Pvgis

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

Annotations already declare the tool to be read-only, idempotent, and non-destructive. The description adds valuable behavioral details: which models are probed, the free default Workers AI model, the need for a BYO key for Anthropic, and the return format. It does not contradict annotations.

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

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

The description is concise and well-structured, with the core function stated first, followed by model details, return format, and use cases. 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 the tool's complexity (2 models, optional API key, multiple parameters) and the lack of an output schema, the description fully covers the input, output format, and use cases. It provides enough context for an agent to understand when and how to 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?

The input schema already describes all 4 parameters with 100% coverage. The description adds significant context beyond the schema: it explains that 'models' defaults to Workers AI, that '_apiKey' is only needed for Anthropic, and that 'context' helps disambiguate entities. This greatly aids the agent in understanding 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 clearly states the tool's purpose: to probe LLMs for knowledge about an entity and return visibility scores. It uses a specific verb ('probe') and resource ('LLMs'), and distinguishes itself by focusing on AI visibility measurement, which is unique among the listed sibling tools.

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

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

The description provides explicit use cases such as AI-marketing audits and pre-launch brand checks. It explains the default model and how to enable Anthropic via an API key. However, it does not explicitly state when not to use the tool or compare it to alternatives, which would provide even clearer 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?

With no annotations provided, the description carries full burden. It discloses the core behavior: auto-selecting the tool, filling arguments, returning results. It could mention potential limitations (e.g., no side effects, rate limits) but is sufficiently transparent for a query tool.

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 and front-loaded with purpose and usage. It includes examples and a list of data sources. It could be slightly more concise, but 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?

For a simple query tool with one parameter and no output schema, the description provides enough context: what it does, when to use it, and examples. It doesn't detail return format or error handling, but given the tool's simplicity, it is fairly complete.

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

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

The input schema has 100% coverage for the single 'question' parameter with a brief description. The tool's description adds value by giving example questions and clarifying the natural language style, enriching beyond the schema.

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

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

The description clearly states the tool answers natural-language questions by automatically picking the right data source. It distinguishes from sibling tools by emphasizing it routes across 300+ sources, so the agent doesn't need to select a specific pack/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 a user asks…' and provides concrete examples (e.g., 'What is X?', 'Find Z'). It implies when not to use—when you already know which specific Pipeworx tool to call. This is clear and helpful for an AI agent.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds value by detailing the fan-out to different data packs and the classification of bets, which goes beyond what annotations provide. 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 verbose at 4 sentences, containing detailed context that could be streamlined. It front-loads the action but includes some marketing language that adds length without critical information.

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

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

Given no output schema, the description explains the output as an 'evidence packet plus a simple market-vs-model comparison'. It covers the main functionality and inputs well, though a bit more on output format would be helpful.

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

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

Schema coverage is 100%, and the description adds significant meaning: it explains that 'market' can be a slug, URL, or question text, and describes the 'depth' enum values (quick vs thorough). This is above and beyond the schema.

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

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

The description clearly states it researches a Polymarket bet by pulling Pipeworx data, resolving the market, classifying the bet, and returning an evidence packet. This distinguishes it from sibling tools like ask_pipeworx or compare_entities, which are more general.

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 use cases like 'should I bet on X?' and 'is there edge in this bet?', providing clear context. However, it does not explicitly state when not to use this tool or mention alternatives, which would have made it stronger.

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?

With no annotations provided, the description carries the full behavioral burden. It discloses data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), specific fields retrieved, and output format (paired data with citation URIs). It also notes efficiency gains, but lacks details on error handling or idempotency. Overall, good transparency given the constraints.

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 dense paragraph of about 100 words, front-loaded with the main purpose. It covers use cases, data sources, and return format efficiently without wasted words. Minor improvement could be better structuring for readability, but it is concise and 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?

Given the tool's complexity (two entity types, different data sources, paired citations), the description provides sufficient context. It explains what data is pulled for each type and mentions citation URIs. However, it omits potential limitations (e.g., geographic scope, output structure details). No output schema exists, but the description compensates moderately.

