Press Release

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds value by detailing the return structure (score, confidence, signals, raw_response) and noting that _apiKey is passed directly to Anthropic. 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 concise (3-4 sentences) with a clear front-loaded purpose. Every sentence adds value, covering purpose, mechanics, and use cases 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 tool's complexity (4 parameters, no output schema), the description covers key aspects: how to use, return format, and use cases. It lacks details on error handling or interpretation of scores, but richness of annotations compensates.

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

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

Schema description coverage is 100%, providing baseline 3. The description enhances this by explaining the default model, the purpose of each parameter (e.g., context for disambiguation), and the relationship between _apiKey and models.

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

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

The description clearly specifies the action ('Probe one or more LLMs'), the resource (LLMs), and the output ('score visibility 0-100 per model'). It differentiates from siblings like 'scan_competitor_ai_presence' by being general-purpose rather than competitor-specific.

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

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

The description explains default vs. optional models and provides use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring'). However, it does not explicitly state when not to use this tool or compare directly to siblings.

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

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

With no annotations, the description carries full burden for behavioral transparency. It discloses that the tool selects the best data source, fills arguments, and returns results. However, it does not explain what happens if the question cannot be answered or if multiple tools are relevant.

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

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

The description is concise (3 sentences plus examples) and front-loaded with the core action. The examples are helpful but could be integrated more succinctly.

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 input schema (1 param, no output schema) and the tool's straightforward purpose, the description provides sufficient completeness for an agent to invoke it correctly. It covers what the tool does and how to use it.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning beyond the schema by explaining how the parameter is used ('describe what you need') and providing examples, but does not specify format 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 the tool's purpose: asking questions in plain English and getting answers from the best available data source. It distinguishes itself from siblings by emphasizing natural language input and automatic tool selection, contrasting with tools like discover_tools or remember.

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 use (natural language queries) and includes examples, but does not explicitly state when not to use it or mention alternatives. Given the tool's unique role as a natural language interface, this 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?

Annotations declare readOnlyHint=true and destructiveHint=false, and the description aligns by noting it pulls data, fans out to packs, and returns evidence. It adds context about classification and comparison, with 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 4-5 sentences, efficiently covering purpose, inputs, processing, and output. The final sentence about 'core demo product' is slightly promotional but not wasteful. Minor room for tightening.

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

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

Despite no output schema, the description clearly defines what is returned (evidence packet plus market-vs-model comparison) and the processing steps (resolve, classify, fan-out). It fully equips an agent to understand the tool's role and output.

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 detailed descriptions. The tool description adds value by explaining that the 'market' parameter resolves the market and classifies the bet, going beyond the schema's format-only description. The 'depth' parameter is not elaborated 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 explicitly states it researches Polymarket bets by pulling Pipeworx data, specifying inputs (slug, URL, question text) and outputs (evidence packet, market-vs-model comparison). It clearly distinguishes from siblings focused on other queries, like 'ask_pipeworx' for general questions.

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

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

The description provides clear use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. However, it does not explicitly state when not to use or name alternatives, leaving some ambiguity.

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

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

Without annotations, the description partially discloses behavior (returns paired data, sources like SEC EDGAR) but omits details on read-only nature, auth, rate limits, or error states.

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

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

Four crisp sentences with front-loaded core purpose, no wasted words, and clear structure from general to specific.

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

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

Given no output schema, the description adequately explains return data (paired data + URIs, fields per type). Missing details like pagination or format, but sufficient for a straightforward 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% (baseline 3), but the description adds specific examples (tickers, drug names) and lists returned fields per type, enriching parameter meaning.

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

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

The description explicitly states the tool's purpose (compare 2-5 entities side by side) and distinguishes it from sibling tools like ask_pipeworx and resolve_entity by focusing on comparison in one call.

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 efficiency (replaces 8-15 calls) but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites.

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

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

The description discloses key behaviors: it searches by natural language description, returns the most relevant tools, and is designed for discovery. Since no annotations are provided, the description must carry the burden, and it does so effectively by stating the search mechanism and result type. A slight deduction because it doesn't mention any potential limitations (e.g., indexing delay) or side effects, but overall it's clear.

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 that each add value: the first states the action, the second describes the result, and the third gives usage guidance. No unnecessary words or repetition.

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 that there is no output schema, the description adequately explains what the tool returns (most relevant tools with names and descriptions). The context of having 500+ tools and the instruction to call it first provides sufficient completeness for a search/discovery tool.

