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

With no annotations, the description carries the behavioral burden. It discloses the critical non-obvious behavior that a lagging subgraph returns stale data rather than an error, and that the tool succeeds but answers about the past. It doesn't detail return format or units, but the provided context is valuable.

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

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

Two sentences, front-loaded with the main purpose, followed by a concise rationale. Every word earns its place with no redundancy or filler.

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 low-complexity tool with one enum parameter and no output schema, the description covers the core purpose, when to use it, and a critical behavioral nuance. It could mention the unit of 'how far behind' (e.g., blocks vs seconds), but the phrase 'chainhead' implies block distance, so 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 already fully describes the single parameter (subgraph enum with 'Which subgraph to check'), so the description adds no extra parameter semantics. Baseline 3 is appropriate since schema coverage is 100%.

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 ('Report') and names the exact resource (subgraph) and metric (how far behind chainhead by reading _meta block). It clearly distinguishes itself from sibling data-getter tools by focusing on freshness rather than data content.

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

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

The description gives explicit situational guidance: 'Worth calling before trusting any current or latest number' and explains why (stale data returns successfully). It doesn't explicitly name alternative tools, but the context of sibling data getters makes the use case 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 provided, and the description does not disclose behavioral traits such as read-only nature, required permissions, rate limits, or side effects. The agent has no information beyond the tool name and description.

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, concise sentence that covers the core purpose. It is front-loaded and contains no redundant information. However, it could be slightly expanded to include more context without becoming 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?

With only one parameter and no output schema, the description is minimal. It does not specify what performance metrics are included or how the results are formatted. Given the complexity of P&L data, more context would be helpful for agent decision-making.

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 already describes the account parameter as 'Ethereum address of the trader (lowercase)' with 100% coverage. The description adds no additional meaning beyond what the schema provides, so a baseline of 3 is appropriate.

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

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

Description clearly states the tool gets a trader's P&L and performance metrics from a specific subgraph. The verb 'Get' and resource 'P&L and performance metrics' are specific, and the source 'Beefy P&L subgraph' distinguishes it from other trader-related tools like get_trader_profile or get_user_positions.

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

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

No guidance on when to use this tool vs alternatives. For instance, it doesn't clarify whether this should be used over get_trader_profile or get_user_positions for P&L-related queries. No context about prerequisites or use cases.

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 data scope (recent fills, aggregate totals) and the V2-only limitation. However, it does not mention pagination, default limit, or return format beyond the totals, leaving minor gaps.

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

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

Two sentences, front-loaded with the core functionality and output, followed by a concise usage prerequisite. Every sentence earns its place with no redundant phrasing.

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 two-parameter read tool, the description sufficiently explains purpose, input source, and scope. The lack of an output schema is mitigated by the explicit mention of 'recent fills' and 'aggregate totals.' Minor omission is the precise definition of 'recent' (e.g., time range).

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 reinforces that builderId comes from get_builder_leaderboard, but the schema already states this. No additional parameter semantics are provided beyond what the schema offers.

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

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

The description clearly states that the tool returns recent fills and aggregate totals for a specific Polymarket CLOB V2 builder code. It distinguishes itself from sibling tools like get_recent_activity by specifying builder code and V2-era scope, and by referencing get_builder_leaderboard.

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 instructs to call get_builder_leaderboard first to obtain a builderId, providing a concrete usage workflow. The 'V2-era only' constraint gives an exclusion and contextualizes when the tool is applicable.

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 responsibility for behavioral disclosure. It explains the definition of builder, the V2-only block constraint, and why data is unavailable for V1. This goes well beyond the tool name, though it doesn't mention return format or potential 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?

Three sentences, each adding essential value: the main action, the crucial definition of 'builder', and the temporal scope. No filler or redundant phrasing; information is front-loaded with the most important part first.

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 two-parameter read-only tool without an output schema, the description covers the core context: what it ranks, the key concept of builder, and the applicable data range. It could explicitly state the return format, but the ranking intent is clear enough.

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 already documents both parameters with descriptions (coverage 100%), so the description doesn't need to add much. It does restate the ranking metrics ('routed volume, order count or fees') that map directly to the orderBy enum values, but no additional meaning is provided 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 opens with a clear action: 'Rank Polymarket CLOB V2 builders by routed volume, order count or fees.' It defines the nuance of 'builder' and explicitly contrasts with V1 subgraphs, making the tool's unique scope unmistakable.

