disify

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds key behavioral traits: default model is free, Anthropic requires BYO key, and returns per-model results.

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

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

Concise: two sentences plus a use-case summary. Essential information is front-loaded, 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?

Despite no output schema, description outlines returned structure (per-model fields + combined view). Combined with rich annotations, it provides sufficient context for correct invocation.

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%. Description adds context: default model explanation, why _apiKey is needed, and context parameter for disambiguation.

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 brand/topic knowledge and scores visibility (0-100). It distinguishes from siblings like check_domain or entity_profile by focusing on AI-specific visibility scoring.

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

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

Explicitly mentions use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) but does not specify when not to use or provide direct 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 effectively describes key traits: the tool acts as an intelligent intermediary ('Pipeworx picks the right tool, fills the arguments'), handles natural language input, and returns results. However, it lacks details on limitations like rate limits, error handling, or data source constraints, which would be helpful for a tool with no annotation coverage.

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

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

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by elaboration on the mechanism and usage guidelines. Each sentence earns its place by adding clarity or examples without redundancy. The structure efficiently conveys essential information in a compact form.

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 processing with tool selection), no annotations, and no output schema, the description is reasonably complete. It explains the tool's behavior, usage, and parameter semantics effectively. However, it could improve by mentioning output format or potential limitations, as the absence of an output schema leaves return values unspecified.

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 baseline is 3. The description adds value by explaining the parameter's purpose beyond the schema's 'Your question or request in natural language': it emphasizes 'plain English' queries and provides concrete examples, enhancing understanding of what constitutes a valid question. This compensates well, though it doesn't detail format constraints beyond natural language.

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

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

The description clearly states the tool'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'), distinguishing it from sibling tools like check_domain or validate_email that perform specific validations rather than natural language queries.

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 provides usage guidelines: 'No need to browse tools or learn schemas — just describe what you need.' It includes examples ('What is the US trade deficit with China?', 'Look up adverse events for ozempic', 'Get Apple's latest 10-K filing') that illustrate when to use this tool versus alternatives, emphasizing its role for natural language queries rather than structured tool invocations.

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

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

Beyond the annotations (readOnlyHint=true, openWorldHint=true), the description discloses that the tool resolves the market, classifies the bet, fans out to relevant data packs, and returns an evidence packet with market-vs-model comparison. This adds valuable context about internal behavior and output.

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

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

The description is well-structured, starting with the main purpose, then input formats, internal logic, output, and use cases. It is concise with no redundant sentences, though it could be slightly tighter by combining some clauses.

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 mentions 'evidence packet plus a simple market-vs-model comparison' but does not detail the packet's contents. It adequately covers the main functionality and use cases for a complex tool, but missing output details slightly reduces completeness.

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

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

Schema coverage is 100%, so baseline is 3. The description restates that market can be a slug, URL, or question text, and depth can be quick or thorough, but adds no new detail beyond the schema descriptions. It does not explain the difference between quick and thorough in terms of fan-out logic beyond what is in the enum description.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data, with specific verb (research) and resource (Polymarket bet). It differentiates from siblings like ask_pipeworx by being purpose-built for betting edge, noting that agents using this tool convert better than those discovering packs individually.

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 use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It implies this is the recommended tool for bet research but does not explicitly discuss when not to use it or compare to sibling tools.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe how it works—such as whether it queries an external API, uses a local database, has rate limits, or returns specific result formats. This leaves significant behavioral gaps for the agent.

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

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

The description is a single, clear sentence that efficiently conveys the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse and understand quickly.

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 (one parameter, no output schema, no annotations), the description is minimally adequate. It explains what the tool does but lacks details on behavioral traits, usage context, and output format, which could hinder an agent's ability to use it effectively in varied scenarios.

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 'domain' parameter well-documented in the schema itself. The description adds no additional parameter semantics beyond implying the domain is checked for disposable/temporary email associations, which aligns with the schema's purpose but doesn't provide extra details like format constraints or examples 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 clearly states the tool's purpose: checking if a domain is associated with disposable/temporary email services. It specifies the verb 'check' and resource 'domain', but doesn't explicitly differentiate from the sibling 'validate_email' tool, which likely validates email addresses rather than domain types.

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?

No guidance is provided on when to use this tool versus the sibling 'validate_email' tool. The description implies usage for domain checking, but offers no explicit context, exclusions, or alternatives, leaving the agent to infer when this specific check is appropriate.

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

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

Discloses return format (paired data + resource URIs) and data sources per type. No annotations exist, so description carries full burden. Does not explicitly state non-destructive nature, but context implies 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?

