tollbooth

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

Annotations are empty, so the description must fully disclose behavior. It mentions output fields but lacks information on rate limits, authentication, data freshness, or other operational traits.

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 clearly conveying the tool's function with no redundancy. Front-loaded with key 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?

Lacks output schema and details on result format, pagination, or error handling. The description gives a hint of output content but is not fully complete for an agent to use without assumptions.

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 description does not add further meaning beyond the schema's parameter 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 specifies the verb 'Search' and the resource '20M+ FDA adverse drug event reports via openFDA', clearly distinguishing it from sibling tools like drug_labels and drug_recalls.

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, nor any exclusions or context. The description merely states what it does.

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 empty annotations, the description carries the full burden. It only says 'Get' without elaborating on side effects, authorization needs, rate limits, or return format. The behavioral trait is 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 efficient sentence with no redundant words. It is front-loaded and compact.

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 one parameter and no output schema, the description covers the essential purpose and enumerates valid profiles. No missing information.

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

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

Schema coverage is 100%, and the schema already describes the 'profile' parameter with the same list. The description adds no additional meaning beyond the schema; 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 'Get' and the resource 'drop-in agent security policy templates', and enumerates the specific profiles. It distinguishes itself from siblings like 'check_agent_policy', which likely checks rather than retrieves templates.

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, nor any exclusions or prerequisites. The description only states what it does without context for appropriate usage.

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

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

With no annotations provided, the description carries the full burden. It states the return types (threats, detection hints, etc.) which is good, but does not mention idempotency, read-only nature, rate limits, or any required authentication. For a query tool, this 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 a single sentence that immediately conveys the core purpose and output details with zero wasted words. It is well 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 and a simple query tool, the description adequately explains what is returned (threats, detection hints, mitigations, rule IDs). However, it could mention if results are paginated or if there is a limit on returns.

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 input schema already describes each parameter (id, category, severity) with clear definitions. The description adds no additional context beyond what is in the schema, earning a 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 tool queries the OWASP Agentic Top 10 threat catalog (ASI01-ASI10) and specifies the type of information returned (threats, detection hints, mitigations, detection rule IDs). This clearly distinguishes it from sibling tools like cve_search or mcp_supply_chain_iocs.

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 for OWASP Agentic threat queries but does not explicitly state when to use vs alternatives or mention any prerequisites or exclusions. For example, it could contrast with general vulnerability searches.

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 empty annotations, the description carries full burden for behavioral disclosure. It states what the tool returns but does not disclose whether it is read-only, requires authentication, has rate limits, or any side effects. For a scoring tool, the lack of explicit read-only hint or behavioral caveats is a gap.

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 front-loads the main purpose and includes both inputs and outputs. It is efficient with no filler, but could be slightly improved by separating the return items for 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 has 4 required parameters (all boolean and an array) and no output schema, the description explains the concept ('lethal trifecta') and lists return values. It provides enough context for a low-complexity scoring tool, though a bit more detail on what 'decomposition advice' entails would help.

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 description's phrase 'private data + untrusted content + outbound actions' maps to the boolean parameters but adds no new nuance. The 'compensating_controls' array is mentioned in the description only as part of the return, not in parameter semantics. The schema already fully documents the parameters, so the description adds minimal 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 tool scores an agent's 'lethal trifecta risk' and lists its components (private data, untrusted content, outbound actions). It also specifies the outputs (risk level, missing controls, decomposition advice). This provides a specific verb-resource pair and distinguishes it from sibling tools like 'agent_security_policies' or 'check_agent_policy' that focus on broader security or policy checking.

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 no guidance on when to use this tool versus alternatives, nor does it mention prerequisites, when not to use it, or which sibling tools might be more appropriate for different contexts. It is purely functional.

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?

Annotations are empty, so the description must carry the full behavioral burden. It only says 'airport details' without specifying whether the operation is read-only, any authentication needs, rate limits, or side effects. The description adds 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?

The description is concise with one front-loaded sentence. It wastes no words. However, it could be slightly improved by adding more detail without losing conciseness.

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 lookup tool with one parameter and no output schema, the description should clarify what details are returned. It omits any information about the output structure or content, leaving the agent without essential context for using the response.

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 has 0% description coverage; the tool description adds that the single parameter is an ICAO code, providing some meaning. However, it does not clarify the expected format or any constraints, leaving the agent to infer what a valid ICAO code looks like.

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 airport details by ICAO code, identifying the verb (lookup/get) and resource (airport) with a specific key. However, it does not differentiate from sibling tools like flight_intel, and lacks specifics on what details are returned.

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, no context on prerequisites or exclusions. Usage is only implied: when an ICAO code is known and airport details are 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?

Annotations are empty, so description bears full weight. It implies a read operation but does not disclose any behaviors like data freshness, rate limits, or whether historical data is returned. No contradictions.

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 is well front-loaded and efficient. No wasted words, though could be slightly expanded without losing conciseness.

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 rich sibling set, the description is too sparse. It does not explain return structure or limitations, leaving the agent underinformed.

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

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

Schema description coverage is 0%. Description only says 'for any location', adding minimal meaning beyond schema. No details on coordinate format, bounds, or required precision.

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 PM2.5, UV index, and pollutants for any location via lat/lon, distinguishing it from general weather tools. However, it does not explicitly differentiate from similar air quality tools like openaq_air.

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 openaq_air or weather_current_global. The description lacks 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?

Annotations are empty, so the description carries full burden. It indicates the tool performs scoring and computes viral flags but does not disclose side effects (e.g., readOnly status), rate limits, or data sources beyond mentioning proxies. Limited behavioral disclosure.

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 lines covering key outputs and sources, front-loaded. Some jargon ('proxies', 'Polymarkets') reduces clarity, but overall 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 exists, so the description should explain return values. It mentions velocity scoring, momentum_score, and viral flag but lacks format details and clarity on 'proxies'. Adequate but not fully 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%, providing parameter descriptions. The description adds minimal value beyond the schema, only contextualizing 'query' as a topic filter and 'window' as time range. Baseline score 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?

The description states the tool computes attention momentum scores from HN and Reddit, with outputs like points/hr, momentum_score, and viral flag. While specific, the jargon ('Polymarkets', 'proxies') may obscure the purpose, and it does not differentiate from sibling tools like reddit_search or hn_frontpage.

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. The description lacks explicit when-to-use/when-not-to-use instructions or comparisons to siblings.

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

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

With no annotations, the description carries the full burden. It only states it's a forecast but does not disclose data freshness, output format, or any behavioral traits. Minimal disclosure.

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 is extremely concise and front-loaded. It is appropriately sized for a zero-parameter tool, though slightly lacking in 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 no parameters, output schema, or annotations, the description provides minimal context: only that it's a NOAA aurora forecast. It lacks specifics on time range or output format, but may be adequate for a simple 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?

There are no parameters; schema coverage is 100%. The description adds no parameter information, but none is needed. Baseline high due to zero parameters.

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

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

The description clearly states it provides the NOAA aurora oval forecast, specifying both the resource and source. However, it does not differentiate from sibling tools like space_weather or space_weather_kp.

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. There is no mention of context, prerequisites, or when this tool is preferred over similar weather or space weather tools.

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

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

Annotations are empty, so the description bears full burden. It provides no behavioral information such as whether the operation is read-only, any rate limits, or what happens with invalid addresses. The description only states the basic function.

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, consisting of a single sentence. It is front-loaded with the core purpose. However, it may be too terse, missing necessary details for a well-rounded description.

