GoCreative Agent API

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

No annotations are provided, but the description discloses cost ($0.005–$0.05 per call) which adds transparency. It does not mention other behavioral traits like read-only nature, rate limits, or data retention, but the name implies non-destructive analysis.

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

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

The description is three sentences long, no wasted words. It front-loads the main purpose, includes an example, and provides cost information efficiently.

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 one parameter, no output schema, and no annotations. While it explains the input and cost, it does not describe the return format or structure of the audit results, which is important for an agent to process the 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?

Only one parameter with 0% schema coverage. The description adds meaning by specifying the 'owner/repo' format (e.g., 'vercel/next.js'), which is not evident from the schema's title 'Owner Repo'.

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 audits a GitHub repo for security signals like CVEs, last commit age, license, and contributor count. It uses a specific verb and resource, and the purpose is distinct from sibling tools like lookup_github or enrich_github.

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

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

Explicitly says to use for OSS supply-chain risk scoring and provides an example call. However, it does not explicitly mention when not to use or compare to alternatives like lookup_github_releases.

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 carry the burden. It mentions cost ($0.005–$0.05 USDC) and the batch size limit, but does not disclose input format expectations, error handling, or behavior when exceeding 50 symbols.

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 lines with no fluff. It front-loads 'BULK' and key functionality, making every word earn its place.

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

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

Given the single parameter and no output schema, the description partially covers inputs and outputs (prices + 24h change) but lacks details on return format, pagination, or error states. It is 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?

With 0% schema description coverage, the description must compensate. It states the parameter is for 'crypto symbols' but does not specify the format (e.g., comma-separated, array). No additional meaning beyond the schema is 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?

Description clearly states it provides 'prices + 24h change' for 'up to 50 crypto symbols in one call', distinguishing it from similar tools like crypto_price by using 'BULK' and specifying the batch limit.

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 'BULK' prefix and mention of 'up to 50 symbols' imply it is for multiple cryptocurrencies, contrasting with single-symbol alternatives. However, no explicit when-not-to-use or alternative tool names 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 provided, so description must reveal behavioral details. It mentions cost and domain limit but does not describe the types of DNS records returned, any rate limits, or that it is a read-only operation, leaving significant gaps.

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

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

Two concise sentences with no wasted words; the 'BULK' prefix immediately communicates purpose, and the cost info is efficiently included.

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?

Describes the tool's core function and constraints, but lacks detail on output contents (what DNS record types are returned) and any prerequisites, which is needed since no output schema exists.

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

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

Schema coverage is 0%, but description clarifies that the single 'arg' parameter should be a comma-separated list of domains and imposes a limit of 25, providing essential usage guidance beyond the raw 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?

Description clearly states it performs DNS lookups for up to 25 domains in a single bulk call, distinguishing it from the sibling tool lookup_dns by specifying the bulk nature and capacity.

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?

Implies use for multiple domains via 'up to 25 domains' and 'comma-separated', but does not explicitly state when to prefer this over the single-domain alternative lookup_dns, nor provides any exclusion 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?

No annotations exist, so the description carries the full burden. It discloses validation type (syntax+MX) and cost, but lacks details on error handling, rate limits, or input formatting (e.g., spaces).

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 concise lines: the first defines functionality and format, the second states cost. Every word adds value, no 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?

The description adequately covers purpose and cost against a backdrop of many sibling tools, but lacks output specs (no output schema) and behavioral details like authentication or rate limits, which would help an agent assess 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 provides no description (0% coverage), but the description specifies the format (comma-separated) and capacity (up to 50), adding essential meaning beyond the schema.

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

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

The description clearly states the tool validates up to 50 emails via bulking, specifying syntax and MX checks. It immediately distinguishes from single-email validation siblings like lookup_email_validate.

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 bulk use (BULK, comma-separated) and mentions cost, but does not explicitly say when to choose this tool over its single counterpart. No alternative tool is named.

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

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

No annotations are provided, so the description must disclose behavioral traits. It mentions the batch nature and cost per call, but does not cover error handling (e.g., invalid repo format, partial failures), authentication requirements, or whether the tool is read-only.

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 no filler. The first sentence front-loads the core purpose and format, and the second adds cost information. Every word contributes value.

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

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

With no output schema, the description should explain what 'repo metadata' includes (e.g., stars, description) and handle edge cases. It lacks details on error handling and output structure, relying on the agent's implicit knowledge. The cost mention is good but incomplete without authentication or rate limit 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?

