Trade Intel

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

Annotations indicate read-only, idempotent, non-destructive behavior. The description adds context: the default model is free, Anthropic requires a BYO key (passed through), and returns per-model scores with raw_response. It does not contradict annotations and provides useful cost and authentication details.

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

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

The description is concise (3 sentences), front-loaded with action and return value. Every sentence provides essential information without redundancy.

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

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

No output schema, but the description details the return structure (score, confidence, signals, raw_response per model + combined view). All parameters are covered, and use cases are clear. The description is complete for an agent to use the tool effectively.

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

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

Schema coverage is 100% with good parameter descriptions. The description adds semantics: default model behavior, that omitting 'models' array probes only Workers AI, that _apiKey is conditional, and that 'context' helps disambiguate. This adds value beyond the schema.

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

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

The description clearly states the tool's purpose: probing multiple LLMs about a brand/product/topic and scoring visibility (0-100) per model. It specifies the default model, optional Anthropic probing with API key, and the return structure. This distinguishes it from sibling tools like scan_competitor_ai_presence by focusing on visibility scoring across specific models.

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: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also explains when to pass _apiKey (for Anthropic probes). However, it does not explicitly exclude scenarios or compare to alternatives, which would elevate it to a 5.

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

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

The description discloses that Pipeworx picks the right tool and fills arguments, implying delegation, which is key behavioral context beyond annotations (none provided). It doesn't detail failure modes or data sources.

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 three sentences, front-loaded with purpose and examples, 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?

For a single-parameter tool with no output schema, the description is complete enough, providing examples and explaining the delegation mechanism. However, it could mention limitations or data coverage.

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

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

Schema coverage is 100% with a single 'question' parameter. The description adds value by explaining it accepts natural language, but the schema already describes it as 'Your question or request in natural language'.

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

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

The description clearly states the tool answers natural language questions by selecting the best data source, which distinguishes it from siblings like trade_bilateral_analysis or trade_macro_dashboard that are domain-specific.

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 to use this tool for plain English questions and mentions it handles tool selection automatically, but does not explicitly state when not to use it or name alternatives.

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

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

Annotations already indicate readOnlyHint=true, safe and idempotent. The description adds process details (resolving, classifying, fanning out) and output (evidence packet, market-vs-model comparison), fully disclosing behavior. No contradiction.

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

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

The description is four sentences, front-loaded with the primary purpose, then input format, then process, then use cases. No redundant information; 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?

The description covers input, process, and output sufficiently for a read-only research tool. It mentions the return (evidence packet and comparison) despite no output schema. Could add more detail on the 'market-vs-model comparison' but is adequate.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining the 'market' parameter can be a slug, URL, or question text, which the schema description does not explicitly cover. This adds user guidance.

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

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

The description clearly specifies the action (research a Polymarket bet) and the resource (Pipeworx data). It details the process: resolving the market, classifying, fanning out to packs, and returning evidence. This distinguishes it from sibling tools like 'ask_pipeworx' or 'validate_claim'.

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 example queries ('should I bet on X?') and states it is the core demo product, implying it is the primary tool for bet research. However, it does not explicitly name alternatives or when not to use this tool, which would improve clarity.

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

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

No annotations exist, so description must carry the burden. It discloses return data and URIs but omits behavioral traits like error handling, rate limits, or what happens if an entity is not found. Adequate but not comprehensive.

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

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

Two sentences with no fluff. First sentence defines the core action and types; second sentence specifies return data and efficiency benefit. 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 no output schema, the description adequately describes return values and resource URIs. It covers the main use case but lacks details on partial results or errors. Still fairly complete for a comparison 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 the description adds significant meaning: it details what data is returned for each entity type (e.g., revenue for company, adverse-event counts for drug), going beyond schema field 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 it compares 2–5 entities side by side, specifies two types (company and drug) and the data returned for each. It distinguishes itself from sequential single-entity 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?

While it describes when to use (comparing multiple entities of same type), it does not explicitly state when not to use or alternatives like using sequential calls for single entities. However, it implies efficiency over sequential calls.

