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Pulltrader Seller Economics

by app.pulltrader

Identify a trading card from a text description

identify_card

Resolve a natural-language trading-card description into structured fields (player/athlete, year, set, card number, parallel, grader, grade, sport/category). Use this first when a user names a card in prose and you need its canonical fields before looking up sales or market value. Returns a confidence level and which fields were resolved. This does NOT price the card or return sales — use search_card_sales or summarize_card_market for that. Trading cards only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe card description to identify, e.g. '2018 Prizm Luka Doncic Silver PSA 10'.

TDQS

A4.5/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses behavior: returns confidence and resolved fields, does not price or return sales, and scope is 'trading cards only'. However, could mention whether multiple results are possible or if it's deterministic.

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

Conciseness5/5

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

Four sentences: first declares core function, second gives usage context, third clarifies non-behaviors, fourth limits scope. Every sentence earns its place with no waste.

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

Completeness5/5

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

Given low complexity (1 param, no output schema, no nested objects), the description is fully adequate. Covers purpose, usage, behavior, and boundaries completely for an AI agent.

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

Parameters3/5

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

Schema coverage is 100% with the single 'query' parameter already described. Description adds an example but no additional semantic detail beyond the schema. Baseline 3 is appropriate.

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

Purpose5/5

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

Description clearly states it resolves natural-language trading-card descriptions into structured fields, and explicitly distinguishes from pricing/sales tools by stating what it does not do.

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

Usage Guidelines5/5

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

Explicitly says to use first when needing canonical fields before sales lookup, and directs to specific siblings for pricing/sales, providing clear when-to-use and when-not-to-use guidance.

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

All Pulltrader Seller Economics toolsInspect
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TDQS

A4.6/5.0
Disambiguation5/5

Each tool targets a distinct operation: calculating prices, comparing costs, explaining methods, identifying cards, searching sales, summarizing markets, and fetching price history. No functional overlap exists between any pair.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., calculate_required_sale_price, compare_selling_costs, explain_selling_method) with no mixed conventions or abbreviations.

Tool Count5/5

With 7 tools covering pricing, fee comparison, method explanations, card identification, sales search, market summary, and price history, the set is well-scoped for a seller economics domain—neither too sparse nor bloated.

Completeness5/5

The tool surface comprehensively covers the seller economics lifecycle: card identification, market data (recent sales, price history, summary), fee/cost comparison, target-pricing calculation, and method explanation. No obvious dead ends for the stated purpose.

Resources