ipinfo

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

Annotations already indicate read-only, non-destructive, idempotent behavior. Description adds output format and cost implication for Anthropic, but doesn't disclose potential rate limits or retry behavior.

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

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

Three sentences, no wasted words. Purpose, usage, and output are front-loaded. Every sentence earns its place.

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

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

Despite no output schema, the description covers return structure. With 4 well-documented params and clear use cases, the tool is adequately described. Minor gap: no mention of timeouts or result caching.

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

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

All parameters have schema descriptions (100% coverage). Description adds value by explaining the default model and the purpose of _apiKey, beyond what schema provides.

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

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

The description clearly states it probes LLMs for entity visibility and returns scores. It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on general visibility scoring.

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 usage context (AI-marketing audits, pre-launch checks) and when to use the API key. Lacks explicit mentions of avoiding this tool when direct competitor scans are needed.

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 burden of behavioral disclosure and does this well by explaining that Pipeworx 'picks the right tool, fills the arguments, and returns the result' - revealing the tool's intelligent routing behavior. It doesn't mention rate limits, authentication needs, or error conditions, but for a query tool with no annotations, this provides substantial behavioral context.

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 perfectly structured and front-loaded: the first sentence establishes the core functionality, the second explains the automation benefit, and the third provides concrete examples. Every sentence earns its place with zero redundant information, making it highly efficient while remaining comprehensive.

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

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

For a single-parameter query tool with no output schema, the description provides excellent context about what the tool does and when to use it. The examples help illustrate the range of possible queries. The main gap is the lack of information about return formats or error handling, but given the tool's simplicity and the absence of an output schema, this is reasonably 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 schema has 100% description coverage with the parameter well-documented as 'Your question or request in natural language'. The description adds minimal value beyond this by mentioning 'plain English' and providing examples, but doesn't elaborate on parameter constraints or formats beyond what the schema already states. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('Ask a question', 'get an answer') and resources ('best available data source'), distinguishing it from siblings like lookup_ip or discover_tools by emphasizing natural language processing rather than specific technical operations. It explicitly mentions that Pipeworx handles tool selection and argument filling, which is unique 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 provides explicit guidance on when to use this tool vs alternatives: 'No need to browse tools or learn schemas — just describe what you need' clearly positions this as the tool for natural language queries when you don't want to manually select tools. The examples further illustrate appropriate use cases like factual questions, data lookups, and document retrieval.

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 behaviors: resolves market, classifies bet, fans out to right packs, returns evidence and comparison. Consistent with annotations (readOnlyHint, openWorldHint, non-destructive). 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?

Single paragraph, front-loaded with purpose, then inputs, outputs, usage. Each sentence adds value. Slightly long but well-structured and information-dense.

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

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

No output schema, but description explains return format (evidence packet + comparison). Covers classification and fan-out behavior. Adequate for complexity of tool.

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

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

Schema coverage is 100%, but description adds meaning: market accepts slug, URL, or text; depth options explained (quick vs thorough) and default. Provides context beyond schema.

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

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

The description clearly states it researches a Polymarket bet by pulling Pipeworx data. It specifies inputs (slug, URL, question text) and outputs (evidence packet, market-vs-model comparison). It distinguishes from sibling tools by being the core demo product that combines multiple data 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?

Explicitly says when to use with example queries ('should I bet on X?', 'what does the data say?'). Implies when not to use (e.g., raw data needs other tools). Suggests agents that use this convert better.

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

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

No annotations are provided, so the description carries full burden. It discloses data sources (SEC EDGAR, FDA, CT.gov) and output format (paired data + URIs). No destructive behavior is implied, and permissions are not mentioned, which is acceptable for a read-like 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 two sentences long, well-structured, and front-loaded with the core functionality. Every sentence adds value without redundancy.

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

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

Given no output schema, the description adequately covers return format (paired data + resource URIs) and data sources. It is sufficient for the tool's complexity, though it could hint at error handling.

