litvm-tcg-oracle
Server Quality Checklist
Latest release: v1.0.4
- Disambiguation5/5
Each tool has a distinct purpose: search_cards for discovery, get_price for pricing, simulate_price for simulation, get_market_snapshot for overview, get_merkle_proof for verification, and oracle_status for system health. No overlap or ambiguity.
Naming Consistency4/5Most tools follow a consistent verb_noun pattern like get_price, search_cards, simulate_price. However, oracle_status is a noun_noun pattern and doesn't start with a verb, which is a minor inconsistency.
Tool Count5/5With 6 tools, the server is well-scoped for a TCG price oracle. Each tool serves a necessary function without redundancy, and the count is appropriate for the domain.
Completeness5/5The tool surface covers all essential operations for a price oracle: card search, price retrieval, market overview, price simulation, on-chain verification, and system status. No obvious gaps.
Average 4.2/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 13 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Business Source License 1.1.
This repository includes a README.md file.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full weight. It discloses returns (current, low, history) and notes the data powers Monte Carlo simulation. While rate limits and error handling are omitted, the output schema covers return format, making this sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with purpose and input options. The Monte Carlo sentence adds context but could be trimmed; it remains acceptable without being overly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has low complexity with an output schema. The description covers main outputs and input options. It does not mention pagination or prerequisites (e.g., using search_cards to find product IDs), but for a straightforward price tool, it is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds critical context: the exclusive-or relationship between card_name and product_id, and that card_name uses fuzzy search. This goes beyond the schema's individual parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets 'the latest market price and historical price data for a trading card,' with specific verbs and resources. It distinguishes from siblings like search_cards (search vs get) and simulate_price (simulation), but does not explicitly differentiate from get_market_snapshot.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when needing price data for a card and specifies input options (card name or product ID). It does not provide explicit when-not-to-use guidance or alternatives like search_cards for finding product IDs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden. It discloses catalog size, active vs. catalog-only entries, and the return of product IDs. It implies a safe read operation but does not explicitly state nondestructive nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the main purpose. It contains useful details but could be slightly more concise without losing information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search over 433K items), the description provides good context: catalog size, games, and return value usage. Output schema exists, so return values are handled. Minor gap: missing detail on search behavior (e.g., fuzzy matching).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and schema descriptions are present. The description adds example search terms and mentions game filter 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.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search 433K+ trading card products by name using full-text search' and lists specific games and what is returned. It distinguishes from siblings by noting that product IDs are needed for get_price and get_merkle_proof.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly guides usage by stating it returns product IDs for other tools and mentions catalog details. However, it lacks explicit when-not-to-use or alternative guidance 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.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the forecast is distribution-free, deterministic, never-under-protective, and that risk values are honest out-of-sample. It also mentions 'two plain-English letter grades.' However, it could be more explicit about side effects or return behavior beyond the grade mention.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (~100 words) and front-loaded with the main purpose. Every sentence adds value, distinguishing the tool and its methodology without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter and the existence of an output schema, the description is fairly complete. It explains methodology and key traits, but could briefly mention what the output schema covers to enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage for the single parameter 'card_name'. The description does not add additional meaning beyond the schema's example. Baseline score is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get the calibrated conformal risk forecast for a trading card.' This is a specific verb+resource. It distinguishes from siblings like 'simulate_price' by explicitly contrasting with Monte Carlo simulation and positioning itself as the 'honest default forecast.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage guidance by calling it the 'recommended, honest default forecast' and contrasting with Monte Carlo simulation. It explains why it's distribution-free and never-under-protective, helping the agent understand when to prefer this tool, but does not explicitly state when not to use it or name specific alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the description fully discloses that the tool returns highest-value cards with current market and low prices. No hidden side effects are mentioned, and the read-only behavior is evident.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Description is four sentences, front-loaded with the core purpose, and each sentence adds essential information without redundancy. Extremely efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, the description covers the main functionality. An output schema exists (not shown) so return format details are not required. Minor gap: does not explicitly state descending sort order for 'sorted by value', but it's reasonably implied.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, providing baseline 3. The description adds value by clarifying that 'game' filters by game name and 'limit' controls number of cards, plus the output is sorted by value with price details—beyond the schema's concise descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves a market overview of top trading cards sorted by value for a specific game, listing supported games. This distinctively differentiates from sibling tools like get_price (single card) and search_cards (search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining a high-level market snapshot, but does not explicitly state when to prefer this tool over alternatives or provide any usage restrictions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It clearly states it performs a live on-chain read via Caldera RPC, not cached data, and lists return fields. However, it omits potential behavior like latency or endpoint reliability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections for purpose, behavior, and returns. It is fairly concise for the depth provided, though slightly verbose in listing return fields.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no params and an output schema exists, the description adequately covers the use case. It explains the live nature and return fields, but could mention error handling or edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so baseline is 4. The description adds significant value by detailing the four return categories (MerklePriceOracle, TCGPriceOracleV2, Network, Database), which is beyond the empty input schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states 'Get live status of the TCG Price Oracle on LitecoinVM,' providing a specific verb and resource. It distinguishes from siblings like get_price or get_market_snapshot by focusing on overall oracle health.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining live on-chain oracle status, but lacks explicit guidance on when to use this tool versus alternatives (e.g., get_price for individual prices). No exclusions or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavioral traits: it returns a Merkle proof array, only works for actively-priced products (market_price > 0), and zero-price entries cannot be proven. It also explains the overall mechanism (Merkle root on LiteForge).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections, front-loading the purpose. While slightly verbose, every sentence adds value (e.g., why it matters, the oracle process, the zero-price note). It could be tightened but remains effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown but indicated), the description still provides essential context about what the tool returns (Merkle proof array) and its application. It also covers limitations (zero-price items) and the overall workflow, making it complete for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter (product_id) described as 'TCGPlayer product ID.' The description adds a concrete example ('e.g. 98580 for Shadowless Charizard') and explains the role of the parameter in verification, adding value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with 'Get a Merkle proof for on-chain price verification on LitecoinVM,' clearly stating the specific verb and resource. It distinguishes this tool from siblings like get_price by emphasizing cryptographic verification versus trusting a server.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use this tool (to verify prices on-chain without trusting a server) and provides context about the underlying oracle. It does not explicitly mention alternative tools or when not to use it, but the context implies other tools (like get_price) for trusted scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description fully bears the transparency burden. It explains the math, data sources, calibration steps, and fallback priors in detail. However, it does not explicitly state if the tool is purely read-only or mention potential side effects, but the nature of simulation implies safety. Could also disclose rate limits or data freshness.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is lengthy but well-structured with bullet points and clear sections. It efficiently explains a complex process without unnecessary fluff. Slightly verbose, but every sentence adds value for the intended technical audience.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of Monte Carlo simulation, the description covers data source, calibration steps, fallback behavior, and output expectations. It references a sibling tool for default forecasting, setting complete context. With an output schema likely present, this description is fully adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema covers all 4 parameters with descriptions, achieving 100% coverage. The description adds context like 'vectorized numpy simulation paths' and 'percentile forecast bands' that enrich understanding beyond schema, but does not introduce new parameter constraints not already in schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs a Monte Carlo price simulation for trading cards, using specific verbs and resource. It distinguishes itself from the sibling 'get_forecast' by positioning as the stochastic Monte Carlo alternative, making the purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use this tool vs 'get_forecast': 'For the honest DEFAULT forecast... use get_forecast. This tool is the stochastic Monte Carlo alternative.' It also mentions opt-in, providing clear guidance on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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