lorem

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

Disclosures beyond annotations: returns per-model {score, confidence, signals, raw_response} and combined view, clarifies Anthropic billing is direct. No contradiction with annotations, which are all non-destructive and read-only.

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 functionality, 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?

Despite no output schema, description fully explains return structure. Covers all aspects needed for a probe tool with given annotations.

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

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

Schema coverage is 100% with good descriptions. Description adds value by explaining default model, how _apiKey works, and context usage, enhancing understanding beyond schema.

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

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

The description clearly states the tool probes LLMs for knowledge about an entity and returns a visibility score, with specific verb and resource. It distinguishes from sibling tools by focusing on AI visibility measurement.

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 clear context (AI-marketing audits, pre-launch checks, competitive monitoring) but lacks explicit when-not or direct alternatives. However, it does specify default model and optional Anthropic key 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: 'Pipeworx picks the right tool, fills the arguments, and returns the result,' which adds value beyond basic purpose. However, it lacks details on limitations, error handling, or performance traits, 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 appropriately sized and front-loaded, starting with the core purpose, followed by mechanism and examples. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

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

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

Given the tool's complexity (natural language query processing) and no output schema, the description is mostly complete. It explains the input method and provides examples, but lacks details on output format or error cases, which could be improved for full 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?

The input schema has 100% description coverage, so the baseline is 3. The description adds minimal semantic context by framing the parameter as 'Your question or request in natural language' and providing examples, but does not significantly enhance the schema's documentation of the single required 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: '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 also distinguishes from siblings by emphasizing natural language input versus other tools that might require structured parameters or browsing.

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 this tool: 'No need to browse tools or learn schemas — just describe what you need.' It implies usage for natural language queries versus more technical alternatives, but does not explicitly name when-not-to-use scenarios or list specific sibling alternatives, keeping it at a 4.

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

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

The description adds significant behavioral context beyond annotations: it discloses the internal process of resolving the market, classifying the bet, fanning out to packs, and returning an evidence packet with market-vs-model comparison. This aligns with the readOnlyHint and openWorldHint annotations without contradiction.

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

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

The description is front-loaded with the core purpose and progressively adds detail on inputs, process, and use cases. Every sentence serves a purpose, though it could be slightly trimmed without losing value.

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

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

Despite lacking an output schema, the description fully explains the return value (evidence packet + market-vs-model comparison) and the internal fan-out logic. With only two parameters, it covers all necessary context for an AI to use the tool effectively.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the depth parameter's behavior ('quick = 2-3 evidence sources, thorough = full fan-out') and the market parameter's accepted formats (slug, URL, question text). This goes 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 explicitly states the tool's function: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It distinguishes itself from siblings by focusing specifically on Polymarket bets, while other tools like ask_pipeworx or compare_entities serve broader 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?

The description provides direct use cases: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?".' It does not explicitly state when not to use it, but the context and sibling tools make the scope 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 explains data sources (SEC EDGAR for companies, FDA for drugs), return format (paired data + pipeworx:// URIs), and the efficiency benefit. It does not cover auth requirements or rate limits, but for a read-only comparison tool the key traits are disclosed.

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 core purpose, and every sentence adds distinct information (scope, type-specific data, return format, efficiency gain). 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 no output schema, the description provides a high-level summary of return data (paired data + URIs). While it does not detail the exact structure, it is sufficient for an agent to understand what to expect. The tool is not overly complex.

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%, baseline 3. The description adds value by explaining how to specify values for each type (tickers/CIKs for company, drug names for drug) and provides examples. This goes beyond the schema's enum and min/max constraints.

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 in one call, distinguishes between company and drug types, and lists specific data returned (e.g., revenue, adverse-event counts). It also highlights efficiency by replacing 8-15 sequential calls, which differentiates it from siblings like ask_pipeworx or resolve_entity.

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 this tool (comparing entities) and gives examples for each type. It implies it's for batch comparisons but does not explicitly exclude cases where single entity lookup or different comparisons are needed. Sibling tools like ask_pipeworx cover other queries, providing implicit 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?

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 gives some behavioral insight into the output format. However, it lacks details on rate limits, error handling, or performance characteristics, leaving gaps in behavioral context.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by usage guidance, with no redundant information. Both sentences earn their place by providing essential clarity and instructions, making it highly efficient and well-structured.

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

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

Given the tool's moderate complexity (search functionality with 2 parameters) and no output schema, the description is mostly complete. It explains the purpose, usage context, and output format, but lacks details on behavioral aspects like limitations or error cases, which could enhance completeness 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 description coverage is 100%, so the schema already documents both parameters ('query' and 'limit') with descriptions. The description does not add any parameter-specific details beyond what the schema provides, such as examples or usage nuances, resulting in a baseline score of 3.