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

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

No annotations are provided, so the description carries the full burden. It states the tool 'Returns the most relevant tools with names and descriptions,' which is useful but lacks details on ranking, exact return format, or whether it's read-only. It does not contradict anything, and a score of 3 is reasonable given the minimal behavioral disclosure beyond the 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 extremely concise: two sentences that front-load the purpose and critical usage advice. Every sentence is essential and there is no redundant or extraneous information.

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

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

Given the tool's simplicity (2 parameters, no output schema, no nested objects) and the presence of sibling tools, the description covers the key aspects: what it does, when to use it, and the return type (tools with names and descriptions). It could be slightly more explicit about the return format or sorting, but it is largely complete for a search tool with clear 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 description need not add much. The description does not elaborate on the parameters beyond what the schema already provides (e.g., query and limit). However, it does not introduce confusion and the schema is clear, so a baseline of 3 is appropriate.

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

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

The description clearly states the verb ('Search') and resource ('Pipeworx tool catalog'), specifies the purpose ('find the right tools for your task'), and distinguishes this from siblings by its unique role as a discovery/search tool, which is further emphasized by the usage guidance to call it first.

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 the agent to 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear when-to-use guidance and implies when not to use it (when you already know the tool). No alternative tools are named, but the context of 500+ tools and the first-call instruction effectively differentiate 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?

Since no annotations are provided, the description carries the full burden. It states it returns pipeworx:// citation URIs and replaces 10-15 sequential calls. However, it does not explicitly describe read-only behavior, authentication needs, or potential side effects, but the context implies a read-only profile retrieval.

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

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

Four sentences, each adding distinct value: purpose, content details, return format, and usage guidance. No redundancy, front-loaded with the core action.

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

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

Given the complexity of bundling multiple data sources, the description covers what data is included (SEC, XBRL, patents, news, LEI) and the return format (pipeworx:// URIs). It lacks an explicit output schema but the content description suffices for an AI agent to understand 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?

Both parameters (type, value) have schema descriptions with enum for type and examples for value. The description adds significant meaning: only 'company' supported, and value must be ticker or CIK, advising to use resolve_entity for names. This goes 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 'Full profile of an entity across every relevant Pipeworx pack in one call' and lists specific data types (SEC filings, XBRL, patents, news, LEI). It distinguishes itself from siblings like resolve_entity and compare_entities by its comprehensive scope.

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 (for a company profile) and when not to use 'For federal contracts call usa_recipient_profile directly'. It also advises using resolve_entity if only a name is available, providing clear alternatives.

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

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

No annotations are provided, so the description must carry full behavioral burden. It correctly indicates the destructive nature ('delete'), but does not disclose whether deletion is permanent, irreversible, or affects other operations. A score of 3 is appropriate as it conveys the core behavior but lacks depth.

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

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

The description is a single sentence that is direct and front-loaded. It contains no filler and every word is necessary.

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 is simple (single parameter, no output schema), the description adequately conveys the purpose. However, it does not specify what happens on success or failure (e.g., returns confirmation, error if key missing), which would be beneficial.

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 parameter 'key'. The description adds no extra meaning beyond the schema's description. Baseline 3 is appropriate.

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

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

The description 'Delete a stored memory by key' clearly states the verb ('delete'), the resource ('stored memory'), and the mechanism ('by key'). It effectively differentiates from siblings like 'recall' (retrieve) and 'remember' (store).

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 when to use it (when you need to delete a memory), but provides no guidance on when not to use it or alternatives. Given the sibling context, it does not explicitly contrast with similar tools, though the name and purpose 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 (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) already provide safety profile. The description adds specific steps: fetches page, extracts title/description/key links, emits markdown format. 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?

Three sentences, no filler. Each sentence adds value: what it does, how it works, and use cases. Perfectly sized for quick understanding.

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

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

Covers purpose, behavior, and usage. Missing details about error handling or invalid URLs, but for a simple read-only tool with idempotent annotation, this is sufficient. Could mention output format more explicitly, but overall adequate.

