imgflip

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

Annotations indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context about probing LLMs, scoring, and the cost model for Anthropic (BYO key). This goes beyond standard annotations and provides actionable 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 two sentences, front-loaded with the core purpose and immediately followed by key details (default model, API key option, returns). No unnecessary words; every sentence contributes to 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?

Given the tool has 4 parameters, no output schema, and annotations present, the description covers what it does, how to use, and the return format (per-model scores, confidence, signals, raw_response, combined view). It is complete for effective use, though an explicit output structure example could 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 clear descriptions. The tool description adds value by explaining the default model for 'models', the cost implication for '_apiKey', and examples for 'entity' and 'context'. This enhances understanding beyond what the schema provides.

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

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

The description clearly states the tool probes LLMs for knowledge about an entity and returns visibility scores (0-100) per model. It specifies the default model, optional Anthropic support, and lists use cases like AI-marketing audits and pre-launch brand checks. This differentiates it from sibling tools by unique functionality.

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

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

The description provides clear context for when to use the tool (visibility audits, brand checks) and explains usage of default vs. Anthropic models. It does not explicitly mention when not to use or name alternative sibling tools, but the context is sufficient for an AI agent to decide.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it accepts plain English questions, automatically selects and invokes appropriate tools, handles argument filling, and returns results. However, it doesn't mention potential limitations like rate limits, authentication requirements, or error handling scenarios, leaving some behavioral aspects unspecified.

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

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

The description is perfectly structured and concise. The first sentence clearly states the core functionality. The second sentence explains the automation mechanism. The third sentence provides usage guidance. The final part offers three diverse examples. Every sentence earns its place, with no wasted words or redundant 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 complexity (natural language interface with automated tool selection) and the absence of both annotations and an output schema, the description does an excellent job explaining what the tool does and how to use it. However, it doesn't describe what the output looks like (structure, format, potential errors), which would be helpful since there's no output schema. The examples hint at possible answer types but don't fully compensate.

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

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

The schema description coverage is 100%, so the schema already documents the single 'question' parameter. The description adds meaningful context by emphasizing 'plain English' and 'natural language,' and provides three concrete examples that illustrate the expected format and scope of questions. This goes beyond the schema's basic documentation of the parameter type.

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 specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'). It distinguishes itself from sibling tools by emphasizing natural language interaction without needing to browse tools or learn schemas.

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

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

The description provides explicit guidance on when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It includes three concrete examples ('What is the US trade deficit with China?', 'Look up adverse events for ozempic', 'Get Apple's latest 10-K filing') that illustrate appropriate use cases, making it clear this is for natural language queries rather than structured tool 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?

Beyond the readOnlyHint and openWorldHint annotations, the description details the tool's behavior: it pulls Pipeworx data, resolves markets, classifies bets, fans out to appropriate data packs, and returns an evidence packet with a comparison. 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?

The description is four sentences, front-loaded with the main purpose, and each sentence provides valuable information without redundancy. It is appropriately concise for a complex tool.

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

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

Given the lack of an output schema, the description adequately explains the return format ('evidence packet plus a simple market-vs-model comparison') and the fan-out logic. However, it does not mention error handling or edge cases, which is a minor gap.

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 does not add meaning beyond what the schema already provides for both parameters (market and depth), merely restating input types and depth options.

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 identifies the tool as 'Research a Polymarket bet' and distinguishes it from sibling tools like ask_pipeworx by highlighting its specific role in aggregating data for Polymarket bets and its status as the core demo product.

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 with example queries like 'should I bet on X?', and implies it is the preferred method over other tools by noting agents that use it convert better. It lacks explicit when-not-to-use examples but provides strong contextual cues.

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

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

No annotations exist, so the description carries full burden. It discloses return format (paired data + URIs) but omits details on permissions, rate limits, or error behavior. 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?

Three sentences, each essential: purpose, type-specific details, and return/value-add. No redundancy, front-loaded with main 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?

No output schema; description mentions 'paired data' but lacks structure details. Lacks info on error handling or edge cases. Sufficient for basic understanding but not fully 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 covers both parameters fully. Description adds value by specifying metrics returned for each type and giving clear examples (tickers/CIKs, drug names), which aids selection.

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 with specific metrics per type (company/drug). It distinguishes from sibling tools like resolve_entity or ask_pipeworx by being a bulk comparison 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 mentions it replaces 8-15 sequential calls, implying efficiency and suggesting use when multiple comparisons are needed. However, it does not explicitly state when not to use it or list alternative tools.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'the most relevant tools with names and descriptions,' which adds behavioral context about the output. However, it lacks details on error handling, rate limits, or performance characteristics, leaving some gaps in transparency.