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

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

The description clearly states the tool's purpose with a specific verb ('Search') and resource ('Pipeworx tool catalog'), and distinguishes it from siblings by emphasizing its role in discovery when many tools are available. It explicitly mentions returning 'the most relevant tools with names and descriptions,' which clarifies the output.

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 alternative context (vs. not using it). It clearly directs the agent to use it for initial discovery in large catalogs.

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

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

No annotations provided, so description carries full burden. It describes the data returned and implies read-only behavior by stating it replaces sequential calls, but does not explicitly declare idempotency or safety. Overall, behavior is inferable but not fully explicit.

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 with no superfluous words. Front-loaded with core purpose and key differentiators. Every sentence adds value.

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

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

Given no output schema, description covers return format (pipeworx:// URIs) and data sources comprehensively. It addresses sibling tool resolve_entity and a specific exclusion (usa_recipient_profile). Lacks error handling or edge cases, but sufficient for typical 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 coverage is 100%, but description adds critical context beyond enum/string descriptions: explains type only supports 'company', value can be ticker or CIK, and names not supported (with alternative). This significantly aids agent understanding.

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 every relevant Pipeworx pack in one call' and lists specific data included (SEC filings, revenue, patents, news, LEI). It also notes that for federal contracts, usa_recipient_profile should be used, distinguishing this tool's scope.

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

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

Explicitly states when NOT to use this tool: 'For federal contracts call usa_recipient_profile directly (too slow to bundle).' Also advises using resolve_entity first if only a name is available, providing clear alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool deletes a memory, implying a destructive mutation, but doesn't clarify if deletion is permanent, requires specific permissions, has side effects, or what happens on success/failure. This leaves significant gaps for a mutation tool.

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

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

The description is a single, efficient sentence with zero waste. It's front-loaded with the core action ('Delete') and resource, making it easy to parse quickly. Every word earns its place without redundancy.

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

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

For a destructive mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., permanence, error handling), usage context, and expected outcomes, which are critical for safe and effective tool invocation by an AI agent.

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

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

The input schema has 100% description coverage, with the 'key' parameter documented as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format or examples. With high schema coverage, the baseline score of 3 is appropriate, as 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 action ('Delete') and the resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'recall' or 'remember', but the verb 'Delete' distinguishes it as a removal operation rather than retrieval or storage.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing memory to delete), exclusions, or how it relates to sibling tools like 'recall' (for retrieval) or 'remember' (for storage).

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

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

The description adds value beyond annotations by explaining the read-only, safe operation (fetching, extracting, emitting) and the output format ('single text blob'). Annotations already indicate safe behavior, so the description supplements with useful process 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: the first explains the main action, the second lists use cases. It is front-loaded with the core purpose and contains no extraneous words.

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

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

Given the simple inputs (2 params, one required) and no output schema, the description sufficiently covers the tool's behavior and output. It explains the output format and typical use cases, though it omits error handling or rate limits.

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 both parameters described. The description does not add meaning beyond the schema (e.g., url is 'Full URL', max_links default 25). No extra parameter context is provided.

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

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

The description clearly states that the tool generates an llms.txt file for any URL for AI crawlers, listing the exact actions (fetch, extract, emit) and output format. It distinguishes itself from siblings by being the only tool focused on llms.txt generation.

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 lists three use cases (client indexing, own project, competitor auditing), providing clear context for when to use. It lacks explicit exclusions or alternatives, but no direct sibling exists, so the guidance is adequate.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool strips HTML tags from responses, which is a useful behavioral trait beyond basic generation. However, it lacks details on rate limits, error handling, or other operational aspects.

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 with two concise sentences that are front-loaded with the core purpose and a key behavioral detail (stripping HTML), with no wasted words or 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 low complexity (simple text generation), no annotations, and no output schema, the description is reasonably complete by specifying the output format and content type. However, it could benefit from more context on when to use versus the sibling tool or handling edge cases.

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

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

Schema description coverage is 100%, so the schema fully documents the two parameters (count and length). The description does not add any meaning beyond what the schema provides, such as explaining the semantic impact of length options or count ranges, meeting the baseline for high schema coverage.

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

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

The description clearly states the specific action ('generate lorem ipsum placeholder text as plain paragraphs') and resource ('placeholder text'), distinguishing it from the sibling tool 'generate_with_options' by specifying the output format ('plain paragraphs') and content type ('lorem ipsum').

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 for generating placeholder text with plain paragraphs, but does not explicitly state when to use this tool versus the sibling 'generate_with_options' or provide any exclusions or alternatives, leaving the context somewhat vague.