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

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

The input schema already has high coverage (100%) with descriptions for both 'query' and 'limit'. The tool description adds value by framing how to use the 'query' parameter ('natural language description of what you want to do') and hints at the purpose of the tool, which complements the schema. However, it doesn't add additional details about 'limit' beyond what's in the schema.

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

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

The description uses a specific verb-resource pair ('Search the Pipeworx tool catalog') and clearly states the purpose: returning relevant tool names and descriptions. It distinguishes itself by instructing the agent to call this FIRST when many tools are available, which sets it apart from siblings like 'ask_pipeworx' or 'recall'.

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

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

The description explicitly tells when to use this tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear context for usage and implies alternatives (other tools are for different purposes).

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?

Despite no annotations, the description reveals data returned (SEC filings, revenue, patents, news, LEI) and mentions pipeworx:// URIs. It notes bundling limitations but doesn't cover error handling or performance.

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?

Extremely concise: two sentences plus a list. Front-loaded with purpose, every sentence adds essential information. No wasted words.

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

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

No output schema, so description attempts to compensate by mentioning URI returns and listing data categories. However, the exact return structure is not detailed, which may be needed for a complex tool replacing 10–15 calls.

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

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

Schema coverage is 100%, and the description adds value by explaining that type is only 'company' currently and value must be ticker or CIK, explicitly noting names not supported.

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

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

The description clearly states 'Full profile of an entity across every relevant Pipeworx pack in one call' and lists specific data sources for company type. It distinguishes from siblings like compare_entities 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?

Explicit guidance provided: 'For federal contracts call usa_recipient_profile directly (too slow to bundle).' Also clarifies unsupported inputs (names) and recommends resolve_entity first.

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

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

No annotations are provided, so the description must carry the behavioral burden. It clearly states the action is destructive (delete), which is sufficient. However, it doesn't disclose whether deletion is irreversible or if there are side effects.

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

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

The description is a single, concise sentence with no wasted words. It is front-loaded with the action and resource.

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 (1 parameter, no output schema), the description is complete enough for the agent to understand its function. No additional details are strictly necessary.

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

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

Schema coverage is 100%, so the schema already describes the 'key' parameter as 'Memory key to delete'. The description adds no additional meaning beyond what the schema provides, so a 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 verb 'Delete' and the resource 'a stored memory by key', making the tool's purpose unambiguous. It effectively distinguishes from siblings like 'remember' (create) and 'recall' (retrieve).

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

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

The description implies when to use this tool (when you need to remove a stored memory) but does not explicitly state when not to use it or mention alternatives among siblings like 'recall' for reading.

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 behavioral context beyond annotations by explaining the process (fetch, extract, emit) and output format. Annotations already declare readOnlyHint and idempotentHint, and the description does not contradict them, providing a coherent picture of a safe, non-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 concise with three sentences: the first states the primary function, the second outlines the process, and the third lists use cases. It is front-loaded with essential information and contains no redundant 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?

The description fully explains what the tool does, how it works, and what output to expect, compensating for the lack of an output schema. It provides sufficient context for an AI agent to use the tool effectively within the set of siblings.

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

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

The input schema has 100% coverage with descriptive parameter names and descriptions. The description does not add additional meaning beyond the schema, meeting the baseline expectation for this dimension.

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 (generate), the resource (llms.txt file for any URL), and the intended audience (AI crawlers). It distinguishes itself from sibling tools by focusing on a specific output format and use case, which is unique among the listed siblings.

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

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

The description provides specific use cases (client site indexing, personal project drafting, competitor auditing) that help an AI agent determine when to invoke the tool. However, it does not explicitly mention when not to use it or compare it to 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 provided, so description bears full burden. It discloses the feedback destination, rate limiting, and that it's free. However, it does not specify whether feedback is anonymous, if confirmation or response is given, or how it is processed. Minor gaps.

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

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

Very concise: two sentences plus a short bullet-like list cover purpose, usage, guidelines, and rate limit. No filler; every sentence adds value. Front-loaded with main purpose.

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

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

Given the tool's simplicity (3 parameters, no output schema), the description fully covers purpose, usage, and operational constraints. An agent can correctly invoke the tool based on this description alone.

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 has 100% description coverage, so baseline is 3. The description adds value by specifying content guidelines for 'message' (1-2 sentences, 2000 char max, no verbatim prompts) and clarifying that 'context' is optional. It complements the schema well.