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

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

The description is front-loaded with the core purpose, followed by usage guidelines, all in two efficient sentences with zero waste. Every sentence earns its place by providing essential information without redundancy or fluff.

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

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

Given the tool's complexity (a search function with natural language querying) and no output schema, the description is mostly complete. It explains the purpose, usage, and output format, but could benefit from mentioning error cases or limitations. However, it adequately covers the essentials for an agent to use it 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 the schema already documents both parameters thoroughly. The description does not add any additional meaning or context about the parameters beyond what the schema provides, such as examples or usage tips. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), and distinguishes it from sibling tools by emphasizing its role in discovering tools among 500+ options. It explicitly tells what the tool does beyond just the name.

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

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

The description provides explicit guidance on when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task'), including a specific condition (500+ tools) and timing (first). It also implies an alternative (not using it when fewer tools are available), making usage 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?

Describes the data returned (SEC filings, revenue, patents, news, LEI) and citation URIs, plus performance benefit. No annotations provided, so description carries burden. No mention of side effects or rate limits, but appropriate for a read-only tool.

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

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

Description is detailed but each sentence adds value. It's front-loaded with main purpose, then enumerates contents. Slightly long but appropriate 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?

Without output schema, the description thoroughly explains what is returned (multiple data types, citation URIs). It also provides context on when to use sibling tools, making it self-contained and actionable.

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

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

Schema coverage is 100%, but description adds significant meaning: clarifies type only supports 'company', explains value formats (ticker or CIK), and warns names not supported, directing to resolve_entity. This goes beyond schema descriptions.

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

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

The description clearly states it returns a full profile of an entity across multiple packs, lists specific data types included, and distinguishes from siblings like resolve_entity and compare_entities.

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

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

Explicitly says when to use (for comprehensive company profile) and when not (for federal contracts, use usa_recipient_profile). Also suggests resolve_entity for name resolution. Could mention when to use compare_entities as alternative.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Delete' clearly indicates a destructive mutation, the description doesn't specify whether deletion is permanent, whether it requires specific permissions, what happens on success/failure, or any rate limits. This leaves significant behavioral gaps for a destructive 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?

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately scannable and understandable.

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

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

For a destructive tool with no annotations and no output schema, the description is inadequate. It doesn't explain what 'stored memory' means in this context, what the deletion consequences are, what format the key should be in, or what to expect as a result. Given the complexity of a memory deletion operation, more context is needed.

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

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

The schema has 100% description coverage, with the single parameter 'key' already documented as 'Memory key to delete'. The description adds minimal value beyond this, merely restating that deletion is 'by key' without explaining what constitutes a valid key or how keys relate to stored memories.

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

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

The description clearly states the action ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't explicitly distinguish from sibling tools like 'recall' or 'remember', but the destructive nature of 'delete' provides implicit differentiation from read operations.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'recall' (which likely retrieves memories) or 'remember' (which likely stores memories). There's no mention of prerequisites, error conditions, or typical use cases for deleting stored memories.

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. Description adds process (fetches page, extracts title/description/key links) and output format (standard markdown blob), adding value beyond annotations.

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

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

Two sentences, front-loaded with the main action and output, followed by use cases. Every word earns its place; no redundancy.

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

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

For a simple tool with 2 parameters and no output schema, description fully covers purpose, process, output format, and use cases. Mentions output is ready-to-use text blob, ensuring completeness.

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

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

Schema coverage is 100% and description does not elaborate on parameter details beyond what schema provides. Baseline score maintained as description adds no extra semantics.

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

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

Description clearly states it generates an llms.txt file for AI crawlers, specifying verb (Generate), resource (llms.txt file), and context. Distinct from siblings like 'scan_competitor_ai_presence'.

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 (client indexing, drafting, auditing), providing clear context. Lacks explicit when-not-to-use statements, but use cases are sufficiently clear.

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

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

No annotations are provided, so the description carries the full burden. It describes the tool's behavior by specifying what data is returned (name, image URL, etc.) and the source (Imgflip), but it does not disclose traits like rate limits, authentication needs, or potential errors. The description is informative but lacks depth on operational constraints.

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

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

The description is a single, well-structured sentence that efficiently conveys the tool's purpose, resource, and output details without any wasted words. It is front-loaded with the core action and includes all necessary information concisely.