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 (one parameter, no output schema), the description covers the basic purpose but lacks details on what the response includes (e.g., how balance is returned, transaction history format). It is minimally adequate but not 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 0% description coverage, and the description does not add any meaning beyond the parameter name 'address'. It does not specify the expected format (e.g., base58 vs bech32) or constraints. The parameter name alone provides basic 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?

Description states it retrieves Bitcoin address balance and transaction history, but lacks an explicit verb like 'Retrieve' or 'Get'. The purpose is clear but minimal, and does not differentiate from siblings like 'btc_mempool_fees' or 'crypto_price_simple'.

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 guidelines provided. The description does not specify when to use this tool, any prerequisites (e.g., valid address format), or alternatives among sibling tools.

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

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

Annotations are empty, so the description carries full burden. It says 'recommended' but does not disclose whether it's a read operation, rate limits, data source, or how fees are computed. This is insufficient for an agent to understand behavioral traits.

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

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

The description is short and front-loaded, but it could be more informative without being verbose. One sentence is concise, but simplicity does not excuse lack of 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 no output schema, the description should explain the return format (e.g., sat/byte, fees per transaction priority). It lacks context on what 'recommended' means (e.g., based on mempool congestion). Completeness is low.

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 coverage is 100% trivially. The description adds nothing about parameters, but none exist, 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 that the tool provides recommended Bitcoin network fees, which is a specific resource and action. It distinguishes from sibling tools like btc_address_balance (balance) and crypto_price_simple (price), so the agent can easily understand what it does.

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 no guidance on when to use this tool versus alternatives, nor any conditions or context. For example, it does not explain that this is for fee estimation or when to prefer it over other fee-related tools.

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

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

No annotations exist, so the description must disclose behavioral traits. It fails to explain how business days are calculated (e.g., which days are considered weekend/holiday), whether start and end dates are inclusive, or what happens if days_ahead is used.

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 but at the expense of essential information. It consists of only three words, which is insufficient for an agent to understand how to use the tool correctly.

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, no annotations, and three undocumented parameters, the description is completely inadequate. The tool's behavior, inputs, and outputs are all unclear, leaving an AI agent with no actionable guidance.

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

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

Schema description coverage is 0%. The description adds no meaning to the parameters. It does not explain the role of start_date, end_date, or days_ahead, nor their allowed formats or relationships.

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 'Business days calculator' indicates the general domain but is vague. It does not specify whether the tool calculates the number of business days between two dates, adds business days to a date, or something else. Given the input schema with start_date, end_date, and days_ahead, the exact operation is ambiguous.

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 guidelines are provided. The description does not indicate when to use this tool versus any of the many sibling tools. There is no context about typical use cases or 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?

Annotations are empty, so the description must cover behavioral traits. It does not state whether the tool is read-only, whether it respects robots.txt directives, or if it fetches files from the remote server. This lack of detail leaves important behavioral ambiguity for an agent.

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 of 12 words, perfectly concise and front-loaded with the key information. Every word is necessary and no fluff exists.

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 is simple with one parameter and no output schema. The description covers the input and the files checked but omits what the output will look like or how results are structured, leaving the agent to guess the return format.

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 'domain'. The description adds no further meaning beyond the schema's description ('Domain to check, e.g. example.com'), which is already clear. 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 'check' and the specific resources (robots.txt, llms.txt, security.txt, agent.json). It conveys the purpose of retrieving crawl/discovery signals for a domain. However, it does not explicitly differentiate from sibling tools like agent_security_policies, which have similar scopes.

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 'before an agent touches a site', giving context. However, it offers no guidance on when not to use it or mention alternatives among siblings, leaving the agent to infer when this tool is the best choice.

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 behavioral disclosure. It fails to state whether the tool is read-only, has rate limits, or any side effects. For a tool that likely queries an external API, this is a significant gap.

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 short (one phrase), which could be considered concise, but it lacks essential structure and detail. It does not front-load critical info or use formatting to aid readability.

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 simple parameter set, the description should explain what the returned citation graph contains (e.g., list of papers, metadata). It does not, making it incomplete for the agent to understand the tool's output.

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

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

Schema description coverage is 0%, and the description does not explain the parameters. 'paperId' and 'direction' are not defined anywhere, leaving the agent to guess their meaning (e.g., what values for 'direction', how to obtain a paperId).

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 'Citation graph via Semantic Scholar' indicates the tool retrieves a citation graph for a paper, but it does not specify what type of graph (e.g., citing papers, references, or both). This vagueness makes it less clear compared to sibling tools like 'paper_details' which have more specific names.

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?

There is no guidance on when to use this tool versus alternatives like 'search_papers' or 'paper_details'. The description does not mention prerequisites or typical use cases, 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?

Annotations are empty, so the description bears full responsibility for behavioral disclosure. It only states it searches studies, with no mention of rate limits, data freshness, authentication needs, or that it is read-only. The behavioral traits are largely implicit.

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 efficiently conveys the source, scope, and key filters.

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 3 parameters (1 required), no output schema, and no annotations, the description provides adequate context: the source, version, and filterable aspects. However, it does not explain return format or pagination, making it 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 the baseline is 3. The description adds marginal value by mentioning 'sponsors, phases' which are not explicit parameters but hints at search capabilities. It does not elaborate on parameter syntax or format 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 a specific verb ('Search') and resource ('480K+ clinical studies via ClinicalTrials.gov v2'). It lists key aspects (Status, sponsors, phases, results) that clearly distinguish it from siblings like search_pubmed which searches biomedical literature.

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 explicit guidance on when to use this tool versus alternatives (e.g., adverse_events, drug_labels). It lacks context about when not to use it or what complementary tools exist.

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?

Annotations are empty, so the description must disclose behavioral traits. Only states 'scan', implying read-only, but lacks details on return format, cost, or side effects. This is insufficient 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 sentence with no fluff, but it is too brief to be fully informative. It sacrifices completeness for conciseness.

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, no parameter details, and no annotations, the description is incomplete. It does not explain what the scan returns, how results are structured, or how to use the limit parameter.

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 one optional parameter 'limit' with no description. Schema coverage is 0%, and the description does not explain the parameter's meaning or usage, adding no value beyond the schema.

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

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

The description specifies the platform (Polymarket), type (negRisk combinatorial arbitrage), and action (scan). It distinguishes from sibling tools like cross_platform_arb_scan by focusing on Polymarket and negRisk. However, 'negRisk' may not be clear to all agents, slightly reducing clarity.

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 sibling tools like polymarket_event_scan or cross_platform_arb_scan. The description does not provide any context for selection or exclusion.

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?

Annotations are empty, so description bears full burden. It only says 'Search' implying read access, but no mention of permissions, rate limits, or return behavior. Minimal disclosure.

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?

Extremely concise (one sentence) but under-specified. Not front-loaded with key information; it's too brief to be 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 0% schema coverage, no output schema, and a vague description, the tool is incompletely described. Missing essential behavioral and input context.

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

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

Schema description coverage is 0%, and the description adds no meaning about the parameters 'query' and 'jurisdiction'. Fails to compensate for missing schema 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?

The description states a verb and resource ('Search company registries'), but it's vague and doesn't specify the scope or differentiate from sibling tools like 'edgar_filings' or 'trademarks_search'. A specific verb+resource but lacking distinction.

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. No context about scenarios or exclusions.

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 present, the description carries full burden of behavioral disclosure. It only states the basic lookup action and omits critical traits: read-only nature, API rate limits, authentication requirements, data freshness, or whether it supports partial IDs. The agent cannot infer safety or limitations from this description alone.