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 significant meaning beyond the schema by explaining what 'type' values entail (e.g., 'company' yields revenue, net income) and giving concrete examples for 'values' parameter. This aids correct invocation.

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

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

The description clearly states that the tool compares 2-5 entities side by side, specifying metrics for 'company' and 'drug' types. It distinguishes itself from siblings like ask_pipeworx or resolve_entity by focusing on comparative analysis.

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 when to use the tool (for comparing companies or drugs) and notes efficiency gains (replacing 8-15 sequential calls). It does not explicitly state when not to use or offer alternatives, 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?

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it's a search operation that returns relevant tools, and it should be called first in large tool environments. However, it doesn't mention potential limitations like rate limits, authentication requirements, or error conditions that might be important for a discovery 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 perfectly concise and front-loaded. The first sentence states the core functionality, the second explains the return value, and the third provides crucial usage guidance. Every sentence earns its place with no wasted words.

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

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

Given the tool's moderate complexity (search operation with 2 parameters) and no output schema, the description is reasonably complete. It explains what the tool does, when to use it, and what it returns. The main gap is the lack of output format details (what the returned tool information looks like), which would be helpful since there's 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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't elaborate on query formulation strategies or limit implications). Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('search', 'returns') and resources ('Pipeworx tool catalog', 'most relevant tools with names and descriptions'). It distinguishes from siblings like 'get_my_ip' and 'lookup_ip' by focusing on tool discovery rather than IP-related operations.

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 usage guidance: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This clearly indicates when to use this tool versus alternatives, establishing it as an entry point for tool discovery in large catalogs.

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, description carries full burden. It lists return data (citations, sources) and mentions performance bundling, but does not explicitly state it's read-only or discuss authentication/rate limits. Still, it is transparent about scope and excluded use cases.

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

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

Description is concise (about 5 sentences), front-loaded with the main purpose. Every sentence adds value—no filler, clear structure.

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

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

Despite no output schema, the description details what is returned (SEC filings, revenue, patents, news, LEI) and notes citation URIs. It explains why federal contracts are excluded. For a complex multi-source tool, this is 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?

Schema coverage is 100%, and description adds significant meaning beyond schema: clarifies that only 'company' type is supported, explains value format (ticker or CIK), and warns that names are not supported, recommending resolve_entity. This is highly informative.

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 returns a 'full profile of an entity across every relevant Pipeworx pack in one call', with specific data sources listed for type='company'. It distinguishes from sequential calls and mentions the sibling tool usa_recipient_profile.

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

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

Explicitly says when to use (replaces 10-15 sequential calls) and when not to use (federal contracts, use usa_recipient_profile). Also advises using resolve_entity if only a name is available, providing a clear alternative.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. 'Delete' implies a destructive mutation, but there's no information about permissions required, whether deletion is permanent or reversible, what happens if the key doesn't exist, or any rate limits. The description states what happens but not how it behaves.

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 perfectly concise - a single sentence with zero wasted words that immediately communicates the core functionality. Every word earns its place, and the structure is front-loaded with the essential information.

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

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

For a destructive mutation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address critical context like what 'stored memory' means in this system, what confirmation or response to expect, error conditions, or integration with sibling tools in the memory management workflow.

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 the single 'key' parameter adequately. The description adds no additional semantic context about key format, constraints, or examples beyond what the schema provides, meeting the baseline for high schema coverage.

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

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

The description clearly states the action ('Delete') and target resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'recall' or 'remember', but the verb 'Delete' strongly implies this is a removal operation rather than retrieval or storage.

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

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

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'recall' (likely for retrieval) and 'remember' (likely for storage), there's no indication of when deletion is appropriate, what prerequisites exist, or what happens after deletion.

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 explains the process ('Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format') which adds detail beyond the annotations. Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior, and the description aligns with these without contradiction.

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

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

The description is concise and well-structured: first sentence states purpose, second explains process, third lists use cases. Every sentence is informative and there is no redundancy or unnecessary detail.

