undesirables-mcp-server

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

Discloses that it's free, returns an agent-complete object, and lists all output fields. Lacks details on potential errors or authentication, but overall transparent for a read-only 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?

Well-structured with clear first sentence, output details, usage scenarios, and a tip. Slightly lengthy but every statement 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 provides comprehensive details on returned fields and example queries. Covers all necessary context 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?

Adds meaning beyond schema: explains that either card_name (resolved to best match) or product_id can be used, and that card_name resolves to best match. This is crucial for proper invocation.

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 conformal-calibrated 30-day price forecast and letter grades for a single card, distinguishing it from sibling tools like grade_card which likely only return grades.

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 when to use (e.g., 'is this card a safe hold?', 'what's the 30-day outlook?') and provides an alternative endpoint for market overview via tip.

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

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

No annotations are provided, so the description carries the burden. It mentions 'FREE — no payment required' and lists the dashboard contents, but does not disclose if it is read-only, requires authentication, or any side effects.

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

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

The description is short and front-loaded with key information: purpose, contents, and usage. It is efficient but could be slightly improved by integrating the parameter explanation.

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 one parameter and no output schema, the description covers the tool's purpose and when to use it, but fails to explain the 'game' parameter or describe the output format beyond listing metrics.

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 only parameter 'game' has 0% schema description coverage and is not explained in the description. The description adds no meaning beyond the schema, leaving the agent to guess its purpose and valid 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?

The description clearly states the verb ('View') and resource ('TCG Oracle's public prediction accuracy dashboard'), and details the contents (mean absolute error, hit rates, etc.). It distinguishes from siblings like 'card_forecast' and 'grade_card' which focus on other operations.

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 provides a use case: 'when a user asks "how accurate is the grading AI?"' and notes the tool is free. No explicit exclusion of alternatives, but the specific scenario sufficiently guides the agent.

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 present, so the description carries full burden. It discloses the 3-stage pipeline (Qwen, OpenCV, BGS), the return of subgrades and ROI verdict, and importantly the payment details ($0.10 per call via x402 with three accepted rails). This adequately informs the agent about cost and operational 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 front-loaded with the core pipeline and return values, which is good. However, it includes tangential details like the specific Solana settlement audit note and three accepted rails, which could be shortened or moved to a separate field. It is structured with bullet points but still slightly verbose.

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

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

Given the complexity (multi-stage AI, payment, no output schema), the description is fairly complete: pipeline, output (subgrades, overall score, ROI verdict), and payment details. It lacks explanation of the 'game' parameter and does not describe the output format explicitly, but overall it provides sufficient context for an agent to use the tool effectively.

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 0% with no parameter descriptions. The description explicitly mentions 'image_url' only once ('a user has a card image') but does not explain the 'game' parameter (default 'Pokemon') or its purpose. The agent may guess game is the card game type (e.g., Pokemon, Magic), but clarity is lacking.

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 with a specific verb ('grade'), resource ('trading card image'), and details the three-stage AI pipeline. It distinguishes itself from siblings like 'card_forecast' and 'grade_or_not' by specifying PSA/Beckett calibration and ROI verdict.

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 ends with an explicit usage condition: 'Use this when: a user has a card image and wants to know what grade it would receive from PSA or Beckett.' While it does not mention when not to use or compare directly to siblings, the context is clear and sufficient for an agent to decide.

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 it's a paid tool ($0.10 USDC per call) and explains it uses multiple data sources to compute ROI. Does not mention side effects or mutation, but as a financial calculator it is reasonably 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?

Concise at three sentences plus a usage note. Starts with the core question, then explains functionality and output. Efficient but could be slightly more 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 no output schema and no annotations, the description covers main purpose and key outputs (verdict and profit scenarios) but omits parameter details and output structure. Insufficient for fully informed invocation without further schema details.

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 0%, so the description must compensate. It mentions AI prediction, fee schedules, shipping costs, and market values, which relate to parameters like raw_price, service_tier, predicted_grade, but does not explicitly define each parameter or their formats. Lacks essential mapping for agent use.

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 answers the question 'Should I grade this card?' by combining AI grade prediction with financial factors to give a GO/NO-GO verdict. It distinctly focuses on financial decision, differentiating from sibling tools like grade_card which likely handles the grading prediction itself.

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 'Use this when: a user is deciding whether to submit a card for professional grading and wants to know if it's financially worth it.' Provides clear usage context, though lacks explicit when-not-to-use or alternative 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?

Discloses the paid nature ($0.025 USDC per call) and payment method, which is critical behavioral info. Also notes a historical bug fix regarding cost. With no annotations provided, this adds significant value beyond the name and schema.