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 explicitly states the tool sends feedback to the Pipeworx team and lists specific use cases: bug reports, feature requests, missing data, or praise. It clearly distinguishes from sibling tools which are for asking questions, comparing entities, etc.

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

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

Description provides explicit when-to-use guidance (for specific feedback types) and includes operational constraints (rate limit of 5 messages per day) and content guidelines (describe in terms of Pipeworx tools/data, avoid verbatim prompts).

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, openWorldHint, idempotentHint, destructiveHint=false. The description adds valuable behavioral context: data source (CF analytics-engine), privacy (no PII), aggregation details (pack, tool, count), and caching behavior (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?

The description is slightly verbose but front-loaded with the core function. Each sentence adds value (use cases, data source, caching). No filler, but could be tightened without losing meaning.

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

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

For a simple read-only aggregation with only one parameter and no output schema, the description covers everything an agent needs: what it returns, time windows, use cases, data provenance, and caching. No gaps.

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

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

Only one parameter (window) with full schema description coverage. The description adds nuance beyond the enum values: 'shorter windows surface what's hot right now; longer windows show steady-state demand.' This helps the agent choose wisely, even though the schema already provides the 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 states it returns top tools, top packs, and total call volume over a recent window, with specific timeframes (24h, 7d, 30d). It uses a specific verb ('returns') and resource ('what other AI agents are calling on Pipeworx'), making the tool's purpose unambiguous even without the title.

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

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

Three explicit use cases are provided, helping the agent decide when to use this tool. However, it does not explicitly mention when not to use it or compare it to sibling tools (e.g., discover_tools), though the context of 'what other AI agents are calling' is self-limiting.

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 that the tool searches markets, groups them, and checks monotonicity. Describes cross-event search behavior. Annotations (readOnlyHint, openWorldHint) are consistent and not contradicted. Adds value beyond annotations with mode 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?

Single paragraph but well-structured: starts with main purpose, then modes with examples. Not overly verbose, but packs a lot of information. Could be slightly more concise, but effective.

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

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

For a tool with no output schema, it describes return format (ranked opportunities with reasoning). Covers complexity of two modes and cross-event search. Example usage is embedded. Completeness is adequate.

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

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

Schema coverage is 100% but description adds significant meaning: explains two modes, gives example values, and clarifies the purpose of each parameter. Compensates for lack of output schema with return format info.

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

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

Clearly states it finds arbitrage opportunities on Polymarket by checking monotonicity violations. Distinguishes two modes (event and topic) and explains their difference. Provides specific verb and resource.

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

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

Explicitly describes when to use each mode with concrete examples (event slug vs topic). Explains why topic mode catches cases that event mode misses. Does not explicitly mention when not to use or alternatives, but the guidance is clear.

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

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

The description provides detailed behavioral information beyond the annotations: it explains that the tool is read-only (consistent with readOnlyHint=true), uses external data sources (FRED, coinpaprika), groups by asset, computes model probability, and ranks by edge. It covers the computational steps and output format, adding significant value over the annotations alone. No contradictions.

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

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

The description is a single paragraph that is well organized, front-loading the main action and then explaining the model and process. Every sentence contributes useful information, though it could be slightly shortened without losing clarity. Overall efficient and 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 output schema, the description adequately explains that the tool returns 'top N ranked by edge magnitude with suggested trade direction.' All parameters are documented in the schema, and the description covers the logic, data sources, and use case. For an analytical scanning tool, this is 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 description coverage is 100% with each parameter already well-documented (e.g., 'Top N edges to return after ranking. Default 10, max 25.'). The description adds minimal extra meaning beyond the schema, such as mentioning 'returns top N ranked by edge magnitude' which aligns with the limit parameter. 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 tool scans high-volume Polymarket markets and returns those with the largest disagreement between Pipeworx data and market price, ranking by edge magnitude and suggesting trade direction. It uses specific verbs (scan, return) and resource (Polymarket markets), and the purpose is further clarified by the phrase 'built for the what should I bet on today question.' It distinguishes itself from siblings by describing its unique model and ranking process.

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 identifies the use case: 'agents/users discover opportunities without paging through hundreds of markets by hand.' This provides clear context for when to use the tool. However, it does not explicitly mention when not to use it or contrast with the sibling tool 'polymarket_arbitrage,' which might have overlapping functionality. Thus, score 4 for clear context but no exclusions.

