Tinder Bio

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

Annotations declare readOnly=true, idempotent=true, non-destructive, so the safe-read behavior is covered. Description adds details: per-model results, scoring, and that API key is passed through 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?

Two sentences with no wasted words. Front-loaded with the main action and output. Every sentence adds value, and the structure is 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?

The tool has moderate complexity (multiple models, optional keys, scoring). Description covers the return structure (per-model score, confidence, signals, raw_response + combined view). Missing error handling or rate limit notes, but acceptable for a read-only tool with good annotations.

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

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

Schema coverage is 100% but description adds meaning: explains default model for 'models' param, clarifies '_apiKey' is for Anthropic and is passed directly, and 'context' helps disambiguate. This goes beyond the schema's brief 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?

Description uses specific verb 'Probe' and resource 'LLMs' with output 'visibility score (0-100)'. Distinguishes from sibling tools like 'scan_competitor_ai_presence' by focusing on general knowledge probing and scoring across models.

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

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

Explicitly states use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Mentions default model and BYO key for Anthropic. Lacks explicit when-not-to-use but provides sufficient context for selection.

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

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

Describes behavior as picking the right tool and filling arguments, which goes beyond annotations (none provided). However, does not detail limitations (e.g., data freshness, latency, or when it might fail). Still, the description is honest about the autonomous routing.

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 at 4 sentences. Front-loaded with the core purpose. Examples are helpful but could be trimmed to 1-2. 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 the tool's simplicity (1 param, no output schema) and rich annotations (none), the description is largely complete. It explains the autonomous behavior and gives examples. Could mention that it may not handle domain-specific jargon or that answers depend on available tools.

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 only one parameter 'question'. The description adds context by explaining it's natural language and providing examples, but the schema already describes it well. 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?

Clearly states the tool accepts a plain English question and returns an answer from the best data source. Distinguishes itself from siblings by acting as a natural language router, unlike others like discover_tools or tinder_bio_generate.

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

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

Explicitly tells when to use: 'just describe what you need', implying it's for broad questions. Provides concrete examples, making it clear this is the go-to for quick answers without needing to browse other 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?

Annotations (readOnlyHint, openWorldHint) are consistent. Description adds detail about fan-out logic, classification, and output structure 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?

Well-structured paragraph with clear progression: action, inputs, processing, use cases. Every sentence adds value; could be slightly more concise but not verbose.

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

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

Given complexity (no output schema), description covers input formats, core processing, and output shape. Lacks details on evidence packet content but sufficient for an agent.

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

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

Schema covers all parameters; description adds value by explaining input formats (slug, URL, question text) and depth options (quick vs thorough with source counts).

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 specifies verb ('Research'), resource ('Polymarket bet'), and unique processing (classification, fan-out, comparison). Distinguishes from siblings like ask_pipeworx which is more general.

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

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

Explicitly lists use cases ('should I bet on X?', etc.) and contrasts with alternative approaches. Lacks explicit 'when not to use' but provides sufficient guidance.

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

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

With no annotations, description transparently discloses it returns paired data and resource URIs from specific sources (SEC EDGAR, FDA, clinical trials). No mention of side effects or auth needs, but sufficient for a read-only comparison 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?

Two sentences total: first states purpose and types, second adds return info and efficiency gain. No unnecessary words; every sentence earns its place.

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

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

Given the complexity of comparing 2–5 entities and no output schema, description adequately covers data sources and return types. However, lacks details on 'paired data' format and example URIs, which could further enhance 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?

Beyond 100% schema coverage, description adds concrete examples for each parameter (e.g., tickers vs drug names) and clarifies the allowed entity types and value formats, significantly aiding parameter understanding.

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

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

Description uses specific verb 'Compare' and resource 'entities', clearly differentiating from siblings like 'resolve_entity'. Defines two entity types with distinct data fields, which is precise and unambiguous.

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

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

Explicitly states it replaces 8–15 sequential calls, implying efficiency for bulk comparisons. Clear context for when to use (comparing multiple companies/drugs), but lacks explicit 'when not to use' or alternatives beyond the sibling list.

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, description must carry behavioral burden. It discloses that the tool returns 'most relevant tools with names and descriptions' and that it's for a catalog of 500+ tools. However, it doesn't specify whether the tool is read-only or has side effects, though search is inherently safe. Slight gap but still good.