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 defines 'type' and 'values' with min/max and enum. The description adds value by explaining the distinction between company and drug types and the data each retrieves, but this is largely contextual rather than essential parameter semantics. Baseline 3 is appropriate.

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

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

The description clearly states the tool's function: comparing 2–5 companies or drugs side by side. It lists specific use-case phrases and distinguishes between company and drug types with different data sources. It also highlights that it replaces multiple sequential calls, reinforcing its purpose against sibling 'entity_profile'.

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

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

The description explicitly specifies when to use the tool, including user queries like 'compare X and Y' and 'X vs Y'. It differentiates between company and drug types. However, it does not explicitly mention when not to use it or name alternative tools, though the context implies it is preferred over multiple single-entity calls.

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 discloses that the tool returns the top-N most relevant tools with names and descriptions. It does not contradict any annotations (none provided) and no further behavioral details are needed given the simple search nature. Could mention pagination or error handling, but current level is sufficient.

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

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

Description is front-loaded with purpose, concise (2 sentences), and includes a list of domains which is information-dense without being verbose. Every part of the description earns its place.

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

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

Given the tool's simplicity and that no output schema is provided, the description covers purpose, when to use, and parameter details. It could mention what happens if no results are found, but that is a minor gap. Overall, it is complete enough for an agent to invoke correctly.

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

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

Schema coverage is 100% (query and limit described). The description adds substantial meaning beyond the schema: query is described as a 'Natural language description' with examples, and limit has a default and max. This fully clarifies 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?

Description begins with a clear verb+resource: 'Find tools by describing the data or task.' It lists many specific domains (SEC filings, financials, etc.) and distinguishes itself from sibling tools by stating it is for browsing/searching/discovering, while siblings are specific tools for specific tasks.

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: 'Use when you need to browse, search, look up, or discover what tools exist...' and provides a clear directive: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This guides the agent on prioritization and context.

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

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

No annotations provided, so the description carries full burden. It specifies the tool returns recent SEC filings, fundamentals, patents, news, and LEI with citation URIs. However, it does not explicitly state it is read-only, mention rate limits, or clarify how recent 'recent' is.

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 dense paragraph but front-loads the purpose and usage. It is efficient but could benefit from better structuring into clear sections for improved 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?

The description covers the tool's aggregated output well, including specific data types and citation format. However, it lacks details on pagination, limits, or whether all data sources are always included, which would make it more complete for this complex tool.

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

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

Schema coverage is 100%, so baseline 3. The description adds value by clarifying that 'names not supported' and directing to resolve_entity, which goes beyond the schema's parameter 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: 'Get everything about a company in one call.' It lists specific data sources (SEC filings, fundamentals, patents, news, LEI) and distinguishes from siblings like 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?

Explicit usage guidance: 'Use when a user asks "tell me about X"...' and provides example queries. Also notes when not to use: 'Names not supported — use resolve_entity first,' directly referencing an alternative sibling 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?

The description states the deletion action but lacks detail on error handling, permission requirements, or whether deletion is permanent. Without annotations, more behavioral context would be helpful.

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 succinct sentences: one for purpose, one for usage. No redundant words, highly 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 simple one-parameter tool without output schema, the description is nearly complete. Could mention return value or success behavior, but overall adequate.

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

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

Schema coverage is 100% and the description doesn't add information beyond the schema's 'Memory key to delete.' 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 explicitly states 'Delete a previously stored memory by key,' which is a specific verb and resource. It clearly distinguishes from siblings like 'remember' (store) and 'recall' (retrieve).

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

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

Provides explicit when-to-use scenarios: 'when context is stale, the task is done, or you want to clear sensitive data.' Also references related tools 'remember' and 'recall' for pairing.

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, which align with the description's process of fetching and extracting. The description adds behavioral details (fetches page, extracts title/description/key links, emits standard markdown format), providing 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 a single paragraph of three sentences, front-loading the main purpose. It is efficient with no wasted words, though a more structured layout (e.g., bullet points for use cases) could improve 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?

