Tarot Draw

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

Annotations already declare read-only and idempotent behavior. The description adds return structure details and cost transparency for Anthropic, which is valuable 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?

Four sentences, front-loaded with purpose, no filler. Each sentence adds unique value: purpose, model details, returns, use cases.

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

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

Given no output schema, the description explains return format adequately. Covers purpose, usage, cost, and use cases. Missing scoring methodology, but overall sufficient for an API probe 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 baseline is 3. The description adds minor context about default model and API key usage, but does not significantly enhance parameter understanding beyond the schema.

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

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

The description clearly states it probes LLMs and returns visibility scores, with specific use cases. However, it does not explicitly distinguish this tool from siblings like 'scan_competitor_ai_presence', so misses the highest bar.

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?

Gives clear guidance on default vs. Anthropic model, including cost implications, and lists use cases. Lacks explicit alternatives or when not to use, but the context is strong.

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 discloses that the tool selects the best data source and fills arguments automatically, which implies some decision-making but does not detail limitations or behaviors like latency, scope of data sources, or potential errors. Still, it is mostly transparent for a high-level 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 concise: three sentences plus examples. It is front-loaded with the main purpose and efficiently adds examples for clarity. Every sentence adds value.

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

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

Given the tool's simplicity (one parameter, no output schema), the description is sufficiently complete. It explains the input and the behavior. However, it lacks details on what happens if the tool cannot find an answer, but that is a minor gap for a high-level query tool.

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

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

Schema description coverage is 100%, and the description adds meaning beyond the schema by explaining that the question should be in natural language and providing examples of what constitutes a valid request. This compensates for the schema's minimal 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 answers questions in plain English by selecting the best data source and filling arguments. It distinguishes itself from sibling tools by abstracting away tool selection and schema learning, which is a unique value proposition.

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 (when you need an answer from data without browsing tools) and provides examples. It does not explicitly exclude cases or mention alternatives, but the sibling tools are different enough (e.g., discover_tools, remember) that the usage 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?

Despite annotations already indicating readOnlyHint=true and destructiveHint=false, the description adds significant behavioral context: it resolves the market, classifies the bet type, fans out to relevant data packs, and returns a structured output. 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 moderately sized and front-loaded with the main purpose. While every sentence adds value, it could be slightly trimmed without losing clarity. Still, it is well-structured and effective.

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

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

Given no output schema, the description clearly explains the output: an evidence packet plus market-vs-model comparison. It covers the tool's functionality, inputs, and role in the product. The tool has only 2 parameters, both well-documented, so no gaps.

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

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

The description adds meaning beyond the input schema: for 'market', it explains it can be a slug, URL, or question text; for 'depth', it clarifies the enum values 'quick' vs 'thorough' and the default. Since schema coverage is 100%, the description still enhances 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 the tool's purpose: research a Polymarket bet by pulling relevant Pipeworx data. It specifies input types (slug, URL, question text) and output (evidence packet plus market-vs-model comparison), clearly distinguishing it from sibling tools as the dedicated bet research tool.

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

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

The description provides concrete usage examples: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It implies when to use this tool over alternatives by noting it is the core demo product and that agents using it convert better.

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 discloses return format (paired data + URIs) and data sources (SEC EDGAR, FDA), but does not mention rate limits, auth, or other behavioral traits. Adequate but not rich.

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

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

Three sentences, front-loaded with purpose. No redundant information. Every sentence is valuable.

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 explains return format, data sources, and efficiency benefit. Covers key aspects for a comparison tool. Missing edge cases 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 coverage is 100%, baseline 3. Description adds meaning by detailing the specific fields returned per 'type' (e.g., revenue for company, adverse events for drug), which is 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?

Description clearly states verb 'compare', resource 'entities', and specifies data fields per type. Distinguishes from siblings like resolve_entity and ask_pipeworx by focusing on side-by-side comparison.

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

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

Description provides context for when to use (side-by-side comparison, efficient replacement for multiple calls). Does not explicitly state when not to use or name alternatives, 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?

