that 'version' defaults to latest when omitted.

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: a composite check for adding an npm package, covering license, advisories, size, and more. It distinguishes from siblings by specifying NPM-only in v1 and pointing to deps.dev:version for other ecosystems.

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

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

Explicitly states when to use ('is X safe / popular / small') and provides an alternative for non-NPM ecosystems ('PyPI / Maven / Cargo / Go fall under deps.dev:version directly').

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds value by specifying return fields (names, types, descriptions, ATK/DEF, etc.) but does not disclose edge cases like empty query behavior or rate limits.

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: first introduces the search functionality and filters, second lists return fields. No fluff, front-loaded, and every sentence is informative.

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

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

With no output schema, the description lists return fields but does not specify the response structure (e.g., array) or behavior when no parameters are provided. Still fairly complete for a search tool with good annotations.

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 each parameter is documented. The description summarizes the filter options but does not add new meaning beyond grouping; the fuzzy name mention is already in schema.

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

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

The description clearly states the tool searches Yu-Gi-Oh! TCG cards by multiple criteria (fuzzy name, type, attribute, race, archetype, level) and lists the return fields, making the purpose distinct from siblings like get_card or random_card.

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 finding cards matching given criteria but does not explicitly say when to use this vs alternatives like get_card for specific cards or random_card. No exclusions or when-not-to-use guidance.

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

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

Beyond annotations (readOnly, openWorld, idempotent, non-destructive), description details return values (verdict types, citation, percent delta) and notes it replaces multiple calls. 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, each serving a purpose: trigger phrases and functionality. Front-loaded with examples. Efficient and clear.

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

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

For a one-param tool with full schema and annotations, the description covers domain, return values, and efficiency improvement. No output schema but explains output well.

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

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

Schema coverage is 100% with good description. Description adds context and examples but doesn't significantly enhance parameter semantics beyond schema.

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

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

Description clearly states the tool is for natural-language claim verification with specific trigger phrases and domain (company-financial claims). It distinguishes from siblings like entity_profile or resolve_entity.

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 it (when needing to check factual correctness) and provides domain limitation. Lacks explicit when-not-to-use or alternative tools.

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