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 (0 parameters, no output schema, no annotations), the description is reasonably complete. It explains what the tool does and what data it returns. However, it could be more complete by including behavioral details like rate limits or error handling, but for a read-only tool with no parameters, it 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?

The tool has 0 parameters, and the schema description coverage is 100% (as there are no parameters to describe). The description does not need to add parameter semantics, so it appropriately focuses on the tool's output and scope. A baseline of 4 is applied since no parameters exist.

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 specific action ('Get'), resource ('top 100 most popular meme templates from Imgflip'), and scope ('including name, image URL, dimensions, and text box count'). It provides a complete picture of what the tool does without being tautological, and since there are no sibling tools, differentiation is not needed.

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 by specifying the scope ('top 100 most popular meme templates'), but it does not provide explicit guidance on when to use this tool versus alternatives or any prerequisites. With no sibling tools, the lack of comparative guidance is less critical, but it still lacks explicit when/when-not instructions.

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

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

With no annotations, the description carries the full burden. It discloses the rate limit (5 per day) and the important constraint to avoid including end-user prompts verbatim. It could mention whether sending is synchronous or if there's a confirmation, but it covers key behavioral traits.

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

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

Three sentences with no fluff: first sentence states purpose, second lists use cases, third adds constraints and rate limit. Highly front-loaded and efficient.

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

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

For a simple feedback tool with a well-documented schema, the description is nearly complete. It omits return value details but that is acceptable for a 'send and forget' action. The nested context parameter is adequately explained in the schema.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value beyond schema by providing the 'do not include prompt' rule for the message parameter, which is a critical usage nuance not captured in schema types alone.

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

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

The description clearly states the tool sends feedback to the Pipeworx team and lists specific categories (bug reports, feature requests, missing data, praise). It distinguishes itself from sibling tools like 'ask_pipeworx' by being the dedicated feedback channel.

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 when to use (bug reports, feature requests, etc.) and provides constraints: describe in terms of Pipeworx tools/data, do not include user prompt verbatim, and rate limit of 5 per day. This gives clear guidance with no 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?

Annotations already indicate readOnly, idempotent, non-destructive. Description adds that data is derived from CF analytics-engine, contains no PII, and is cached 5min-1h. No contradictions.

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

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

Description is efficiently structured with the main action first, followed by use cases and implementation details. Every sentence adds value without redundancy.

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

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

For a simple tool with one optional parameter and no output schema, the description fully explains what is returned (top tools, packs, call volume), how it works (aggregated signal, no PII), and caching behavior. This is complete for agent usage.

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

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

Schema covers 100% of parameters. Description adds semantic guidance: shorter windows for current trends, longer for steady-state demand. This enhances understanding beyond the enum 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 clearly states the tool returns trending tools, packs, and call volume over configurable time windows. It differentiates from sibling tools like 'discover_tools' and 'ask_pipeworx' by focusing on aggregated usage trends.

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

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

Description explicitly lists three use cases (discovering hot data sources, confirming canonical tools, aligning with agent demands). It doesn't explicitly state when not to use, but the use cases provide clear context and imply alternatives are for different needs.

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

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

Beyond annotations (readOnly, openWorld, non-destructive), the description details internal behavior: walks child markets, searches across events, groups related markets, checks monotonicity, and returns ranked opportunities with reasoning. No contradiction with annotations.

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

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

The description is a single well-structured paragraph that starts with purpose, then details two modes with examples, explains why cross-event mode exists, and ends with return information. No redundant sentences.

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

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

Given no output schema, the description explains return format (ranked opportunities with direction and reasoning), which is sufficient but could specify exact fields. It also explains the tool's logic without missing critical details for an agent to use it correctly.

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

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

Schema coverage is 100% with each parameter already described. The description adds value by linking each parameter to a mode and providing concrete examples, especially for 'topic', which improves semantic understanding beyond the schema.

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

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

The description clearly states the tool finds arbitrage opportunities via monotonicity violations on Polymarket. It distinguishes itself from siblings like bet_research and polymarket_edges by focusing on cross-market price consistency, not just betting research or graph edges.

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

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

The description explicitly describes two modes with usage scenarios: event mode for a single event's child markets, and topic mode for cross-event searches. It provides an example where topic mode is necessary (separate cutoff events), implicitly guiding the agent when to choose each.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false. The description adds extensive behavioral detail: V1, crypto-price bets, lognormal model from FRED + coinpaprika, grouping by asset, single fetch of price history, and ranking by |edge|. This goes well beyond annotations, providing full transparency on the tool's 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?