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

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

Given the tool's simplicity (2 parameters, no output schema), the description provides all necessary context: what it does, how it works, and what the output is ('a single text blob'). Annotations cover safety and semantics, making the description 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?

Input schema coverage is 100% with both parameters documented. The description does not add significant meaning beyond the schema; it only echoes the default and max for max_links. Baseline score of 3 is appropriate since the schema already provides sufficient 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 generates a llms.txt file for AI crawlers, specifying the verb 'generate' and the resource 'llms.txt'. It distinguishes itself from siblings by listing specific use cases like indexing a client's site or drafting for own project.

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 concrete use cases ('getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor'), giving clear context for when to use it. However, it does not explicitly mention when not to use it or compare with 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what information is retrieved (geolocation and network info) but doesn't mention behavioral traits like rate limits, authentication needs, response format, or potential errors. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 efficiently conveys the tool's purpose without any wasted words. It's front-loaded with the key action ('Get') and resource, making it easy to parse. Every part of the 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 has no annotations, no output schema, and 0 parameters, the description is minimal. It states what the tool does but lacks context on behavioral aspects (e.g., response format, error handling) and doesn't differentiate from siblings. For a tool that retrieves potentially sensitive geolocation data, more completeness is needed to guide an agent effectively.

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

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

The tool has 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so it naturally compensates by focusing on the tool's purpose. Baseline for 0 parameters is 4, as it appropriately avoids unnecessary parameter details.

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 geolocation and network information for the current request's originating IP address.' It specifies the verb ('Get'), resource ('geolocation and network information'), and scope ('current request's originating IP address'). However, it doesn't explicitly differentiate from the sibling tool 'lookup_ip' (which presumably looks up other IPs), so it doesn't reach the highest score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'lookup_ip' or clarify that this tool is specifically for the current request's IP, while 'lookup_ip' might be for arbitrary IPs. There's no explicit when/when-not usage advice, so it scores low.

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 burden. It discloses the return data structure (city, region, etc.) and that it's a lookup operation, but lacks details on error handling, rate limits, authentication needs, or data freshness. It adequately describes what the tool does but misses some behavioral traits.

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, front-loaded with the core purpose and followed by specific return details. Every sentence adds value with no wasted words, making it highly efficient and well-structured.

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

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

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is mostly complete. It clearly states the purpose, usage, and return values. However, without an output schema, it could benefit from more detail on the return format or error cases, but it's sufficient for basic understanding.

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 schema description coverage is 100%, with the parameter 'ip' fully documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints, so 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 tool's purpose with specific verbs ('Get geolocation and network information') and resource ('for a specific IP address'). It distinguishes from the sibling tool 'get_my_ip' by specifying it's for looking up a provided IP rather than retrieving the user's own IP.

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 context by stating it's for a 'specific IP address,' which differentiates it from the sibling tool 'get_my_ip' that likely retrieves the user's own IP. However, it doesn't explicitly state when to use this tool versus alternatives or provide exclusions.

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

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

With no annotations, the description carries full burden. It discloses rate limiting (5 per identifier per day) and that the tool is free. It does not describe side effects or returns, but for a feedback tool this is sufficient. 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 four sentences, front-loaded with purpose and use cases, then content guidelines and rate limit. Every sentence provides essential information with no redundancy or fluff.

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

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

Given the tool's simplicity and lack of output schema, the description covers purpose, usage, content guidelines, and rate limits. It is complete enough for an agent to invoke correctly. Minor gap: no mention of response or confirmation after sending.

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 well-described parameters. The description adds value by advising users to describe their attempts without including the end-user's prompt verbatim. This goes slightly beyond the schema's property descriptions, but baseline is 3 due to high coverage.

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 sends feedback to the Pipeworx team and enumerates specific use cases (bug reports, feature requests, missing data, praise). This distinguishes it from sibling tools like ask_pipeworx or discover_tools, which serve different purposes.

