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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive nature. The description adds that the default model is free, and that `_apiKey` is passed directly to Anthropic. It also describes the return structure. No contradictions.

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

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

The description is three sentences, front-loaded with the primary action and purpose. Every sentence adds value: action, default model, optional key, return format, use cases. 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 tool with 4 parameters and no output schema, the description covers the main behavioral aspects and return format. It lacks explicit mention of rate limits or cost implications (beyond BYO key), but is otherwise 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 coverage is 100%, so baseline is 3. The description adds extra context: explains `_apiKey` is BYO and passed through, and that `context` helps disambiguate. This adds meaning beyond the schema descriptions.

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

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

The description clearly states the tool probes LLMs about an entity and returns a visibility score per model, with a specific verb ('Probe') and resource ('LLMs for what they know'). It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on generic brand/topic visibility scoring.

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

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

The description mentions use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also explains when to use the optional `_apiKey` and the default model. However, it does not explicitly exclude alternative tools or provide when-not-to-use guidance.

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

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

The description discloses key behavioral traits: 'Pipeworx picks the right tool, fills the arguments, and returns the result.' However, it omits limitations like handling of ambiguous questions, failure modes, or data freshness. Since no annotations are provided, the description carries the full burden, and this level of disclosure is only moderate.

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 structured into three sentences and an example list, with the main action front-loaded. It is concise overall, though it includes some repetition between 'No need to browse tools' and 'just describe what you need'. Still, every sentence serves a 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 tool's simplicity (one parameter, no output schema, no annotations), the description sufficiently explains its meta-functionality and how to use it. It adequately conveys the concept of routing questions to underlying tools, though it could be more complete by mentioning potential failure scenarios or scope.

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 describes the single parameter 'question' as 'Your question or request in natural language', achieving 100% coverage. The description adds significant context by explaining how the parameter is processed, providing examples, and stating that answers come from the best data source. This goes beyond the schema's bare definition.

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: 'Ask a question in plain English and get an answer from the best available data source.' It uses specific verbs and resources, and distinguishes itself from sibling tools like compare_entities or search_events by positioning itself as a general-purpose question-answer interface.

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: 'No need to browse tools or learn schemas — just describe what you need.' It gives concrete examples and contrasts with using individual tools. However, it does not explicitly state when not to use it or provide alternatives.

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

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

The description details internal behavior: market resolution, bet classification, fan-out logic tailored to bet type (e.g., crypto+fred+gdelt for BTC). It also states the output includes a market-vs-model comparison. This adds context beyond the readOnlyHint and openWorldHint 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 moderately long but every sentence adds value. It is front-loaded with the main purpose and methodical, though slightly verbose in explaining internal logic. Could be slightly trimmed but still effective.

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

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

For a tool with 2 parameters and no output schema, the description fully explains the return value (evidence packet + comparison), the processing steps, and the use cases. No gaps remain.

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

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

Schema coverage is 100% (both parameters described). The description adds nuance for 'market' (three input formats) and 'depth' (quick=2-3 sources, thorough=full fan-out), which enriches the schema definitions.

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

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

The description clearly states the tool researches Polymarket bets by pulling relevant Pipeworx data. It specifies input formats (slug, URL, question text), the process (resolves, classifies, fans out), and the output (evidence packet and comparison). This differentiates it from sibling tools like ask_pipeworx or 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?

Explicit usage examples are given: 'Use for "should I bet on X?", "what does the data say...", "is there edge...".' It implies agents should prefer this over discovering packs themselves, but does not explicitly name alternatives, leaving some ambiguity.

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

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

No annotations are provided, so the description carries full burden. It details the output (paired data + URIs) and constraints (2-5 entities), but lacks information on side effects, permissions, rate limits, or data freshness. Adequate but not exhaustive.

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

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

The description is two sentences, front-loaded with the core purpose, and every sentence adds essential information without redundancy.

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

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

The description covers the tool's purpose, parameters, and output format despite lacking an output schema. It omits error handling and format details, but for a 2-param tool with simple types, it is fairly complete.

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

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

Schema coverage is 100%, so schema already explains parameters. The description adds value by listing example fields for each type (e.g., revenue, adverse-event count), providing context beyond the schema's enum and array 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 compares 2-5 entities side by side, specifies two types (company/drug) with distinct data points, and highlights efficiency gains over sequential calls, distinguishing it from siblings like search_events.

