NightWatch Live Intelligence

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

With no annotations, the description provides behavioral context: no wallet needed, free tier limit of 100 req/day, and hints at returning an API key. It does not fully explain side effects like key replacement, but is adequate for a registration 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?

Three concise sentences front-load the primary action and outcomes, with 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?

For a simple 1-parameter tool with no output schema and absent annotations, the description covers purpose, usage order, and limits. It omits details about how to use the API key, but remains fairly complete.

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

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

Schema description coverage is 100%, with the parameter agent_name already described. The description adds no extra parameter detail, 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 the verb 'Register' and the resource 'as a NightWatch agent', with the outcome 'get an API key'. It distinguishes itself from sibling tools like agent_contribute or agent_status by explicitly stating this is the first step.

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 advises 'Call this first before submitting discoveries', giving a clear usage order. However, it does not mention when not to use or alternatives, but for a registration tool this 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?

No annotations provided, so description carries full burden. It discloses that contributions are verified (evidence + no duplicate) and lead to Cherry credits and reputation building. It does not detail auth requirements, rate limits, or exact failure modes, but provides sufficient behavioral context.

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

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

The description is fairly concise and front-loaded with the core purpose. It packs detail into one paragraph but could be slightly tighter. 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?

Given the tool has 6 parameters, no output schema, and nested objects, the description covers the distinction between kinds and required fields. However, it does not describe the API response or error states, which leaves a gap for an agent to understand what is returned.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the 'kind' enum semantics, the requirement of evidence_url, and the structure of 'fields' for metadata. It also clarifies rejection criteria 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's purpose: leave a sourced contribution to earn Cherry. It distinguishes between the 'observation' and 'metadata' kinds with specific rules, and contrasts with the read-only sibling tools (e.g., get_price, get_stats) by being a write/contribution 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 explains when to use each kind: observation for factual claims with evidence, metadata for filling token data fields. It also states what is rejected (uncited contributions). However, it does not explicitly mention alternative tools or when not to use this tool, though siblings are all read-only.

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 must disclose behavior. It mentions that the tool returns status data (contributions, balance, tier) and notes persistence, but lacks details on side effects, authentication needs, or rate limits. It is adequate but not thorough.

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-loading the purpose and efficiently covering key information. Every sentence adds value, with no redundancy.

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

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

Given no output schema and no annotations, the description adequately conveys the tool's purpose and output. It could be more specific about the output format, but it is 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?

The tool has zero parameters, so the baseline is 4. The description exceeds baseline by explaining the output (contributions, balance, tier), providing additional context beyond the empty input 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 that the tool shows the agent's standing, including contributions (total/verified/pending), Cherry balance, and reputation tier. It uses specific verbs ('see your standing') and distinguishes from sibling tools like agent_connect and agent_contribute, which focus on other actions.

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 advises calling the tool to see your standing and notes that presence persists across calls. It implicitly suggests when to use it, but does not explicitly state when not to use it or provide alternatives.

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

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

Discloses that data is sourced with citation URLs, refuses uncited data, and lists three categories of returned data. No annotations are provided, but the description sufficiently covers behavior.

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

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

The description is thorough but slightly verbose; however, it is well-organized with clear examples and usage guidance. Could be tightened slightly but remains effective.

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 complex tool (returns multiple data types) and simple input schema, the description fully explains the output structure and use case. No output schema exists, but the description compensates.

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 a description for the single parameter. The description adds value by noting fuzzy matching and examples of accepted formats (slug or name).

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 tool as a knowledge graph lookup for companies, with specific examples of entities and types of returned data. It distinguishes itself from sibling tools like get_price or get_price_stats by focusing on fundamentals, relations, and citations.

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

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

Explicitly states 'Use this BEFORE reasoning about a company's fundamentals, competitors, supply chain, or a hedge on its equity perp', providing clear when-to-use guidance.

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 bears full responsibility. It discloses a null-safe behavior ('has_data=false when no HF capture exists'), which is valuable. However, it does not specify whether the operation is read-only, permission requirements, rate limits, or performance characteristics, leaving significant 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 extremely concise—two sentences with no redundancy. It front-loads the key outputs and includes a crucial behavioral note (null-safety). 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 complexity of microstructure data and no output schema, the description does a decent job listing output fields. However, it lacks structural details (e.g., whether fields are nested, always present) and does not contextualize the tool among siblings or provide usage heuristics. It is adequate but not 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?

Input schema has 100% coverage with clear descriptions for both parameters (symbol, exchange). The description adds no further parameter-level detail, so it does not exceed the baseline. The mention of 'token' is generic and not parameter-specific.

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 defines the tool as retrieving high-frequency orderbook microstructure metrics (quality score, quote persistence, etc.), distinguishing it from sibling tools like get_price or get_stats. The verb is implied but the resource and outputs are well-specified.

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 compared to alternatives (e.g., get_token_intel). The description does not mention prerequisites, limitations, or preferred contexts, leaving the agent to infer usage from the output listing 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 provided, so the description carries full burden. It details the gate mechanics: contract identity check, withdraw/deposit door check, chain canonicalization, and unknown-skip. Also lists return fields. This provides good transparency beyond the basic operation.