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 states exactly when to use this tool: to discover who is actually routing flow, a question V1 subgraphs cannot answer. It also constrains usage to V2-era data (from block 84902353, migration 2026-04-28). While it doesn't name sibling tools as alternatives, the use case is explicit.

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 must carry behavioral disclosure. It states the tool is read-only (returns data) and describes the output fields. It does not cover errors, permissions, or rate limits, but for a simple lookup tool this 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?

Three concise sentences: first states purpose and output, second reinforces role, third gives usage guidance. No extraneous information, properly front-loaded.

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

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

Given no output schema, the description adequately explains the return values (token IDs, prices, sizes, status) and how to use the output with other tools. It does not mention error handling or pagination, but for a simple query tool with one parameter, this is sufficient.

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

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

Schema coverage is 100% for the single parameter. The description adds context by explaining the parameter's role ('by condition ID') and how the output (token_id) relates to other tools, which adds meaning beyond the schema's basic hex string description.

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

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

The description clearly states the tool gets CLOB market details by condition ID, listing specific return fields (token IDs, live prices, sizes, status). It also positions itself as a bridge between on-chain IDs and CLOB data, distinguishing it from sibling tools.

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

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

The description explicitly advises using the returned token_id values with several sibling tools (get_live_prices, etc.), providing a clear next-step context. It implicitly indicates usage when you have a condition ID and need trading data, but does not explicitly state when not to use it.

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 must carry the burden. It mentions the data source ('Beefy P&L subgraph'), implying a read-only operation. However, it does not disclose any specific behavioral traits such as data freshness, pagination behavior, or any rate limits. With no annotations, a score of 3 reflects marginal value beyond the basic read 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 two sentences, directly stating the purpose and the intended use case. Every sentence adds value, and the key information is front-loaded. No redundancies or filler.

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 has only one parameter with schema documentation, no output schema, and no annotations, the description provides sufficient context for an agent to understand what the tool does, what data it returns (volume, fees, etc.), and when to use it. A minor gap is the lack of information about the output format, but it is still 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 input schema has 100% description coverage for the only parameter ('days'), with clear bounds and default. The description does not add extra meaning beyond what the schema provides. According to the rubric, baseline is 3 when schema coverage is high, so this score 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 verb 'Get', the resource 'daily platform statistics', and lists specific metrics (volume, fees, trader counts, market activity). It distinguishes itself from sibling tools like get_global_stats by specifying 'from the Beefy P&L subgraph' and focus on daily 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?

The description explicitly says 'Use this for trend analysis and historical performance', indicating the appropriate context. It does not provide exclusions or alternatives, but the usage intent is clear and sufficient given the tool's simplicity.

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 the full burden. It fails to disclose behavioral traits such as data freshness, pagination, or any side effects. The description merely defines the resource without adding operational 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 extremely concise with two sentences that convey purpose and context without any unnecessary words or repetition.

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 low complexity of the tool (one optional parameter, no output schema), the description is mostly adequate but lacks details on the output format or any example usage. It is sufficient for a simple retrieval but not fully comprehensive.

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 description coverage for the sole parameter 'first' is 100% (it includes a numeric description). The tool description does not add any further meaning beyond what the schema already provides.

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

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

The description clearly states the action ('Get') and the specific resource ('Polymarket markets that were disputed during the UMA oracle resolution process'). This uniquely identifies the tool among siblings that also retrieve market data but target different aspects.

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

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

The description explains what disputes are ('high-signal events') implying a use case for monitoring contentious outcomes, but does not provide explicit guidance on when to use this tool versus alternatives like get_market_resolution or search_markets.

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 exist, so the description bears full burden. It correctly indicates the tool is a read operation returning aggregate data, but does not disclose potential rate limits, data freshness, or caching behavior. For a simple stat, the description is adequate but minimal.

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 sentence that efficiently conveys purpose and result. No wasted words; every element 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 parameters and no output schema, the description fully covers what the tool does and returns. It provides complete context for an agent to select and invoke this 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?

The tool has zero parameters, so baseline is 4. The description adds meaning by defining what 'global open interest' represents (aggregate USDC locked in outstanding positions). This is sufficient for an agent to understand the output.

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 the total global open interest across all Polymarket markets. The verb 'Get' and resource 'global open interest' are specific and unambiguous. It implicitly distinguishes from sibling tools like 'get_market_open_interest' which operate per market.

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?

Usage context is implied by the name and description (global vs per-market), but no explicit guidance is given on when to use this tool versus alternatives like 'get_market_open_interest'. No exclusions or prerequisites are mentioned.