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

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

No annotations are provided, so the description carries the full burden. It states the tool generates HTML with optional elements but lacks behavioral details: no information on output format (e.g., HTML string), error handling, rate limits, or side effects. For a generation tool with no annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the core purpose ('Generate lorem ipsum HTML') and lists optional features without waste. Every word contributes to understanding the tool's functionality, making it appropriately concise and well-structured.

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

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

Given no annotations and no output schema, the description is incomplete for a tool with 6 parameters and generation functionality. It doesn't explain what the output looks like (e.g., HTML string), how optional elements are integrated, or any behavioral constraints. For a tool of this complexity, more context is needed to be fully helpful.

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 fully documents all 6 parameters. The description adds minimal value beyond the schema by listing optional elements (headers, code blocks, unordered lists, ordered lists), which correspond to some boolean parameters. However, it doesn't explain parameter interactions or provide additional context, meeting the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose: 'Generate lorem ipsum HTML' with specific optional elements (headers, code blocks, unordered lists, ordered lists). It distinguishes from the sibling 'generate_paragraphs' by mentioning these HTML formatting options, though it doesn't explicitly contrast them. The verb 'generate' and resource 'lorem ipsum HTML' are specific.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions optional features but doesn't explain when to choose this over 'generate_paragraphs' or other text generation tools. There are no prerequisites, exclusions, or context for selection.

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

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

With no annotations, the description carries the burden. It discloses rate limiting (5 messages per identifier per day) and that it's 'Free'. The behavior of sending feedback is straightforward and no side effects are expected. Slightly lacking on whether feedback is stored or returned, but acceptable for a feedback tool.

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

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

The description is extremely concise: two sentences covering purpose, usage guidelines, and rate limit, plus a note about being free. Every sentence adds value, and the structure front-loads the core purpose.

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

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

Given the tool's simple nature (feedback submission) and lack of output schema, the description is sufficiently complete. It covers what, when, and how to use it. It does not explain return format, but for feedback tools, that is acceptable. The rate limit and privacy note ('do not include...') add necessary 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 description coverage is 100%, so each parameter is already described. The description adds value by giving additional context for the message parameter (describe what you tried, avoid verbatim prompts) and clarifying the purpose of each type in the schema. This enhances the schema's information.

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

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

The description clearly states the tool is for sending feedback to the Pipeworx team, listing specific use cases (bug reports, feature requests, data gaps, praise) and instructions on what to include. It is distinct from sibling tools which focus on knowledge retrieval or generation.

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 (for feedback) and provides guidelines on what to include (describe what you tried in terms of Pipeworx tools/data, avoid verbatim prompts). It also mentions rate limits. It does not explicitly exclude alternative tools, but the purpose is clear enough.

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

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

Annotations already declare read-only and idempotent. The description adds valuable behavioral details: data is derived from CF analytics-engine, contains no PII, is cached for 5min to 1h, and the return format includes (pack, tool, count). 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 with a bulleted list of use cases. It is front-loaded with the main purpose, every sentence adds value, and there is no extraneous text.

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 read-only tool with one parameter and no output schema, the description covers purpose, parameters, use cases, data origin, privacy, and caching. It is complete enough for effective use.

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

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

The schema already describes the 'window' parameter with enum values. The description adds semantic context about trade-offs (short windows for hot trends, longer for steady-state), which enhances understanding beyond the schema.

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

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

The description clearly states it returns top tools, packs, and call volumes over recent windows. It distinguishes itself from siblings by focusing on trending AI agent activity, which no other sibling does.

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

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

Three concrete use cases are provided: discovering hot data, confirming canonical tool choices, and checking alignment with agent needs. No explicit exclusions or alternatives, but the context is clear and sufficient.

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

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

Annotations already provide readOnlyHint=true and openWorldHint=true. The description adds detailed behavioral context: scanning for monotonicity violations, walking child markets, searching across events, grouping, and ranking opportunities. 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 concise yet comprehensive: two sentences that front-load the purpose and then detail the modes in a structured paragraph. 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 read-only tool with no output schema, the description adequately explains returns ('ranked opportunities with suggested trade direction + reasoning'). It covers both modes thoroughly and addresses the complexity of arbitrage detection across events.

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

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

Schema coverage is 100% with both parameters described. The description adds meaningful context by detailing the two modes, providing example values (e.g., 'when-will-bitcoin-hit-150k' and 'Strait of Hormuz traffic returns to normal'), and explaining how each parameter affects tool 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's purpose: 'Find arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets.' It introduces two modes (event and topic) and distinguishes itself from sibling tools like bet_research and polymarket_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 explains when to use each mode: event mode for a single Polymarket event slug, topic mode for cross-event searches. It also explains why cross-event mode is necessary, addressing a common pitfall where single-event mode misses monotonicity across separate 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?