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

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

Schema coverage is 100% with clear parameter descriptions. The tool description does not add significant new semantics beyond what the schema provides. Baseline score of 3 is appropriate.

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

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

Clearly states the verb 'Generate' and the specific resource 'llms.txt file for any URL'. Distinguishes itself from sibling tools (e.g., ai_visibility_check, scan_competitor_ai_presence) by focusing on a specific output format and use case for AI crawlers.

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 lists three use cases: indexing client site, drafting own llms.txt, auditing competitor. Provides clear context but does not explicitly state when not to use. Good guidance for the AI agent.

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

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

With no annotations provided, the description carries full burden. It discloses rate limits and that it's free, but does not mention whether the tool returns a confirmation or has any side effects. This leaves some uncertainty for the AI agent.

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, efficient paragraph with no redundancy. Every sentence contributes essential information, and it is front-loaded with the core 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 simplicity of the tool and full schema coverage, the description is reasonably complete. It covers purpose, usage constraints, and behavioral rules. However, it could mention what the agent should expect after calling (e.g., success indication), though no output schema makes this less critical.

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 advising users to describe what they tried in terms of Pipeworx tools/data, which provides meaningful context beyond the schema's parameter descriptions.

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

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

The description explicitly states the tool is for sending feedback to the Pipeworx team, listing specific use cases (bug reports, feature requests, missing data, praise). This clearly distinguishes it from sibling tools like 'ask_pipeworx' and 'discover_tools', which serve different functions.

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 guidance on what to include (describe attempts with Pipeworx tools/data) and what to exclude (end-user's prompt verbatim), along with rate limits. It doesn't explicitly state alternatives, but the context makes it obvious that this is not for queries or data 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds useful context: derivation from CF analytics engine, no PII, and caching behavior (5min-1h). This goes beyond the 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 front-loaded with the core purpose, followed by bullet-like use cases, and ends with data source and caching notes. Every sentence serves a purpose, and the length is appropriate for the tool's simplicity.

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 one parameter, no output schema, and comprehensive annotations, the description covers all necessary aspects: what is returned, caching, aggregation method, and privacy. No gaps.

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

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

Schema coverage is 100% with a single enum parameter and a schema description. The description reiterates the window values and adds a note about shorter vs. longer windows, but does not add significant new meaning 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 returns top tools, top packs, and total call volume over time windows. It specifies the resource ('what other AI agents are calling on Pipeworx') and lists three explicit use cases, distinguishing it from siblings like 'discover_tools'.

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

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

The description provides clear use cases (discovering hot data sources, confirming canonical tool, seeing alignment) but does not explicitly state when not to use or list alternative tools. The context is strong enough for an agent to infer appropriate usage.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, destructiveHint=false, which align with the description. The description adds rich behavioral context: two modes, how they work internally, and what is returned (ranked opportunities with reasoning). 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 well-structured with clear sections ('TWO MODES') and front-loaded main purpose. It is slightly verbose but every sentence adds value. Could be trimmed marginally.

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

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

Given the tool's complexity (two modes, cross-event search, returns opportunities with reasoning) and the absence of an output schema, the description is complete. It explains inputs, modes, returns, and provides examples. Annotations cover safety.

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 extra context beyond the schema by explaining each mode's behavior and providing examples, helping the agent choose between them.

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 arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets.' It specifies two modes (event and topic) with concrete examples, differentiating from potential siblings like polymarket_edges by explicitly describing cross-event mode's unique capability.

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 each mode: event mode for a single event slug, topic mode for cross-event searches. It provides a concrete example where cross-event mode catches cases single-event misses. However, it doesn't explicitly state when not to use the tool or mention 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?

Annotations indicate read-only and non-destructive behavior. The description provides rich behavioral details beyond annotations: V1 covers crypto-price bets using a lognormal model from FRED and live coinpaprika price, groups by asset, fetches price history once, computes model probability, and ranks by |edge|. No contradiction.

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

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

The description is slightly long but well-structured, starting with purpose, then methodology, then output. Every sentence adds value, though it could be slightly more concise.