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

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

Three sentences, each earning its place: purpose, return value, and when to use. 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 no annotations, the description adequately covers purpose and usage. Missing details like exact result format or pagination, but for a search tool with simple schema, this is sufficient.

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 little beyond schema: it repeats the query example pattern and mentions default/max limit. No extra semantic value beyond what the schema provides.

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

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

Description clearly states the tool searches a tool catalog by natural language query and returns relevant tool names and descriptions. The verb 'search' and resource 'tool catalog' are specific, and it distinguishes from siblings by being the only search/discovery tool among them.

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 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear when-to-use guidance and implies the alternative is browsing or guessing, making it a recommended first step.

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?

Given no annotations, the description discloses that the tool returns pipeworx:// citation URIs and bundles multiple sources. However, it lacks details on potential errors, authentication requirements, or performance implications, which would be useful for an 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 two sentences, well-structured with purpose first, then details and alternatives. Every sentence adds value, no redundancy.

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

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

For a complex tool bundling multiple data sources, the description explains what is returned (citation URIs) and provides alternatives. It lacks details on the return structure or error handling, but overall 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% and the description adds significant context: 'type' is limited to 'company', and 'value' explains allowed formats (ticker or CIK) with a note that names are not supported, directing to resolve_entity.

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

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

The description clearly states the tool returns a full profile of an entity across multiple data sources (SEC, XBRL, USPTO, GDELT, LEI) for type='company'. It distinguishes from sibling tool 'usa_recipient_profile' and implies it replaces many sequential 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?

The description explicitly says when to use (comprehensive profile) and when not (federal contracts, use usa_recipient_profile). It also provides guidance to use resolve_entity when 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?

No annotations are provided, so the description must carry the behavioral burden. It states that the operation is deletion, which is inherently destructive, but does not mention reversibility, confirmation, or side effects. It is adequate but not 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?

A single sentence of 6 words, front-loaded with the verb. Every word earns its place. No 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?

The tool has one simple required parameter, no output schema, and no nested objects. The description is sufficient for basic understanding, but could mention what happens if the key does not exist or if the deletion is successful.

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% (the only parameter 'key' is described as 'Memory key to delete'). The description adds no extra meaning beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description 'Delete a stored memory by key' is a specific verb+resource combination. It clearly states what action is performed and on what, and distinguishes it from siblings like 'remember' (store) 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 usage by stating the action, but does not explicitly mention when to use this tool versus alternatives like 'remember' or 'recall'. However, given the sibling names, the distinction is clear without explicit guidance.

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

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

Annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false) already signal safety. The description adds that it fetches the page, extracts title/description/key links, and outputs a text blob, which aligns with annotations and provides operational context without contradiction.

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

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

The description is three sentences, front-loaded with the main action. Every sentence earns its place by covering purpose, process, output location, and use cases, with no superfluous content.

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

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

For a simple read-only tool with 2 parameters and no output schema, the description covers the inputs, process, output format, and placement (site-root/llms.txt). It lacks error handling details but is sufficient given the low 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% with clear descriptions for both parameters (url and max_links). The description does not add new semantic meaning beyond what the schema already provides, so it meets the baseline of 3.

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

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

The description starts with 'Generate a production-ready llms.txt file for any URL so AI crawlers can index the site cleanly', which is a specific verb+resource. It details the process (fetch, extract, emit) and output, distinguishing it clearly from siblings without explicit 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 lists three explicit use cases (getting a client's site indexed, drafting for own project, auditing competitor's AI visibility), providing clear context for when to use. However, it does not specify when not to use or contrast with sibling tools like scan_competitor_ai_presence.

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

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

No annotations are provided, so the description carries the full burden. It discloses the rate limit of 5 messages per identifier per day and gives content rules. This is sufficient for a feedback submission tool with no destructive or complex 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 three sentences long, front-loaded with the purpose, and every sentence adds value. There is no redundancy or fluff.

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

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

For a tool with 3 parameters and no output schema, the description covers the purpose, usage guidelines, rate limiting, and content rules. It does not describe return values, but that is unnecessary for a feedback submission tool. It is complete enough for an agent to use 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?

While the input schema already describes all parameters (100% coverage), the description adds extra value by explaining how to structure the message (describe what was tried, avoid verbatim prompts) and clarifying enum values for the type parameter. 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 the verb 'Send feedback' and the resource 'to the Pipeworx team', listing use cases like bug reports, feature requests, missing data, or praise. It distinguishes this tool from siblings by focusing solely on feedback, unlike others that perform searches or tool discovery.