Beyond annotations (readOnlyHint, destructiveHint false), the description details the process: scanning top markets, grouping by asset, fetching price history once, computing model probabilities using lognormal model from FRED and live coinpaprika, ranking by edge, and outputting top N with suggested direction. This fully discloses behavior and data sources.

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

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

The description is a well-structured paragraph with the main purpose front-loaded. It is informative but slightly verbose with technical details like 'V1 covers crypto-price bets' and model specifics, yet every sentence adds value. A bit of trimming could improve conciseness.

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

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

Given no output schema, the description adequately describes the output (top N ranked by edge magnitude with suggested direction) and the workflow. It covers the use case, data sources, and ranking logic, making it complete enough for an agent to decide 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 coverage is 100% with clear descriptions for all three parameters (limit, window, min_edge_pp). The description adds no extra semantics beyond repeating defaults already in schema, 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?

The description clearly states it scans high-volume Polymarket markets to find ones where Pipeworz data disagrees with market price, using a specific verb 'scan' and resource 'Polymarket markets'. It further specifies it's for crypto-price bets and returns ranked edges with trade direction, 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 explicitly says it's built for the 'what should I bet on today' question to discover opportunities without manual paging, giving clear context. However, it does not explicitly mention when not to use it or compare to siblings like polymarket_arbitrage.

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 (readOnlyHint, idempotentHint), the description adds that output includes leg-by-leg prices as raw probabilities (0-1) and spreads in percentage points, and explains how modes auto-fetch or override. No contradictions with annotations; additional context is valuable.

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

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

The description is concise (~100 words), front-loaded with purpose, then provides motivation, modes, and output format. Every sentence adds value; no fluff.

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

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

Despite no output schema, the description explains return values (prices and spreads). It covers purpose, usage, modes, and output adequately for a read-only, idempotent tool with optional parameters.

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?

Though schema coverage is 100%, the description explains the interplay between topic and explicit parameters, clarifying that explicit overrides topic. This adds semantic meaning beyond individual param 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 computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It specifies the verb (compute), resources (two venues), and scope (same event), and distinguishes from siblings like polymarket_arbitrage by emphasizing the cross-venue focus and two modes.

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 the context of pricing disparities and arbitrage signals, and outlines two usage modes (topic macros vs. explicit tickers). It does not explicitly contrast with sibling tools, but the clear purpose and mode guidance effectively tell when to use this 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 provided, the description carries full burden and does well by explaining key behavioral aspects: it retrieves from both current and previous sessions, supports both single-key retrieval and listing operations, and clarifies the relationship between parameter presence and behavior. It doesn't mention error handling or performance characteristics, keeping it at 4 rather than 5.

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 zero waste. The first sentence states the dual functionality clearly, and the second provides essential usage context. Every word earns its place, and the structure is front-loaded with core functionality.

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

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

For a single-parameter tool with no annotations and no output schema, the description provides good coverage of what the tool does, when to use it, and how parameters affect behavior. It doesn't describe return format or error conditions, but given the tool's simplicity, this is reasonably complete.

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

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

The schema has 100% description coverage, so baseline is 3. The description adds meaningful context by explaining the semantic effect of omitting the key parameter ('omit to list all keys') and connecting the parameter to the tool's dual functionality, providing value beyond the schema's technical 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?

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 like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations.

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

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

The description provides explicit usage guidance: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It also explains when to omit the key parameter ('omit key to list all keys'), creating clear conditions for different use cases.

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

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

No annotations are provided, so the description carries full responsibility. It discloses parallel data source fan-out, date formats, and return structure (structured changes, count, URIs). It does not mention pagination, rate limits, or error conditions, but these are not critical for basic use.

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

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

The description is a single paragraph of 4-5 sentences, efficiently conveying purpose, usage, and parameters. It is front-loaded with the key action. Could be slightly more structured, but overall compact and informative.

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

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

Given no output schema, the description adequately describes the return shape (structured changes, total_changes count, pipeworx:// URIs). It covers parameter details and use cases. However, it lacks mention of pagination or limits, which would improve completeness for complex queries.

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 beyond schema: examples for 'since' (ISO and relative), suggests typical values ('30d', '1m'), and explains 'value' as ticker or CIK. This enriches 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 the tool's purpose: retrieving changes about an entity since a given time. It specifies the verb 'fans out' and lists the data sources (SEC EDGAR, GDELT, USPTO), distinguishing it from siblings like entity_profile 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?