The description is a cohesive paragraph of 4 sentences, each contributing value: purpose, technical details, use case. It is front-loaded with the primary purpose. Minor verbosity remains, but overall it is well-structured for its 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 (multiple internal steps, external data sources, and no output schema), the description covers the essential behavior, return value (top N edges with direction), and input parameters. It lacks details on error handling or output format details, but for a discovery tool, it is sufficiently complete.

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

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

Input schema coverage is 100% with detailed descriptions for all three parameters, including defaults. The description does not add significant new meaning beyond the schema, only referencing the defaults in prose. Thus, baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: scanning high-volume Polymarket markets to find those with the largest disagreement between Pipeworx data and market prices. It specifies the verb (scan, return), resource (Polymarket markets), and unique value (edge detection). The 'what should I bet on today' use case further clarifies its purpose, distinguishing it from siblings like polymarket_arbitrage.

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

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

The description explains when to use the tool (for discovering betting opportunities without manual browsing) but does not explicitly state when not to use it or mention alternatives (e.g., polymarket_arbitrage for arbitrage). The context is clear but lacks exclusions or comparative guidance.

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

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

Annotations declare readOnly, idempotent, non-destructive. Description adds behavioral details: two modes, auto-fetching for topics, custom pairings, and output specification (leg-by-leg prices, spread formula). No contradiction with annotations; adds useful context beyond them.

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

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

Description is a single paragraph that efficiently covers purpose, modes, and output. Every sentence adds value, though it could be slightly more structured (e.g., bullet points). Still clear and not verbose.

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, description adequately explains output (leg-by-leg prices in 0-1, spread in percentage points). It covers parameter modes and safety. Missing explicit JSON structure, but sufficient for an agent.

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

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

Schema covers 100% of parameters with descriptions. Tool description adds meaning by explaining the two modes (topic vs explicit) and how parameters interact (explicit overrides topic). This clarifies usage beyond schema alone.

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

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

Description clearly states 'Cross-venue spread between Kalshi and Polymarket for the same resolving question', identifying the specific verb (compute spread) and resources (two venues). This distinguishes it from sibling tools like 'polymarket_arbitrage' which likely focuses on within-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?

Description explains two modes (topic shortcuts and explicit parameters) and notes that price differences of 2-25pp provide real arb signals, indicating when to use. It does not explicitly exclude alternatives, but the clear mode descriptions guide 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool's behavior (retrieve or list memories) and persistence across sessions, but lacks details on error handling (e.g., what happens if key doesn't exist), performance (e.g., rate limits), or security (e.g., access controls). It adds some context but is 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?

The description is appropriately sized and front-loaded: the first sentence states the core functionality, and the second adds contextual guidance. Every sentence earns its place with no redundant or vague information, making it efficient and easy to parse.

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

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

Given the tool's low complexity (1 optional parameter, no output schema, no annotations), the description is largely complete. It covers purpose, usage, and parameter semantics adequately. However, it lacks details on return values (since no output schema) and behavioral traits like error handling, leaving minor gaps for a retrieval tool.

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

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

The schema description coverage is 100%, so the schema already documents the parameter 'key' and its optional nature. The description adds semantic meaning by explaining the dual functionality: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' This clarifies the parameter's role beyond the schema's technical description, compensating well for the single parameter.

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

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

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings by specifying it's for retrieving context saved earlier, unlike 'remember' (store), 'forget' (delete), or 'discover_tools' (list tools).

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

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

The description provides explicit guidance on when to use this tool vs alternatives: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It also clarifies the context: 'Use this to retrieve context you saved earlier in the session or in previous sessions,' which implicitly distinguishes it from tools like 'get_memes' (likely unrelated) and 'discover_tools' (meta-tool).

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

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

With no annotations, the description carries full behavioral burden. It describes parallel fan-out to SEC EDGAR, GDELT, USPTO, and mentions the return structure (structured changes, total_changes count, pipeworx:// URIs). It implies a read-only operation without stating it explicitly, but covers core behavior well.

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

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

The description is extremely concise: two well-structured sentences. The first sentence states the overall purpose and behavior for company type, the second explains the since parameter format and return structure. Every clause contributes necessary information 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 tool's moderate complexity (3 required params, no output schema, no annotations), the description covers purpose, usage, parameter details, and return format comprehensively. It lacks explicit error handling or failure scenarios, but is otherwise complete for typical use.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds value by specifying that 'since' accepts ISO date or relative formats (with examples like '7d', '30d', '1y'), and suggests using '30d' or '1m' for typical monitoring. It also clarifies that 'value' can be a ticker or CIK, and 'type' is limited to 'company'.