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

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

Given the complexity of scanning markets and computing edges, the description is very complete: it explains the model, data sources, and output (top N ranked by edge magnitude with suggested trade direction). No output schema, but description covers return format.

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

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

Schema coverage is 100%, and each parameter already has a clear description. The description adds minimal extra meaning beyond the schema definitions (e.g., methodology context is not about parameters). Baseline 3 is appropriate.

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

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

The description uses specific verbs like 'scan', 'return', and 'ranks' to clearly state the purpose: scanning high-volume Polymarket markets and returning those where Pipeworx data disagrees most. It distinguishes itself from sibling tools like polymarket_arbitrage by focusing on edge discovery.

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 it is 'built for the what should I bet on today question' and helps discover opportunities, providing clear context for when to use. However, it does not explicitly mention when not to use or alternatives.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds behavioral details: it returns leg-by-leg prices and spreads, and explains mode behavior (override). No contradictions.

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

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

The description is a single paragraph of four sentences, front-loading the purpose. It is informative without excessive verbosity. Minor room for trimming but 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?

With no output schema, the description adequately explains return values (leg-by-leg prices, spreads). It covers both modes and their interplay. Missing error handling details, but overall sufficient for a read-only tool with clear structure.

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

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

Input schema has 100% coverage with descriptions. The description adds meaning by listing all topic shortcuts, explaining override semantics, and clarifying that explicit params override topic-mapped values.

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 states the tool computes cross-venue spread between Kalshi and Polymarket for the same question. It clearly distinguishes from siblings by focusing on spread calculation and mentioning two modes, whereas siblings like 'polymarket_arbitrage' likely differ.

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 explains two usage modes (topic shortcuts vs. explicit tickers) and when to apply each. It provides context on why the spread exists (arb signal). However, it does not explicitly state when NOT to use the tool 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 provided, so description carries burden. It states the tool retrieves or lists memories, which is clear. However, it doesn't disclose if retrieval modifies anything, requires authentication, or has rate limits. Adequate but minimal beyond the basic function.

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

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

Two sentences, front-loaded with the action, no wasted words. Every sentence adds value.

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

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

Given low complexity (1 optional param, no output schema), the description fully explains the tool's behavior: retrieve specific or list all. Could mention return format, but not essential for a simple memory tool.

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

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

Schema coverage is 100% with one optional parameter described. The description adds 'list all stored memories (omit key)' which adds context beyond the schema's 'omit to list all keys'. This adds moderate value, so baseline 3 is appropriate.

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

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

Clearly states the verb 'retrieve' and resource 'memory by key' or 'list all stored memories', distinguishing it from siblings like 'remember' (store) 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?

Explicitly says when to use: 'to retrieve context you saved earlier', and implies when not to (use 'remember' to save). Lacks explicit exclusion or alternative names beyond the tool name itself.

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 details key behaviors: parallel fan-out to multiple sources, supported date formats, and return structure. It covers input formats and outputs but omits potential errors or rate limits, though the tool is likely safe.

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 concise paragraph that efficiently communicates purpose, behavior, parameters, and usage. No superfluous information; 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 no output schema, the description explains the return values (structured changes, count, URIs). It covers all parameters and typical use cases, making it complete for an agent to use effectively.

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

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

Schema coverage is 100%, and the description adds significant value: explains the 'since' parameter with examples and recommended values, clarifies 'type' limitation, and provides examples for 'value'. This goes beyond the basic schema descriptions.

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

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

The description clearly states the tool's purpose: retrieving what's new about an entity since a point in time. It specifies behavior for type='company' with data sources, and differentiates from sibling tools by focusing on change monitoring rather than static profiles.

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 to use for 'brief me on what happened with X' or change-monitoring workflows. However, it lacks guidance on when not to use or alternatives, which slightly reduces the 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 the full burden. It discloses that storage is session-based, with authenticated users getting persistent memory and anonymous sessions lasting 24 hours. This is good behavioral context, but it does not mention any side effects (e.g., overwriting existing keys), limits on key/value sizes, or whether the tool can fail silently.