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

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

No annotations are provided. The description discloses the data source combination (Main and Orderbook subgraphs), which is behavioral context. However, it does not mention any side effects, auth needs, rate limits, or expected return structure. More transparency would be beneficial.

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

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

Two sentences, front-loaded with purpose. No wasted words. Every sentence adds value: the first defines the action and scope, the second details the data source integration.

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 should hint at return fields. It only says 'aggregate statistics' which is vague. Siblings like get_daily_stats are likely more specific. Missing key output details makes it incomplete.

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 are no parameters, so schema description coverage is 100%. Baseline for 0 params is 4. The description adds no param info, but none 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?

The description clearly states 'Get aggregate Polymarket platform statistics' and distinguishes by specifying it combines data from Main and Orderbook subgraphs. Among siblings like get_market_info or get_account_pnl, this is the only global aggregate tool.

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

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

The description implies use when overall platform stats are needed, but provides no explicit when-to-use or when-not-to-use guidance. No alternatives are mentioned, leaving the agent to infer context from sibling 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?

No annotations are provided, and the description does not disclose any behavioral traits such as data freshness, rate limits, error conditions, or whether the price is retrieved from cache or real-time. The description carries the full burden for transparency but only states the action.

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, concise sentence that immediately conveys the tool's purpose without any extraneous information. Every word earns its place.

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, lack of output schema, and no annotations, the description is minimally adequate. However, it lacks details on return format, possible errors, and usage context, which would make it more complete for an agent.

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 described as 'CLOB token ID'. The description does not add any additional meaning or context beyond what the schema provides, so a baseline score of 3 is appropriate.

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

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

The description clearly states the tool retrieves the last trade price for a Polymarket outcome token, using a specific verb 'Get' and resource 'last trade price'. It distinguishes itself from sibling tools like get_live_prices or get_orderbook_trades by focusing on the last traded price from the CLOB.

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

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

No guidance on when to use this tool versus alternatives like get_live_prices or get_orderbook_trades. It does not mention prerequisites, context, or exclusions, leaving the agent to infer usage from the name alone.

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, and the description does not disclose behavioral traits such as rate limits, order book depth, or whether 'full' means all levels; minimal 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?

Single sentence efficiently conveys purpose and output, with 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?

Adequate for a simple retrieval tool with one parameter; could mention depth limits or pagination but is largely complete as-is.

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

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

Schema coverage is 100% for the single 'tokenId' parameter, so the description need not add much; it provides no additional context beyond the schema's 'CLOB token ID'.

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

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

Description clearly states it retrieves the full real-time order book (bids and asks) for a Polymarket outcome token from the CLOB, distinguishing it from sibling tools like get_live_prices or get_orderbook_trades.

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

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

No explicit guidance on when to use this tool vs alternatives like get_orderbook_trades or get_live_spread; usage is implied by 'real-time' but lacks explicit 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?

Since no annotations are provided, the description carries the full burden. It discloses the tool returns the current best price for a given side, which is a read operation. It does not detail response format or error handling, but the core behavior is clear.

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 efficient sentences with no wasted words. The first sentence states the purpose, the second explains input sourcing. Perfectly front-loaded.

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

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

For a simple tool with 2 params and no output schema, the description is adequate but incomplete. It does not specify the return format or any edge cases. Siblings with more complex output might set a higher bar, but for this tool it is minimally viable.

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 extra context on where to obtain token IDs (search_markets/get_market_info, get_clob_market), which aids usage 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 the tool gets real-time CLOB prices for Polymarket outcome tokens and returns the best price for buying or selling. It specifies the source of token IDs, distinguishing it from siblings like get_live_orderbook and get_price_history.

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

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

The description gives clear context on when to use the tool (real-time best price) and references related tools for obtaining token IDs. However, it does not explicitly mention when not to use it or alternatives for full order book data.

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 behavioral disclosure. It accurately states the tool returns real-time data, but does not mention potential latency, refresh rates, or whether it is a snapshot vs. streaming. For a simple read operation, this is adequate but could be improved.

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 consists of two concise sentences: the first states the function, the second lists returned data and use case. Every word is informative with no redundancy or unnecessary detail.

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 low complexity (one parameter, no output schema, no annotations), the description sufficiently covers what the tool does, what it returns, and its purpose. No obvious gaps exist for an agent to effectively use this tool.

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

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

The single parameter tokenId is described in the input schema as 'CLOB token ID', and the description adds no further explanation. With 100% schema description coverage, the baseline is 3, and the description does not provide additional semantics beyond what the schema already conveys.