Three sentences, front-loaded purpose, no wasted words. Efficiently conveys key 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?

Complete for a comparison tool given no output schema or annotations. Explains return data per type and resource URIs. No mention of error handling, but acceptable.

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

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

Schema covers both params (100% coverage). Description adds meaning: explains values format (tickers vs drug names) and examples, enriching 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 it compares 2-5 entities side by side, specifying metrics per type (company: financials, drug: trial data). It differentiates from siblings as no other tool does 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?

Indicates it replaces 8-15 sequential calls, implying use for multi-entity comparison. However, lacks explicit when-not-to-use or alternative guidance.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it's a search operation (implied read-only), returns 'the most relevant tools with names and descriptions', and suggests it's for initial discovery. However, it doesn't mention potential limitations like rate limits, authentication needs, or error handling. For a tool with no annotations, this is good but not comprehensive.

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

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

The description is two sentences, front-loaded with the core purpose, followed by usage guidance. Every sentence earns its place: the first explains what the tool does, and the second tells when to use it. There is zero waste or redundancy, 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 complexity (search with natural language query), no annotations, no output schema, and 100% schema coverage, the description is mostly complete. It covers purpose and usage well but lacks details on output format (beyond 'names and descriptions') and behavioral constraints. For a discovery tool, this is sufficient but could be enhanced with more context on results.

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) thoroughly. The description adds no additional parameter semantics beyond what's in the schema. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description, which applies here.

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 ('Search the Pipeworx tool catalog'), the resource ('tool catalog'), and the method ('by describing what you need'). It distinguishes this tool from its siblings (check_domain, validate_email) by focusing on tool discovery rather than domain or email validation. The purpose is explicit and well-defined.

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.' This gives clear context (large tool catalog) and a specific recommendation (use first), which helps the agent distinguish it from alternatives. No exclusions are mentioned, but the guidance is strong and actionable.

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

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

No annotations provided, so description carries the burden. It discloses that the tool returns citation URIs, aggregates multiple sources, and replaces sequential calls. It hints at performance (too slow to bundle for federal contracts) but doesn't mention side effects, rate limits, or authentication requirements. Generally transparent for a read 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 (5 sentences) with no redundant information. Each sentence adds value: purpose, content list, output detail, efficiency claim, and exclusion for federal contracts.

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

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

For a tool with 2 straightforward parameters and no output schema, the description sufficiently explains inputs, output (citation URIs), and the benefit of aggregated profiling. It doesn't cover edge cases like partial data or limits, but given the simplicity, it is fairly complete.

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

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

Schema coverage is 100% (baseline 3). The description adds meaningful context: type is limited to 'company' currently, and value accepts ticker or CIK with a specific format hint, plus guidance to use resolve_entity for names. This goes beyond the schema descriptions.

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

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

The description clearly states it provides a 'full profile of an entity across every relevant Pipeworx pack' and enumerates specific data sources (SEC filings, XBRL, patents, news, LEI). It distinguishes from sibling tools by mentioning 'resolve_entity' as a prerequisite and 'usa_recipient_profile' as an alternative for federal contracts.

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

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

Explicitly says when to use: for a comprehensive entity profile replacing 10-15 calls. Also says when not to use: for federal contracts, use usa_recipient_profile. Advises using resolve_entity if only a name is available.

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 'Delete', implying a destructive mutation, but lacks details on permissions needed, whether deletion is permanent or reversible, error handling (e.g., if the key doesn't exist), or side effects. This is a significant gap 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 no wasted words. It is front-loaded with the core action ('Delete'), making it easy to scan and understand quickly.

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 destructive nature, no annotations, and no output schema, the description is incomplete. It fails to address critical context such as what 'delete' entails (permanent removal?), authentication requirements, or what happens on success/failure. For a mutation tool with minimal structured data, this leaves the agent under-informed.

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 schema fully documents the single parameter 'key'. The description adds no additional parameter semantics beyond implying deletion targets a memory by its key, which aligns with the schema. With zero parameters needing extra explanation, a baseline of 4 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 specific action ('Delete') and resource ('a stored memory by key'), distinguishing it from sibling tools like 'recall' (likely retrieves) and 'remember' (likely creates). It precisely communicates the tool's function without being vague or tautological.

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 does not mention prerequisites (e.g., that a memory must exist to delete it), exclusions, or comparisons to siblings like 'recall' or 'remember', leaving the agent to infer usage context.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds behavioral details: fetches page, extracts title/description/key links, outputs standard markdown format. No contradictions.

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

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

Description is concise, front-loads the primary purpose, and every sentence adds value. 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?