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 (comparing entities). It implies alternatives by noting it 'replaces 8–15 sequential agent calls,' but does not name specific alternative tools or explicitly state when not to use it.

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

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

No annotations are provided, so the description carries the full burden. The description implies a read-only search operation but does not explicitly state safety or behavioral traits like side effects, rate limits, or permissions. It is adequate but not thorough.

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

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

The description is concise at two sentences, front-loading the action ('Search...') and then providing secondary guidance. No unnecessary words, earning 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 purpose and usage adequately for a simple search tool. Without an output schema, it might have explained the return format but it is not essential. Most information an agent needs is present.

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 the schema already describes both parameters. The description adds minimal additional meaning beyond the schema, such as clarifying that 'query' is a 'natural language description'. The description does not significantly enhance understanding beyond the schema.

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

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

The description clearly states the verb 'Search', the resource 'Pipeworx tool catalog', and what it returns ('most relevant tools with names and descriptions'). It distinguishes itself from sibling tools by being the only search tool on this server.

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

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

The description explicitly says 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task', providing clear when-to-use guidance. It does not mention when not to use it or list alternatives, but the context is clear.

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

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

No annotations present, but description details the exact data returned (SEC filings, revenue, patents, news, LEI) and that it replaces 10–15 calls. Could mention that it may take longer due to multiple 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?

Concise yet informative; uses bullet-like structure. Slightly dense but efficient.

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

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

Even without output schema, the description enumerates all returned data types (SEC filings, XBRL financials, patents, news, LEI) and citation URIs, providing complete context.

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 context: value format (ticker or CIK) and that names require resolve_entity first. No additional detail on optional 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?

Clearly states it returns a full profile of an entity across multiple data sources, distinguishing from siblings like resolve_entity and usa_recipient_profile.

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

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

Explicitly tells when to use (for comprehensive company profile) and when to avoid (for federal contracts, directs to usa_recipient_profile).

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 present, so the description carries the burden. It indicates aggregation (read-only implied) but does not explicitly state that no data is modified or any behavioral traits like security or rate limits.

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

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

Two sentences with no wasted words. The first sentence defines the action and scope, the second provides use context. Well front-loaded.

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 tool has no output schema and 5 optional parameters. The description does not specify what the response structure looks like (e.g., list of countries with counts), which is necessary to use the tool correctly.

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

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

The input schema has 100% parameter description coverage. The description adds general context ('aggregate by country, over a date range') but does not significantly enhance understanding beyond the schema.

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

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

The description clearly states it aggregates event and fatality counts by country over a date range. However, it does not differentiate from sibling tools like search_events.

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

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

The description provides a use case ('cross-country comparison, time-bounded risk snapshots') but does not offer any guidance on when to avoid using this tool or mention alternatives. Sibling search_events might serve a different purpose but no comparison is 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?

No annotations are provided, so the description must disclose behavioral traits. It only states 'delete,' which is minimally informative. It does not mention idempotency, error handling for missing keys, or any side effects, leaving significant gaps.

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 sentence with no unnecessary words. It is front-loaded and to the point, earning 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?

Given the simplicity of the tool (one parameter, no output schema), the description is almost complete but lacks behavioral details such as return values or error conditions. It meets minimum viability but has clear 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?

The input schema has 100% coverage with the parameter described as 'Memory key to delete.' The description adds no additional meaning beyond what the schema already provides, 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 'Delete a stored memory by key,' providing a specific verb and resource. It distinguishes from sibling tools 'remember' (store) and 'recall' (retrieve), making purpose unambiguous.

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

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

The description implies usage when deletion of a memorized value is needed, but it offers no explicit guidance on when to use this tool versus alternatives or any exclusion criteria. Basic implied 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 already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds concrete behavior: fetches page, extracts title/description/key links, emits standard markdown format. No contradictions.

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

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

Two concise sentences plus a bullet list of use cases. No redundant information, front-loaded with core action. Every sentence serves a 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 tool's simplicity (2 params, no output schema), the description covers purpose, behavior, output format, and use cases adequately. No missing critical information.

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

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

Schema description coverage is 100% with clear parameter descriptions. Description does not add new meaning beyond what schema provides, though it mentions output format. Baseline 3 is appropriate.

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

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

Description clearly states the tool generates an llms.txt file for any URL, specifying the action and resource. It distinguishes from sibling tools, none of which produce llms.txt files, and provides a specific purpose 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?