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

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

The description is front-loaded with the primary purpose. It is somewhat lengthy but each sentence adds value. Minor redundancy could be trimmed, but overall it is structured well.

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 explains all key return values (viable, reason(s), per-chain doors, contract identity verdicts, chosen route). The tool has moderate complexity and the description covers it thoroughly, making it complete for an AI agent.

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

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

Schema coverage is 100%, but the description adds extra context: buy_ex is the withdraw side and sell_ex is the deposit side, with examples (e.g., gateio, bithumb). This adds value beyond the schema's 'venue' 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's purpose: returns a cross-venue PAIR GATE verdict on whether a BUY-to-SELL trade is executable. It specifies inputs (base, buy_ex, sell_ex) and outputs, and distinguishes from siblings by being the primary identity+mobility check before any cross-venue trade.

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

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

The description explicitly states this is the primary check before any cross-venue trade, giving clear usage context. However, it does not explicitly state when not to use or mention alternatives, though sibling tools do not overlap.

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 explains the output (numeric price in USD), null-safe behavior (returns null with note if not found), and the dual-dex structure (crypto vs equity). It adds context beyond a minimal description, though lacks details like rate limits or authentication.

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 (3-4 sentences) with clear front-loading of the core action. Every sentence adds value: purpose, supported assets, return type, null safety. No fluff 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?

For a simple price tool with no output schema, the description covers return value, error handling (null safety), and both use cases (crypto and equity). It is self-contained and sufficient for correct invocation.

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%, baseline 3. The description adds meaning by explaining the market parameter's purpose (crypto vs equity dex) and giving concrete symbol examples (BTC, ETH, SMSN, NVDA). This goes beyond the schema descriptions.

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

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

The description uses a specific verb+resource ("Get the LIVE mid price") and clearly distinguishes this tool from siblings. It lists supported assets (crypto majors and equities) and explicitly states it answers quote requests like 'what's X trading at?'.

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

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

The description provides clear usage context by specifying the types of questions it answers (quote requests). It does not explicitly mention when not to use it or name alternative sibling tools, but the context is strong enough to guide correct invocation.

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

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

With no annotations provided, the description carries full burden. It discloses the data source (HyperLiquid candles, real data) and default window. It does not explicitly declare read-only or mention rate limits, but the intent 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?

Description is concise, front-loaded with purpose, and each sentence adds value. No unnecessary words, and it efficiently packs usage guidance, examples, and data source.

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 (current price, high, low, open, % changes). It covers the main use case and parameter behavior. Minor omissions like maximum window or error handling, but overall complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds substantial value by explaining the market examples (crypto vs equity), default hours, and the meaning of returned percentage changes, which goes 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 uses specific verbs and resources ('Get intraday/window OHLC stats') and clearly differentiates from sibling tools like get_price and get_stats by detailing the drawdown, bounce, and change from open statistics.

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

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

Explicitly states when to use this tool with example queries ('how far off the high/low', 'today's high and low') and provides market examples. However, it does not explicitly mention when not to use it or list alternatives.

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 carries full burden. It discloses shadow mode, paper-only moves, and the absence of real capital actions. It does not explicitly state read-only, but the context implies safety.

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 despite the detailed field list, with every sentence adding necessary context. It front-loads the core purpose and avoids redundancy.

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

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

Given no output schema, the description fully enumerates the returned data (floor/cap bands, status, flags, FX, logistics). It also provides operational context (shadow mode) and pointers to related endpoints, making it self-contained.

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

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

No parameters are defined; schema coverage is 100% by absence. Description adds value by explaining what the single implied input (the fund state) contains, meeting the baseline for 0 parameters.

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

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

The description clearly specifies the tool returns the 'DECISION STATE' of the Quartermaster shadow fund, listing all included fields. It distinguishes from separate live-balance endpoints, making the tool's exact scope obvious.

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?

While it references separate endpoints for live outcomes (guiding away from misuse), no explicit when-to-use vs. siblings is given. Sibling tools are not mentioned, but the contrast with other endpoints is helpful.

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

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

No annotations are provided, so the description must disclose behavior fully. It lists output categories but omits details like read-only nature, performance impact, or data freshness guarantees. Agent lacks critical 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?

A single sentence that is concise, front-loaded with the purpose, and contains no unnecessary words. Every part of the description contributes to understanding.

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 tool with zero parameters and no output schema, the description is largely complete by listing the categories of returned statistics. However, it could benefit from noting if the data is real-time or cached.

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?

No parameters exist, and schema coverage is 100%. The description adds value by explaining what the output contains, such as exchange coverage and token counts. This compensates for the lack of parameter documentation.

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 specifies verb 'get' and resource 'platform-wide statistics', listing categories like exchange coverage, token counts, scanning status, and data freshness. This effectively distinguishes it from sibling tools that focus on specific data types.

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 needing broad statistics but does not explicitly state when to use or not use this tool versus alternatives like get_price or get_token_intel. No exclusions or context are 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?