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

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

The description is three sentences, front-loaded with the core action. It efficiently covers purpose, usage, and persistence. Could be slightly more concise by removing 'in your session memory' since that is implicit, but overall 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 simple tool (two required string parameters, no output schema), the description is mostly complete. However, it lacks information about whether storing an existing key overwrites the value, what happens on failure (e.g., memory full), and the fact that the memory is per-session. These gaps are minor but notable for a complete picture.

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

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

The input schema already provides good descriptions for both parameters (key and value), and coverage is 100%. The description adds context about what kinds of values to store (findings, addresses, preferences, notes) and provides example key conventions (e.g., 'subject_property'). This enhances the schema's meaning.

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

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

The description clearly states the action ('store a key-value pair'), the resource ('session memory'), and distinguishes the tool by naming its purpose: saving intermediate findings, user preferences, or context across tool calls. This differentiates it from siblings like 'forget' and 'recall'.

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

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

The description explicitly states when to use the tool ('save intermediate findings, user preferences, or context across tool calls') and provides context about persistence differences between authenticated and anonymous sessions. It does not explicitly mention when not to use it or name alternative tools, but the usage context is clear.

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

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

With no annotations provided, the description carries the full burden. It discloses inputs, outputs, version constraints, and that it's a single-call replacement. It does not detail error handling or authentication, but for a read-like resolution tool, the transparency 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 two sentences: the first sentence states the main action, the second adds details (version, supported type, examples, output, efficiency). It is front-loaded, concise, and every word 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?

For a simple 2-parameter tool with no output schema, the description provides enough context for an agent to use it correctly. It explains inputs, outputs, and efficiency. Missing error handling or edge cases, but given the simplicity, it is sufficient.

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

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

Schema coverage is 100% (both parameters have descriptions). The description adds context by listing accepted value types (ticker, CIK, name) and providing examples. It also explains the output, enriching the schema beyond the basic type definitions.

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

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

The description clearly states the core function: 'Resolve an entity to canonical IDs across Pipeworx data sources in a single call.' It specifies accepted inputs (ticker, CIK, name) and outputs (ticker, CIK, company name, resource URIs). It also differentiates from alternatives by saying '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 explicitly states when to use: for entity resolution to canonical IDs, replacing multiple calls. It notes the version scope (v1, type='company') and provides examples. However, it does not explicitly mention when not to use or how it relates to sibling tools like ask_pipeworx.

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

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

Annotations already provide readOnlyHint, idempotentHint, etc. The description adds behavioral context: it probes each entity with ai_visibility_check, ranks results, and returns a list with score, confidence, and signal density. Does not contradict annotations.

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

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

The description is two sentences that front-load the main action. It is concise but could be slightly tighter by removing the 'Useful for' phrase. Overall, it 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 no output schema, the description adequately covers the output format (ranked list with fields). It mentions the dependency on ai_visibility_check and the competitive audit context. It could mention entity count limits (2-8) explicitly, but schema handles that.

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

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

Schema coverage is 100%, baseline 3. The description adds value by explaining that the first entity in the array is treated as the subject, and that models default to workers-ai. This enriches the schema descriptions beyond just their names.

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 specifies that the tool compares AI visibility across multiple entities side-by-side, using ai_visibility_check to probe and rank. It distinguishes itself from sibling ai_visibility_check by focusing on multi-entity comparison.

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

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

The description explicitly states the tool is useful for competitive AI-marketing audits and gives an example question. It implies when to use it versus single-entity checks, though it could be more explicit about 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, the description must carry behavioral transparency. It discloses that the tool combines multiple data sources and notes that FRED key is optional for dollar index. However, it does not mention potential side effects (e.g., rate limits, cost, or that it is read-only). The description adds value beyond structured fields but lacks completeness.

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-loading the main purpose and then adding supporting details. It is efficient, but the first sentence could be slightly more concise. 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 complexity (combines multiple data sources) and lack of output schema, the description covers the main inputs and data sources but does not explain the output structure, which would help the agent anticipate the return value. It is adequate but incomplete for full 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?