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 includes necessary information but the bug audit note makes it longer than ideal. While informative, it could be more concise by focusing on core functionality and payment, without the detailed bug backstory.

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

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

Given the simple tool (1 optional param, no output schema, no annotations), the description covers functionality, usage, and payment. It does not describe the output format in detail, but for a snapshot tool this is 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?

There is one optional parameter 'game' with 0% schema coverage. The description implies it can filter by game ('across all 25 supported card games'), but does not specify that the parameter accepts game names, the default behavior, or valid values. More explicit parameter documentation is needed.

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 it provides a daily TCG market snapshot with top movers, gainers/losers, and volume leaders across 25 card games. Distinguishes from sibling tools like 'trending_cards' and 'card_forecast' by specifying daily snapshot and cross-game scope.

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

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

Explicitly states when to use: when user asks 'what's trending?' or 'what cards are going up/down?'. No explicit when-not-to-use or alternatives, but the context is clear and helpful.

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 cost and mentions the computational method (optimization), but does not explain side effects, required permissions, error handling, or whether the tool is read-only. Some behavioral info is present, but not 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?

The description is six sentences, each well-focused: purpose, method, output, cost, use case. No filler or redundancy. Ideal length for the tool's complexity.

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 (advanced optimization) and lack of output schema, the description provides a reasonable overview of outputs and use. However, it omits details on the 'days' parameter, risk tolerance values, and how results are presented. Leaves some questions unanswered.

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 0%, so description must compensate. It explains 'cards' as comma-separated names, 'budget' as a number, and 'risk_tolerance' in context. However, it does not mention the 'days' parameter or its purpose, leaving a gap. Coverage is partial but adds value beyond the schema.

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

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

The description clearly states it optimizes a portfolio using specific models (Markowitz, Merton jump-diffusion) and lists specific outputs (position sizing, allocation weights, Sharpe ratios, rebalancing recommendations). It also specifies when to use it: when a user has a budget and wants allocation across cards, distinguishing it from siblings like card_forecast or simulate_price.

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 includes an explicit 'Use this when' section, providing clear guidance on the tool's purpose: a user with a budget wanting allocation. It does not explicitly state when not to use it or list alternatives, but the use case is well-defined. The cost mention ($0.50 USDC per call) adds practical context.

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 only mentions 'FREE — no payment required.' It does not disclose how the recommendation is generated, error handling, rate limits, or any side effects. This leaves significant behavioral uncertainty.

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 (4 sentences plus bullet example list) with no wasted words. It front-loads the core purpose, adds key info (free, usage guidance), and uses clear bullet examples.

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 recommendation tool with no annotations and no output schema, the description is quite complete. It covers purpose, when to use, and parameter meaning. However, it lacks any description of the output format or structure, which would help an agent process the recommendation.

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 0%, so the description must compensate. It explains the 'goal' parameter as 'Describe your goal in natural language' and gives four concrete examples. This provides rich semantic context beyond the plain schema definition.

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: 'get a recommended sequence of TCG Oracle API calls to accomplish it.' This is a specific verb+resource. It distinguishes from siblings because recommend_workflow is the only tool that outputs a multi-step workflow plan.

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?

Explicit usage guidance is provided: 'Use this when you're not sure which tool to call first, or need a multi-step workflow recommendation.' This tells the agent exactly when to invoke this tool over siblings.

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

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

No annotations were provided, so the description carries full burden. It discloses that the tool is free, explains search behavior (wide net with card name alone, refining with set), and advises against including rarity words. It also describes how results can be used with other tools (via product_id). This provides good behavioral insight.

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 relatively long but well-structured. It starts with purpose and pricing, then usage scenario, then detailed search tips. Each sentence adds value, though slightly verbose. Still, it earns a 4.

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 (search across many products, multiple parameters, no output schema), the description is quite complete. It covers usage, limitations, result composition, and how to use results with other tools. It even provides fallback advice for failed searches.

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 0%, so the description must add meaning. It extensively explains the 'query' parameter with examples and tips. The 'game' and 'limit' parameters are implied but not detailed; however, the query parameter explanation is thorough enough to compensate.

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: 'Search 449K+ TCG products across 25+ card games. Returns card names and IDs, plus current market prices.' It uses a specific verb ('search') and resource ('TCG products'). The purpose is distinct from sibling tools like 'card_forecast' or 'grade_card'.

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 this tool: 'Use this when: a user asks about a specific card, wants to find cards, or needs current pricing.' It also provides detailed search tips and what to avoid (e.g., rarity words), giving clear guidance on usage.