Description lists explicit use cases (getting a client's site indexed, drafting for own project, auditing competitor) but does not specify when not to use or alternatives. Still provides clear context.

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

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

Discloses rate limit (5 per identifier per day) and states it's free, but lacks details on submission confirmation, response, or asynchronous behavior. With no annotations, the description carries the burden but could be more transparent about what happens after sending.

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

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

Description is concise, with purpose stated first, followed by usage guidance and constraints. It is efficient and avoids redundancy, though a slightly more structured format (e.g., bullet points) could improve readability.

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

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

For a simple feedback tool, the description covers purpose, usage, rate limits, and content restrictions. No output schema exists, but the schema fully documents parameters. Missing details like confirmation behavior, but overall adequate.

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

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

Schema coverage is 100% and parameter descriptions in the schema are thorough (enum options, context structure). The description adds no extra parameter guidance, so baseline 3 is appropriate.

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

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

Description clearly states the tool sends feedback to the Pipeworx team, listing specific use cases: bug reports, feature requests, missing data, praise. It distinguishes from sibling tools like ask_pipeworx or discover_tools, which serve different purposes.

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

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

Explicitly describes when to use (for various feedback types) and provides guidance on content (describe in terms of Pipeworx tools/data, omit end-user prompt). Also mentions rate limits. While it doesn't discuss alternatives, the tool is unique and the guidelines are highly 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 declare readOnly, openWorld, idempotent, non-destructive. Description adds data source (CF analytics-engine), privacy (no PII), and caching behavior (5min-1h).

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?

Well-organized: main outcome first, then use cases, then technical details. No superfluous information.

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

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

For a simple tool with one optional parameter and no output schema, description covers purpose, usage, behavior, parameter semantics, and caching. Fully 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?

Parameter 'window' has enum and schema coverage 100%. Description adds semantic guidance: shorter windows for hot trends, longer for steady-state demand.

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

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

The description clearly states the tool returns top tools, packs, and call volume over a window. It provides concrete use cases and distinguishes 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?

Three explicit use scenarios are listed, giving clear context. Missing explicit when-not-to-use, but the examples imply appropriate contexts.

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 significant behavioral details beyond annotations: it walks child markets, extracts dates/thresholds, sorts them, and reports violations. It also describes the return format. Annotations (readOnlyHint=true, openWorldHint=true) are consistent with the description.

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

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

The description is well-structured, starting with the core function, explaining the concept with an example, and concluding with the return format. Every sentence is informative and earns its place. It is appropriately sized for the 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?

Given the tool's complexity (arbitrage detection with monotonicity) and lack of an output schema, the description is complete. It explains the rule, the process, and the return structure. No critical information is missing.

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

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

Schema description coverage is 100%, and the description does not add significant new information about the 'event' parameter beyond what the schema provides. It restates that it takes a slug or URL, which is already in 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 finds arbitrage opportunities by checking monotonicity violations in Polymarket events. It provides a concrete example (e.g., BTC price thresholds) and explains the logic. The purpose is unique among siblings, as no other sibling tool implies arbitrage detection.

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

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

The description explains when to use the tool: when you have a Polymarket event slug or URL and want to find arbitrage due to price ordering violations. It does not explicitly mention when not to use or provide alternatives, but the context is clear.

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

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

Description goes well beyond annotations (readOnlyHint, openWorldHint) by detailing the internal model (lognormal from FRED + coinpaprika), process (scans, groups, fetches price history once), and output (top N ranked by edge magnitude with suggested trade direction). No contradictions with annotations.

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

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

Description is a single paragraph front-loaded with the main action. Every sentence adds value, explaining the model version, assets, and ranking logic. Slightly verbose with technical details but remains efficient overall.

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 complex tool involving ranking across markets, the description covers inputs (limit, window, min_edge_pp), process (V1 model, grouping, ranking), and output (top N with edge magnitude and direction). No output schema exists, so description adequately compensates.

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

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

Input schema has 100% coverage with descriptions for all three parameters (limit, window, min_edge_pp). Description adds no parameter-specific meaning beyond what schema already provides, so baseline score of 3 is appropriate.

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

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

Description uses specific verb 'Scan' and identifies resource 'highest-volume Polymarket markets' with clear outcome 'return where Pipeworx data disagrees most with market price'. Distinguishes itself from sibling tools like polymarket_arbitrage by focusing on edge discovery for betting opportunities.

