Ipify

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

Annotations already declare readOnlyHint and openWorldHint. The description adds behavioral context: routing logic, argument filling, and citation URI format. No contradictions with annotations. It does not detail rate limits or authentication, but the added context is valuable.

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 but front-loaded with the key usage directive ('PREFER OVER WEB SEARCH'). Each sentence adds distinct information; however, slight trimming could improve conciseness without losing meaning.

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 is provided, but the description clarifies return structure (structured answer with citations). It covers purpose, usage, and examples adequately for a routing tool. Minor gaps like expected response format details remain.

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

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

Schema covers 100% of parameters (only 'question'). The description goes beyond by specifying query intents ('what is', 'look up', etc.) and domain examples, which enriches the schema's generic 'natural language' prompt.

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 routes questions to thousands of verified data sources for authoritative structured data with citations. It contrasts with web search, provides specific domains (SEC, FDA, etc.), and lists example queries, leaving no ambiguity about what the tool does.

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 strongly advises preferring this tool over web search and enumerates many factual query types and example use cases. It lacks explicit 'when not to use' scenarios but provides ample guidance for appropriate invocation.

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

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

Annotations already indicate read-only, open-world, and non-destructive behavior. The description adds rich behavioral detail: it resolves the market, classifies the bet into categories, fans out to specific data packs based on classification, and returns an evidence packet plus a comparison. This fully discloses what the tool does internally and what to expect, exceeding what annotations alone provide.

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

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

The description is relatively long but front-loaded with the core purpose. Each sentence adds value, including internal workflow and use case examples. It could be slightly more concise, but the structure is logical and the information density is justified given the tool's complexity.

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

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

The description covers input (market and depth), internal process (classification, fan-out), output (evidence packet, comparison), and use cases. Despite no output schema, the description adequately describes what is returned. The complexity of the tool (multiple data packs, classification) is well addressed.

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%, but the description adds meaningful context: it explains that 'market' can be a slug, URL, or question text, and it details the 'depth' enum values (quick vs thorough) with their implications. This enhances understanding beyond the schema alone.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data in one call. It specifies the action (research, pull data) and the resource (Polymarket bet). It distinguishes itself from siblings by claiming it's the core demo product that provides bet-relevant context without needing to discover packs, though it does not explicitly name alternatives.

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 provides use cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. It also implies when to use by stating agents that get bet-relevant context here convert better, suggesting it's the preferred tool for bet research. No explicit exclusions, but the guidelines are clear and actionable.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/trials for drugs) and output format (paired data + citation URIs). This goes beyond annotations without contradiction.

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

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

The description is well-structured with the main action first, then usage examples, then details per type. Every sentence adds value without redundancy. It is concise for the amount of information provided.

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

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

Given the presence of annotations and 100% schema coverage, the description is quite complete. It explains what data is retrieved, from where, and gives usage examples. However, it does not describe the exact return structure (e.g., JSON format), though this is mitigated by the lack of output schema requirement.

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?

Both parameters have descriptions in the schema (100% coverage), so baseline is 3. The description adds meaning by explaining what data is returned for each type: revenue/net income for companies, adverse events/approvals/trials for drugs. This clarifies the purpose of the 'values' parameter.

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

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

The description clearly states the tool's purpose: 'Compare 2–5 companies (or drugs) side by side in one call.' It specifies the verb 'compare', the resource types (companies/drugs), and distinguishes from siblings like entity_profile which handles single entities.

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

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

The description provides explicit usage cues: 'Use when a user says "compare X and Y", "X vs Y", etc.' It also notes that it replaces 8–15 sequential agent calls, indicating efficiency. However, it does not explicitly compare to sibling tools like entity_profile for single entity queries.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety profile is clear. The description adds value by specifying that it returns 'top-N most relevant tools with names + descriptions' and listing covered topics, but does not reveal potential side effects (none expected) or any caveats beyond what annotations imply.

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

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

Description is front-loaded with the key action and provides necessary context. At 82 words, it efficiently covers purpose, examples, and usage guidance. Slightly verbose in example list, but each 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?

For a simple tool with two parameters and no output schema, the description is complete: it explains what it does, when to use it, what returns, and parameter specifics. No critical gaps given the tool's straightforward nature.