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

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

Discloses default and optional models, output format (percentiles, parameters, confidence intervals), and pricing. Without annotations, it carries full burden and does so well, though it could mention any side effects or mutability.

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, front-loaded with purpose, and every sentence adds value. 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?

Given the tool's complexity (5 params, no output schema), the description covers the default model, optional models, output format, and cost. It lacks details on error handling or rate limits but is largely 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 description coverage is 0%, but the description only hints at parameters like 'days' (3 months), 'model' (options), and 'card_name'/'current_price'. It fails to explain 'simulations' or provide detailed parameter semantics, leaving agents to infer.

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 with specific verbs ('Predict future trading card value') and distinguishes it from siblings like 'card_forecast' by focusing on price simulation with multiple models.

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

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

Includes explicit usage context ('Use this when: a user wants to know what will this card be worth in 3 months?'), but lacks guidance on when not to use or direct comparison to siblings.

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

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

With no annotations, the description carries the full burden. It discloses that the tool is free ('FREE — no payment required'), and describes the content (open predictions with lock_hash, merkle root, on-chain tx) to indicate verifiability and immutability. This adds behavioral context beyond the schema.

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, using only 4 sentences. It front-loads the core purpose, then gives usage guidance, and ends with parameter description. No redundant or irrelevant information.

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

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

Given the tool's simplicity (single parameter, no output schema), the description adequately covers the return content and purpose. It could provide a bit more detail on the return format, but the description of 'every open prediction and its recent scored results' with verification details is sufficient for an agent to understand its behavior.

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 0%, so the description must compensate. It explains 'token_id: minted soul, 1-273', providing the meaning and valid range of the integer parameter, which the schema lacks.

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 provides the full public record for one soul, including all open predictions and recent scored results. This distinguishes it from sibling tools like 'souls_in_wallet' (which lists souls) and 'check_accuracy' (which presumably checks accuracy). The verb 'retrieve' is implicit but the resource 'record for a specific soul' is specific.

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 includes explicit usage guidance: 'Use this when a user wants to inspect a specific soul's calls in detail, or wants to verify one'. It also explains why this tool is valuable for verification. However, it does not explicitly mention when not to use it or name alternative 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 fully discloses behavioral traits: it's free, reads from Ethereum mainnet, is non-private, and explains the maturity schedule for ratings. The 'IMPORTANT' note clarifies expected behavior for unrated souls.

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

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

The description is well-structured with clear sections and front-loaded essential info. Every sentence adds value, and it avoids redundancy.

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

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

Given the simple parameters, lack of output schema, and no annotations, the description is remarkably complete. It covers source, permissions, return format, and edge cases like the maturity schedule.

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 0%, but the description explains both parameters: 'address' as an EVM address and 'calls' with range and default. This adds meaning beyond the schema's types and defaults, though it could include more details about valid 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?

The description clearly states it shows every soul a wallet holds with prediction records and recent calls, using specific verbs and resources. It distinguishes from sibling tools like 'soul_calls' which likely focus on individual souls.

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 usage scenarios (e.g., 'Use this when: someone asks what Undesirables they own, how their souls are performing...'), but does not explicitly state when not to use it or name specific alternative 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?

No annotations are provided, so the description carries full responsibility for disclosing behavior. It does not explicitly state that this is a read-only query or describe what the tool returns (e.g., a list of scores, ordering, limits). The mention of automatic posting is a system behavior, not clearly attributed to this tool, which could confuse an agent about whether invoking the tool triggers posting.

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 compact and front-loaded with the core identity. The second sentence about automatic posting adds relevant context but could be seen as tangential to the tool's direct use. Overall, it is efficient and each sentence contributes some 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?

For a zero-parameter tool with no output schema or annotations, the description covers the main purpose and distinguishes the board's content. It lacks explicit return format or read-only status, but given the simplicity of the tool, the description is reasonably complete. A bit more detail about the output would make it fully 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?

The tool has zero parameters, so the schema is trivially fully covered. The description adds helpful context about the data structure (agent entries with {'agent': true} and a model label), but there is no parameter-specific information to provide. The baseline of 4 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 identifies the resource as 'The Syndicate's shared 'Biggest Scores' leaderboard' and explains its unique quality (humans and AI agents on one board). However, it lacks an explicit verb like 'retrieve' or 'view', so while the purpose is unambiguous in context, it doesn't fully meet the 5-point criterion of a specific verb+resource.