Description clearly states it returns 'most relevant tools' and indicates it performs a search, which implies no side effects. No annotations provided, so the description carries full burden; it does well but could mention if it modifies state or has auth needs.

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 and then usage guidance. No wasted 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?

Given simplicity (2 params, no output schema, no nested objects), the description is nearly complete. It explains what the tool does, when to use it, and how to call it. Could optionally mention that it searches tool descriptions, but not necessary.

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

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

Schema coverage is 100%, so baseline is 3. The description provides a concrete example query ('analyze housing market trends') that adds meaning beyond schema. For the limit parameter, description doesn't add extra semantics beyond schema's default and max, but overall 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?

Description clearly states it searches the Pipeworx tool catalog by describing what you need, returning relevant tools with names and descriptions. It uses a specific verb ('search') and resource ('tool catalog'), and distinguishes from siblings by its unique discovery purpose.

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 it's a prerequisite before 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?

With no annotations, the description bears full responsibility for behavioral disclosure. It clearly states what data the tool returns and that it outputs 'pipeworx:// citation URIs.' It implies a read-only, batched operation. However, it does not explicitly mention idempotency, side effects, or error conditions, though for a query tool this is minor. The description is transparent enough for safe usage.

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, using four sentences with no fluff. The first sentence states the overall purpose, followed by a list of included data, then output specification, and finally an alternative for a specific case. Structure is front-loaded and efficient.

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

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

For a tool with two parameters and no output schema, the description provides a thorough overview of what the profile includes, the output format, and when to use an alternative. It covers the essential context an agent needs to invoke the tool 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 covers 100% of parameters with descriptions. The description adds no new parameter-level information beyond what the schema provides (e.g., 'value: Ticker or zero-padded CIK'). Since schema coverage is high, the baseline score of 3 is appropriate; 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 clearly states that this tool returns a 'Full profile of an entity across every relevant Pipeworx pack in one call' and lists the specific data sources (SEC filings, revenue, patents, news, LEI). It distinguishes itself from sibling tools by noting that for federal contracts one should call usa_recipient_profile, and indirectly from resolve_entity by stating names are not supported. The verb+resource is specific and leaves no ambiguity.

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 not to use this tool: 'For federal contracts call usa_recipient_profile directly (too slow to bundle).' It also advises using resolve_entity first if only a name is available. This clearly helps the agent decide between 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 must cover behavioral traits. It says 'Delete' implying destructive action, but does not mention irreversibility, authorization needs, or what happens if key doesn't exist.

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: one sentence with essential info. No wasted words, but could be slightly improved by front-loading the key parameter importance.

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?

Tool is simple (1 param, no output schema), but description omits behavioral details (e.g., idempotency, error handling) that would help an agent use it correctly.

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

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

Schema coverage is 100% (1 parameter described in schema). Description adds no additional meaning beyond schema, so baseline 3 is appropriate.

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

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

The description clearly states the action ('Delete'), the resource ('stored memory'), and the identifier ('by key'). It distinguishes itself from sibling tools like 'recall' (retrieve) and 'remember' (store).

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 the tool is for deleting memories, but does not explicitly state when to use it vs. other tools or any prerequisites (e.g., memory must exist). Sibling tools are not mentioned.

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, idempotentHint, destructiveHint) already indicate safety. The description adds process details: fetching, extracting title/description/key links, and emitting standard markdown. No contradictions, and it enriches understanding 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 four sentences, each serving a clear purpose: purpose, process, output, use cases. No redundant words, front-loaded with key information. Very efficient.

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

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

Given no output schema, the description fully explains output as a 'single text blob ready to drop at site-root/llms.txt'. It covers input, process, output, and use cases, leaving no gaps for this low-complexity 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?

Input schema has 100% coverage for both parameters (url and max_links) with descriptions. The description mentions 'key links' and 'link entries' but does not add additional semantic meaning beyond the schema. Baseline 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 a production-ready llms.txt file for any URL, specifying the actions (fetch, extract, emit) and output format. It distinguishes itself from sibling tools which are domain-specific or otherwise unrelated.

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 use cases: getting a client's site indexed, drafting for own project, or auditing competitor visibility. It does not mention when not to use or direct alternatives, but the clear context suffices.