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

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

The description starts with 'What's new about an entity since a given point in time,' which clearly states the verb (get changes) and resource (entity over time). It explains the fan-out to multiple data sources for type='company', and differentiates from sibling tools like entity_profile (profile at a point) and compare_entities (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 recommends use for 'brief me on what happened with X' or change-monitoring workflows, providing clear when-to-use guidance. It does not mention when-not-to-use, but the context is well-defined.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool performs a write operation ('store'), specifies persistence characteristics ('authenticated users get persistent memory; anonymous sessions last 24 hours'), and hints at session scope. However, it doesn't cover potential limitations like size constraints or error conditions.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by usage context and behavioral details. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.

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

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

Given the tool's moderate complexity (write operation with persistence nuances), no annotations, and no output schema, the description is mostly complete. It covers purpose, usage, and key behavioral aspects like persistence rules. However, it lacks details on return values or error handling, which would be helpful for full contextual 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 description coverage is 100%, so the schema already fully documents both parameters (key and value). The description adds minimal value beyond the schema by implying usage examples ('findings, addresses, preferences, notes') but doesn't provide additional syntax, format, or constraints. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('store a key-value pair') and resource ('in your session memory'), distinguishing it from siblings like 'recall' (likely retrieval) and 'forget' (likely deletion). It explicitly mentions what gets stored ('intermediate findings, user preferences, or context across tool calls'), making the 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 provides clear context on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but does not explicitly state when not to use it or name alternatives. It implies usage for persistence across tool calls, which helps differentiate from temporary storage, but lacks explicit exclusions or sibling comparisons.

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

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

No annotations provided, so description carries full burden. It explains the return value (ticker, CIK, name, URIs) and that it replaces multiple calls, but lacks details on error handling, authentication, or limitations.

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 with no unnecessary words. It is front-loaded with purpose and provides essential details efficiently.

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, it adequately describes return values. Could mention error behavior or case sensitivity, but overall it is fairly complete for a simple lookup tool.

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

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

Schema coverage is 100% but description adds value by specifying accepted formats (ticker, CIK, name) and the v1 limitation (type='company'). This 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 verb 'resolve' and the resource 'entity to canonical IDs', specifying it consolidates multiple lookups. It distinguishes from sibling tools like ask_pipeworx and discover_tools by focusing on entity resolution.

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

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

The description provides clear context on when to use (single call to replace 2-3 lookups) and examples. However, it does not explicitly state when not to use or mention 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?

Discloses internal process (probing each entity with ai_visibility_check), ranking by score, and output details (score, confidence, signal density). Annotations confirm read-only and idempotent behavior; description adds process transparency 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?

Three sentences, front-loaded with the main purpose, each sentence adds essential information without redundancy.

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

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

Given the complexity (multi-entity, nested probes), the description covers workflow, input constraints, and output format. Schema and annotations complement completeness. No output schema, but return fields are described.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the 'entities' role (first entry as subject) and clarifying that 'models' can be omitted for default. This goes beyond schema descriptions.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, using 'ai_visibility_check' for each entity and ranking results. This distinguishes it from sibling 'ai_visibility_check' (single entity) and 'compare_entities' (more generic).

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

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

Provides a concrete use case ('competitive AI-marketing audits') and implicitly contrasts with single-entity check. However, it does not explicitly state when not to use the tool or name 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?

With no annotations, the description carries full burden. It explains the output (verdict, structured form, actual value, citation, delta) and briefly describes the internal process (NL parsing → entity resolution → data lookup → comparison). This gives a good understanding of behavior without needing 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. The first front-loads the main purpose, and the second efficiently adds scope, output details, and efficiency benefit. No superfluous content.

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 being a complex tool (multi-step fact-checking), the description covers input, output, and scope constraints. With no output schema, it adequately explains return values. Minor gaps like error handling or success conditions are acceptable for this length.

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% description coverage, and the description repeats the schema's example. It adds no new semantic information beyond the schema, so a baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: fact-checking natural-language claims against authoritative sources. It specifies the domain (company-financial claims for public US companies) and sources (SEC EDGAR + XBRL), which distinguishes it from siblings like 'ask_pipeworx' or 'compare_entities'.

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

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

The description provides clear context on when to use the tool (for fact-checking financial claims) and mentions it replaces multiple sequential calls, implying efficiency. However, it does not explicitly state when not to use it or list alternatives for out-of-scope claims.

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