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

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

Adds substantial context beyond annotations: explains that the tool fetches prices from two venues, describes the typical price gap (2-25pp), and details the output structure (leg-by-leg prices and spread). 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 informative but slightly verbose. It front-loads the purpose and then details modes and returns. While every sentence adds value, it could be somewhat more concise without losing clarity.

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 fully compensates by detailing return values (leg-by-leg prices and spread). Combined with annotations, it provides complete guidance for a complex tool with multiple 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?

Schema coverage is 100% with descriptions for each parameter. The tool description enriches semantics by explaining the two operational modes, listing topic presets, and providing explicit ticker examples, clarifying parameter interplay beyond raw schema.

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

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

The description clearly defines the tool's function as computing cross-venue spread between Kalshi and Polymarket. It uses specific verbs ('cross-venue spread') and distinguishes two modes (topic vs explicit ticker), setting it apart 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 each mode via concrete examples ('fed', 'btc') and explicit ticker formats. However, it does not explicitly state when to prefer this tool over sibling tools like polymarket_arbitrage, though the unique focus on Kalshi–Polymarket spread is implicit.

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

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

No annotations are present, so the description must fully disclose behavior. It only states the output structure (headline, body, etc.) but does not reveal behavioral traits such as whether tone selection influences language, if the tool edits grammar, or whether it works iteratively. The lack of any behavioral context beyond the output format is a significant gap.

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 concise sentences, front-loaded with the action verb and resource, immediately clarifying the tool's purpose. Every word is essential; no fluff 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 has 4 parameters (one with enums) and an output schema, the description is minimally adequate: it explains the input-to-output transformation but omits parameter details. Since there is an output schema, return values need no extra explanation. However, completeness suffers from ignoring tone and optional fields, leaving the agent without guidance on how to use them.

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 0%, so the description must compensate by explaining parameter meanings. It mentions 'announcement text' and 'formatted release' but does not define the 'tone' enum options, the role of 'company' and 'location', or any parameter constraints. The description adds minimal value beyond the schema's property names.

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

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

The description uses the specific verb 'Transform' and clearly identifies the resource 'announcement into a professional press release'. It lists the output components (headline, body, boilerplate, media contact), which distinguishes this tool from unrelated siblings, making the purpose unmistakable.

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 implicitly states when to use the tool (when you have announcement text to turn into a press release). Sibling tools are diverse (e.g., ask_pipeworx, compare_entities), so no explicit exclusion is needed. However, it lacks explicit conditions like 'use for official public announcements' or alternatives, which would elevate the score.

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

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

No annotations provided, so description carries full burden. It mentions retrieving previously stored memories, implying it is a read-only operation. However, it doesn't disclose whether retrieval affects memory state (e.g., marks as accessed) or any limits on number of memories.

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

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

Two sentences, front-loaded with purpose, 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?

Given no output schema and single optional parameter, description adequately covers retrieval behavior. Could mention if listing all memories returns keys only or full content.

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 parameter 'key' with description, but description adds meaning by explaining that omitting key lists all memories. This adds value beyond the schema.

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

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

Clearly states the verb (retrieve/list), resource (memory by key), and distinguishes from 'remember' by specifying retrieval vs. storage. Also clarifies that omitting key lists all memories.

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

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

Explicitly says when to use ('to retrieve context you saved earlier') and notes that omitting key lists all memories. Could be improved by mentioning when to use alternatives like 'remember' or 'forget'.

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 fully explains behavior: parallel fan-out to multiple sources, ISO and relative date formats, and return structure (structured changes + total_changes count + pipeworx:// URIs). It is transparent about what the tool does and returns.

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 dense paragraph but packs all necessary information. It could be improved with bullet points for readability, but it is front-loaded with the core purpose and concise overall.

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

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

Given no annotations and no output schema, the description covers behavior, inputs, return format, and use cases comprehensively. It leaves no major gaps for an agent to understand 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?

Although schema coverage is 100%, the description adds meaningful context: type is limited to 'company' (not in schema), since supports relative formats with examples ('7d', '30d', '3m', '1y'), and value can be ticker or zero-padded CIK. This enriches the schema.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It provides specific details for type='company', listing the data sources (SEC EDGAR, GDELT, USPTO) and the parallel fan-out behavior, making it distinct from siblings like entity_profile.

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

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

The description explicitly recommends usage for 'brief me on what happened with X' or change-monitoring workflows, providing clear context. It does not explicitly state when not to use or list alternatives, but the guidance is sufficient for 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?