For a simple read-only tool, the description covers core functionality, output format, and use cases. There is no output schema, but the description states 'Output is a single text blob ready to drop at site-root/llms.txt', which is sufficient given the tool's simplicity.

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

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

The input schema covers both parameters with descriptions (100% coverage). The description does not add new semantics beyond what the schema provides (e.g., mentioning 'Full URL' is already in the schema). Baseline of 3 is appropriate.

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

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

The description clearly states the verb 'generate' and the resource 'llms.txt file' for any URL, specifying the purpose of making sites indexable by AI crawlers. It distinguishes itself from sibling tools like 'entity_profile' or 'bet_research' by focusing on a specific output format.

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

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

The description provides explicit use cases (indexing client sites, drafting for own projects, auditing competitors), giving clear context for when to use. However, it does not mention when not to use the tool or suggest alternatives, which prevents a 5.

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 empty. The description does not disclose behavioral traits such as data source, accuracy, rate limits, or side effects. It implies a read operation but does not state it.

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 sentence, front-loaded with key purpose. No wasted words, but could benefit from more structure (e.g., bullet points for output types).

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; description is brief. Does not explain return format, units, or how to interpret the monthly averages. Insufficient for a tool with 7 parameters and complex outputs.

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 71%. The description adds 'global / diffuse / direct irradiation' which are not in parameter descriptions, but does not explain parameters like 'angle' or 'optrad'. Minimal added 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?

The description clearly states 'Long-term monthly average global / diffuse / direct irradiation on horizontal / inclined plane', specifying the resource, verb, and scope. It distinguishes from siblings like 'tmy' and 'pv_performance'.

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 usage context provided; no mention of when to use this tool vs alternatives like 'tmy' or 'pv_performance'. The description does not guide the agent 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?

Discloses rate limits (5 per identifier per day), cost (free, not counted against quota), and impact (team reads digests daily, affects roadmap). With no annotations, the description carries the burden and handles it well.

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

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

Description is front-loaded with purpose, followed by usage guidance and behavioral notes. Each sentence adds value, though slightly verbose. Still 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?

Comprehensively covers the tool's behavior, usage, and constraints. No output schema, but not needed for a feedback submission tool. Context signals indicate moderate complexity, and the description fully addresses it.

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

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

Schema has 100% coverage with descriptions. The description adds context for the 'type' enum and reinforces usage for 'message'. Provides examples of what to include, adding value beyond the schema.

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

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

The description clearly states the tool's purpose: to send feedback to Pipeworx team. It lists specific feedback types (bug, feature, data_gap, praise) and distinguishes from sibling tools by focusing on feedback rather than queries or data retrieval.

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

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

Provides explicit when-to-use scenarios (bug, feature, data_gap, praise) and instructions on how to describe issues in terms of tools/packs. Does not explicitly state when not to use, but the positive guidance is strong and sufficient.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds context about derivation (CF analytics-engine), no PII, and caching behavior (5min-1h). No contradiction with annotations.

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

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

Four sentences front-loaded with purpose, followed by use cases, then technical details. No wasted words; every sentence adds value.

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

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

Given only one simple parameter and no output schema, the description covers what the tool returns (top tools, top packs, total call volume), data sourcing, and caching. It is fully self-contained for an agent to invoke correctly.

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

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

Schema coverage is 100% for the single 'window' parameter with enum values. The description adds meaning beyond the schema by explaining that shorter windows show what's hot and longer windows show steady-state demand, going above baseline of 3.

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

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

The description explicitly states the tool returns top tools, top packs, and total call volume over a recent window (24h, 7d, 30d). It clearly distinguishes from sibling tools like 'discover_tools' which lists all tools, by focusing on trending signals.

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 (discovering hot data sources, confirming canonical choice, aligning use case). It implies when to use but does not explicitly exclude alternatives or mention when not to use, which would push it to 5.

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 (readOnlyHint=true, openWorldHint=true, destructiveHint=false) are complemented by description detailing that it walks child markets in event mode and searches/checks monotonicity across events in topic mode. No contradiction and adds 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?