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

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

Description provides explicit guidance on when to use the tool (for feedback types) and what content to include (describe what you tried in terms of Pipeworx tools/data). It also mentions the rate limit. However, it does not explicitly state when not to use it or point to alternatives, though siblings are distinct enough.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable context: data derived from CF analytics-engine, no PII, cached 5min-1h. 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 (~100 words), front-loads the core purpose, and uses bullet points for use cases. Every sentence adds value with no redundancy.

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

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

For a low-complexity tool (1 param, no output schema), the description fully covers purpose, usage, behavior, and parameter semantics. No gaps remain for an agent to select and invoke correctly.

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

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

The single parameter 'window' has full schema coverage with enum values. The description adds meaning beyond the enum by explaining that short windows surface hot topics and longer windows show steady-state demand.

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

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

The description clearly states that the tool returns 'what other AI agents are calling on Pipeworx right now', specifying top tools, packs, and total call volume. It distinguishes itself from siblings (e.g., discover_tools) by focusing on aggregated trending data.

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

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

The description lists three concrete use cases (discovering hot data sources, confirming canonical choices, aligning with agent demand). It implicitly guides when to use but does not explicitly state when not to use or offer alternatives.

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

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

The description elaborates on the tool's behavior beyond annotations: it explains the monotonicity logic, the process of walking child markets, extracting dates/thresholds, and the output structure. This aligns with annotations (readOnlyHint, openWorldHint, destructiveHint) and adds valuable context.

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 each sentence adding value. It explains the concept, input, logic, and output in a logical order. There is minor redundancy (e.g., 'Pass a Polymarket event slug or URL' could be shorter), 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 fully explains the return format (list of entries with fields). Given the tool's simplicity (one parameter) and the richness of context provided, it is complete and leaves no major gaps 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?

The input schema already describes the 'event' parameter well (100% coverage). The description adds extra clarity by specifying it can be a slug or URL, going beyond the schema's description. This is helpful but the schema already does the heavy lifting.

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

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

The description clearly states the tool's purpose: finding arbitrage opportunities by checking monotonicity violations within Polymarket events. It uses specific verbs ('find', 'checks') and resources ('arbitrage opportunities'). It distinguishes itself from sibling tools like 'bet_research' by focusing on a specific arbitrage strategy.

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 when to use the tool (for time-based or threshold-based markets within the same event) and provides guidance on input format (slug or URL). However, it does not explicitly mention when not to use it or compare with alternative tools, leaving some ambiguity for edge 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?

The description details the internal process: scanning top markets, grouping by asset, fetching price history once, computing model probability, and ranking by edge. It mentions data sources (FRED, coinpaprika) and output (top N with trade direction). Annotations (readOnlyHint, openWorldHint, destructiveHint) are consistent, and the description adds significant behavioral context beyond them.

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

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

The description is concise at about 100 words and 6 sentences. It front-loads the main purpose in the first sentence, then efficiently explains the model, process, and use case without redundancies. Every sentence contributes value.

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

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

Given the tool's complexity (scanning, computing edges, ranking), the description is complete. It covers what the tool does, how it works (algorithm, data sources), what it returns (top N with suggested trade direction), and its scope (V1, crypto-price bets). No output schema exists, but the description adequately describes the return format. Annotations support the description.

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

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

All three parameters are fully described in the input schema with clear defaults and limits. The tool description does not add extra parameter information beyond what is in the schema. Since schema coverage is 100%, a score of 3 is appropriate as the description is not required to repeat schema details.

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 scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price. It explains the model (V1, crypto-price bets, lognormal model) and the ranking by edge magnitude. It distinguishes from sibling tools like polymarket_arbitrage by focusing on edge computation using specific data 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 targets the 'what should I bet on today' question, providing clear context for use. It notes that V1 covers crypto-price bets, implying a scope limitation. However, it does not explicitly state when not to use this tool or compare with alternatives like polymarket_arbitrage, which is a minor gap.

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 non-destructive. The description adds context: typical delta (2-25pp), the two modes, and returns leg-by-leg prices and spread. It does not cover edge cases like missing matches, but adds good value.