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 with zero wasted words. It front-loads the action and resource, then lists what the tool returns. 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?

For a single-parameter tool with no output schema, the description covers the core purpose and expected outputs. However, it lacks detail on ID format, return structure (e.g., fields beyond parties/filings/dates), and limitations (e.g., only federal courts, RECAP coverage). It is adequate but incomplete for optimal 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%, so the baseline is 3. The description adds 'federal court docket' and 'via RECAP archive' but does not clarify the expected format of docket_id (e.g., case number format, court abbreviation). The parameter's schema description is generic ('Docket ID'), and the tool description adds marginal context without specifying syntax or validation.

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: 'Look up a federal court docket by ID via CourtListener RECAP archive.' It specifies the resource (federal court docket), the action (look up), the data source (RECAP archive), and the return type (parties, filings, dates). It distinguishes itself from siblings like court_opinions and judges_search by focusing on docket records.

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 no guidance on when to use this tool vs alternatives. It implies use when you have a docket ID, but does not mention prerequisites, exclusions, or when to choose another tool. For example, no mention of whether state court dockets are excluded or what to do if the ID is not found.

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 does not mention rate limits, pagination, result limits, error handling, or output format. Only 'Keyless' is noted, which is a credential requirement, not behavioral.

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 that conveys essential information with no 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?

Given the lack of output schema and annotations, the description should explain what the tool returns (e.g., list of opinions, metadata, text). It fails to disclose any output characteristics, pagination, or limitations, leaving the agent underinformed 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 each parameter already has a description. The description adds 'by keyword, court, date', which maps to the three parameters but does not add new semantic details 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 clearly states the verb 'Search' and the resource 'US federal court opinions', and specifies filtering by keyword, court, and date. It also mentions the data source (CourtListener/Free Law Project) and notes that no API key is needed ('Keyless'). This distinguishes it from siblings like court_docket or citation_graph.

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 court_docket or judges_search. The description only notes 'Keyless' as a feature, but does not explain when it is appropriate to use this tool or when to avoid 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?

With no annotations, the description conveys it only provides intelligence (no execution), uses conservative fee estimates, and requires verification. It could mention data freshness or rate limits.

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 concise sentence that front-loads the core action and constraints. 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?

Covers purpose and usage well but lacks output structure information. Given no output schema, a brief note on return format (e.g., matched pairs with edges) 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%, so the schema already documents all parameters. The description adds no extra parameter meaning beyond the topic context.

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 matching live Polymarket and Kalshi markets for a topic, comparing asks with fee estimates, and states 'Intelligence only'. It distinguishes from siblings like polymarket_event_scan and combinatorial_arb.

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 'Intelligence only; verify both rulebooks before acting', which informs the agent not to act blindly. However, it does not name alternative tools for specific 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 present, the description must fully disclose behavioral traits. It only mentions 'via CoinGecko,' indicating an external data source, but does not describe any side effects, rate limits, authentication needs, or response behavior. As a price retrieval tool, it is presumably read-only, but this is not explicitly stated. The minimal disclosure leaves significant uncertainty about how the tool behaves beyond its basic function.

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 at only four words, which aids quick scanning. However, it sacrifices critical information; every word should earn its place. The brevity here leads to omissions in usage guidelines, parameter semantics, and behavioral details, making it less effective than a slightly longer but more informative description.

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 (2 string parameters, no output schema) and the presence of many similar sibling tools, the description is incomplete. It does not describe the output format, possible error conditions, or example usage. An agent needs more context to integrate this tool reliably, especially without any annotations or schema descriptions.

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

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

Schema description coverage is 0%, so the description should explain the parameters. The description does not mention 'coin' or 'currency' at all. Although the parameter names are somewhat self-explanatory, the tool offers no information about valid values (e.g., ticker symbols, full names) or default behavior. This fails to add value beyond the raw schema and leaves the agent with insufficient guidance to use the tool correctly.

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 cryptocurrency prices using CoinGecko, establishing a specific verb ('get price') and resource ('cryptocurrency price'). It is not a tautology and distinguishes itself from sibling tools like btc_address_balance and btc_mempool_fees, which serve different purposes. However, it lacks detail on whether it returns current, historical, or some other price type.

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 no guidance on when to use this tool versus alternatives. Given the large number of sibling tools, including other financial tools like currency_rates and polymarket_market_scan, the absence of usage context leaves the agent without clear criteria for tool selection. No when-not or alternative recommendations are provided.

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

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

With no annotations, the description carries the full burden, but it only states 'Live currency exchange rates.' It does not disclose behavioral traits such as data freshness, rate limits, authentication needs, or what happens if parameters are omitted. The agent gets minimal insight into the tool's 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 extremely concise at four words, which is structurally clean but overly terse. It could be expanded slightly to include key details without losing conciseness. As is, it is too brief to be fully useful.

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 two parameters, no required fields, no output schema, and no annotations, the description is severely incomplete. It lacks information on return format, parameter usage, and any constraints. An agent would have insufficient context to use the tool correctly.

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

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

The input schema has two string parameters (base, target) with no descriptions and 0% schema description coverage. The description does not explain what these parameters represent (e.g., currency codes) or how to use them. It adds no meaning beyond the schema, leaving the agent to guess.

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 states 'Live currency exchange rates,' which conveys the general purpose but lacks specificity. It does not use a verb like 'get' or 'fetch,' and fails to clarify that it likely returns a specific rate based on base and target parameters. It vaguely distinguishes from siblings like 'crypto_price_simple' but does not explicitly differentiate.

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. There is no mention of use cases, prerequisites, or exclusions. The description is too terse to help an agent decide when to invoke this tool over similar ones.

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?

Annotations are empty, so the description bears full responsibility for behavioral disclosure. However, the description only states the tool searches a database, with no information about rate limits, read-only behavior, return format, or any other behavioral traits.

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 (7 words) but at the expense of necessary detail. It does not use a front-loaded structure to convey key information efficiently; instead, it omits critical usage context.

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 3 unannotated parameters, no output schema, and empty annotations, the description fails to provide adequate context. The agent cannot determine how to invoke the tool correctly or what to expect from results.

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

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

The input schema has 3 parameters with 0% description coverage, and the tool description does not add any meaning to the parameters. The agent receives no guidance on what 'cve_id', 'keyword', or 'severity' are expected to contain or how they relate to the search.

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 'Search' and the specific resource 'NIST NVD CVE database', making the tool's purpose immediately obvious and distinct from the many sibling tools which cover different domains.

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, such as other security-related tools in the sibling list. No exclusions, prerequisites, or usage context 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?

Annotations are empty, so the description carries full burden. It does not disclose if the tool is read-only, what modifications occur, or any rate limits. 'Fingerprint' suggests reading but is unverified.

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 with no wasted words, front-loading the key action. Could be slightly more structured but is 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 exists, yet the description does not hint at the return format (e.g., JSON object with signals). For a fingerprinting tool, this omission reduces 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 coverage is 100% with a clear description of the 'url' parameter. The description adds no extra meaning beyond the schema, meeting the baseline but not compensating.

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 fingerprints a URL for multiple signal types (framework, analytics, etc.). It is specific and distinguishes from siblings like 'scrape' which might extract page 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?

No guidance on when to use this tool versus alternatives like 'scrape' or 'enrich_lead'. No context on prerequisites or limitations, leaving the agent to infer usage.

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

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

Annotations are empty, so the description must disclose behavior. It merely states 'data' without indicating whether the tool is read-only, what it returns, or any side effects. This is insufficient.

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 short, but this is under-specification rather than conciseness. It fails to provide essential details that a few more sentences could cover.