Concise single paragraph that front-loads the purpose, then briefly explains modes, and ends with return information. Every sentence serves a purpose 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?

While the description covers modes and return values well, it could mention that results are based on current market data and may change. Overall it's complete for the tool's complexity, given 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?

Schema coverage is 100% with descriptions, but the description adds significant value: explains how each parameter maps to a mode, provides examples, and clarifies that 'event' is for single-event checks and 'topic' triggers cross-event search.

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 by checking monotonicity violations. It distinguishes two modes (event and topic), making the purpose specific and differentiated from siblings 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?

Explicitly guides when to use each mode: 'event' for a single Polymarket event slug and 'topic' for cross-event searches. It explains why cross-event mode catches cases missed by single-event mode, providing concrete examples.

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 (readOnlyHint=true, destructiveHint=false) are consistent. Description adds detailed behavioral context: it covers crypto-price bets using a lognormal model from FRED and live coinpaprika price, scans top markets, groups by asset, fetches price history once, computes model probability, and ranks by |edge|. This goes well 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 information-dense with each sentence adding value. It front-loads the core purpose and then explains the process. Slightly verbose in the middle section, but overall well-structured and to the point.

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

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

Despite no output schema, the description explains return values (top N ranked by edge magnitude with suggested trade direction). It covers purpose, process, parameters, and return. Could be more explicit about output structure, but sufficient for a moderately complex tool.

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

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

Input schema has 100% description coverage for all 3 parameters, clearly defining limit, window, and min_edge_pp. The description does not add additional parameter-specific details beyond what the schema provides, so baseline 3 is appropriate.

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

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

Description clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price. It specifies the verb 'scan' and resource 'Polymarket markets', and distinguishes itself from sibling tools like 'polymarket_arbitrage' by mentioning its purpose for identifying opportunities.

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

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

The description explicitly says it's built for the 'what should I bet on today' question, guiding agents to discover opportunities without manual paging. It does not explicitly mention when not to use it or contrast with alternatives, but the intended use case 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 declare read-only, idempotent, open-world, and non-destructive. The description adds value by detailing output structure (leg-by-leg prices, spread in percentage points) and explaining the data source difference. No contradictions.

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

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

The description is concise (3 sentences) and front-loaded with the main purpose. Some minor redundancy (TWO MODES in caps) but overall efficient.

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

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

Despite no output schema, the description adequately explains return values (prices, spread). Parameters are fully covered. Complexity is moderate and the description is sufficient for an AI agent to invoke correctly.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds meaning by explaining the interplay between topic and explicit overrides, and listing valid topic values. This goes beyond the schema.

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

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

The description uses specific verbs ('cross-venue spread') and clearly identifies the resource (Kalshi and Polymarket events). It distinguishes the tool from siblings like polymarket_arbitrage by focusing on spread between two venues.

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

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

The description explains two modes (topic shortcuts vs explicit tickers) and why to use it (real arb signal). It could be improved by explicitly stating when not to use it or comparing to sibling tools, but the context is clear.

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

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

With no annotations, the description must convey behavior. It states the tool returns estimates and monthly breakdowns, which is useful but lacks details on side effects, permission requirements, or what happens with invalid inputs. It is adequate but not rich.

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 long, with no redundancy or filler. It efficiently conveys the core purpose and outputs. Every word earns its place.

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

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

Given 8 parameters and no output schema, the description is minimal. It explains what the tool does and what it returns but does not detail how parameters influence results or provide examples. It is functional but could be more comprehensive.

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

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

The input schema has 100% coverage with descriptions for all 8 parameters. The tool description adds no additional parameter meaning beyond what the schema already provides. Thus, it meets the baseline of 3.

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

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

The description clearly states the verb 'Model', the resource 'PV system output', and the scope 'annual + monthly'. It returns specific estimates, making the purpose unambiguous. None of the sibling tools appear to overlap with this PV modeling function.

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 explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or when it is inappropriate to use. The context is purely implied by the task.