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 discloses the rate limit and that the tool is 'Free.' While it doesn't detail backend behavior (e.g., who receives feedback, how it's stored), for a simple feedback tool this is adequate. No contradiction with annotations.

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

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

The description is three sentences, each serving a distinct purpose: purpose, usage guidance, and rate limit. It is front-loaded with the key action and use cases, making it easy to parse 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 simplicity (feedback submission), the description covers all essential aspects: purpose, what to include/avoid, and constraints (rate limit). It is complete for an agent to decide if and how to invoke this tool.

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

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

Schema description coverage is 100%, with all parameters well-described. The description adds extra context beyond schema (e.g., 'do not include the end-user's prompt verbatim' and the rate limit), which helps an agent craft appropriate messages.

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 purpose ('Send feedback to the Pipeworx team') and explicitly lists use cases (bug reports, feature requests, missing data, praise). It distinguishes itself from sibling tools (e.g., ask_pipeworx, compare_entities) by being the designated feedback channel.

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

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

The description provides clear usage context: it tells what to include (what was tried in terms of Pipeworx tools/data) and what not to include (end-user prompt verbatim). It also specifies the rate limit (5 messages per day). Although it doesn't explicitly state when not to use it, the purpose is sufficiently scoped.

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

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

Beyond annotations (readOnlyHint, openWorldHint, etc.), description adds that data is derived from CF analytics-engine, no PII, just counts, and cached 5min-1h depending on window. No contradiction with annotations.

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

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

Three tightly written sentences: first defines output, second lists use cases, third adds technical details. No filler, front-loaded with core 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?

For a read-only, idempotent tool with one parameter and no output schema, the description fully explains return values, caching, and privacy. No gaps.

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

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

Schema coverage is 100% with a single parameter 'window'. Description adds context that shorter windows surface hot items and longer windows show steady-state demand, adding value beyond the schema's enum values.

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

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

Description states the tool returns top tools, top packs, and total call volume over a recent window, with clear window options. It distinguishes from siblings by focusing on trending data, which is unique among the listed sibling tools.

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

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

Lists three specific use cases (discovering hot data sources, confirming canonical choice, seeing alignment) but does not mention when not to use or provide alternatives. The guidelines are clear and helpful, but lack 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?

Annotations (readOnlyHint, openWorldHint) already indicate safety. The description adds value by detailing the internal steps: walking child markets, extracting dates/thresholds, sorting, and reporting pairs. 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 a single paragraph that explains the concept, provides an example, and states input and output format. It is front-loaded and efficient, though slightly lengthy.

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

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

The description fully explains the tool's purpose, logic, input, and output structure (list of objects with specific fields). No output schema exists, so the description compensates 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?

Schema coverage is 100% and the schema already describes the 'event' parameter as a slug or URL. The description reiterates this without adding new semantic detail beyond what is 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 finds arbitrage opportunities by checking monotonicity violations, with a specific example and the input (event slug/URL). It distinguishes itself from sibling tools like bet_research and validate_claim by focusing on arbitrage detection.

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

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

The description explains when to use the tool (for events with multiple date/threshold markets) and the underlying logic, 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?

Annotation readOnlyHint, openWorldHint, destructiveHint are consistent. The description adds valuable behavioral details: uses a lognormal model, fetches price history once, ranks by edge magnitude, provides trade direction. No contradictions.

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

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

The description is front-loaded with the main purpose and provides necessary details. It is slightly verbose but well-structured and each sentence adds value.

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

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

Given no output schema, the description sufficiently explains the return value (top N ranked by edge magnitude with trade direction). The model and process are adequately described for the tool's 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 description coverage is 100%, so baseline 3 is appropriate. The description does not add extra meaning beyond the schema's parameter descriptions, which are already clear.

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

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

The description clearly states the tool scans high-volume Polymarket markets and identifies those with largest disagreement between Pipeworx data and market price. It uses specific verbs and distinguishes from siblings like polymarket_arbitrage.

