Pulltrader Seller Economics

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

With no annotations, the description carries full burden. It explains deterministic behavior, use of dated fee schedules, and that eBay/competitor figures are estimates. It clarifies the role of acquisition_cost. Minor gap: no mention of output format, but overall 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 comprehensive but well-structured, front-loading the core function and then adding usage guidance. Every sentence adds value, though it is slightly lengthy. 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 11 parameters and no output schema, the description covers core logic, usage context, and behavioral nuances. It could mention output type, but the schema descriptions already detail inputs. Overall sufficient for a complex tool.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds behavioral context beyond the schema, such as how target_net changes with acquisition_cost and that fixed fees apply per order. This enhances 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 the tool computes the per-item sale price to achieve a target net payout after fees for a specific selling method. It clearly distinguishes from siblings like compare_selling_costs and get_card_price_history by listing exclusions.

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 when-to-use scenarios (e.g., 'what do I need to list this at to walk away with $X') and explicit when-not-to-use instructions (e.g., not for market value lookups or non-trading-card categories). It offers thorough context for appropriate invocation.

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

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

With no annotations, the description fully discloses behavioral traits: calculations are deterministic using dated fee schedules, competitor marketplaces off by default, only fixed-price seller fees modeled, and advice to present figures as estimates without claiming universal cheapest. This provides clear expectations for the agent.

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

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

The description is a single, well-structured paragraph with front-loaded purpose. Every sentence adds value: use cases, exclusions, behavioral notes, and presentation guidance. 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 11 parameters and no output schema, the description covers all needed context: what the tool does, when to use it, limitations, parameter grouping, and behavioral assumptions. It is complete enough for an agent to select and invoke correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant context beyond parameter descriptions, such as explaining defaults for methods, the meaning of seller_plan tiers, and the nuance of seller_covers_fees. It helps the agent understand parameter interactions, justifying above baseline.

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 estimated fees and net seller proceeds across multiple selling methods for trading cards. It specifies the verb (compare), resource (seller proceeds), and scope (trading cards). It also explicitly distinguishes from tools that price cards or cover non-trading-card categories.

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 when-to-use scenarios (e.g., 'when a seller asks what they would keep/net/take-home') and when-not-to-use (do not use for pricing cards or non-trading-card categories). It also explains that competitor marketplaces are off by default and that only fixed-price fees are modeled.

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

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

With no annotations provided, the description carries full burden. It clearly states the tool returns a 'plain, structured explanation' and lists what it covers, but does not mention behavioral traits like read-only nature, external calls, or side effects. It is transparent about its output but lacks depth in behavioral context.

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

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

The description is concise (~100 words), front-loaded with the core purpose, then lists methods, usage guidance, and exclusions. Every sentence adds value without redundancy.

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

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

Given the tool's complexity (explaining selling methods), the description covers purpose, included methods, usage cases, and distinguishes from siblings. Without an output schema, it adequately describes the return type as a 'plain, structured explanation'. It provides sufficient context for an AI agent to select and 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?

Schema coverage is 100% with the 'methods' parameter's enum values documented in the schema. The description repeats the list of methods but adds the context of defaulting to all supported methods. It does not add significant semantic value beyond the 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 tool returns a structured explanation of selling methods, lists the specific methods covered, and details the aspects explained (ownership, fulfillment, venue, fees, caveats). It distinguishes itself from sibling tools by explicitly excluding use cases for computing payouts or prices.

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 ('when a seller asks how a method works, what fees a platform charges, or how Pulltrader selling options differ') and when not to use it, providing alternatives (compare_selling_costs and calculate_required_sale_price).

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

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

With no annotations, the description discloses that data is built from sold-sale samples and vendor TCG history, and that estimates are not financial advice. Adds context about data source and limitations, though could elaborate on recency or response 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?

Four sentences, front-loaded with core purpose. Every sentence serves a distinct role: purpose, usage triggers, parameter explanation, and data limitations. 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?

Absent an output schema, the description mentions 'chart-ready price-history series' and components (buckets, median, trend) but does not specify the exact JSON structure or field names an agent would need to parse the response. Adequate for basic understanding but incomplete for automation.