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, no parameter documentation, and no annotations. The description does not address expected output, data types, or any usage constraints, making it incomplete for practical use.

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

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

The input schema has a single 'query' parameter with no description and 0% schema description coverage. The description does not explain the purpose or format of the query parameter, leaving the agent with no semantic guidance.

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 'CDC disease outbreak data' vaguely indicates the tool provides outbreak data but lacks a specific verb (e.g., search, list, get) and does not differentiate from siblings like 'clinical_trials' or 'drug_labels'.

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 such as 'clinical_trials' or 'food_safety'. There is no mention of context or 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?

Without annotations, the description carries full burden for behavioral disclosure. It indicates a read operation ('resolution') and mentions the external service (Cloudflare DoH), but does not disclose rate limits, caching behavior, error handling, 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 extremely concise (two words plus provider info) and to the point. It could benefit from slightly more structure or bullet points, but it's 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?

For a simple tool with no output schema and minimal parameter documentation, the description is insufficient. It lacks details on what the tool returns (e.g., IP addresses, records array) and how to interpret results.

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 has no descriptions for parameters 'domain' and 'type'. The description does not explain that 'type' likely refers to DNS record type (e.g., A, AAAA). With 0% schema description coverage, the description fails to add meaning beyond parameter names.

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 'DNS resolution via Cloudflare DoH' clearly states the tool's purpose: performing DNS lookups using Cloudflare's DNS-over-HTTPS service. It distinguishes itself from sibling tools, none of which are DNS-related.

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 vs alternatives, or any prerequisites or limitations (e.g., Cloudflare DoH restrictions). The agent receives no context for optimal usage.

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

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

No annotations exist, so description bears full burden. It mentions the data source and types but lacks details on rate limits, authentication, pagination, or whether results are truncated. Minimal behavioral disclosure.

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, action-first, with key details. No filler or redundancy. 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?

For a search tool with no output schema, the description omits the return format, pagination behavior, and exact fields. An AI agent may struggle to interpret the response 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 high (100%) with both parameters described. The description adds no parameter-specific semantics; it lists result fields instead. 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 verb 'Search' and the resource 'official FDA drug labels via openFDA'. Lists specific content types (dosage, warnings, contraindications, active ingredients) which distinguish it from sibling tools like adverse_events.

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 searching drug labels but provides no explicit guidance on when to use or alternatives. No exclusion criteria or context for sibling tools.

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

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

No annotations are present, so the description must provide behavioral context. It omits important details like API authentication, rate limits, return format, or whether results are limited to recent recalls.

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 conveying purpose and key fields. Efficient and front-loaded, though could be slightly more structured.

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

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

For a search tool with no output schema, missing details on pagination, date filtering, and result format make 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?

Schema coverage is 100%, with descriptions already specifying limit range and query usage. The tool description adds no additional meaning to the parameters.

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

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

The description clearly states it searches FDA enforcement recalls for drugs, devices, and food via openFDA, mentioning key fields like severity and classification. However, it does not differentiate from sibling tools like food_recall_check or product_recalls.

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, no prerequisites or exclusions 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 empty annotations, the description must fully convey behavior. It only mentions 'multi-year time series' but does not explain read-only nature, potential rate limits, authentication needs, or data freshness. The brevity leaves significant behavioral 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 a single, concise sentence with no redundancy. However, it could be slightly more structured by starting with an action verb like 'Retrieve' to improve scanability.

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 (2 parameters, no output schema), the description is adequate but lacks details about return format, time series granularity, or limitations. It does not fully prepare the agent for interpreting results.

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 context by mentioning example indicators (GDP, CPI) and implies ISO country codes, but does not specify expected formats or enumeration. Baseline 3 is appropriate as schema already documents 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 explicitly lists the types of economic indicators (GDP, CPI, unemployment, trade data) and the data source (World Bank API), making the tool's purpose clear. It distinguishes itself from siblings like fred_series by specifying a different provider.

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, such as fred_series or nedbalk for similar data. There is no mention of prerequisites or scenarios where the tool is inappropriate.

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 but only states the tool's function. It does not disclose behavioral traits such as being read-only, rate limits, data freshness, or pagination behavior, leaving uncertainty for the agent.

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 redundancy. However, it could be expanded slightly to include critical usage details without sacrificing conciseness.

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 3 parameters, no output schema, and empty annotations, the description is insufficient. It lacks information on output format, supported companies, and typical use cases, making it inadequate for reliable agent invocations.

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

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

Schema coverage is 0%, so the description must compensate. It mentions parameters (query, ticker, form_type) in a shallow way ('by company, form type, or ticker') but does not explain the query parameter's format or how 'company' maps to it. Minimal added value over parameter names.

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 'search', the resource 'SEC EDGAR full-text filings', and the filtering criteria 'by company, form type, or ticker'. It effectively distinguishes this general filing search from more specific sibling tools like sec_8k_velocity.

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 no guidance on when to use this tool versus alternatives. It lacks any mention of appropriate contexts, prerequisites, or exclusions, which is important given the diverse sibling set.

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 lists the output data but omits details on rate limits, authentication, or error handling. It is transparent about the read-only nature but lacks depth on behavioral constraints.

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, no fluff. The first sentence states action and outputs, the second provides context. 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?

For a simple tool with one parameter and no output schema, the description covers expected outputs and use case. It could mention validation or failure behavior, but overall it 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?

The schema already has a description for the single parameter, but the description adds 'e.g. stripe.com or https://stripe.com' which clarifies acceptable formats. This extra example adds value beyond the schema.

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

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

The description clearly states 'Enrich a company domain' and enumerates specific returned data (e.g., company name, tech stack). It effectively distinguishes this comprehensive tool from more focused siblings like detect_stack or dns_records_lookup.

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 usefulness for lead scoring and prospect research, implying when to use it. However, it does not explicitly state when not to use or mention alternative tools for specific data points.

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 that extraction is limited to 'visible' content and specifies the data types (emails, phones, social links, URLs). However, it does not mention potential limitations (e.g., reliance on rendered HTML, inability to handle JavaScript-heavy pages, or response structure).

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 of only 13 words, efficiently conveying the tool's purpose with no wasted content. It is front-loaded with the key verb and output types.

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 one parameter, no output schema, and no annotations, the description adequately covers the extraction types. It lacks explicit mention of output format or potential errors, but the low complexity justifies a high score.

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 fully documents the single 'url' parameter with a description. The tool description adds the context 'from a page', but this is already implied by the schema's 'The URL to inspect'. No additional semantic value 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 states a specific verb 'Extract' and a clear resource: visible emails, phone strings, social links, and contact-related URLs from a page. This distinguishes it from siblings like 'scrape' which returns raw page content, and 'enrich_lead' which focuses on business lead enrichment.

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 extracting contact information from a page, but does not explicitly state when to use it over alternatives like 'scrape' or when not to use it (e.g., for non-renderable pages). No exclusions or prerequisites 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?

No annotations exist, so description must disclose behavioral traits. It only mentions the data source (USAspending.gov) but omits key details like rate limits, data freshness, authentication, or pagination.

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?

Extremely concise at 5 words, but this comes at the cost of informative content. The description could be longer to add value without being 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 no output schema, no annotations, and two undocumented parameters, the description fails to provide a complete understanding of the tool's behavior and return format.

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

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

Schema description coverage is 0%, and the description adds no meaning to the 'query' and 'agency' parameters. The description does not explain how these parameters are used or what values they accept.

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 'Federal contracts via USAspending.gov.' identifies the resource and source, but lacks a verb or specific action. It distinguishes from siblings like federal_grants and federal_spending only by resource name, not usage context.