Covers input, process, output format, and use cases. Despite no output schema, the description explains what the tool returns (single text blob). Complete for a simple 2-parameter read-only tool.

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

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

Schema coverage is 100% with descriptions for both parameters. The description does not add additional meaning beyond what the schema provides, such as format or constraints. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool generates an llms.txt file for any URL, specifying the verb (Generate) and resource (llms.txt file). It distinguishes itself from sibling tools like ai_visibility_check which have different purposes.

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

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

Provides explicit use cases: indexing a client's site, drafting for own project, auditing competitors. While it doesn't explicitly exclude alternatives, the context is clear enough for an agent to decide 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, the description discloses a key behavioral trait: rate-limited to 5 messages per identifier per day. It also implies non-destructive sending. Slightly missing details on return value or error handling, but sufficient.

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: three sentences covering purpose, usage guidelines, and rate limit. Front-loaded with key action and use cases. No redundant 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 nature of feedback submission and full schema coverage, the description covers all necessary context: what it does, when to use, what to include/exclude, and constraints. No output schema needed.

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

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

Schema descriptions cover all parameters (100% coverage) with detailed enum explanations. The description adds value by instructing on message content and excluding end-user prompt, enhancing clarity 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's purpose: 'Send feedback to the Pipeworx team.' It enumerates specific use cases (bug reports, feature requests, missing data, praise) and is distinct from sibling tools like ask_pipeworx or check_domain.

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: use for feedback types, describe in terms of Pipeworx tools/data, avoid end-user prompt verbatim. It also notes a rate limit. While no direct alternatives are given, the sibling tools serve 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?

Annotations already declare readOnly, idempotent, non-destructive. Description adds valuable context: data derived from CF analytics engine, no PII, cached 5min to 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?

Two sentences plus bulleted use cases. Front-loaded with main purpose. 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?

Given one simple parameter, no output schema, and annotations covering safety, the description covers data source, privacy, caching, and usage scenarios. Complete for an aggregation 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 100% so baseline 3. Description adds semantic guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This helps the agent choose values effectively.

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

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

Clearly states it returns top tools, packs, and call volume over a window. Specific verb and resource. Distinct from siblings as no other tool provides trending data. Gives three explicit use cases.

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?

Lists three specific use cases (discovering hot data sources, confirming canonical tool, aligning use case). Does not explicitly mention when not to use or alternatives, but context is clear enough for agent to decide.

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

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

Annotations already indicate read-only, non-destructive behavior. Description adds behavioral details: monotonicity checking, both modes' behavior, and that returns ranked opportunities with trade direction. It does not cover potential rate limits or data freshness but provides sufficient transparency 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?

Description is a single paragraph with clear logical flow: initial purpose statement, then two modes listed with explanation, and final sentence on return value. No unnecessary words; every sentence adds value.

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

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

For a tool with no output schema, the description adequately explains return type ('ranked opportunities with suggested trade direction + reasoning'). It covers both modes and their rationale. Missing specifics on output fields (e.g., probability values, arbitrage magnitude) but overall complete enough for an AI agent to invoke correctly.

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

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

Input schema already provides descriptions for both parameters (event and topic). Description adds value by explaining the two modes, giving concrete examples (e.g., 'when-will-bitcoin-hit-150k', 'Strait of Hormuz traffic returns to normal'), and clarifying when each parameter should be used. This goes beyond the schema alone.

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

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

Description clearly states verb 'Find' and resource 'arbitrage opportunities on Polymarket' with specific method 'checking for monotonicity violations'. Two modes ('event' and 'topic') are distinguished, and the description differentiates this tool from potential siblings by explaining cross-event mode catches cases single-event mode misses.

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 clear context for when to use each mode with examples (e.g., event slug for single-event, topic for cross-event). However, it does not explicitly mention when not to use the tool or compare to other sibling tools like bet_research or polymarket_edges.

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

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

Annotations already declare readOnlyHint, openWorldHint, and destructiveHint, but the description adds substantial behavioral context: it fetches price history once, groups by asset, computes model probability from FRED and coinpaprika, ranks by edge magnitude, and returns suggested trade direction. No contradictions with annotations.

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

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

The description is well-structured, front-loading the main purpose and then elaborating on methodology and use case. It is informative without being overly verbose, though minor redundancy could be trimmed.

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 that the tool returns top N ranked by edge magnitude with suggested trade direction, which is sufficient for understanding the output shape. It covers domain, inputs, and algorithm steps adequately.