Includes explicit use cases: 'brief me on what happened with X' or change-monitoring workflows. It notes that type is currently limited to 'company', implying a constraint. However, it does not 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the persistence differences between authenticated users (persistent memory) and anonymous sessions (24-hour lifespan). This adds valuable context beyond the basic storage function, though it doesn't cover potential limitations like storage size 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?

The description is perfectly front-loaded with the core purpose in the first sentence, followed by usage context and behavioral details. Every sentence earns its place by adding distinct value without redundancy, making it appropriately sized and efficiently structured.

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

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

Given the tool's moderate complexity (storage with persistence rules), no annotations, and no output schema, the description does well by explaining the core function, usage, and key behavioral traits. However, it doesn't specify what happens on success/failure or return values, leaving some gaps that an output schema would typically cover.

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

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

The schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add significant meaning beyond what's in the schema properties, maintaining the baseline score of 3 for adequate but not enhanced parameter semantics.

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

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

The description clearly states the tool's purpose with specific verbs ('store a key-value pair') and resource ('in your session memory'). It distinguishes from siblings like 'recall' (retrieval) and 'forget' (deletion) by focusing on storage, and provides concrete examples of what to store (intermediate findings, user preferences, context).

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

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

The description provides clear context on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), which implicitly differentiates it from retrieval or deletion tools. However, it lacks explicit guidance on when NOT to use it or named alternatives for similar storage needs, preventing a perfect score.

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

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

No annotations are provided, so the description carries the full burden. It discloses the return values (ticker, CIK, company name, resource URIs) and indicates the operation is a lookup (resolve). It does not mention side effects or authentication, but for a simple lookup tool, this is adequate.

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

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

The description is concise with three sentences, no fluff. The main purpose is front-loaded, and each sentence contributes useful 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 input schema fully describes parameters and no output schema exists, the description sufficiently explains the tool's behavior and return values. It covers what the tool does, what inputs it accepts, and what outputs it produces, making it 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%, providing the baseline. The description adds value by explaining the type parameter's v1 support and giving concrete examples for the value parameter (e.g., 'AAPL', '0000320193', 'Apple'), which enhances understanding beyond the schema.

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

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

The description clearly states the tool resolves an entity to canonical IDs across Pipeworx data sources. It specifies the verb 'resolve' and resource 'entity', and distinguishes from siblings 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 explains when to use the tool, mentioning it replaces 2–3 lookup calls and accepts ticker, CIK, or name. It does not explicitly state when not to use or list alternatives, but the context is clear for the intended use case.

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 adds significant behavioral detail beyond annotations: it probes each entity with ai_visibility_check, ranks by score, surfaces most/least recognized, and returns ranked list with score, confidence, signal density. 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?

Three sentences, front-loaded with main purpose, no fluff. Every sentence adds meaningful 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?

Despite no output schema, description adequately specifies return structure (ranked list with score, confidence, signal density). Covers probing mechanism and use case thoroughly.

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 explaining that the first entity is treated as the 'subject' for narrative, and rest are competitors. Also clarifies the role of models and _apiKey.

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 AI visibility across multiple entities side-by-side, using a specific verb (Compare) and resource (AI visibility). It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (not AI-specific).

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

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

Provides clear context for when to use: competitive AI-marketing audits, with an example question. Implicitly suggests using ai_visibility_check for single entity, but does not explicitly state when not to use this 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?

No annotations are provided, so the description carries full burden. It describes the output (verdict, structured form, value with citation, percent delta) but does not mention safety, rate limits, or idempotency. For a fact-checking tool, this is adequate but could explicitly state it is read-only.

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 consists of two succinct sentences, each carrying essential information. The first sentence states the purpose, and the second adds scope and output details. No unnecessary words.

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

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

Given the tool has one simple parameter, no output schema, and no annotations, the description is sufficiently complete. It covers the supported domain, output format, and mentions edge cases like the 'unsupported' verdict. All necessary information for an agent to decide to use the 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?

There is only one parameter 'claim' with a schema description. The tool description adds examples of valid claims, which provides additional context beyond the schema. Since schema coverage is 100%, baseline is 3, and the examples elevate it to 4.

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

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

The description clearly states the tool's purpose: fact-check a natural-language claim against authoritative sources. It specifies the supported domain (company-financial claims for public US companies via SEC EDGAR + XBRL). This differentiates it from siblings like entity_profile 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 explains when to use the tool: for company-financial claims. It also mentions that it replaces 4-6 sequential agent calls, indicating efficiency. However, it does not explicitly state when not to use it or provide alternatives, though the sibling list is available.

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