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?

No annotations present, so description carries full burden. Discloses scope (per identifier), listing behavior when key omitted, and pairing with other tools. Does not cover error cases or rate limits, but for a simple read operation this is adequate.

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 tightly written sentences, each adding value. No redundancy, front-loaded with action.

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 (1 optional param, no output schema), the description fully explains input, behavior, scope, and related tools. 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 covers 100% of parameters, description adds the 'list all keys' behavior for the omitted key case, which is beyond the schema description. Provides practical usage context.

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

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

Description clearly states the tool retrieves a saved value or lists all keys, with specific verb-resource relationship. It distinguishes from sibling tools remember and forget by naming them.

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

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

Explicitly describes when to use (look up stored context) and pairs with remember/forget. Lacks explicit 'do not use when' but the positive guidance is clear and context-rich.

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?

No annotations exist, so the description must provide behavioral context. It discloses that the tool fans out to three sources in parallel, the format of 'since', and the return structure. However, it omits potential limitations like rate limits or result 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?

The description is concise, packing necessary information into a single paragraph without redundancy. It starts with a clear purpose statement and efficiently covers usage, parameters, and output.

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

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

Given no output schema, the description explains return types (structured changes, count, URIs) and data sources. It could elaborate on the structure of 'changes', but overall it provides sufficient context for an agent to understand the tool's behavior.

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

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

The input schema covers all parameters with descriptions, but the tool adds value by explaining the 'since' format (ISO vs relative shorthand) and giving examples for 'value'. This goes beyond the schema's basic 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's purpose: to find recent changes for a company. It provides specific example queries and distinguishes itself from siblings like 'entity_profile' by focusing on time-bound updates across multiple sources.

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 suggests when to use the tool with example user queries. It does not discuss when not to use it or compare to sibling tools directly, but the context makes its niche 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?

Discloses key-value scoping, persistence differences for authenticated vs. anonymous users, and 24-hour retention for anonymous sessions, despite no annotations provided.

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

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

Description is front-loaded with purpose and guidelines, but slightly verbose; could be tightened without losing clarity.

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

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

No output schema exists and description omits what the tool returns (e.g., success confirmation), leaving the agent uninformed about the response 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 coverage is 100%, so baseline is 3. Description rephrases key and value roles but adds little beyond examples; does not introduce new constraints or format requirements.

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 saves data for later reuse, provides concrete examples (resolved ticker, target address), and distinguishes from sibling tools like recall and forget.

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

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

Explicitly states when to use ('when you discover something worth carrying forward') and pairs with alternatives ('Pair with recall to retrieve later, forget to delete').

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?

No annotations provided, so description carries full burden. It discloses the tool returns IDs and citation URIs, and is a read-only lookup. Could mention missing identifier handling, but overall sufficient.

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, front-loaded with main purpose, no redundant content. Every sentence provides value: purpose, examples, workflow, and benefits.

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 simple tool with 2 params and no output schema, description covers purpose, input usage, return values, and workflow context. Could add edge cases, but is complete enough for agent to use correctly.

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

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

Schema coverage is 100%, but description enriches parameter meaning with examples (e.g., 'For company: ticker (AAPL), CIK...') and clarifies usage beyond schema descriptions.

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

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

Clearly states 'Look up the canonical/official identifier for a company or drug,' specifying verb and resource. Provides examples and distinguishes from siblings by noting it replaces multiple lookup calls.

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

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

Explicitly says 'Use when a user mentions a name and you need the CIK...' and 'Use this BEFORE calling other tools that need official identifiers,' offering clear contextual guidance and workflow placement.

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 no destructiveness. The description adds useful behavioral context: it probes each entity using ai_visibility_check internally, ranks results, and surfaces scores. No contradictions.

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

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

The description is a single paragraph of three sentences that front-loads the purpose. It is efficient and avoids verbosity, though it could be more structured (e.g., bullet points for output metrics).

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 explains the process (probe, rank, return list with metrics) and the role of the first entity. It covers most necessary information for an agent to use the tool correctly, though it could mention error cases or limits on entity count.