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

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

Input schema has 100% description coverage for all three parameters. The description provides context on how parameters are used (e.g., 'Top N edges to return') but does not add significant new meaning beyond what the schema already specifies.

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 Polymarket markets to find those where Pipeworx data disagrees with market price, specifically for crypto-price bets. It differentiates from siblings like bet_research and polymarket_arbitrage by focusing on edge discovery for betting opportunities.

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

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

The description explicitly says it's built for the 'what should I bet on today' question and helps agents/users discover opportunities without manually paging through markets. While it doesn't provide explicit when-not-to-use or alternatives, the context implies its utility for opportunity discovery.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds behavioral context by explaining that the tool fetches prices from two venues and returns leg-by-leg prices and spreads. No contradiction and adds value beyond annotations.

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

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

The description is a single paragraph but conveys all necessary information efficiently with clear structure: purpose, context, modes, and return values. No redundant sentences.

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

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

Given the tool's complexity (cross-venue spread with two modes) and lack of output schema, the description adequately explains return values (leg-by-leg prices and spread). It is largely complete, though missing potential error handling details.

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

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

Schema description coverage is 100%, baseline 3. The description adds significant meaning by explaining the two modes, providing examples for 'kalshi_event_ticker' and 'polymarket_event_slug', and clarifying how they override topic-mapped sides.

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: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It distinguishes itself from siblings like 'polymarket_arbitrage' by specifying it is cross-venue and explains the two modes (topic and explicit).

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 by detailing two modes (topic for pre-mapped macros, explicit for custom pairings) and explains the context of price differences due to different participant pools. It does not specify when not to use, but the instructions are clear.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: retrieving memories by key, listing all memories when key is omitted, and accessing memories from current or previous sessions. It doesn't mention error handling, performance characteristics, or data persistence details, but covers the core operational behavior adequately.

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 concise with two sentences that each earn their place. The first sentence states the core functionality with parameter guidance, and the second provides usage context. There's zero waste or redundancy, and information is front-loaded appropriately.

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

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

For a simple retrieval tool with 1 parameter and 100% schema coverage but no output schema, the description provides good contextual completeness. It explains what the tool does, when to use it, and how parameters affect behavior. The main gap is lack of information about return format or error conditions, which would be helpful given no output schema exists.

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 the 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 parameter usage to the tool's purpose ('retrieve a previously stored memory by key'). This provides valuable guidance beyond the schema's technical specification.

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. The description explicitly mentions retrieving context saved earlier in sessions.

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 specifies when to omit the key parameter to list all memories versus when to include it for specific retrieval. This gives clear context for when to use this tool versus alternatives.

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

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

No annotations provided, so description carries full burden. It explains parallel fan-out to multiple sources, supported `since` formats, and return structure (structured changes, total_changes, pipeworx:// URIs). Could be improved by detailing exact return schema, but still sufficient.

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 of 3-4 sentences, front-loaded with purpose, then details. No redundant or unnecessary information. 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 no output schema and no annotations, the description provides adequate context: purpose, parameters, usage scenarios, and return format. However, lacks explicit differentiation from siblings beyond usage hint. Still sufficient for an agent to decide and invoke correctly.

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

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

Schema has 100% description coverage (baseline 3). Description adds value by detailing `since` formats ('7d', '30d', etc.) with examples, explaining `value` as ticker or CIK, and confirming only 'company' for `type`. This goes beyond schema descriptions.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It specifies the resource (entity changes) and action (get changes since a time), with detailed behavior for type='company'. This distinguishes it from sibling tools like entity_profile or ask_pipeworx.

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 'Use for "brief me on what happened with X" or change-monitoring workflows.' This provides clear when-to-use guidance. However, no explicit when-not-to-use or alternative tools mentioned, but the positive guidance is strong 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?

With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it explains persistence differences (authenticated vs. anonymous sessions with 24-hour limit) and the cross-tool call context. However, it doesn't mention potential limitations like storage size, rate limits, or error conditions.

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

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

The description is perfectly front-loaded with the core purpose in the first sentence, followed by usage context and behavioral details. Every sentence earns its place with no wasted words, making it highly efficient and scannable.

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 2-parameter tool with no annotations and no output schema, the description provides good context about persistence behavior and usage scenarios. However, it doesn't explain what happens on successful storage (confirmation? stored value?) or potential error cases, leaving some gaps in 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?

With 100% schema description coverage, the schema already fully documents both parameters. The description doesn't add any additional parameter semantics beyond what's in the schema properties, so it meets the baseline for high schema coverage without providing extra value.