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 siblings such as federal_grants, federal_spending, or gov_contract_fit_brief. The description provides no 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?

Annotations are empty, so description carries full burden. Only hints at parameters ('Agency, eligibility, deadline') but does not disclose behavioral traits like read-only, auth needs, or rate limits.

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 with no waste, front-loaded with the main action. Extremely concise.

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 search tool with 3 parameters, but lacks mention of return format, pagination, or authentication. Could be more 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%, so baseline is 3. The description adds 'Agency, eligibility, deadline' which are not explicit parameters, providing marginal context 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 searches Grants.gov for open federal funding opportunities, which is a specific verb and resource, distinguishing it from sibling search tools for other data sources.

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, no context or exclusions provided among many sibling search tools.

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

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

With no annotations, the description carries the full burden. It correctly indicates a read operation ('Search'), but does not disclose any additional behavioral traits such as rate limits, authentication requirements, or pagination behavior. This is acceptable for a simple search tool, but leaves gaps for an unaware agent.

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 efficient and immediately 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?

Given the tool's simplicity (3 params, no nested objects, no output schema), the description is mostly complete. It could mention result format or search scope (e.g., 'full text'), but this is a minor omission. The schema already covers parameter details.

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

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

Schema coverage is 100% with each parameter described. The description adds no further meaning beyond the schema (e.g., for 'type' it lists possible values but no additional context like format or required status). Since the schema is thorough, the 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?

The description clearly states the action ('Search'), the resource ('the daily Federal Register'), and the document types covered ('proposed rules, final rules, presidential notices'). However, it does not differentiate from sibling tools like 'regulations_search', which could cause confusion for an agent choosing between similar options.

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, or when not to use it. The description only states what it does, leaving the agent to infer usage context from the name and description 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 description bears full responsibility. It mentions API source but lacks details on read-only nature, rate limits, data freshness, pagination, or error behavior. Critical behavioral traits 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?

Single, front-loaded sentence with no wasted words. Efficiently conveys core purpose in 13 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 3-parameter tool with no output schema, description is too sparse. It does not explain return format, result structure, pagination, or data temporal range, leaving the agent without key operational context.

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 clear parameter descriptions, so baseline is 3. However, description mentions 'keyword' which is not a parameter in the schema, causing confusion and reducing clarity. This discrepancy lowers the score.

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 verb 'Search', resource 'federal budget', and scope 'by agency, recipient, or keyword' via 'USAspending.gov API'. Effectively distinguishes from sibling tools like federal_contracts or federal_grants.

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 is implied: use when searching federal budget data. However, no explicit when-to-use, when-not-to-use, or alternatives compared to sibling tools, 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?

No annotations are present, so the description carries full burden for behavioral disclosure. It only says 'search', implying non-destructive behavior, but lacks details on return format, pagination, authorization needs, or other behavioral traits.

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, concise and to the point. It could be slightly more efficient, but it carries necessary information without fluff.

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 does not fully cover what to expect from results or how to use the tool effectively. It is too brief for a search tool that presumably returns a list of resources.

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, which is sufficient but not enhanced.

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 a curated atlas of agent-useful resources, specifying categories like APIs, self-hosted tools, payment rails, etc. This differentiates it from sibling tools that are specific endpoints.

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 explicitly state when to use this tool versus siblings. However, the context of a curated atlas implies it is for discovering relevant tools among many, but no explicit guidance 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?

Annotations are empty, so the description must disclose behavioral traits. It states the tool uses an open data source (adsb.lol), implying read-only access, but does not discuss permissions, rate limits, data freshness, or any potential side effects. The detection claim lacks detail on accuracy or limitations.

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, but the first sentence packs many concepts (exec-jet, military, geofence, tail filter, detection) into a dense structure. The second sentence is clear. Some reformatting could improve readability, but it is not overly 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 no output schema and no annotations, the description should explain what the tool returns. It mentions 'intel' and 'detection' but does not describe the structure or content of the output, leaving the agent uncertain about the data format.

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?

All 3 parameters have schema descriptions (100% coverage), so baseline is 3. The description adds value by explaining that airport codes define a geofence, tail numbers filter specific aircraft, and mentions detection of specific models (B-52/F-35/E-3). This enriches 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 indicates the tool provides intelligence on executive jets and military aircraft, referencing specific airports (TEB, VNY, DCA), tail filtering, and detection via adsb.lol. It also hints at use in M&A and defense betting. While not perfectly precise, it distinguishes well from siblings focused on other domains.

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 'M&A / defense Polymarket edge' as potential use cases, but provides no explicit guidance on when to use this tool versus alternatives. No when-not-to-use or alternative tool names are 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?

No annotations exist, so the description must disclose behavioral traits. It only says 'search', which is generic. It does not state that it is read-only, mention API rate limits, or describe any side effects.

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

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

The description is extremely concise (one sentence) and front-loaded. However, it is so brief that it sacrifices necessary detail, which prevents a higher 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?

Given the lack of schema coverage, output schema, and annotations, the description is insufficient. It does not explain return format, pagination, or how results are structured, leaving 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?

Schema coverage is 0%, so the description must compensate. It does not explain what the 'query' parameter expects (e.g., free text, product name, date range). No format or example provided.

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 it searches openFDA food enforcement and recalls, which is a specific verb and resource. However, among sibling tools like food_safety, drug_recalls, and product_recalls, it does not differentiate itself.

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

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

No explicit guidance on when to use this tool versus alternatives like food_safety or product_recalls. The context implies use for food recall data, but lacks exclusions or when-not-to-use scenarios.

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 empty annotations, the description must disclose behavioral traits. It only states 'openFDA food enforcement' without indicating whether the tool is read-only, whether it returns data, or any constraints like rate limits or pagination. This provides no actionable 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?

While extremely short, the description is under-specified rather than concise. It lacks structure, front-loading minimal domain information without any useful content. Every word should earn its place, but here they are too sparse to be helpful.

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 absence of output schema and the complexity of food enforcement data, the description is completely inadequate. It does not explain return values, optional parameters, or any other details needed for correct invocation. The tool is essentially opaque.

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

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

Schema description coverage is 0%, yet the description adds no meaning to the 'query' parameter. It does not explain what types of queries are accepted, expected format, or how it affects results. The tool name alone does not compensate.

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

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

The description 'openFDA food enforcement' vaguely indicates the domain but fails to specify the action (e.g., search, list, get). It does not distinguish this tool from siblings like 'food_recall_check' or 'drug_recalls' which have more specific names and purposes.

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 over alternatives. There is no mention of context, prerequisites, or exclusions, which is critical given the many related sibling tools in the food/recall domain.

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 description provides no behavioral context beyond data source. Agent doesn't know if it's read-only, what operations it performs, or any side effects.

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

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

Single phrase is brief but sacrifices necessary detail. It's front-loaded but incomplete.

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 covers only data source, lacking return format, parameter usage, and sibling differentiation. Agent likely cannot use this tool effectively.

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

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

Description provides no explanation of parameters limit and series_id, even though schema coverage is 0%. Agent has no context on how to use them.

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 states it deals with FRED economic data series but lacks a specific verb or resource scope. It doesn't distinguish from sibling tools like economic_indicators or fred_surprises.

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 information on when to use this tool versus siblings like economic_indicators or fred_surprises.

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?

Annotations are empty, so description carries full burden. It discloses output format (CSV), public domain, no key needed, and rate limit (TokenBucket 2/s polite). It also mentions spread and inversion flag computation. However, it doesn't explicitly state read-only nature or side effects, and the scoring mechanism is unclear.