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

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

The description explicitly frames the tool for the 'what should I bet on today' question and explains its advantage over manual browsing. It does not list when not to use or name alternatives, 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by explaining that the tool fetches real-time prices from both venues and computes spreads, and it details the return format (leg-by-leg prices in 0-1, spread in percentage points). No contradiction with annotations.

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

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

The description is concise (~100 words) and well-structured: purpose first, then rationale, two modes with bullet-like clarity, and return format. Every sentence adds value with 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?

Given no output schema, the description covers the essential details: modes, parameter usage, and return format. It lacks explicit mention of error handling or edge cases (e.g., when no matching event exists), but for a read-only tool, it is mostly 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%, so baseline is 3. The description adds value by explaining how the parameters relate: the 'topic' parameter provides pre-mapped shortcuts, while explicit parameters override the topic-mapped side. This clarifies interaction beyond the schema's individual 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: computing cross-venue spread between Kalshi and Polymarket for the same resolving question. It specifies the verb 'spread' and the resource 'cross-venue', and distinguishes itself from siblings by focusing on cross-venue arbitrage, contrasting with potentially internal Polymarket tools.

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

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

The description explicitly outlines two modes ('topic' and explicit parameters), providing clear guidance on when to use each. It explains the rationale behind the spread (different participant pools) but does not explicitly state when not to use the tool or mention alternative tools for single-venue analysis.

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. Clearly states that the tool retrieves or lists memories, which is non-destructive. However, does not mention any side effects, rate limits, or whether listing returns full contents or just keys. Still, core behavior is 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?

Two sentences, no filler. Front-loaded with the core action. 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 no output schema, description could explain what is returned (e.g., full memory content or just summary). But for a simple retrieval tool, the description is adequate. No annotations, but the tool is straightforward.

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. Description adds value by clarifying the two usage modes (with key vs without) and the purpose of the parameter. Slightly redundant with schema but useful context for an agent.

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 verbs ('retrieve', 'list') and a clear resource ('stored memory by key'). Distinguishes between single retrieval and listing all, and explicitly states the use case: 'retrieve context you saved earlier'. Differentiates from sibling tools like 'remember' and 'forget'.

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 guidance on when to use the tool ('to retrieve context you saved earlier'), and describes two distinct usage modes (with key vs omit key). Does not mention alternatives but clearly implies this is for retrieval, not storage or forgetting.

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 fully discloses behavioral traits: parallel fan-out to three sources, accepted date formats (ISO and relative), and return structure (structured changes + count + URIs). This is comprehensive and leaves no ambiguity about side effects or behavior.

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 remarkably concise: three sentences that front-load the purpose, then detail behavior and use cases. Every sentence adds essential information without redundancy.

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

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

Given the tool's complexity (parallel fan-out, multiple sources, date formats, rich return), the description covers all necessary aspects: what it does, how it works, input details, and output structure. No output schema exists, so the description's mention of 'structured changes + total_changes count + pipeworx:// URIs' fills that gap completely.

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

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

Although schema coverage is 100%, the description adds significant value by explaining the since parameter's format options with examples ('7d', '30d', '3m', '1y'), clarifying the type parameter's current limitation ('Only company supported'), and specifying acceptable value formats (ticker or CIK). This enriches the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It then elaborates on the specific behavior for company entities, detailing the parallel fan-out to SEC EDGAR, GDELT, and USPTO. This differentiates it from sibling tools like entity_profile, which likely provide static profiles rather than temporal changes.

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

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

The description explicitly recommends usage for 'brief me on what happened with X' or change-monitoring workflows, providing clear context. However, it does not mention when not to use this tool or name alternatives among siblings, missing an opportunity to guide agent 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, so description carries full burden. It discloses persistence behavior: authenticated users get persistent memory, anonymous sessions last 24 hours. This adds important context beyond basic store 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?

Three sentences, each purposeful: function, use case, persistence details. No wasted words, 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 simple schema (2 string params), no output schema, and no nested objects, the description is complete. It covers what, why, and behavioral notes, leaving no gaps.

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

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

Schema description coverage is 100%, so baseline is 3. Description adds value by explaining the purpose of stored data (findings, preferences, context) and providing example keys, enhancing agent understanding of parameter usage.