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 retrieves the real-time bid-ask spread for a Polymarket outcome token, specifying returned fields (best bid, best ask, spread). It is distinct from sibling tools like get_live_orderbook or get_last_trade, as it focuses on the spread rather than full order book depths or individual trades.

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 notes the tool is useful for assessing market liquidity and trading costs, implying when to use it. However, it does not contrast with alternatives such as get_live_orderbook or get_last_trade, nor does it specify when not to use this tool (e.g., if full order book detail is needed).

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, and the description does not disclose any behavioral traits like permissions, rate limits, or data scope beyond the minimal 'outcomes and volumes'.

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?

Single sentence efficiently conveys the tool's purpose with no unnecessary words, well-structured.

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

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

Description is adequate but does not elaborate on how this tool differs from the many sibling get_* tools; lacks completeness in guiding selection choice.

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 covers all parameters with descriptions, so the description adds no extra meaning beyond what the schema already provides.

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

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

Description clearly states the tool gets market/condition data including outcomes and volumes, specifying the source subgraph, which distinguishes it from siblings like get_market_info.

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

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

No guidance on when to use this tool versus alternatives such as get_market_info or search_markets; the description lacks any contextual usage advice.

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, but description clearly indicates a read operation (get, returns). It mentions returning full metadata from Gamma API, no side effects hinted. Could mention idempotency or rate limits, but acceptable.

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, each packed with useful information. No fluff. Front-loaded with purpose and immediate action.

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?

Without output schema, description sufficiently explains what is returned (full metadata, specific fields). Mentions key uses, making it complete for an API 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 has 100% coverage with clear descriptions for each parameter. Description adds value by explaining the relationship (conditionId used for other calls) and that either slug or conditionId is sufficient.

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?

States clearly it gets Polymarket market info by slug or condition ID, returns full metadata including description, prices, outcomes, CLOB token IDs. Distinguishes from sibling tools by mentioning conditionId usage for other queries.

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 (get market metadata) and what not to use for (CLOB data, OI, resolution), providing alternatives like get_clob_market, get_market_open_interest, get_market_resolution.

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 must carry the full burden of behavioral disclosure. It only states that the data is from the Open Interest subgraph, but does not mention data freshness, rate limits, pagination, or any other behavioral traits. For a read operation, more details are expected.

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 highly concise with two sentences. It immediately states the primary function and then adds a distinguishing remark. Every sentence adds value without unnecessary 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?

The tool has no output schema, so the description should cover what the response contains. It does not explain the return format, pagination, or structure of the market data. For a tool returning a list of markets, this lack of completeness is a significant gap.

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 100% description coverage for all three parameters, so the schema already explains each parameter. The tool description adds no additional semantic value beyond what is in the schema, earning the baseline score of 3.

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

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

The description clearly states the action ('Get the top Polymarket markets') and the specific metric ('ranked by open interest'). It also distinguishes this tool from siblings by noting that no other Polymarket subgraph tracks open interest, making the purpose unambiguous.

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 when to use this tool (when open interest data is needed) but does not explicitly state when not to use it or provide alternatives. Given the sibling list includes 'get_global_open_interest', more guidance on the distinction would improve score.

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 must convey behavioral traits. It honestly describes a read operation, but lacks details on data freshness, pagination, or potential side effects. It is adequate but not rich.

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

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

Two short, clear sentences with no unnecessary words. The key information is front-loaded.

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

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

With no output schema, the description does not detail the return format (e.g., fields of positions). It implies P&L but is vague. Adequate given the parameter richness.

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

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

Input schema has 100% description coverage, so the schema already explains each parameter. The description adds no additional meaning for the parameters 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.

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

The description clearly states the tool retrieves top positions for a specific market token, using a specific verb ('Get') and resource. It distinguishes from sibling tools like get_user_positions or get_account_pnl which focus on individual accounts.

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 mentions 'from the Beefy P&L subgraph' but does not provide explicit guidance on when to use this tool vs alternatives, nor any prerequisites or exclusions. The context signals show many sibling tools, but no comparison is given.

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 the output fields and statuses, but does not mention that the operation is read-only, side effects, potential errors, or authentication requirements. Additional behavioral context would improve 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?

A single sentence that efficiently communicates the tool's purpose and output without redundancy. Every word contributes 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 adequately describes the returned information (statuses, prices, dispute history). Omits pagination details but these are captured in the schema. Sufficient for an agent to understand what to expect.

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

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

Input schema covers 100% of parameters with descriptions and enums. The description adds no new meaning beyond confirming the tool's purpose. Baseline of 3 is appropriate as schema already provides sufficient semantics.