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

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

The description explicitly states when to use the tool (bug reports, feature requests, etc.) and provides guidelines on what to include (Pipeworx tools/data context) and exclude (end-user's prompt verbatim). It also mentions rate limits. However, it does not explicitly state when not to use it or list alternatives, though no alternative exists among 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?

Adds caching behavior, data source (CF analytics-engine), privacy (no PII), and the self-aggregating nature beyond annotations. No contradiction.

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

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

Well-structured with purpose first, then use cases, then technical details. Slightly verbose but no wasted 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?

For a simple tool with one parameter and no output schema, the description fully explains return content, caching, and usage context.

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

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

The description adds context to the 'window' parameter by explaining the effect of short vs long windows, which is not 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 returns trending tools and packs based on recent call volume, with specific windows. It distinguishes from siblings by focusing on trending signals.

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

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

Explicitly lists three use cases for when to use the tool, but does not mention when not to use or alternatives among siblings. Context is clear enough.

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

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

Annotations already mark the tool as read-only and non-destructive. The description adds valuable behavioral context: it searches Polymarket, groups related markets, checks monotonicity, and returns ranked opportunities with reasoning. No contradictions. The description goes beyond annotations by detailing the internal logic and output structure.

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

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

The description is concise yet comprehensive. It starts with the primary purpose, then clearly presents two modes in separate bullet-like sentences, followed by a rationale for cross-event mode and a summary of returns. Every sentence provides essential information with no filler.

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 (two modes, cross-event search), the description covers all necessary context. Input schema covers 100% of parameters, annotations cover safety, and the description explains output (ranked opportunities with trade direction and reasoning). No output schema exists, but the description provides sufficient expectation of returns. The tool's logic and when each mode applies is fully explained.

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?

Both parameters are fully described in the schema (100% coverage). The description adds extra meaning by explaining the mode each parameter corresponds to and giving usage examples. This elevates understanding beyond the plain schema descriptions, such as clarifying that 'topic' triggers a cross-event search.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations. It names two modes ('event' and 'topic') with distinct scopes, and the action verb 'find' plus resource 'arbitrage opportunities on Polymarket' is specific. It naturally distinguishes from sibling tools like 'polymarket_edges' which likely explore edges.

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

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

The description explicitly explains when to use each mode: 'event' for a single event's child markets, 'topic' to catch cross-event cases where each cutoff is a separate event. It gives concrete examples (e.g., 'Strait of Hormuz…') and explains why cross-event mode is needed. No explicit 'do not use' but the guidance is clear enough for correct selection.

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

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

The description details the read-only behavior, data sources (FRED, coinpaprika), computation steps (grouping, fetching price history once, model probability), and ranking. Annotations (readOnlyHint=true) are consistent, and the description adds rich behavioral 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 informative but slightly verbose. It is well-structured with a clear problem statement, technical details, and intended use. Could be slightly trimmed without losing value.

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

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

Given no output schema, the description specifies return values (top N ranked by edge magnitude with trade direction). It fully explains the model scope and limitations (V1, crypto-price bets), making the tool self-contained.

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

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

Schema description coverage is 100%, so baseline is 3. The description does not add additional meaning beyond schema definitions but provides context for the parameters within the tool's purpose.

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, computes edge using Pipeworx data, and returns ranked opportunities. It distinguishes from siblings like 'polymarket_arbitrage' by focusing on edge discovery.

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 targets the 'what should I bet on today' use case, implying when to use. It lacks explicit exclusions or alternatives but provides clear context for application.

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

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

Annotations indicate read-only, idempotent, non-destructive behavior, and open-world assumption. The description adds behavioral details about the two modes and the return structure, including leg-by-leg prices and spreads. 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 and concise, presenting the purpose, modes, and return values in a clear, scannable manner. It avoids unnecessary detail while being informative.

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

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

Given the absence of an output schema, the description adequately explains the output format (leg-by-leg prices in probability, spread in percentage points). Parameters and modes are fully covered, providing a complete picture for the agent.