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

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

The description is short but cryptic, using abbreviations and fragmented phrasing. It is front-loaded with 'FRED rates surprise' but lacks sentence structure. While concise, it sacrifices 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?

Tool has 2 optional parameters and no output schema. Description does not fully explain the output beyond 'csv' and components. The surprise score meaning, delta scoring details, and data source (FRED API) are omitted. Given no output schema, the description should elaborate on return values.

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 (days, min_score). Description does not add meaning beyond the schema; it mentions 'delta scoring' but not min_score. Baseline 3 is appropriate as the schema already documents parameters adequately.

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 lists components (DGS10, DGS2, spread, inversion flag, delta scoring) but lacks a clear verb stating the tool's action. It vaguely conveys a derived metric computation but doesn't explicitly say 'calculates' or 'computes'. It distinguishes from siblings like fred_series by implying a derived output, but the purpose remains somewhat ambiguous.

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 versus alternatives. While it mentions 'public-domain no-key' and rate limiting, it doesn't specify contexts or exclusions. Siblings include fred_series and economic_indicators, but the description offers no decision criteria.

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 empty annotations, the description notes it is 'Intelligence only' and 'no direct model API resale,' which signals that it will not execute model generation. It does not disclose potential side effects, rate limits, or the exact meaning of 'live,' leaving behavioral expectations partially unspecified.

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

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

The description is compact: two sentences covering scope and constraints. The model list is informative without being redundant, and every phrase contributes to understanding 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?

The tool has no output schema and empty annotations, so the description must convey what to expect. It covers the input domain and content types, but the nature of the response ('intelligence') is vague, and no details are given about the data source or response format.

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?

Since the schema provides no parameter descriptions, the description's enumeration of models (Wan, VACE, LTX) and topics (settings, comparisons, gotchas) helps infer that the 'model' parameter likely accepts one of these names and 'query' requests particular intelligence. However, the relationship between the two optional parameters and their accepted formats remains ambiguous.

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's domain (generative video/image workflow intelligence) and lists specific models, distinguishing it from broader sibling tools. However, it lacks an explicit action verb like 'get' or 'query', relying on the noun 'intelligence' to convey the function.

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 should be used when needing intelligence on the listed generative video/image models, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it reference alternative tools. The 'no direct model API resale' note hints at a limitation but does not guide tool 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?

With no annotations provided, the description carries full burden. It mentions the output format and data sources but does not disclose behavioral traits such as rate limits, authentication needs, how confidence is computed, or what triggers different alert levels. The tool appears read-only but that's inferred, not stated.

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 efficiently convey purpose and output format with no wasted words. However, a bulleted list or structure could improve scannability. It is appropriately 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 the tool's complexity (4 parameters, no output schema, no annotations), the description covers the high-level purpose and output but omits details about the signals array structure, default parameter behaviors, edge cases, and performance implications. Adequate but incomplete for fully autonomous agent 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 parameter descriptions already present. The description adds high-level context about data sources but does not elaborate on parameter meanings beyond the schema. Thus it meets the baseline without adding significant 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 it is a 'Composite geopolitical intervention signal' and lists specific data sources (GDELT, BBC, AlJazeera, ADS-B, prediction markets). It explains the output is 'alert_level + signals array'. While it distinguishes from many siblings by being composite and intervention-focused, it does not explicitly compare with other similar alert tools like 'regulatory_pulse'.

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 no guidance on when to use this tool versus alternatives, no prerequisites, and no when-not-to-use scenarios. The function is stated but the context of use is left implicit.

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 must disclose behavior. It does not mention that the tool is read-only, what data it returns, or any side effects. Completely opaque.

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 short (6 words), but this is under-specification rather than efficient conciseness. It lacks substantive content.

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 annotations, no output schema, and a single parameter, the tool is severely underspecified. No guidance on errors, return values, or rate limits.

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 0% description coverage for the 'repo' parameter. The description adds no meaning beyond the parameter name. No format or example provided.

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 'GitHub repo intelligence' is vague, indicating the tool deals with repository information but not specifying what kind (e.g., metadata, stats, activity). It is not a tautology but lacks specificity.

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 siblings like github_trending. The description gives no context for appropriate usage.

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

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

With no annotations, the description carries the full burden. It discloses that the tool uses the public GitHub Search API without authentication, and details the filtering logic (stars >20, recent push, sort). However, it omits potential rate limits, pagination behavior, or error handling. These are useful but not critical gaps given the disclosure of key traits.

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 short sentences that convey the essential information. It is front-loaded with the core purpose and immediately provides the key criteria. No extraneous text, 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?

The description explains the source and criteria but does not describe the output format (e.g., what fields each repo includes) or pagination behavior. Since there is no output schema, this omission leaves the agent uncertain about the response structure. Given moderate complexity (3 parameters, no output schema), additional context on return values 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 covers all three parameters with descriptions, giving 100% coverage, so the baseline is 3. The description adds value by explaining the underlying selection criteria ('stars >20 + recent push') that the parameters influence, helping the agent understand how 'limit', 'language', and 'since_days' affect results beyond their basic constraints.

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 GitHub trending repos with criteria of stars >20 and recent push, sorted by stars descending. It also mentions it uses the public GitHub Search API with no key required, making the purpose specific and unambiguous. While it doesn't explicitly differentiate from sibling tools like github_repo_intel, the purpose is well-defined.

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 dev tooling and open source virality ('Momentum proxy'), but it does not provide explicit guidance on when to use this tool versus alternatives like github_repo_intel for detailed repo info. There are no when-not-to-use statements or clear context for exclusion of other 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?

No annotations are provided, so the description carries the full burden. It states the tool returns data but does not disclose whether it has side effects, requires authentication, or has rate limits. The description is too brief for a mutation-aware agent.

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 communicates purpose and outputs, but could be more structured by explicitly listing parameters or usage tips.

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, empty annotations, and four undocumented parameters, the description is incomplete. It does not explain the 'limit' parameter, how parameters interact, or what the output format is beyond a vague list.

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

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

Schema coverage is 0% and the description only vaguely mentions inputs ('service keyword, agency, or company domain') without linking to the schema properties (keyword, agency, company_domain, limit). It fails to explain the 'limit' parameter or any constraints.

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 'Score' and names the resource 'federal contract awards', clearly distinguishing it from sibling tools like 'federal_contracts' that might simply list contracts. It also lists return fields (incumbents, agencies, amounts, pursuit notes).

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 for scoring contract fit but does not provide explicit when-to-use or when-not-to-use guidance. No alternatives are mentioned, though siblings exist (e.g., federal_contracts, federal_spending).

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 caching (10min), data source (Algolia), and sorting logic. Being a read-only tool, no destructive behavior; but does not explicitly state it's non-destructive or mention rate limits. Adequate for a simple read 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?

Two sentences, front-loaded with purpose, no wasted words. Every sentence 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?

Missing description of return values (no output schema). Agent cannot know what fields each story will contain. Significant gap for such a 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 description adds little beyond what schema already provides for parameters 'limit' and 'min_points'. Dwell formula is about behavior, not parameters.

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

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

The description clearly states it retrieves Hacker News frontpage stories, using Algolia and sorted by a custom 'dwell' metric. It distinguishes from siblings by being HN-specific and mentioning the unique sorting metric.

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 ('attention-momentum edge') but no explicit guidance on when to use vs alternatives, nor any exclusions or 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?