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 specifies the tool retrieves UMA oracle resolution status for Polymarket markets, enumerating the possible statuses (initialized, proposed, disputed, resolved) and data fields (prices, dispute history). This distinguishes it from sibling tools like get_market_data or get_disputed_markets.

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 usage when resolution information is needed, but does not explicitly state when to use this tool over alternatives like get_market_info or get_disputed_markets. No 'when not to use' or clear usage context is provided.

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 implies a safe read operation but does not explicitly state if it requires authentication, rate limits, or any side effects. The transparency is adequate but could be improved by confirming read-only nature.

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

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

Description is extremely concise with two sentences. The first sentence immediately states the purpose, and the second adds useful context. No wasted words or 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?

Without an output schema, the description could elaborate on what the response contains (e.g., fields like timestamp, moderator, action). It only says 'when and how' but is vague. For a tool with simple parameters, it is minimally adequate but not fully 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?

Input schema coverage is 100% with both parameters having descriptions. The description adds no extra meaning beyond the schema; it does not explain parameter formats or provide examples. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: retrieving moderator revisions/updates for Polymarket markets. It specifies the action ('get') and the resource ('moderator revisions'), and adds context about showing when and how moderators intervened, differentiating it from siblings like get_market_resolution which likely returns current resolution.

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 does not provide any guidance on when to use this tool versus alternatives. It lacks explicit when-to-use, when-not-to-use, or references to sibling tools. Users are left to infer usage from the purpose alone.

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 exist, so description must cover behavior. It notes hourly snapshots, default count of 168 (1 week), but does not describe return fields or pagination. Adequate but not fully transparent for a tool without output 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?

Two sentences, front-loaded with action and purpose, no extraneous words. Highly concise and well-structured.

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

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

With 3 parameters, no output schema, and no annotations, the description covers core purpose and data source. Could mention response format but overall is contextually sufficient for a read-only historical tool.

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

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

Schema coverage is 100%, so baseline is 3. Description adds no parameter details beyond the schema, only referencing conditionId source. No additional semantic value.

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

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

The description clearly states the action ('Get hourly open interest snapshots'), specifies the resource ('specific Polymarket market'), and implies distinction from siblings like get_market_open_interest (which likely gives current OI).

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 (chart OI trends) and where to get the conditionId (get_market_open_interest or subgraph). Lacks explicit when-not-to-use or alternative comparison, but context is sufficient.

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 implies a read operation but does not disclose details such as rate limits, pagination, or what 'recent' means in terms of time window.

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 sentence, which is concise but not particularly informative. It could include more context without becoming 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 low complexity and no output schema, the description is insufficient. It does not specify timeframes, result format, or how to interpret 'recent'. More context would be helpful, especially with many sibling tools.

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

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

Although schema description coverage is 100%, the description does not add any additional meaning beyond what the schema already provides. The baseline of 3 is appropriate.

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

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

The description clearly states that the tool retrieves recent order fills from the Orderbook subgraph, using a specific verb and resource. While it distinguishes itself from siblings like get_last_trade or get_live_orderbook, it could be more explicit about the 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?

No guidance is provided on when to use this tool versus alternatives like get_last_trade or get_live_orderbook. The agent lacks context to make an informed selection.

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 must carry the full burden. It states the tool 'gets' data, but does not disclose behavioral traits such as whether the eventId can be a slug, the structure of the returned markets, data freshness, or any side effects. The description adds minimal value beyond the tool name.

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 very concise: two sentences, front-loaded with the core purpose, and the second sentence provides essential context about what an event is. No extraneous words or repetition.

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 output schema and annotations, the description is adequate but not comprehensive. It explains that the event comes 'with all its associated markets,' but does not specify the format or structure of those markets. For a tool with many siblings, more detail on what 'associated markets' entails would improve completeness.

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 describes the only parameter 'eventId' as 'Event ID or slug' with 100% coverage. The description offers no additional meaning beyond the schema, so the baseline score of 3 is appropriate. No extra value is added.

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 single Polymarket event with all its associated markets.' It specifies the resource (event) and the action (get), and differentiates from sibling tools like list_polymarket_events (which lists multiple events) and get_market_* (which targets individual markets).

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 retrieving a single event, but it does not provide explicit guidance on when to use it versus alternatives such as list_polymarket_events or get_market_info. No when-not-to-use or exclusion criteria are given, leaving the agent to infer usage from the name and brief description.

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 only says 'returns time-series price points' but omits behavioral traits like read-only nature, time range limits, or response format details.