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

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

Schema coverage is 100%, but description adds value by explaining the aggregation logic (buckets, median) and clarifying that 'query' and 'item' are alternatives. Provides natural-language examples and default interval behavior beyond schema descriptions.

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

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

Clearly states the tool returns a chart-ready price-history series for trading cards, aggregated into day/week/month buckets with median per bucket and overall trend. Distinguishes from siblings like 'search_card_sales' and 'identify_card' by focusing on time-series data.

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 user queries that trigger this tool ('how has the price changed', 'price over time', etc.) and specifies applicability to trading cards only. Lacks explicit mention of when not to use or alternatives, but context from sibling tools fills the gap.

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

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

With no annotations, the description carries full burden. It discloses that the tool returns a confidence level and which fields were resolved, and clarifies it does not perform pricing or sales lookups. However, it does not discuss error behavior or what happens if the card is not identified, which would be helpful for full transparency.

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

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

Two sentences, both front-loaded with purpose and usage guidance. Every sentence adds value—no redundancy or filler. Structure is optimal for quick consumption.

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

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

Given the single parameter, no output schema, and sibling tools, the description sufficiently covers what the tool does and its role in the workflow. It mentions return fields (confidence, resolved fields). Could optionally mention error handling, but not strictly required for this complexity level.

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 one parameter fully described. The description adds meaning by explaining the query as a 'natural-language trading-card description' and providing an example, which augments the schema's 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 specifies the verb 'resolve' and the object 'natural-language trading-card description into structured fields', and explicitly lists the fields (player/athlete, year, set, etc.). It clearly distinguishes from siblings by stating it does NOT price or return sales.

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 ('Use this first when a user names a card in prose and you need its canonical fields before looking up sales or market value') and when not to use ('This does NOT price the card or return sales — use search_card_sales or summarize_card_market for that'). Provides alternative tool names.

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

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

With no annotations, the description carries full burden. It discloses: results are a capped sample, figures are estimates, excludes fees/taxes/shipping, not financial advice, does not place orders, and only for trading cards. Comprehensive.

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

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

Concise, front-loaded with purpose and usage, no redundant sentences. Each sentence adds critical 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 no output schema, the description fully covers what is returned (sold listings + market snapshot) and all behavioral caveats. No gaps for an agent to misuse.

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

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

Schema coverage is 100% but description adds value: explains query is natural-language, item is structured alternative with explicit fields, and limit range. States 'either query or item is required' which is crucial beyond schema.

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

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

The description clearly states it returns a capped sample of recent comparable sold listings plus a market snapshot for a trading card. It uses specific verbs (Return, sample) and specifies the resource (recent comparable SOLD listings). It distinguishes from siblings by noting 'Trading cards only' and focusing on sold comps.

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: 'when a user asks what is this card selling for, recent sales, or comps'. It implies not for non-card items. Lacks explicit alternatives from siblings (e.g., get_card_price_history for price history), but overall 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 bears full responsibility. It discloses that estimates come from recent comparable sales, excludes fees/taxes/shipping, and is not financial advice. It also limits to trading cards. This is sufficient transparency for a read-only estimation tool.

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

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

The description is extremely concise: two sentences that front-load the core value proposition and then add usage cues and limitations. Every sentence serves a purpose with no fluff.

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

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

Given the absence of an output schema, the description explains the output format (median, mean, percentile range, volatility, confidence level) sufficiently. It also notes constraints (recent comparable sales, excludes fees/taxes/shipping). It could mention data freshness or sample size thresholds, but overall it covers the essentials for an agent to use 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?

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the 'query' parameter as 'natural-language card description' with an example, and it lists the fields for the 'item' parameter. This goes beyond the schema's descriptions, providing clearer guidance on how to use each parameter.

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

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

The description clearly states the tool's function: returning a canonical market summary for a trading card, listing specific metrics (median, mean, 10th–90th percentile range, volatility, confidence level). It distinguishes from siblings by specifying 'trading cards only' and contrasting with other related tools like get_card_price_history or search_card_sales.

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: when a user asks 'what's it worth', 'market value', or 'how volatile is this card'. It also explains what the tool does not include (fees, taxes, shipping) and that it's not financial advice. It does not explicitly mention when not to use or list alternatives, but the sibling-tools list provides context.

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