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 provides 100% coverage with descriptions for both parameters (query, limit). The description reinforces that query is 'natural language' and gives limit defaults. No additional semantic information beyond schema, so baseline score is appropriate.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists specific domains (SEC filings, financials, etc.) and distinguishes itself from sibling tools by being a discovery/search tool rather than a specialized query 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 advises to call this 'FIRST when you have many tools available and want to see the option set.' Provides examples of use cases. Could explicitly state when not to use, but context implies it's for exploration before specific tools.

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

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

Annotations already indicate read-only, open-world, non-destructive behavior. The description adds significant value by detailing the exact data returned (SEC filings, financials, patents, news, LEI) and mentions citation URIs. No contradiction with annotations.

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

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

Description is compact and front-loaded with the core purpose in the first sentence. It uses bullet-like structure with commas and lists to convey extensive information without verbosity. Every sentence adds value and 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?

Despite lacking an output schema, the description thoroughly explains the return value composition (SEC filings, financials, patents, news, LEI, citations). It also covers limitations (only company type supported) and prerequisites (need ticker or CIK). This is complete for a complex aggregation tool.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds contextual information beyond schema: example values (AAPL, 0000320193), clarification that names are not supported, and a pointer to resolve_entity for name resolution. This elevates the understanding beyond the raw 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 aggregates comprehensive company data in one call, lists specific data sources (SEC, USPTO, news, GLEIF), and provides example queries like 'tell me about X' or 'research Microsoft'. It distinguishes itself from siblings like resolve_entity by specifying it handles tickers/CIKs directly.

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

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

Description explicitly outlines when to use (user asks for company profile) and when not (if only name given, use resolve_entity first). It provides concrete trigger phrases and clear exclusions, giving the agent precise guidance on tool selection.

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

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

Annotations already set destructiveHint=true, so the description merely confirms the destructive behavior. It adds the nuance of 'clearing sensitive data', but does not detail side effects or reversibility. The annotation carries the main disclosure burden.

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

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

The description is two sentences, each serving a clear purpose: the first defines the action, the second provides usage guidelines. No unnecessary words.

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

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

Given the tool's simplicity (single parameter, no output schema, no nested objects), the description covers purpose and usage adequately. It lacks mention of return value, but for a delete operation this is acceptable and common.

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

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

The only parameter 'key' is fully described in the schema as 'Memory key to delete'. The description adds no further semantic value beyond that, so baseline score applies.

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

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

The description clearly states 'Delete a previously stored memory by key', providing a specific verb and resource. It distinguishes itself from sibling tools like 'remember' and 'recall' by focusing on deletion.

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

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

The description explicitly states when to use the tool: 'when context is stale, the task is done, or you want to clear sensitive data'. It also mentions pairing with siblings, giving context. However, it does not explicitly exclude scenarios where it should not be used.

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 annotations already declare this tool as read-only and non-destructive. The description adds 'gateway egress IP' which clarifies the nature of the IP, but does not reveal additional behavioral traits beyond what annotations provide.

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

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

The description is extremely concise—a single, complete phrase with no unnecessary words. It is front-loaded and efficiently conveys the tool's purpose.

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 zero parameters, no output schema, and simple output, the description is fully complete. The annotations cover safety, and the description tells the agent exactly what to expect.

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

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

There are no parameters, so per the rubric the baseline is 4. The description does not need to add meaning to parameters.

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

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

The description clearly states the tool returns the caller's IPv4 address (gateway egress IP). It uses a specific verb and resource, and distinguishes itself from the sibling tool ipv6.

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?

While the purpose is clear, there is no guidance on when to use this tool over alternatives or any exclusions. It is straightforward due to zero parameters, but lacks explicit usage context.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds value by explaining the fallback to IPv4, which clarifies behavior beyond the annotations. No contradictions are present.

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

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

The description is extremely concise (4 words plus parenthetical), front-loaded with the core purpose, and every part adds meaning. It is an efficient, no-waste description.

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

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

Given the tool has no parameters, no output schema, and annotations cover safety, the description is sufficiently complete. It explains the core function and fallback behavior, making it adequate for a simple lookup tool.

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

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

The input schema has no parameters, and the description does not need to add parameter information. Per the scoring guidelines, 0 parameters earns a baseline of 4.