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

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

Two sentences, front-loaded with purpose, no extraneous information. Highly concise and well-structured.

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

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

Given the simplicity of the tool (3 params, no output schema), the description is adequate but lacks details on return fields or any time range constraints that would fully inform an agent.

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

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

Schema coverage is 100% with descriptions for all parameters. The tool description adds no extra meaning beyond what the schema provides, so baseline score of 3 applies.

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

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

Clearly states 'Get historical price data for a Polymarket outcome token' with the specific resource and purpose ('charting and trend analysis'). Distinguishes from sibling tools like get_live_prices or get_market_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?

No usage guidance provided. Does not explain when to use this tool vs alternatives like get_live_prices for current prices or get_oi_history for open interest.

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 and minimal behavioral detail. 'Recent' is undefined; no mention of ordering, pagination, rate limits, or possible empty results.

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?

Single sentence, clear and focused. Slightly penalized for lack of structure (e.g., no headings or bullet points), but still efficient.

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

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

No output schema and no annotations; description fails to explain what is returned or how results are ordered. Lacks essential context for a tool with optional parameters.

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

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

Schema coverage is 100% with descriptions for both parameters. The tool description adds no additional meaning beyond the schema, so baseline score of 3 is appropriate.

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

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

The description clearly states it retrieves recent splits, merges, and redemptions from the Activity subgraph, using specific verbs and resources. It distinguishes from sibling tools that focus on markets, stats, or trader profiles.

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

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

No guidance on when to use this tool versus alternatives like get_market_data or get_trader_profile. Lacks explicit context for selection.

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 must carry the full burden. It discloses that the schema is 'full' and uses the word 'introspection', which hints at the return type and scope. However, it does not mention any potential large response, error behavior, or security context, leaving room for improvement.

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, efficient sentence with no redundant words. It is front-loaded with the action and resource, making it easy to parse.

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 input (one enum parameter) and no output schema, the description covers the basic purpose but lacks guidance on return format and usage context relative to sibling tools. It is adequate but could be more helpful by stating that the result can be large or used to plan queries.

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 the baseline is 3. The tool description itself adds no parameter information. Notably, the schema parameter description lists only a subset of the enum values ('main, beefy_pnl, slimmed_pnl, activity, or orderbook') while the enum actually contains 11 values, which could confuse the agent. The description does not compensate for this inconsistency.

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

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

The description clearly states the action ('Get'), the resource ('full GraphQL schema'), and the target ('Polymarket subgraph'). It distinguishes the tool from siblings like 'query_subgraph' by specifying introspection rather than data querying.

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 schema discovery, but does not explicitly state when to use it versus alternatives like 'query_subgraph' or 'list_subgraphs'. No exclusions or alternative references are provided, so guidance 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?

No annotations provided, and description only states the core functionality without disclosing behavioral traits like pagination limits, data freshness, or rate limits. For a read-only aggregation tool, minimal transparency beyond schema is offered.

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?

Single sentence, front-loaded with verb and resource, no extraneous information. Efficiently conveys the tool's purpose.

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 list tool with only three optional parameters and no output schema, the description covers the main functionality. It could mention return format or query source, but overall is complete for the scope.

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

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

Input schema covers all three parameters with descriptions, and the description mentions ranking metrics aligning with the orderBy enum. However, description adds no new meaning beyond schema; baseline 3 appropriate due to high schema 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?

Description clearly states the tool retrieves top Polymarket traders ranked by specific metrics like realized P&L, win rate, or volume. It specifies the source (Beefy P&L subgraph) and differentiates from sibling tools focused on individual account data or market details.

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

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

Description does not provide explicit guidance on when to use this tool versus alternatives such as get_trader_profile or get_account_pnl. Usage is implied but not explicitly contrasted with siblings, leaving the agent to infer 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?

With no annotations provided, the description carries full burden. It indicates the tool retrieves data (assumed read-only) but does not disclose behavioral traits such as data freshness, pagination limits beyond the eventLimit parameter, or any side effects. This is minimal context for a data retrieval 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 a single, well-structured sentence that front-loads the purpose and enumerates key data points without extraneous words. Every element is valuable.

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

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

The description lists three categories of data (first appearance, CTF events, USDC flows) but does not specify the structure or types of events. Given no output schema and no annotations, more detail on the returned profile shape would improve completeness. It is adequate but not thorough.

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 covers both parameters (address, eventLimit) fully (100% coverage). The description adds no additional meaning beyond what the schema provides. At baseline, the schema does the work, so a score of 3 is appropriate.