Without annotations, the description carries full burden. It discloses behavioral traits: persistence based on authentication (persistent for authenticated users, 24-hour for anonymous). It also implies the tool is a write operation (store) and doesn't read or delete.

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, each adding value: purpose, use cases, and persistence behavior. 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 and two simple parameters, the description is nearly complete. It covers what the tool does, when to use it, and persistence behavior. Could optionally mention size limits or overwrite behavior, but not necessary.

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

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

Schema coverage is 100% and the schema already provides clear descriptions for both parameters (key with examples, value as free text). The description adds context about the purpose of the parameters (intermediate findings, preferences, notes) but does not add technical details beyond the schema.

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

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

The description clearly states the verb 'store' and the resource 'key-value pair in session memory'. It distinguishes itself from sibling tools like 'recall' (retrieve) and 'forget' (delete).

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

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

The description provides clear context for when to use the tool: to save intermediate findings, user preferences, or context across tool calls. It also mentions authentication persistence and anonymous session limits, though it doesn't explicitly say when not to use it or compare to alternatives.

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

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

No annotations present, so description should carry full burden. It explains it is a single call and replaces multiple lookups, but does not disclose error handling, rate limits, or what happens if entity not found. Adequate but not thorough.

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

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

Two sentences: first states purpose, second details version, input, and output. No extraneous words, front-loaded.

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

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

Given no output schema, description adequately explains what is returned (ticker, CIK, name, pipeworx URIs). Could mention uniqueness of result or error cases, but sufficient for basic 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 description coverage is 100%, baseline 3. Description adds example values for 'value' parameter and clarifies accepted formats, but does not add substantial new meaning 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?

Explicit verb 'resolve' and specific resource 'entity to canonical IDs across Pipeworx data sources'. Clearly distinguishes from sibling tools by stating it replaces multiple lookup calls.

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

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

Provides explicit input formats (ticker, CIK, name) and example values, plus context that it replaces 2–3 calls. Lacks explicit exclusions or when-not-to-use, but usage is clearly implied.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds that it probes each entity with ai_visibility_check, ranks by score, and returns score, confidence, signal density. It also mentions optional API key requirements, providing useful context 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?

The description is structured: purpose first, then details, then use case. It is concise but informative, with no wasted words. Slightly longer than minimal but earns its length.

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

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

Given no output schema, the description explains the return format (ranked list with score, confidence, signal density). It covers behavior, parameters, and usage adequately for a tool with moderate complexity.

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 has 100% description coverage. The description adds meaning: first entity is the 'subject', rest are competitors; explains models default and Anthropic key; context disambiguates. This goes beyond the schema's basic 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, specifies the action (probes, ranks, surfaces most/least recognized), and distinguishes it from siblings like ai_visibility_check by focusing on side-by-side comparison.

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

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

The description explicitly states the use case ('competitive AI-marketing audits') and provides a concrete example. It implies when to use (comparison) vs. single entity checks, though it does not name the alternative tool directly.

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

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

With no annotations provided, the description carries full burden. It details the return values: a verdict (confirmed/approximately_correct/refuted/inconclusive/unsupported), structured form, actual value with citation, and percent delta. It also mentions it is read-only (fact-checking), so no destructive behavior. 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 two sentences long. The first sentence states the purpose and scope; the second summarizes the output and efficiency. It is front-loaded with essential information and contains no unnecessary words or repetition.

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 single parameter, no output schema, and no annotations, the description adequately covers the tool's purpose, input expectations, output format, and a use-case benefit. It does not detail failure modes or exact source behavior, but it provides enough for an agent to decide on invocation. More advanced edge cases (e.g., unsupported claim types) are implicitly handled by the scope restriction.

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 one required parameter 'claim' with a description. The tool's description adds value beyond the schema by specifying the types of claims supported (company-financial, public US companies) and providing examples. Schema coverage is 100%, so baseline is 3; the extra context justifies a 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 domain (company-financial claims, public US companies) and the exact sources (SEC EDGAR + XBRL). This is specific and stands out from sibling tools like compare_entities or entity_profile, which do not perform claim verification.

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

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

The description indicates when to use the tool ('Replaces 4–6 sequential agent calls') and defines its scope ('v1 supports company-financial claims'). It implies alternatives (the sequential calls it replaces) but does not explicitly state when not to use it or list alternative tools for other claim types. This is clear enough for most scenarios.

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