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

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

The description clearly states the tool returns the caller's IPv6 address, with explicit mention of IPv4 fallback. It uses a specific verb+resource structure and distinguishes itself from the sibling tool 'ipv4' by specifying IPv6.

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 does not provide explicit guidance on when to use this tool versus alternatives like 'ipv4'. However, the mention of 'IPv4 fallback' implies it returns IPv4 if IPv6 is unavailable, offering limited contextual guidance. No when-not-to-use or alternative recommendations are given.

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 transparently discloses rate limiting ('5 per identifier per day'), the fact that it's free and doesn't count against quota, and how feedback is processed ('team reads digests daily, signal affects roadmap'). No contradiction with annotations (readOnlyHint=false, destructiveHint=false): the tool writes feedback, aligning with the schema.

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 (4 sentences) with no wasted words. It front-loads the purpose and covers all essential aspects (when to use, how to describe, team action, rate limit, free). 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?

Given the tool's simplicity (3 parameters, no output schema), the description is complete. It explains the purpose, usage guidelines, behavioral constraints, and provides examples of appropriate feedback content. The nested context parameter is described in schema, and the description integrates smoothly.

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%: every parameter has a clear description. The tool description adds no additional parameter-level semantics beyond what the schema already provides. Baseline 3 is appropriate since 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 is for reporting bugs, feature requests, data gaps, or praise to the Pipeworx team. It explicitly differentiates from siblings by specifying use cases: 'Use when a tool returns wrong/stale data (bug)... when a tool you wish existed isn't in the catalog (feature/data_gap)... when something worked surprisingly well (praise).' This level of specificity leaves no ambiguity.

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

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

The description provides explicit when-to-use scenarios (bug, feature, data_gap, praise) and what not to do ('don't paste the end-user's prompt'). It also mentions rate limits and that the tool is free, guiding appropriate usage. Sibling tools like ask_pipeworx or discover_tools are implicitly distinguished.

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

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

Adds significant behavioral detail beyond annotations, explaining the tool walks child markets, searches across events, groups them, checks monotonicity, and returns ranked opportunities with reasoning.

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 and well-structured, front-loading purpose, then explaining modes, then providing cross-event context, ending with output description, all in a single focused paragraph.

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?

Complete description for a read-only exploration tool with no output schema; covers modes, behavior, use cases, and return value sufficiently.

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?

Adds meaning beyond the schema by providing examples for each parameter and explaining how the parameter determines the mode and behavior of the tool.

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

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

Clearly states the tool finds arbitrage opportunities by checking monotonicity violations, and distinguishes two distinct modes (event and topic) which differentiates it from siblings.

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 guidance on when to use each mode, including a concrete example of when cross-event mode is necessary because single-event mode would miss the relationship.

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

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

The description provides rich behavioral context beyond annotations: it explains the lognormal model from FRED and current prices from coinpaprika, the process of grouping by asset, fetching price history once, computing probabilities, and ranking by edge magnitude. Annotations already declare readOnlyHint=true, so this additional detail is valuable and non-contradictory.

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 for the amount of information it conveys, with front-loaded purpose. A few phrases like 'V1 covers crypto-price bets' could be trimmed, but overall it earns its length.

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 ('top N ranked by edge magnitude with suggested trade direction'). It covers parameters, model, use case, and processing steps, making it complete for an agent.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description restates defaults and function but does not add new parameter-level semantics beyond what the schema already conveys. Baseline 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 scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price. It specifies the verb (scan, return, rank), resource (Polymarket markets), and unique analysis (model probability vs market price). This 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 explicitly positions the tool for the 'what should I bet on today' question, giving clear context. However, it does not mention when not to use it or alternatives among siblings, missing some guidance.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false; the description adds that it is scoped to identifier and that omitting key lists all keys, fully disclosing 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, front-loaded with core action, no wasted words, and logically 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?

For a simple tool with one optional parameter and no output schema, the description covers retrieval, listing, scoping, and sibling relationships completely.

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

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

Schema coverage is 100% with description for 'key'; the description adds value by explaining that omitting it lists all keys, which the schema does not explicitly state.

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

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

The description clearly states the tool retrieves a value saved via 'remember' or lists all keys if omitted, distinguishing it from siblings 'remember' (save) and 'forget' (delete).