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

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

The description clearly specifies the verb 'Get' and the resource 'trader's on-chain profile', listing concrete data points (first appearance, CTF events, USDC flows). It distinguishes itself from sibling tools by focusing specifically on trader profile 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?

The description implies the tool is for fetching trader profile information but does not provide explicit guidance on when to use it versus sibling tools (e.g., get_top_traders, get_trader_usdc_flows). No exclusions or contexts are described.

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 must disclose behavioral traits. It mentions the tool shows transfers but does not specify whether it is real-time, cached, or any safety/authentication requirements. The description is adequate for a simple read tool but lacks depth.

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 two sentences long, front-loaded with the core purpose, and contains no unnecessary information. Every word earns its place.

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 (3 parameters, no nested objects, no output schema), the description is fairly complete. It explains the tool's purpose and primary use case. However, it lacks details about the return format or pagination, which would benefit from mention since no output schema exists.

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 100% description coverage, so the schema already explains all parameters. The description adds no additional semantic meaning beyond what is in the schema, meeting the baseline for high coverage but not exceeding it.

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 retrieves USDC deposit/withdrawal history for a trader, specifying inbound and outbound transfers. This distinguishes it from sibling tools like get_account_pnl or get_trader_profile, which focus on other 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?

The description provides a use case: tracking when traders fund or withdraw from Polymarket. While it implies when to use it, it does not explicitly exclude alternatives or state when not to use it.

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 adds the fallback behavior but lacks details on rate limits, authentication, side effects, or response caching.

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

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

Two sentences, front-loaded with purpose, no wasted words. Every sentence earns its place.

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?

No output schema exists, and the description does not explain the return format or fields. For a tool returning positions, more detail on the response structure would be helpful.

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

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

Schema coverage is 100% and already clearly describes the single parameter. The description adds no additional meaning 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 the action (Get), object (user's current positions), and source (Slimmed P&L subgraph), distinguishing it from sibling tools like get_market_positions.

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?

Only minimal guidance is provided: mentions graceful fallback when indexers are behind, but no explicit when-to-use vs alternatives or when not to use.

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 of behavioral disclosure. It reveals critical behavior: the exclusion of exchange contracts and the fact that the V2 exchange's account row would otherwise skew data. It also specifies the V2-era block threshold. This adds meaningful context beyond the schema, though it omits details like pagination or return format.

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

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

The description is three sentences, front-loaded with the primary purpose. The second sentence explains why to use it and the third sets a precise scope. No filler; each sentence contributes essential information for tool selection and invocation.

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 list tool with two optional parameters and no output schema, the description covers purpose, usage guidance, and behavioral nuance. It lacks mention of pagination or return shape, but the tool's simplicity and the presence of a limit parameter make this acceptable. The description is complete enough for an 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 descriptive parameter names and descriptions (limit and orderBy with enum). The description's 'by volume or trade count' aligns with the orderBy enum but adds no new syntax or format details beyond schema. Baseline 3 is appropriate since the schema already explains parameters effectively.

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 'Top real traders on Polymarket CLOB V2 by volume or trade count', specifying both the resource and the sorting metrics. It also distinguishes itself from the sibling 'get_top_traders' by explicitly limiting to V2-era and excluding exchange contracts, making its scope unambiguous.

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 instructs when to use this tool: 'Use this rather than querying accounts directly', and explains why (the V2 exchange itself acts as taker and would outrank humans). This provides a clear alternative and a rationale, satisfying the when/why-not guidance requirement.

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 must carry the full burden of behavioral disclosure. It only states that the tool uses the Gamma API, but fails to mention pagination, rate limits, data freshness, or behavior when no events match. This is insufficient for safe and effective invocation.

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 at two sentences, front-loading the key action and clarification of events. It avoids unnecessary detail. However, it sacrifices some informative context for brevity.

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?

With 6 optional parameters, no output schema, and no annotations, the description is incomplete. It lacks information on return format, example usage, or any constraints like minimum/maximum limit. The conceptual explanation partially compensates but overall leaves significant gaps.

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

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

The input schema covers all 6 parameters with descriptions, achieving 100% coverage. The description adds no parameter-specific meaning beyond the schema; the conceptual explanation of events is helpful but not tied to parameters. Baseline score of 3 is appropriate.

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

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

The description clearly identifies the action as listing events, explains that events bundle multiple markets, and gives a concrete example (US Presidential Election 2024). This effectively distinguishes it from sibling tools like get_polymarket_event (single event) or search_markets.