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

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

The description is 4 sentences, each providing useful information. It is front-loaded with the core purpose. While slightly verbose, it remains clear and focused without redundancy.

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

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

Given no output schema, the description adequately explains return values (leg-by-leg prices, spread in % points) and the two modes. With 3 parameters fully described, it is mostly complete. Could mention error handling for mismatched events.

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 tool description adds the mode concept: topic auto-fetches, explicit overrides. It also enumerates the topic values. This enhances understanding beyond the schema.

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

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

The description clearly states the tool computes cross-venue spreads between Kalshi and Polymarket for the same event. It uses specific verbs ('cross-venue spread') and resources ('Kalshi and Polymarket'), and the description of two modes distinguishes it from siblings like polymarket_arbitrage.

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

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

The description explains when to use the tool (to spot arb signals due to price differences) and how to use it via topic or explicit ticker/slug. However, it does not explicitly contrast with alternatives or mention 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?

With no annotations provided, the description carries the full burden. It discloses that memories can be retrieved from current or previous sessions, which is useful behavioral context. However, it doesn't mention potential limitations like memory size, retrieval speed, or error conditions, leaving some behavioral aspects unclear.

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 perfectly concise with two sentences that each serve distinct purposes: the first explains the core functionality, and the second provides usage context. There's no wasted language, and information is front-loaded effectively.

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 one parameter, 100% schema coverage, and no output schema, the description provides good contextual completeness. It explains what the tool does, when to use it, and how parameters affect behavior. The main gap is lack of output format details, which would be helpful 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?

The description adds meaningful context beyond the schema by explaining that omitting the key parameter triggers listing all memories. With 100% schema description coverage and only one parameter, this additional semantic guidance elevates the score above the baseline of 3 for high coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory by key', 'all stored memories'). It distinguishes from siblings by mentioning 'context you saved earlier' which relates to the 'remember' tool, showing differentiation.

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

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

The description provides explicit guidance on when to use this tool: 'to retrieve context you saved earlier in the session or in previous sessions.' It also specifies when to omit the key parameter to list all memories, offering clear usage instructions.

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 parallel fan-out to SEC EDGAR, GDELT, and USPTO, the parameter formats (ISO date and relative), and the return structure (structured changes, count, URIs). It does not cover potential error cases, rate limits, or authorization requirements, which would improve transparency.

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

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

The description is a single paragraph but highly efficient. It front-loads the core purpose, then packs details on behavior, parameter formats, and return values without extraneous words. Every sentence adds necessary information.

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

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

Given there is no output schema, the description adequately explains the return type (structured changes, total_changes count, pipeworx:// URIs). It also covers parameter nuances and use cases. For a moderately complex tool with 3 parameters and a defined fan-out, this is complete enough for an 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%, so parameters are well-defined structurally. The description adds value by explaining the 'since' parameter's relative formats with examples ('30d', '1m') and clarifying that 'type' is currently only 'company'. This usage guidance 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 starts by clearly stating the verb ('What's new') and the resource ('entity since a given point in time'). It elaborates on the fan-out behavior for 'company' type, distinguishing it from sibling tools like entity_profile or compare_entities. The specific verb-resource combination is unique and well-defined.

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 use cases: 'Use for brief me on what happened with X' or change-monitoring workflows. While it doesn't explicitly mention when not to use or list alternatives, the context of sibling tools makes it clear this is the go-to for change tracking. The lack of exclusion criteria is a minor gap.

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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the storage mechanism (session memory), persistence differences between authenticated vs. anonymous users, and the 24-hour limit for anonymous sessions. This covers important operational context beyond basic functionality.

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 perfectly concise and well-structured in two sentences. The first sentence states the core purpose, and the second provides important behavioral context. Every word earns its place with no redundancy or unnecessary information.

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

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

For a tool with 2 parameters, 100% schema coverage, and no output schema, the description provides good contextual completeness. It explains the tool's purpose, usage context, and important behavioral details about persistence. The main gap is lack of information about return values or error conditions, which would be helpful since there's 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?

The schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema (e.g., it doesn't explain key constraints or value formatting). This meets the baseline expectation when schema coverage is high.

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

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

The description clearly states the tool's purpose with specific verbs ('store a key-value pair') and resource ('in your session memory'). It distinguishes from sibling tools like 'forget' (which likely removes) and 'recall' (which likely retrieves) by focusing on storage. The description goes beyond the name 'remember' by specifying the storage mechanism.

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

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

The description provides clear context on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), which helps the agent understand its role in workflows. However, it doesn't explicitly state when NOT to use it or name alternatives (e.g., when to use 'recall' instead), which prevents a perfect score.

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

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

No annotations are provided, so the description carries full burden. It discloses the return values (ticker, CIK, company name, pipeworx:// URIs) and implies a safe read operation. It does not mention any destructive effects or authentication needs, but given the nature of the tool, 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?

The description is concise with no wasted words. It front-loads the main purpose, then provides version info, input examples, and output details. Every sentence adds value.

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

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

Given the simple tool (2 params, no output schema, no annotations), the description covers what it does, inputs, outputs, and version context. It is complete for an entity resolution tool, though it doesn't detail return format or error cases.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining that the 'value' parameter can be ticker, CIK, or company name with examples, and clarifies the 'type' parameter currently only supports 'company'. 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 verb 'resolve' and resource 'entity', specifies it resolves to canonical IDs across Pipeworx data sources, and provides version context (v1 supports company type). It also distinguishes from sibling tools by noting it replaces 2–3 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?

The description gives explicit examples of accepted input formats (ticker, CIK, name) and states it replaces 2–3 lookup calls, which helps agents decide when to use it. However, it does not provide explicit when-not-to-use instructions or list alternative 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 detail beyond annotations: it explains the tool probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density per entity. Annotations only indicate read-only, idempotent, non-destructive, and open-world, which the description reinforces.

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 at two sentences. It front-loads the core purpose and follows with a use case and expected output. Every sentence adds value with no fluff.

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

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

For a tool with 4 parameters and no output schema, the description adequately explains input (entities, models, apiKey, context) and output (ranked list with score, confidence, signal density). It lacks mention of rate limits or potential external API calls beyond the implied ai_visibility_check, but overall provides sufficient context for selection.

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 minimal extra parameter context (e.g., 'first entry treated as subject'), but the schema already thoroughly describes each parameter. No significant additional 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 compares AI visibility across multiple entities, ranks them, and identifies the most/least recognized. It distinguishes from sibling 'ai_visibility_check' which likely checks a single entity, and from 'compare_entities' which may be more generic.

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 a clear use case (competitive AI-marketing audits) and an example question. While not explicitly stating when not to use, the context of comparing multiple entities versus a single check with 'ai_visibility_check' is implied by the sibling list.

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 return values (verdict, structured form, citation, delta) and overall behavior (fact-checking), but does not mention side effects, authentication needs, or rate limits. The lack of explicit read-only or safety information is a gap, though the tool's read-only nature is implied by description.

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: first states purpose, second narrows scope and sources, third lists outputs and value proposition. No filler; every sentence adds distinct value.

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

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

Given the simple input schema (one parameter) and no output schema, the description fully covers what the tool does, accepts, returns, and its domain restriction. No additional information is necessary for effective use.

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

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

Only one parameter 'claim' with 100% schema description coverage, so baseline is 3. Description adds value by providing examples and clarifying the specific domain (company-financial claims), which enhances understanding beyond the schema's generic 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?

Description clearly states verb 'fact-check' and resource 'natural-language claim against authoritative sources', with specific scope (company-financial claims for US public companies) and supported sources (SEC EDGAR + XBRL). This distinguishes it from sibling tools like ask_pipeworx or compare_entities.

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

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

Description explicitly limits tool to company-financial claims in v1, providing clear domain boundaries. It implies when to use (single-step fact-checking for financial claims) but does not explicitly state when not to use or suggest alternative tools for other claim types.

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