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

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

Description provides explicit use case: 'Built for the what should I bet on today question' and explains the benefit of discovering opportunities without manual paging. While no alternative tools are named or when-not-to-use conditions stated, the context is 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 declare readOnlyHint, idempotentHint, and destructiveHint. The description adds that it returns leg-by-leg prices and spread in percentage points. It could mention that it fetches live data, but the annotations cover the safety profile.

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 that front-loads the purpose and logically flows through modes and output. While efficient, it is relatively long and could benefit from bullet points or shorter sentence structure for easier scanning.

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. It covers the two modes, parameter overriding, and output format. It is complete for moderate complexity, though it omits error handling or output schema details.

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

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

Schema coverage is 100%, but the description adds value by listing example values for topic and explicit parameters, and explaining that explicit parameters override topic-mapped defaults. This clarifies parameter usage beyond the schema.

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

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

The description clearly states the tool computes cross-venue spreads between Kalshi and Polymarket, with two explicit modes (topic shortcuts and custom event tickers). It distinguishes itself from siblings like 'polymarket_arbitrage' by focusing on cross-venue arbitrage.

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

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

The description explains when to use each mode (topic for common events, explicit for custom pairs) and what the output represents. However, it does not explicitly state when not to use this tool or list alternative tools for other scenarios.

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

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

No annotations provided, so the description carries the burden. It discloses both retrieval by key and listing all memories, and mentions persistence across sessions. No side effects are disclosed, but the tool is simple.

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 main action, no wasted words. Efficient and clear.

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

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

For a simple retrieval tool with one optional parameter and no output schema, the description covers both modes adequately and provides context on session persistence.

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 description for the 'key' parameter. The description adds value by explaining that omitting the key lists all memories, clarifying the optional parameter behavior.

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 memory by key or lists all memories, using specific verb 'retrieve' and resource 'memory'. It distinguishes from siblings like 'remember' and 'forget'.

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

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

The description explicitly says to use this tool to retrieve context saved earlier, implying appropriate usage. It does not explicitly state when not to use, but the context makes it clear.

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

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

No annotations provided, so description bears full burden. It discloses the parallel fan-out to SEC EDGAR, GDELT, USPTO; describes accepted input formats; and mentions the return type (structured changes, count, URIs). It does not mention destructive behavior or rate limits, but the disclosed behavior is clear 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 three sentences long, front-loaded with purpose, then details fan-out, parameter usage, output, and use case. Every sentence adds value with no redundancy.

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

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

Given the complexity (multi-source fan-out, relative dates) and no output schema, the description covers return format adequately (structured changes, count, URIs). It could be more specific about the structure of changes, but overall it provides sufficient context for an agent to use the tool correctly.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds some context: for 'since' it provides example values like '7d' and typical use '30d' or '1m'; for 'value' it gives examples. However, most parameter meaning is already in schema descriptions, and the description's main added value is on tool behavior rather than parameter details.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It specifies the verb (get changes) and resource (entity changes), and distinguishes from siblings like entity_profile or search_events by detailing the multi-source fan-out behavior.

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 recommends use cases: 'Use for "brief me on what happened with X" or change-monitoring workflows.' It implies the tool is for aggregated recent activity, but does not explicitly state when not to use it or compare to alternatives like search_events.

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 the burden. It discloses memory persistence for authenticated vs anonymous users, which is sufficient for a simple store operation.

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, concise and focused, no unnecessary words. Information is front-loaded.

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 for a simple store, the description covers most needs. Could mention return value, but not essential.

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 examples. Description adds context about usage and storage duration, providing extra 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 it stores a key-value pair in session memory, listing specific use cases like saving findings and preferences, which distinguishes it from siblings like recall and forget.

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

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

It explains when to use (to save intermediate findings, preferences, context) and mentions persistence differences, but does not explicitly state when not to use or directly compare to 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?

With no annotations provided, the description carries full burden. It explains the tool performs a single call, returns IDs and URIs, and replaces multiple lookups. It does not mention any destructive actions, auth needs, or rate limits, but for a read-like lookup tool this is reasonable.

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 that are well-structured: first sentence states the main function, second adds input details, third describes output and benefit. Every sentence is useful with no redundancy or 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 having no output schema, the description mentions the output format (IDs and pipeworx:// URIs). It covers all necessary aspects for a simple tool with 2 parameters and no nested objects, and even notes it replaces multiple calls.