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 does not provide explicit guidance on when to use this tool versus alternatives. While the purpose is clear, it lacks instructions on filtering scenarios or when to prefer this over search_markets or get_polymarket_event. The context of optional parameters implies usage but does not directly guide 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?

The description indicates a read-only operation ('List'), consistent with the name, but does not disclose any additional behavioral traits such as authentication requirements, rate limits, or pagination behavior. Since annotations are absent, the description carries the full burden but adds only minimal 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 a single concise sentence with no redundant or extraneous information. Every word adds value.

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

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

Given the simplicity (no parameters, no output schema), the description is largely complete. However, it could be enhanced by clarifying the relationship to sibling tools like get_subgraph_schema or query_subgraph, but current level is sufficient for basic understanding.

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 no parameters, and schema coverage is 100%. The description does not need to explain parameters, and it appropriately mentions 'all available' implying no filtering. No compensation required, so baseline 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 states the action (List), the resource (all available Polymarket subgraphs), and includes additional details (descriptions and key entities). It effectively distinguishes from siblings like get_subgraph_schema, which likely deals with schema details, and query_subgraph for executing queries.

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 usage for listing all subgraphs but provides no explicit guidance on when to use this vs. alternatives like get_subgraph_schema. It does not mention when not to use it or any prerequisites.

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 full burden of disclosing behavior. It only states what the tool does ('Execute a custom GraphQL query'), but does not mention whether queries are read-only, what happens on malformed queries, rate limits, or the shape of the response. This is comparable to the 'Update shared drive settings' example, which lacked permission and side-effect context and scored 2.

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, front-loaded sentence with no wasted words. It is appropriately concise for a tool whose name and schema already convey much of the structure. However, it is slightly under-specified for the behavioral side, which prevents a perfect score.

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?

While the schema covers parameters, the description does not explain the intended use case relative to siblings, the nature of a 'subgraph' query, or the response format (especially since no output schema exists). For a generic tool with many specialized alternatives, this is insufficient context for an agent to select and invoke it correctly without additional inference.

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% for all three parameters, so the schema fully documents parameter meanings. The description adds no additional parameter semantics beyond the schema. Per the rubric, a baseline of 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the action ('Execute a custom GraphQL query') and the target resource ('Polymarket subgraph'), which is specific and distinguishes it from the list of specialized sibling tools that return fixed data types. The word 'custom' signals that this is the generic, low-level option rather than a pre-built endpoint.

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

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

No explicit guidance is given about when to use this tool versus the many specialized sibling tools (e.g., get_live_orderbook, get_user_positions). The word 'custom' implies it is for queries not covered by dedicated endpoints, but this is only implied, not stated. There is no exclusion or alternative mention.

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. It mentions 'real-time market discovery' and uses Gamma API, which is informative. However, it fails to disclose safety (e.g., read-only), limitations, error behavior, or response format beyond listed fields, leaving some transparency gaps.

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: two sentences and a list of chaining suggestions. It is front-loaded with the main purpose and every sentence adds value. 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 no output schema, the description adequately explains return values (market metadata). It also provides workflow context via chaining. However, it omits default sort order (descending) and pagination details, though the schema covers these. Overall sufficient for a search 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 the description adds minimal value beyond the schema. It only references the 'query' parameter. The schema already provides clear descriptions for all 6 parameters, including enums and defaults, making the description's marginal contribution limited.

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 searches Polymarket prediction markets by text query and returns specific metadata (question, prices, volume, liquidity, CLOB token IDs). It distinguishes from siblings by mentioning chaining with other tools like get_clob_market and get_live_prices, and the presence of search_markets_enriched implies this is a basic search.

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 says when to use this tool (searching by text query) and provides concrete chaining suggestions for further actions (order books, pricing, etc.). However, it does not explicitly exclude alternatives or state when not to use it, such as preferring search_markets_enriched for more details.

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 combining three APIs but does not mention failure modes, rate limits, or partial failures during enrichment.

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

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

Two sentences, front-loaded with purpose, 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 complexity of multi-API enrichment and no output schema, the description explains what enrichment includes but lacks details on output structure or potential errors.

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

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

Schema coverage is 100%, and schema descriptions are adequate. The description adds minimal extra meaning beyond what schema provides.

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 searches Polymarket markets and enriches them with CLOB prices and resolution status. It explicitly distinguishes from manual chaining and sibling tools like search_markets (basic).

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 says 'no need to chain tools manually,' implying use when enrichment is needed. However, it does not provide explicit when-not-to-use guidance or alternative tools for simpler cases.

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