With no annotations, the description carries full burden for behavioral disclosure. It focuses on output data but does not mention side effects, authentication requirements, rate limits, or whether it is read-only. The 'ONE-call' hint implies a single request but lacks detail.

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 overly verbose, listing many data fields in a single paragraph. It lacks structure (e.g., bullet points) and could be shortened while retaining essentials. The front-loaded purpose is good, but the length impacts clarity.

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

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

Given the tool's complexity (many data fields) and no output schema, the description is fairly comprehensive in enumerating outputs. However, it does not explain how to interpret or use the data for trading decisions, leaving 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?

Schema description coverage is 100%, so baseline is 3. The description does not add extra meaning to the parameters beyond what the schema provides. It lists output data but does not elaborate on parameter usage.

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 this is a 'ONE-call token intelligence for trading decisions' and lists specific data fields. It distinguishes from siblings by explicitly advising 'Call this FIRST before deciding what to do with a token.' The verb 'get' and resource 'token intel' are clear.

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 guides when to use the tool: 'Call this FIRST before deciding what to do with a token.' It provides clear context but does not explicitly state when not to use it or list alternatives.

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 fully disclose behavior. It indicates a read operation for research data but does not mention any potential destructive actions, rate limits, authentication requirements, or other side effects. The lack of transparency is a gap for a tool with no annotation support.

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, short sentence that immediately conveys the tool's purpose. It is front-loaded with the key verb 'Get' and specifies the resource and data types. Every word is informative with no redundancy.

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

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

For a data retrieval tool with no output schema, the description provides a reasonable list of what the research data includes (grade, liquidity metrics, risk assessment, percentile rankings). This helps the agent understand the output's scope, though additional details about the response structure 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?

Schema description coverage is 100%, with both parameters (symbol, exchange) having brief descriptions. The tool description does not add additional meaning or context beyond the schema, such as format constraints or 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 retrieves comprehensive research data for a token, listing specific components like grade, liquidity metrics, risk assessment, and percentile rankings. This differentiates it from siblings like get_price or get_stats, though it could more precisely indicate that input is a trading pair (exchange + symbol), not a token address.

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_price, get_stats, or get_token_intel. The description does not mention when not to use it or provide criteria for selection, leaving the agent 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 provided. Description indicates read-only behavior (returns lists) but does not disclose any additional behavioral traits like potential rate limits or data freshness.

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

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

Two well-structured sentences. First defines the tool's concept, second explains output and optional parameter. No redundant information.

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

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

Given no output schema, the description sufficiently explains return of full club list with grades and optional membership check. No critical missing context for this straightforward 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 for the single parameter. Description adds value by explaining that the symbol checks membership for one base ticker, enhancing understanding 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 returns tokens listed on all monitored exchanges (the strongest listing consensus signal) with grades. It distinguishes itself as the 'unanimous' club among siblings like get_microburst, but does not explicitly contrast use cases.

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 mentions optional symbol parameter for membership check, implying use for consensus signals. No explicit guidance on when not to use or alternatives among siblings.

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

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

Discloses return fields, severity scale, convergence meaning, and outcome states. With no annotations, description provides sufficient read-only behavioral context, though authentication or rate limits are omitted.

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 key information. No fluff; 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?

Covers time range, return fields, and convergence interpretation. Lacks explicit mention of result format (list of objects) but overall sufficient for a simple query tool with two 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 covers 100% of parameter descriptions (symbol and exchange). Tool description adds no extra semantic value beyond the schema's text, so baseline 3 is appropriate.

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

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

Clearly states the tool retrieves early-warning signals for a token over the last 7 days, specifying return fields and interpretation. Distinguishes from sibling tools like get_price or get_microburst as it focuses on warnings.

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?

Implied usage for monitoring risk or anomalies, but no explicit comparison to siblings or when not to use. Lacks guidance on prerequisites or alternatives.

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 takes full burden. It discloses that the search spans 'all monitored exchanges', indicating scope. The tool is likely read-only, which is reasonable for a search. No hidden behaviors like destructive actions or auth requirements are mentioned, but the description is sufficiently transparent for a simple search 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?

Single sentence, no wasted words, front-loaded with action and resource. Perfect for quick comprehension.

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, so the description should ideally mention return format (e.g., list of matching tokens with key details). It does not. However, for a search tool, the purpose is straightforward, and the description is minimally complete for basic use.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds no additional meaning beyond the schema, so the baseline of 3 applies. The description does not explain how the query works (e.g., case sensitivity, partial match) or how exchange filtering behaves.

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 for tokens by symbol or name across all monitored exchanges. This distinguishes it from sibling tools like get_price (which retrieves specific price data) or get_stats (which provides statistics). The verb 'Search' and resource 'tokens' are specific and 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?

No explicit guidelines on when to use this tool versus alternatives. The description implies usage for finding tokens by partial name/symbol, but does not instruct when not to use it (e.g., if exact token address or price is needed, other tools like get_token_balance or get_price would be more appropriate). No exclusions or context about prerequisites.

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