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

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

The input schema covers all three parameters with descriptions. The tool description enhances understanding by explaining how 'topic', 'kalshi_event_ticker', and 'polymarket_event_slug' interact, including override behavior. Schema coverage is 100%, so baseline is 3; the description adds 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 tool's purpose: calculating cross-venue spreads between Kalshi and Polymarket for the same resolving question. It specifies the two operational modes (topic shortcuts and explicit tickers) and what it returns, making it easy to understand the tool's function.

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

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

The description explains when to use the tool (to identify arbitrage signals due to venue price differences) and details the two modes of operation. It does not explicitly state when not to use it or compare it to sibling tools like 'polymarket_arbitrage', but the context is sufficiently clear.

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

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

No annotations are provided, so the description carries the full burden. It discloses that omitting the key lists all memories, which is a behavioral trait. However, it does not mention if retrieval is read-only, any side effects, or persistence across sessions beyond 'earlier in the session or in previous sessions'. Adequate but not exhaustive.

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

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

Two sentences with no fluff. The first sentence states the core functionality; the second provides usage context. Every sentence earns its place.

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

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

For a simple tool with one optional parameter and no output schema, the description is complete enough. It explains both modes (by key and list all) and provides context about retrieving saved information. No major 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?

Schema description coverage is 100% (the one parameter 'key' is described). The description adds the behavioral detail that omitting the key lists all memories, which is not in the schema. This adds value beyond the schema, so score is above baseline 3.

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

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

The description clearly states the tool retrieves a memory by key or lists all memories, with a specific verb ('retrieve') and resource ('stored memory'). It distinguishes itself from 'remember' and 'forget' by focusing on retrieval.

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 this tool ('to retrieve context you saved earlier') and implies the alternative ('omit key' to list all). It does not explicitly mention when not to use it or contrast with siblings like 'remember' or 'discover_tools', but the context is clear.

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

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

No annotations are provided, so the description carries full burden. It explains the parallel fan-out behavior, accepted input formats for 'since', and the return structure (structured changes, count, URIs). It lacks details on error handling or latency, but overall provides sufficient behavioral context.

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

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

The description is concise with no wasted words. It front-loads the core purpose, then efficiently explains fan-out, input formats, output structure, and usage suggestion. Every sentence adds essential information.

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

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

Given the tool's moderate complexity, the description covers the key aspects: fan-out to sources, input parameters, output structure. It lacks discussion of error responses or authentication requirements, but it is largely complete for its intended use case.

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

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

Schema coverage is 100% with good parameter descriptions. The description adds value by clarifying the 'since' formats (ISO date vs relative), the 'value' formats (ticker or CIK), and the 'type' enum. This extra context compensates for the high schema coverage, justifying a score above baseline 3.

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

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

The description clearly states the tool's function: 'What's new about an entity since a given point in time.' It specifies the supported entity type (company) and details the fan-out to multiple sources (SEC, GDELT, USPTO), distinguishing it from sibling tools like entity_profile or compare_entities.

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

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

The description explicitly tells when to use the tool: 'Use for "brief me on what happened with X" or change-monitoring workflows.' While it does not explicitly state when not to use or mention alternatives, the context with sibling tools implies appropriate usage.

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

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

The description discloses persistence behavior (authenticated vs 24-hour TTL), which adds value beyond the absence of annotations. However, it does not mention idempotency, size limits, or whether overwriting is allowed. With no annotations, this is adequate but not exhaustive.

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

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

The description is two concise sentences, each adding distinct value: the first defines the action, the second explains use cases and persistence. No filler 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 simple key-value operation with 100% schema coverage and no output schema, the description adequately covers purpose, usage, and persistence. It lacks explicit mention of return value or error cases, but these are minimal concerns 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%, so the schema already documents both parameters. The description adds no extra semantic detail beyond what the schema provides (e.g., key and value descriptions). Baseline 3 is appropriate as the description does not enhance parameter understanding.

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

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

The description explicitly states 'Store a key-value pair in your session memory', which is a specific verb+resource combination. It clearly differentiates from sibling tools like 'recall' (retrieve) and 'forget' (delete), making the purpose unambiguous.

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

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

The description provides clear usage guidance: 'save intermediate findings, user preferences, or context across tool calls'. It distinguishes between authenticated and anonymous sessions, which is useful context, but does not explicitly mention when not to use it or suggest 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 describes functionality and output but lacks information on error handling, rate limits, case sensitivity, or side effects. Adequate for a read-only lookup, but could be more transparent.

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 purpose, and every sentence adds value. No wasted words.