Annotations are empty, placing full burden on the description. It does not disclose behavioral traits (e.g., read-only, side effects, data aggregation method). 'Signal intel only' is too vague to assess impact or 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 very concise at two sentences, efficiently conveying core purpose. It could be slightly more structured (e.g., separating usage notes), but it avoids verbosity.

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 2 parameters and no output schema or annotations. The description fails to explain what a 'cluster brief' contains, output format, or how to interpret results, leaving gaps for effective 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 description coverage is 0%, so the description must explain parameters. It does not mention 'limit' or 'ticker' at all, leaving their semantics entirely to the schema, which only provides ranges without context.

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: transforming recent SEC Form 4 filings into an 'insider-activity cluster brief.' The phrase 'Signal intel only' distinguishes it from more detailed sibling tools like 'insider_trades' or 'edgar_filings.'

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 hints at usage with 'Signal intel only,' suggesting it's for quick signals rather than deep analysis. However, it does not explicitly state when to use this tool vs. alternatives or provide when-not contexts.

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, and the description does not disclose any behavioral traits such as pagination, rate limits, or data freshness. The minimal statement offers no insight beyond the tool's subject.

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, which is concise, but it omits essential information. It is front-loaded but not adequately structured for utility.

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 two parameters, no output schema, and no annotations, the description fails to provide sufficient context for effective use. Return values, usage examples, and data source details are missing.

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?

With 0% schema description coverage and no parameter explanations in the description, the agent lacks any guidance on how to use the 'limit' and 'ticker' fields. This is a critical gap.

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

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

The description clearly identifies the resource as 'SEC Form 4 insider transactions', which is specific and informative. However, it lacks an explicit verb like 'retrieve' or 'search', making the action slightly ambiguous.

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 vs. siblings like 'insider_cluster_brief' or 'edgar_filings'. The description does not indicate prerequisites or context for 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?

No annotations exist, and the description fails to disclose any behavioral traits such as data returned, rate limits, or destructive nature. It is completely opaque.

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 (one sentence), but it is under-specified for a tool with one parameter. It could be more informative without losing conciseness.

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?

Despite the tool's simplicity, the description lacks context about the output format or what 'geolocate' entails. It is incomplete for practical use by 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?

With 0% schema description coverage, the description adds no meaning beyond the schema. The parameter 'ip' is not explained; the description merely restates the tool's purpose.

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 'Geolocate an IP address' is clear about the verb and resource, but it does not distinguish from siblings like geo_pulse. It is not a tautology but minimal.

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 or any exclusions. The description is too brief to convey 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 empty annotations, description must disclose behavior. Only states data source and input, omits rate limits, data freshness, error handling, or what happens with invalid ISBNs.

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?

Extremely concise (5 words) and front-loaded, but lacks useful information. Every word earns its place, but content is insufficient.

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?

Tool is simple with one parameter and no output schema, but description fails to explain return structure or error behavior. Incomplete for agent to use confidently.

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 0%, and description adds no meaning beyond parameter name 'isbn'. Does not specify expected format (e.g., 10/13 digits, hyphens).

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 book metadata by ISBN using Open Library. Verb 'lookup' and resource 'book' are specific, and no sibling tool has similar function.

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 or conditions for use. No mention of limitations or 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?

The description lacks behavioral details such as read-only nature, authentication requirements, or result structure. With no annotations, the description does not compensate.

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 at three words, but this brevity sacrifices necessary detail. It is not overly long, but it is incomplete.

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 and minimal description, the tool is underequipped for an agent to use correctly. Key aspects like return format, filtering options, and error behavior are missing.

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

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

Schema description coverage is 0% and the description provides no additional meaning for the 'query' or 'court' parameters. The purpose of 'query' is ambiguous (judge name, jurisdiction?).

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 federal and state judges, which distinguishes it from sibling tools like court_docket or court_opinions. However, it does not specify what information about judges is returned.

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. There is no mention of prerequisites, exclusions, or typical 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, the description partially compensates by implying a read operation and listing output fields. However, it does not disclose limitations, pagination, rate limits, or whether the search is exact or partial. Behaviorally adequate but not transparent.

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

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

The description is a single concise sentence, efficient and front-loaded with the action. However, it omits useful details that could be included without verbosity.

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 search tool with 3 parameters and no output schema, the description lacks essential context such as whether multiple parameters are combined with AND/OR, the format of responses, or any default behavior. The minimal information leaves the agent with incomplete 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?

Schema coverage is 100%, so the description adds no new meaning to the parameters. The listed output fields provide some context for what the search returns, but this is separate from parameter 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 states 'Search FEC lobbying disclosure records' with specific verb and resource. It lists key fields (lobbyists, clients, amounts, issues lobbied), but does not explicitly distinguish it from sibling tools like 'federal_contracts' or 'federal_spending'.

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. No mention of prerequisites, typical use cases, or exclusions. The description simply states what it does without contextual direction.

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 behavioral traits are disclosed. Annotations are empty, so the description carries full burden, but it lacks any details about data sources, update frequency, or limitations.

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

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

The description is short and front-loaded, but it is too minimal to be effective. It achieves conciseness at the cost of informativeness.

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 (2 params, no output schema), the description is incomplete. It does not explain what 'marine conditions' entails, leaving the agent without enough context to use the tool confidently.

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 description does not mention the parameters lat and lon. Schema description coverage is 0%, and the description fails to add meaning beyond the schema's type definitions.

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 provides marine conditions for ocean coordinates, distinguishing it from weather and tide tools. However, 'conditions' is broad and could be more specific.

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

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

No usage guidance is provided. The description does not mention when to use this tool over alternatives like water_levels or tide_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?

The description lacks behavioral details beyond stating it is a query-like operation. There are no annotations, so the description bears full responsibility. It does not disclose whether the operation is read-only, if results are cached, rate limits, or what happens when no results are found. The mention of 'email IOCs' is unsupported by the schema, adding confusion.

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 short sentences. The first sentence front-loads the core purpose, and the second sentence adds a memorable metaphor. No 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 lack of output schema and annotations, the description should provide more context about return values, behavior when no match is found, and how results are structured. It does not mention pagination, confirmation of maliciousness, or any other details that would help an agent use the tool effectively in a sequence.

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 provides clear descriptions for both parameters ('C2 host to check', 'npm package name to check'), achieving 100% coverage. The description adds minimal value by grouping them under the umbrella of 'known-malicious...' but also introduces 'versions' and 'email IOCs' not in the schema, which could mislead an agent. Overall, 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 tool queries known-malicious MCP packages, versions, C2 hosts, and email IOCs. It uses a specific verb and resource, and the metaphor 'virus database for MCP' reinforces the purpose. However, it does not explicitly differentiate from siblings like cve_search, and mentions 'versions' and 'email IOCs' not present in the schema, causing slight ambiguity.

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. There are no prerequisites, exclusion criteria, or examples of appropriate use. The description only implies checking package or host names, but fails to mention that both parameters are optional or when to choose one over the other.

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 empty annotations, the description carries the full burden of disclosing behavior, but it does not state that the tool is read-only, what it returns, or any side effects. The name suggests a lookup, but the description never explicitly says it reads or retrieves settings, nor does it mention response structure or limitations. This is a significant 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, compact sentence that front-loads the core purpose ('Community-tested settings...') before listing key topics. It contains no filler words and efficiently communicates the tool's scope. The structure is acceptable, though it is a fragment rather than a full sentence, which slightly reduces 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?

The tool has no annotations, no output schema, and sparse parameter documentation. The description lists many content areas but does not explain input usage, output format, or any behavioral context. This is insufficient for an agent to confidently invoke the tool; the agent must guess parameter formats and expected results. Given the tool's apparent simplicity, a more complete description is expected.