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 states the tool stores a key-value pair in session memory, specifying it saves findings, preferences, or context. It clearly differentiates from sibling tools like 'forget' and 'recall' by focusing on storage.

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

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

The description gives clear usage: save intermediate findings, preferences, context. It implies use across tool calls but does not explicitly state when not to use it or compare 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?

Without annotations, description carries full burden. It discloses that the tool is a read-like operation (resolves and returns data), lists return fields (ticker, CIK, name, URIs), and mentions it replaces multiple calls. No contradictions, but could mention non-destructive nature explicitly.

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 two sentences with no filler. First sentence states global purpose, second provides version details and examples. Every sentence is purposeful and front-loaded.

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

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

For a simple tool with two parameters and no output schema, the description covers purpose, input formats, output components, and efficiency benefit. No gaps given the tool's scope.

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% coverage, description adds value by providing concrete examples for the value parameter (AAPL, 0000320193, Apple) and specifying that only 'company' is supported for type, reinforcing schema constraints.

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

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

The description clearly states the tool resolves an entity to canonical IDs across Pipeworx data sources, specifies supported type (company), and lists accepted input formats (ticker, CIK, name). It distinguishes from siblings by describing a specific entity resolution function that consolidates multiple 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 indicates this tool is for resolving entities in a single call instead of multiple lookups. It provides clear context for v1 (only company type), but does not explicitly state when not to use it or compare to alternatives like ask_pipeworx.

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. The description adds behavioral context: it probes each entity with ai_visibility_check, returns ranked list with score, confidence, signal density. 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 three concise sentences, front-loading purpose and method, then use case and output. Every sentence earns its place with no redundancy.

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

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

Given no output schema, the description adequately explains the return format. It covers input ordering and typical use case. Missing edge cases (e.g., fewer than 2 entities) but overall sufficient for a well-defined tool.

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

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

Schema coverage is 100%, but the description adds semantic value: it explains that the first entity is treated as the 'subject' for narrative, and details the output format (ranked list, score, confidence, signal density), which is not 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 compares AI visibility across multiple entities side-by-side, ranks them by score, and surfaces relative recognition. It effectively distinguishes itself from sibling 'ai_visibility_check' (single entity) and 'compare_entities' (generic) by being specific to AI-marketing audits.

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

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

The description explicitly states the tool is 'useful for competitive AI-marketing audits,' providing clear context. However, it does not specify when not to use or list alternatives beyond implication, so a point off for lacking 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?

Disclosures of non-guaranteed accuracy and no refunds add some behavioral context beyond empty annotations, but lacks information on safety, permissions, or rate limits.

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

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

Three sentences, front-loaded with action, efficient. Could be slightly more structured but no redundant information.

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

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

Output schema exists, so return format is not needed. However, missing usage guidance and behavioral depth for an entertainment tool makes it less 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 descriptions for both parameters, so the description adds no extra meaning beyond what the schema already provides. Baseline of 3 applies.

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 draws a tarot card from the 78-card deck and interprets it, distinguishing it from unrelated sibling tools like ask_pipeworx or compare_entities.

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

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

No guidance on when to use this tool vs alternatives, only disclaimers about accuracy and refunds. Does not mention appropriate scenarios or 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?

No annotations provided, but description discloses return values (verdict types, citation, delta) and limitations (v1 supports only US public company financials), offering sufficient transparency for selection.

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

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

Two concise sentences: first defines purpose and scope, second lists return details. No unnecessary words, effectively front-loaded.

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

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

For a simple single-parameter tool with no annotations or output schema, the description covers purpose, scope, behavior, return format, and limitations, making it 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 coverage is 100% for the single parameter, and description adds value by providing example claims that illustrate expected input format beyond what schema offers.

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 fact-checks claims, specifies domains (company financials) and sources (SEC EDGAR + XBRL), and distinguishes itself from siblings by replacing multiple agent calls.

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

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

Provides clear context for when to use (fact-checking claims, especially financial), but does not explicitly exclude other scenarios or mention alternatives among siblings.

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