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

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

For a simple tool with 2 parameters and no output schema, the description covers inputs, output format, and benefit. It lacks mention of error scenarios or case sensitivity, but is otherwise complete.

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

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

Schema coverage is 100% with parameter descriptions. The description adds value by providing concrete examples for 'value' (ticker, CIK, name) and specifying the current type limitation, going 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', the resource 'entity to canonical IDs', and the context 'across Pipeworx data sources in a single call'. It distinguishes from siblings by noting it replaces 2-3 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?

The description provides specific usage details: v1 supports only type='company', accepts ticker/CIK/name, and returns canonical IDs. It implies efficiency but does not explicitly state when not to use or contrast with 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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds value by explaining that the tool probes each entity via ai_visibility_check and aggregates results into a ranked list with score, confidence, and signal density. It does not contradict annotations and provides detail beyond what annotations convey.

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

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

The description is three sentences long, front-loaded with the main purpose, and each sentence contributes unique information: summary of action, explanation of process, and a concrete example. There is no redundancy or extraneous text.

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

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

Given the tool has 4 parameters (1 required) and no output schema, the description adequately covers the return format (ranked list with score, confidence, signal density) and behavior (probing each entity). It provides sufficient information for an agent to use the tool correctly without additional context.

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

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

Schema coverage is 100%, but the description adds meaningful context: it specifies that the first entity is treated as the 'subject' for narrative, explains the models parameter with supported values and API key requirement, and provides an example for the context parameter. This enhances understandability beyond the schema descriptions.

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

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

The description clearly states the verb 'compare' and resource 'AI visibility', and distinguishes the tool from siblings like ai_visibility_check (which acts on a single entity) and compare_entities by specifying that it probes multiple entities and ranks them. It also includes a concrete use case example, leaving no ambiguity about its function.

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

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

The description provides a clear use case ('competitive AI-marketing audits') and an example query. However, it does not explicitly state when not to use this tool or mention alternative siblings (e.g., ai_visibility_check for single entities). The context is clear but lacks explicit exclusion 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?

Annotations are empty, so the description bears full responsibility for behavioral disclosure. It only says 'red flags embedded' hinting at output content, but doesn't clarify side effects, required permissions, or any constraints. The tool is likely harmless generation, but the description is insufficient.

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

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

Two sentences with no wasted words. The second sentence, while humorous, adds a behavioral hint. It is concise and front-loaded, but the joke may slightly reduce professionalism.

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 8 parameters, many with enums, and no annotations, the description is too sparse. While an output schema exists (reducing need to explain return values), the description fails to provide usage context, parameter guidance, or behavioral clarity. It is far from 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?

With only 13% schema description coverage, the description should compensate by explaining key parameters. Instead, it offers no parameter insights. Parameter names like 'energy' and 'situation' have enums that are cryptic without further explanation. The description adds no semantic 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?

The description clearly states the tool generates a dating profile bio, which is specific and distinguishes it from unrelated sibling tools like ask_pipeworx or compare_entities. The humorous 'red flags' phrase adds a hint of behavior but doesn't detract from clarity.

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 on when to use or not use this tool, no prerequisites, and no mention of alternatives. The description assumes the user knows to call it when they need a bio, but provides no context for decision-making.

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; description carries full burden. It details return values (verdict, citations, delta) and scope (v1, SEC EDGAR+XBRL). However, it does not disclose error handling, rate limits, or whether the tool modifies state (likely not, but not stated).

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 extremely concise, using one informative sentence with bullet-like enumeration of return fields. No wasted words; every part adds value.

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

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

Given complexity (multi-step internal processing) and no output schema, description adequately explains input, domain, return fields, and scope limitations. Could mention error cases or non-US companies, but sufficient for agent 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?

Only one parameter 'claim' with 100% schema description coverage. Description adds natural-language examples and mentions extraction of structured form, but these are complementary rather than essential beyond the schema. Baseline 3 for high coverage is appropriate.

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

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

Description clearly states the tool fact-checks natural-language claims, specifies domain (company-financial for public US companies), and lists return verdict types. It distinguishes itself from siblings by being a specialized fact-checking tool, not a generic ask or data query tool.

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

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

Description explains the tool replaces 4-6 sequential agent calls, implying efficiency for claim verification. It restricts usage to company-financial claims (revenue/net income/cash for public US companies), but does not explicitly mention when not to use it or alternative tools among siblings.

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