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

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

Schema coverage is 100% with each parameter described. The description reinforces this by explaining that 'entities' first entry is the subject, 'models' defaults to workers-ai, and '_apiKey' is needed for anthropic. It also mentions the output structure (ranked list with score, confidence, signal density), adding value beyond the schema.

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

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

The description clearly states the verb 'compare', the resource 'AI visibility across multiple entities', and distinguishes from sibling tool 'ai_visibility_check' which checks a single entity. It also provides a concrete use case example.

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 usage context ('competitive AI-marketing audits') and an example question. It implies not to use for single-entity checks (use ai_visibility_check), but does not explicitly state when not to use or list alternatives beyond the implied sibling.

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 behavior beyond annotations: 'Composite... fans out across deps.dev... and bundlephobia', timeouts ('first measurement can take 5-30s'), graceful degradation ('sources_failed will list it if it times out, the rest still returns'). No contradictions with annotations (readOnlyHint, idempotentHint, openWorldHint true).

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?

Packed with information but still well-structured. Front-loads purpose, then breaks down details and edge cases. Could be slightly trimmed but every sentence is useful.

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

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

Given complexity (multiple sources, partial failures, ecosystem limitation), the description is thorough. Explains return items (summary block, per-advisory detail, links, alternative versions). Covers limitations and fallback tools. No output schema but description compensates.

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

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

Schema coverage is 100%, baseline 3. Description adds value: explains that scoped packages are accepted (e.g., '@types/node'), and version defaults to latest when omitted. This clarifies nuances not in schema.

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

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

Clearly states it is a composite check for npm packages across deps.dev and bundlephobia. Uses specific verbs ('scan', 'check') and resource ('npm package'). Distinguishes from siblings by noting ecosystem limitation (NPM only) and fallback 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 says when to use: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also notes when not to use: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly'. Provides handling for partial failures.

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?

No annotations exist, and the description fails to disclose any behavioral traits—such as whether this is a read operation, side effects, or return format. The agent receives no information about what invoking this tool entails.

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 short but wastes space on a textbook definition rather than tool action. It could be more concise and front-loaded with the verb and resource.

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 lack of annotations, output schema, and parameter descriptions, this single-sentence definition is entirely insufficient. The tool has 4 inputs and no return documentation, rendering it nearly unusable without external knowledge.

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 mention any of the four parameters (latitude, longitude, startyear, endyear). The agent has no clue what values to provide or how they affect results.

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 defines 'Typical Meteorological Year' as a concept but does not state what the tool does (no verb). It reads as a definition, not an action, leaving the agent unclear whether the tool generates, retrieves, or computes the 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?

No guidance on when to use this tool versus alternatives like monthly_radiation or pv_performance. The description provides no context for appropriate usage or exclusion.

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?

With no annotations, the description fully discloses behavioral traits: it returns a verdict with five possible outcomes, includes structured data, actual value with citation, and percent delta. It also notes version limitations (v1). However, it does not mention authentication, rate limits, or side effects, though none seem relevant for a read-only validation tool.

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 a clear lead sentence, usage guidance, and return details. It is concise yet informative, though the initial list of synonyms ('fact-check, verify, validate, or confirm/refute') is slightly redundant.

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 a single required parameter and no output schema, the description provides complete context: what it does, when to use, domain limitations, and what the response contains. It effectively replaces the need for additional documentation.

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% (claim parameter documented). The description adds value by providing example claims ('Apple's FY2024 revenue was $400 billion') and clarifying acceptable phrasing, which goes beyond the schema's brief 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's purpose: fact-checking or validating natural-language factual claims. It uses strong action verbs like 'fact-check, verify, validate, confirm/refute' and distinguishes itself from sibling tools by specifying its specialized scope (company-financial claims) and efficiency gains.

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-case triggers ('Use when an agent needs to check whether something a user said is true') and scope limitations ('v1 supports company-financial claims... public US companies'). It does not explicitly list alternatives or when not to use, but the scope naturally excludes other claim types.

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