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 verb ('store') and resource ('key-value pair in your session memory'), distinguishing it from siblings like 'recall' (retrieve) and 'forget' (remove). It explicitly identifies the tool's function as persistent storage with session 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 about when to use it ('save intermediate findings, user preferences, or context across tool calls'), but doesn't explicitly mention when NOT to use it or name alternatives like 'forget' for removal or 'recall' for retrieval. The guidance is helpful but lacks explicit 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?

With no annotations, the description carries the full burden. It discloses inputs, outputs, and the fact that it's a single call lookup, but does not mention any potential side effects, auth requirements, or error handling. This is acceptable for a read-like operation but could be more detailed.

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

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

The description is two sentences, front-loaded with the core purpose, followed by specific details. Every sentence adds value with no redundancy. Extremely concise.

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

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

Given the simple tool with two required parameters and no output schema, the description adequately explains what it does and returns. It could mention behavior on unresolved entities, but overall complete for the 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 coverage is 100%, so baseline is 3. The description adds value by explaining the input formats (ticker, CIK, name) with examples, and lists the return fields, enhancing understanding beyond the schema.

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

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

The description clearly states the verb 'resolve' and the resource 'entity to canonical IDs', and distinguishes from siblings like ask_pipeworx or check_domain by specifying the exact output and use case.

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

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

The description provides clear context on when to use the tool (e.g., for company entities with ticker, CIK, or name input) and mentions that it replaces 2–3 lookup calls. It lacks explicit when-not-to-use guidance but is 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 declare readOnly, openWorld, idempotent hints. Description adds that it internally calls ai_visibility_check for each entity, ranks results, and surfaces most/least recognized. No contradictions.

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

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

Two sentences, front-loaded with purpose, then details. Every sentence adds value without redundancy.

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

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

For a 4-parameter tool with no output schema, description fully explains behavior (probe, rank, output fields like score/confidence/signal density). No gaps for decision-making.

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. Description adds contextual value: first entity is subject, rest are competitors, and notes that models parameter supports specific providers.

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?

Specifies verb 'Compare', resource 'AI visibility across multiple entities', and method 'probes each entity with ai_visibility_check, ranks by score'. Distinguishes from sibling 'ai_visibility_check' which checks a single 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?

States clear use case: 'competitive AI-marketing audits' and gives an example question. Implies when not to use (single entity should use ai_visibility_check) but does not explicitly list 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, the description carries the full burden. It lists return values (verdict, structured form, actual value, citation, percent delta) and mentions the data source. However, it lacks details on failure modes, authorization needs, or how it handles claims outside the specified scope.

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

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

The description is concise, with four sentences that each add value. It starts with the primary function, then specifies scope, return values, and efficiency benefit. No redundant or unnecessary information.

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

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

Given the tool has one simple parameter and no output schema, the description fully explains the tool's purpose, scope, return values, and efficiency gains. It is complete for an agent to understand when 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?

The input schema has 100% coverage with description for the single 'claim' parameter. The tool description adds meaningful context beyond the schema by specifying the supported claim types (company-financial for public US companies) and the process it replaces, which helps the agent understand the parameter's purpose better.

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 fact-checks natural-language claims against authoritative sources, specifically company-financial claims for public US companies. It distinguishes from siblings like resolve_entity and validate_email by focusing on 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 notes it replaces 4-6 sequential agent calls, implying it should be used instead of a multi-step process. It also specifies the domain (company-financial claims for public US companies via SEC EDGAR + XBRL), providing clear context. However, it does not explicitly state when not to use it or mention 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 lists the types of checks performed (formatting, DNS, disposable, alias), which is helpful, but lacks critical details: it doesn't specify whether this is a read-only operation, what the output format looks like, error handling, rate limits, or authentication requirements. For a validation tool with zero annotation coverage, this 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 is a single, efficient sentence that front-loads the core purpose ('Validate an email address') and enumerates the specific checks without redundancy. Every word earns its place, making it highly 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 the tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers what the tool does but lacks output details, error handling, and differentiation from siblings. With no output schema, the description should ideally hint at return values, but it doesn't, leaving gaps in 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, with the 'email' parameter clearly documented. The description adds no additional parameter semantics beyond what the schema provides (e.g., it doesn't clarify email format expectations or validation rules). Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: validating email addresses with specific checks (formatting, DNS, disposable status, alias detection). It uses specific verbs ('validate', 'check') and identifies the resource ('email address'). However, it doesn't explicitly differentiate from the sibling tool 'check_domain', which might have overlapping functionality.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'check_domain'. It mentions what the tool does but offers no context about appropriate use cases, prerequisites, or exclusions. This leaves the agent without direction on tool selection.

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