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 viewing the leaderboard and mentions that scores post automatically after winning, but it gives no explicit guidance on when to use this tool versus alternatives like syndicate_state or market_snapshot. There are no stated exclusions or alternative tool references, so usage context is only implied.

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 the full burden of behavioral disclosure. It covers return value (events + new state), the one-order-per-crew-member constraint, all allowed action types, and the side effects of an empty order list (world moves, rivals act, rackets pay, heat decays). It does not explicitly state irreversibility or auth requirements, but the commit-like nature is strongly implied by 'Submit' and 'resolved day'.

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 about 100 words but every part carries necessary information: purpose, constraint, action enum, empty behavior, and target source. It is well-structured with line breaks for readability and contains no filler. The long enum list is justified because the schema itself lacks enums.

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 two-parameter tool with no annotations and no output schema, the description sufficiently covers the complex orders parameter, explains the return value, and links to syndicate_state for valid targets. It equips an agent to invoke the tool correctly without needing additional external information.

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 0% and the orders array has an empty item schema, so the description provides all essential parameter semantics: the exact object shape with agentId, targetId, actionType, the full actionType enum, and the empty-list behavior. It also explains where targetId values come from (targets and territory lists in syndicate_state), fully compensating for the schema gap.

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 uses a specific verb 'Submit' with a clear resource: 'one day of orders to your Syndicate game,' and states the outcome ('get the resolved day back'). It distinguishes itself from sibling tools like syndicate_state by focusing on submitting orders to advance and resolve a day.

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?

It provides clear context for when to use the tool: to progress one game day, including the empty-orders pass-day behavior. It also references syndicate_state for target IDs, which orients the agent. However, it does not explicitly name alternatives or state when not to use this tool compared to siblings.

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

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

With no annotations provided, the description carries the full burden. It discloses the side effect of starting a new game when no session_id is given, and describes what is returned (sessionId, crew, capital, target list). It does not mention behavior for invalid session IDs or potential effects on existing sessions, but the 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?

The description is about 90 words in three short paragraphs, with a clear structure: game context, usage instructions, and a strategy tip. The strategy tip referencing sibling tools is helpful but slightly tangential to invoking the tool, and the opening marketing line could be trimmed. Overall, it is efficient but not as maximally concise as possible.

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 lack of an output schema, the description sufficiently explains what a new game returns and that re-reading state returns current game state, plus provides a link to full rules. It does not specify error handling or the exact structure of the returned state, but for a single-parameter tool, it is reasonably 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?

The schema only defines session_id with a default empty string and no description. The description thoroughly explains the parameter semantics: empty/absent session_id starts a new game, while a valid session_id re-reads the current state. This adds essential meaning that fully compensates for the 0% schema description coverage.

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: it is the entry point for the Syndicate game, used to start a new game or re-read current state. It distinguishes itself from siblings like syndicate_move and syndicate_leaderboard by specifying exactly what it does ('start a new game', 're-read the current state') and the resource (game state).

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?

Explicit usage guidance is provided: 'Call with NO session_id to start a new game' and 'Call with your session_id to re-read the current state any time.' It also directs to sibling tools (card_forecast / search_tcg_products) for pricing decisions, offering clear alternatives. A link to full rules further aids usage.

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

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

With no annotations, the description carries the full burden. It discloses several behavioral traits: the metric is price velocity (not sales volume), the response includes a conformal risk model with regime-level constants (not per-card fit), coverage of 25+ games, and a per-call cost. This provides useful context beyond the schema. It does not cover rate limits or auth, but the key pitfalls are addressed.

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 relatively long with a correction note and risk model explanation. While important, it could be more concise. The front-loading is good with the main purpose first. Every sentence adds value, but the length might slow down an AI agent.

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

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

Given no output schema and 3 parameters with no schema descriptions, the description is incomplete. It fails to explain parameter semantics and does not describe the return format or pagination. The risk model caveat is useful, but the missing parameter and output details make it insufficient for effective tool use.

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

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

The input schema has 3 parameters with no descriptions (0% coverage). The description does not explain any parameter's meaning, default, or effect. It mentions 'game' only in the context of coverage, but does not clarify the parameter. This is a major gap, leaving the agent without guidance on how to use parameters.

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

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

The description clearly states the tool returns trending cards by price velocity (drift), which is a specific metric. It distinguishes from sales volume with a correction note. However, it does not explicitly differentiate from sibling tools like card_forecast or market_snapshot, so clarity is good but not outstanding.

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 user asks 'what cards are hot right now?' or 'what's selling the most?' This is direct guidance. However, it does not list alternatives or when not to use it, so it is slightly incomplete.

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