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

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

The schema has 100% coverage with descriptions for both parameters. The description adds significant value by providing concrete examples for 'value' (ticker, CIK, name for company; brand/generic name for drug) and reinforces the 'type' enum. This goes beyond the schema's minimal 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 resolves an entity to canonical IDs across Pipeworx data sources in a single call, specifying support for company and drug types. It distinguishes itself from siblings like 'compare_entities' and 'ask_pipeworx' by being a one-call resolution 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?

The description provides clear context on when to use the tool (when needing canonical IDs for company or drug entities) and gives examples of valid inputs. It does not explicitly state when not to use or list alternatives, but the sibling tools are different enough to imply usage boundaries.

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, idempotentHint=true, destructiveHint=false, so safety profile is covered. Description adds behavioral details: probes each entity sequentially, ranks by score, returns ranked list with score/confidence/signal density. Discloses optional API key requirement for full model access.

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 core action. First sentence packs purpose, mechanism, and output. Second sentence provides use case and return format. Slightly dense but effective; 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 moderate complexity (4 params, no output schema), description fully explains tool behavior, parameters, return structure, and use case. No gaps remain for agent decision-making. Annotations already cover safety and side effects.

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

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

Schema coverage is 100%, baseline 3. Description adds meaning: explains 'entities' array treats first as subject, rest as competitors; clarifies 'models' default behavior ('Omit for just workers-ai'); provides examples for context. Adds value beyond schema documentation.

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

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

Description clearly states verb 'Compare', resource 'AI visibility', and specific actions: probes entities with ai_visibility_check, ranks by score, identifies most/least recognized. Distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic 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?

Explicit use case given: 'competitive AI-marketing audits'. Implies when to use (comparing multiple entities). Does not explicitly state when not to use, but provides context by referencing sibling ai_visibility_check as the underlying probe. Slightly lacks exclusions or alternative suggestions.

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 carries full burden. It lists return fields but does not disclose pagination, rate limits, data freshness, or other behavioral details. Some info (max-per-call 5000) is in schema, not description.

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

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

Three sentences, front-loaded with purpose, filters, and returns. No redundant or extraneous words. Highly 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?

11 optional parameters with no output schema; description lists return fields but not structure. Could mention return format (e.g., array of objects). Adequate for a search 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 all parameters described. Description repeats some filter options but adds no significant new semantics beyond the schema. 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?

Description clearly states it searches ACLED political-violence and protest events, specifies filtering capabilities, and lists return fields. It distinguishes from sibling 'event_counts_by_country' which provides aggregate counts.

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 implies usage for event retrieval with filters, but does not explicitly state when to use this tool over sibling 'event_counts_by_country' or exclusions. No when-not or alternative guidance.

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

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

With no annotations provided, the description carries the full burden. It explains that the tool performs NL parsing, entity resolution, data lookup, and comparison, and returns a structured verdict with citation. It does not disclose side effects or potential limitations like rate limits or handling ambiguous claims, but it is fairly transparent about its process and output.

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

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

The description is concise with four sentences, each serving a clear purpose: stating the overall function, specifying the supported domain, listing return values, and explaining efficiency benefits. It front-loads the primary action without unnecessary details.

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 (NL claim validation) and lack of output schema, the description adequately covers what the tool does, what it returns, and its limitations (v1, specific domain). It mentions possible verdicts including 'inconclusive' and provides citation format, but could be more complete by describing error scenarios or handling of ambiguous claims.

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 already provides full coverage for the 'claim' parameter with description and examples. The tool description adds domain-specific context (company-financial claims) and clarifies that it accepts natural-language factual claims, reinforcing usage beyond the schema. This adds value beyond the parameter definition.

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

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

Description clearly states the tool fact-checks natural-language claims against authoritative sources, specifies the domain (company-financial for public US companies), and lists the return values including verdict, structured form, actual value with citation, and delta. It also explains that it replaces multiple sequential agent calls, making its purpose very specific and distinct.

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 indicates when to use the tool (for company-financial claims via SEC EDGAR + XBRL) and implies it is not suitable for other claims. It mentions it replaces 4-6 sequential agent calls, providing context on efficiency. However, it does not explicitly compare to sibling tools like 'ask_pipeworx' or state when not to use it.

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