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 has two parameters (task, model) with no descriptions and 0% schema coverage. The description does not explain the role of either parameter; it only lists content areas like CFG, steps, and resolution, which are likely values returned rather than input semantics. The required 'model' parameter is implied by the tool name, but 'task' remains ambiguous. The description fails to compensate for the lack of schema 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?

The description clearly identifies the resource as 'community-tested settings for generative video and image models' and lists specific content areas, making the tool's purpose comprehensible. However, it lacks an explicit verb like 'looks up' or 'retrieves', relying on the tool name to convey action. It does not explicitly differentiate from siblings such as gen_video_intel, though the content focus is distinct.

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 no guidance on when to use this tool, when not to use it, or suitable alternatives. There is no mention of context, prerequisites, or situations where other tools might be preferred. The description is purely a content summary with no usage direction.

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 must disclose behavior. It only mentions data source and categories, but omits update frequency, latency, authentication, or any constraints. Minimal 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?

Two sentences, 20 words, front-loaded with purpose. Every word adds value; 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 simple parameterless tool, description covers source and data types. Could include update frequency (e.g., daily), but not essential. Completeness is good given tool complexity.

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 in schema (coverage 100%), baseline is 4. Description adds value by specifying output categories (total debt, debt by instrument), which compensates for lack of params.

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 provides US national debt data to the penny, sourced from Treasury FiscalData, and includes total debt and debt by instrument. This is specific and distinguishes it from many 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?

Usage context is implied (e.g., for exact national debt figures), but there is no explicit guidance on when to use this versus related tools like economic_indicators or fred_series. No exclusions 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?

With no annotations, the description must disclose behavioral traits. It only states 'Search', indicating a read operation, but omits details like rate limits, pagination, error handling, or response format. Agents lack essential 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 very concise (single sentence plus fragment) with no wasted words. However, it lacks structural elements like bullet points or sections that could improve readability for complex tools.

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 mentions data fields but does not explain the return structure or expected format, which is important since there is no output schema. For a search tool, it should clarify whether results are paginated, what a typical response looks like, etc.

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 already described ('Organization name', '2-letter state code'). The tool description adds 'Revenue, expenses, exec comp' which hints at available data but does not enhance parameter meaning 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 action (Search), resource (nonprofit IRS 990 filings), and source (ProPublica Nonprofit Explorer). It also lists key data fields (Revenue, expenses, exec comp) which distinguish it from sibling tools like edgar_filings or federal_contracts.

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 finding nonprofit financial information, but does not explicitly state when to use this tool versus alternatives like edgar_filings or federal_grants. No exclusion criteria or context cues 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, description must carry the behavioral burden. It mentions API version and fallback, but does not explicitly state the tool is read-only, whether authentication is required, or rate limits. The read-only nature is implied but not confirmed.

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 but includes unclear phrases like 'Env / health / geo pulse' and 'For PM market disclaimers' that may confuse agents. A clearer structure identifying output would improve it.

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, description should hint at return structure. It mentions 'lastUpdated' but not other fields or format. For a simple tool, it is adequate but leaves gaps about what the agent can 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?

Schema coverage is 100%, both parameters have descriptions. Description adds context about filtering by city/country but does not add significant meaning 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?

Description clearly states the tool retrieves air quality data (PM2.5/PM10/NO2/O3) by city/country with lastUpdated field, and specifies API version. However, it does not distinguish from sibling tool 'air_quality_index' despite being in a similar domain.

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. Does not mention scenarios where air_quality_index or other weather tools might be more appropriate.

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 empty annotations, the description carries full behavioral disclosure burden. It adds caching behavior ('Cached 1h') and data source ('auto-fetched via public API'), which are valuable beyond bare minimum. No destructive actions are indicated, and no contradictions exist.

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 concise sentences: first lists contents, second covers caching and source, third states purpose. No redundant words, front-loaded with key information. 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?

For a simple listing tool with two optional parameters and no output schema, the description covers caching, data source, and purpose. It does not detail output format or pagination, but given the low complexity and high schema coverage, it is nearly complete. A 4 is reasonable.

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 schema already documents the two parameters (limit, min_context). The description adds overall context but does not enhance parameter meaning beyond what the schema provides. 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 lists models from OpenRouter with specific fields (id, name, context_length, pricing, created). It distinguishes itself from diverse sibling tools by focusing on the model catalog, making its 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 mentions 'For model routing decisions' which implies context but does not provide explicit when-to-use or when-not-to-use guidance, nor does it compare with alternatives like model_settings_lookup. Usage is implied but not fully clarified.

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?

Annotations are empty, and the description provides no behavioral traits (e.g., idempotency, side effects, rate limits). This is a critical 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 extremely short (one sentence), which is concise but sacrifices essential information. It is under-specified rather than 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 lack of annotations, output schema, and parameter descriptions, the tool is insufficiently documented for an agent to use correctly. The description fails to provide minimal context about inputs or outputs.

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

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

Schema description coverage is 0%, and the description does not clarify what 'token_id' and 'condition_id' represent or how they relate to the orderbook imbalance. No additional semantics are provided.

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 identifies the domain (Polymarket CLOB orderbook imbalance) but lacks a verb to indicate the action (e.g., get, fetch). It is not a tautology but is vague about what exactly the tool does.

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 siblings or alternatives. The description provides no context for appropriate usage.

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

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

With no annotations present, the description carries the full burden of behavioral disclosure. It only states 'metadata' without specifying what fields are returned, whether the operation is read-only, any rate limits, or error handling. The description is insufficient for an agent to understand the tool's behavior beyond the basic intent.

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 short (one phrase), but it is underspecified rather than concise. It lacks crucial context and fails to earn its place because it does not convey enough information to guide agent behavior. A good concise description would pack more meaning into fewer 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 that the tool has one required parameter, no annotations, and no output schema, the description should provide enough context about input and output. It does not describe what 'metadata' includes, return format, or error cases. It is incomplete for an agent to use 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 description coverage is 0%, and the description does not explain the 'paperId' parameter at all. It does not specify the format (e.g., Semantic Scholar ID, URL, DOI) or any constraints. The agent is left with no additional meaning beyond the raw schema, which is just a string type.

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 'Paper metadata via Semantic Scholar.' clearly indicates the tool retrieves metadata for a specific paper using Semantic Scholar as the source. The verb is implied (get/retrieve), and the resource is a paper. While it does not explicitly distinguish from siblings like search_papers, the purpose is clear enough for an agent to understand it provides detailed metadata for a given paper ID.

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 guidelines are provided. The description does not mention when to use this tool over alternatives such as search_papers or citation_graph. An agent would have to infer from the tool name and sibling context that it is for fetching details of a known paper, but there is no explicit 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 carries the full burden but only states the search scope. It does not disclose rate limits, read-only nature, external API dependency, or response 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?

A single sentence efficiently conveys the tool's purpose and scope. It is front-loaded and avoids redundancy, though a second sentence for usage guidance would improve it.

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 (2 parameters, no output schema), the description is adequate but incomplete: it lacks response format details and potential limitations.

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%; the description adds context by specifying fields searched ('Titles, assignees, dates, abstracts'), which exceeds the schema descriptions but is not essential.

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 'Full-text patent search via Google Patents' and lists searchable fields (titles, assignees, dates, abstracts), distinguishing it from sibling tools like trademarks_search or search_arxiv.

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 patent search but lacks explicit guidance on when to use this tool vs alternatives (e.g., trademarks_search) or 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.