Schema coverage is 100%, so baseline is 3. The description adds context by listing country code examples and hinting at the purpose of year and _fredKey. However, it does not elaborate on the meaning of the parameters beyond what the schema already provides, so no higher score.

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 performs 'complete bilateral trade analysis between two countries in one call,' combining multiple data sources (trade flows, exchange rates, dollar index). It distinguishes itself from siblings like trade_country_profile and trade_macro_dashboard by specifying the bilateral nature and comprehensive data aggregation.

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 when to use this tool (for comprehensive bilateral analysis) and provides example country codes, aiding selection. However, it does not explicitly state when not to use it or mention alternatives among siblings, which would improve the 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?

The description mentions 'all in one call' indicating batch behavior, but lacks details on data freshness, rate limits, or potential errors. With no annotations, the description carries full burden and is only partially transparent.

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

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

Two sentences, front-loaded with purpose and key details. Every sentence adds value with no waste.

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

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

Given the simple two-parameter input and no output schema, the description is nearly complete. It covers what the tool returns and key usage notes, though return format details could be mentioned.

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 country code examples but no additional semantic context beyond the schema for the year 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 provides a comprehensive trade profile including top import/export partners and commodities. The verb 'trade profile' combined with the resource 'country' is specific and distinguishes it from sibling tools like trade_bilateral_analysis and trade_macro_dashboard.

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 mentions country code examples and the default year behavior. However, it does not explain when to use this tool versus the bilateral or macro dashboard alternatives, leaving room for improvement.

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 for behavioral traits. It states the tool provides a dashboard of indicators and optionally includes FRED data with an API key, but does not disclose whether data is cached, how often it updates, whether API calls are rate-limited, or if the dashboard requires authentication. The optional API key is mentioned but not explained in terms of behavior change.

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, listing indicators in a single sentence and then adding the optional API key detail in another. It front-loads the main purpose and keeps additional information separate. 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 complexity (dashboard with multiple indicators) and no output schema, the description is somewhat complete but lacks details on what the dashboard returns (e.g., format, time range, default behavior without API key). The single optional parameter and no required fields reduce the need for extensive explanation, but more clarity on output would help.

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

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

Schema coverage is 100% with one optional parameter (_fredKey) whose description is basic. The tool description adds context about what the parameter enables ('FRED dollar index and goods/services balance'), which is valuable beyond the schema's brief description. Since there is only one optional parameter, the description adequately supplements it.

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

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

The description clearly states the tool provides US trade macro indicators and lists specific categories (customs revenue, exchange rates, trade balance, etc.). It differentiates from sibling tools like trade_bilateral_analysis and trade_country_profile by focusing on macro-level dashboard data rather than bilateral or country-specific 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 mentions optionally including FRED dollar index with an API key but provides no guidance on when to use this tool vs alternatives, nor any context on prerequisites or typical use cases. The agent is left guessing when this tool is appropriate.

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?

Without annotations, the description carries full burden. It describes the input, process (fact-checking against SEC EDGAR + XBRL), and output (verdict, extracted form, actual value with citation, percent delta). It also explains that it replaces 4-6 sequential agent calls, adding behavioral context. No side effects or errors are mentioned, but it's fairly transparent.

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: two sentences that front-load the purpose and then provide necessary details on scope and return. Every sentence earns its place 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?

With no output schema, the description explains the return values in detail (verdict types, structured form, citation). It also covers the supported domain. Missing are error handling or limitations, but for a single-parameter fact-checking tool, it is largely 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?

Schema description coverage is 100%, so baseline is 3. The tool description adds value by specifying the domain of claims (company-financial) and examples, enriching the parameter meaning beyond the schema's 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 it fact-checks natural-language claims against authoritative sources, specifically company-financial claims for public US companies via SEC EDGAR and XBRL. It uniquely identifies the tool's function and differentiates from siblings like ask_pipeworx or trade analysis tools.

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

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

The description explicitly limits the tool to 'company-financial claims (revenue / net income / cash for public US companies)', providing clear when-to-use guidance. However, it does not explicitly state when not to use it or mention alternatives among siblings.

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