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

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

It explicitly tells when to use the tool ('look up context the agent stored earlier') and mentions pairing with 'remember' and 'forget' as alternatives, plus scoping to identifier.

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

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

Beyond the annotations (readOnlyHint, openWorldHint, destructiveHint), the description reveals the parallel fan-out to SEC EDGAR, GDELT, USPTO and the return structure, adding significant 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 a single, well-structured paragraph that front-loads the purpose, then adds usage hints, parameter details, and output structure without superfluous words.

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

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

With no output schema, the description mentions return elements but lacks precise structure details. It covers all parameters and usage scenarios adequately, though a bit more detail on the output would improve completeness.

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 meaning: explains the 'since' parameter accepts ISO dates and relatives, recommends values, and clarifies the 'type' enum is limited to 'company'.

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: 'What's new with a company in the last N days/months?' and provides specific example queries. It distinguishes itself from siblings like 'entity_profile' by focusing on recent changes.

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 triggers ('Use when a user asks...'), but does not explicitly state when not to use it or mention alternative tools on the same server.

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?

Goes beyond annotations by explaining scoping by agent identifier, persistence differences between authenticated and anonymous sessions (24 hours), and implicit write operation (non-readOnly).

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 concise sentences, front-loaded with purpose, each sentence adding unique 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?

Complete for a simple key-value store tool: explains use case, lifecycle, scoping, and sibling tools; no output schema needed as return is trivial.

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 concrete examples for key (e.g., 'subject_property', 'target_ticker'), supplementing 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?

Clearly states verb 'save' and resource 'data the agent will need to reuse later', distinguishing from sibling tools 'recall' and 'forget' by mentioning 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?

Provides explicit when-to-use guidance with examples like 'resolved ticker, target address', and mentions pairing with recall and forget, but does not explicitly state when not to use.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds value by noting it returns IDs and pipeworx:// citation URIs, and that it replaces 2-3 lookup calls. No contradictions; it fully aligns with annotations.

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

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

The description is concise (3 sentences plus parenthetical examples), front-loaded with the core action, and every sentence provides valuable information. 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?

Despite no output schema, the description clearly states what is returned (IDs plus citation URIs) and provides examples. It covers when to use, what inputs, and what outputs, making it complete 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% and the description adds concrete examples (e.g., 'Apple' → AAPL/CIK 0000320193, 'Ozempic' → RxCUI 1991306) that enrich understanding beyond the schema's enum and text 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: lookup official identifiers (CIK, ticker, RxCUI, LEI) for companies or drugs. It provides concrete examples and distinguishes itself from sibling tools like entity_profile or bet_research by focusing on identifier resolution.

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 tells when to use the tool: when a name is mentioned and official identifiers are needed for other tools. It says 'Use this BEFORE calling other tools that need official identifiers,' providing clear sequencing guidance.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false, which the description does not contradict. The description adds behavioral details: it uses SEC EDGAR + XBRL data, returns a verdict with multiple possible outcomes, and cites sources with pipeworx:// URIs. This goes beyond the annotations, though it does not describe rate limits or authorization requirements.

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

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

The description is three sentences long, front-loading the purpose, giving usage guidance, and then detailing scope and return values. Every sentence is informative and concise, with no repetition 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?

Despite no output schema, the description fully explains the return values: a verdict with five options, extracted structured form, actual value with citation, and percent delta. It also covers the data source and limitations (v1 company-financial). For a single-parameter tool, this is 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 1 parameter with 100% description coverage. The tool description adds meaning beyond the schema by specifying the domain of valid claims (company-financial for public US companies) and providing examples. This enhances the schema's description, though the schema already provides clear examples.

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-check, verify, validate, or confirm/refute a natural-language factual claim'. It specifies the verb ('validate') and the resource ('claim'). It differentiates from siblings by describing its unique capability to replace multiple sequential calls and focusing on company-financial claims, which is distinct from other tools like 'ask_pipeworx' or 'bet_research'.

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

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

The description explicitly states when to use the tool: 'Use when an agent needs to check whether something a user said is true...' and provides example queries. It also delimits the scope ('v1 supports company-financial claims...'). However, it does not explicitly mention when not to use it or list alternative tools for out-of-domain claims.

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