The input schema has a single parameter 'arg' with no description (0% coverage). The description adds meaning by specifying the format as 'owner/repo,owner/repo' and implying comma separation. It also caps at 20 repos. While helpful, it could be more explicit about the exact delimiter and validation rules.

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 tool retrieves repo metadata for up to 20 GitHub repos in one call, using the format 'owner/repo,owner/repo'. This clearly states the verb, resource, and scope, distinguishing it from sibling tools like lookup_github (single repo) and search_github (search).

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

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

The description labels the tool as 'BULK' and gives the input format and maximum count, indicating when to use it for batch operations. However, it does not explicitly mention when not to use it (e.g., for a single repo) or name alternative tools like lookup_github.

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 must convey all behavioral traits. It mentions cost and input format but omits output details, error handling, rate limits, or what happens with invalid IPs. This is a significant gap for a geolocation tool.

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

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

The description is extremely concise—two sentences covering purpose and cost. Every sentence adds value with no redundancy, and key points are front-loaded.

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

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

For a bulk geolocation tool with no output schema, the description fails to explain what data is returned (e.g., country, city, coordinates) or how errors are handled. The cost and input format are helpful, but the output behavior is a critical missing piece.

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 parameter descriptions (0% coverage), but the description adds crucial semantic information: the 'arg' should be comma-separated IP addresses. This compensates for the lack of schema detail, though format specifics (IPv4/IPv6) are missing.

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 geolocation for up to 50 IPs in bulk, using a comma-separated format. It distinguishes itself from the single-IP 'ip' sibling by specifying the bulk nature and the 50-IP limit.

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 multiple IPs with 'BULK' and 'up to 50 IPs in one call,' giving clear context. However, it does not explicitly list alternatives like 'ip' for single IPs, missing an explicit when-not-to-use.

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

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

No annotations are provided, so the description carries the full burden. It discloses a bulk limit of 20 domains and cost, but lacks details on safety (read-only) or error handling. This is adequate but not rich.

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

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

The description is concise with two sentences, front-loading the core functionality and adding cost. However, the missing parameter explanation means it's not as efficient as it could be.

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 minimal parameter info, the description fails to provide sufficient context for correct usage. It lacks how to specify domains and what the output looks like, making 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?

The input schema has a single required 'arg' parameter with 0% schema description coverage. The tool description does not explain how domains should be formatted (e.g., comma-separated, list). This omission hinders correct invocation.

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

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

The description clearly states the tool performs bulk WHOIS lookups for up to 20 domains, listing output fields (registrar, dates, nameservers). This distinguishes it from single-domain WHOIS tools like lookup_whois.

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

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

The description implies use when needing WHOIS for multiple domains, and cost is provided. However, it doesn't explicitly state when not to use or suggest alternatives for single domains.

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 reveals the output in years, months, and days, and states it calculates 'as of today'. Since no annotations are provided, the description carries full burden for behavioral disclosure. It does not explicitly state read-only behavior, but that is implied for a calculator. Cost disclosure adds 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?

The description is short and front-loaded with the main purpose. It is efficient but appears cut off after 'and', which slightly harms completeness. The cost line is placed at the end, which is acceptable but unusual.

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 could better clarify the exact return structure. It mentions the units but not whether the result is an object or simple text. The single parameter is well-described in terms of input, but output details are inferred rather than explicit.

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 must compensate. It specifies the input format ('YYYY-MM-DD') and hints at the parameter's role. However, it does not explain validation, error handling, or constraints beyond the format, leaving some ambiguity.

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 states that the tool calculates exact age in years, total months, and total days from a birthdate. The verb is implied (calculate), and the resource is clearly age. Among many sibling calculators, this one uniquely computes age, so it distinguishes itself well.

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 use cases like verification and eligibility, but does not specify when not to use this tool or contrast it with sibling tools such as 'lookup_age_calculator'. The cost hint is present but no explicit alternatives or exclusions 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 are provided, so the description carries the full burden. It discloses the cost per call ($0.005-$0.05 USDC on Base), which is useful. However, it does not describe error behavior, return format, or any side effects. For a simple conversion tool, this is acceptable but minimally 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?

Two sentences: the first explains the purpose and syntax, the second states the cost. No unnecessary words, front-loaded with the core purpose. 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 conversion tool with one parameter, the description is mostly complete. It covers input format, base range, and cost. It could mention the output format (e.g., returns the converted number as a string) but is not strictly necessary given low complexity. Slight gap, but overall adequate.

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 one parameter 'arg' with no description (0% coverage). The description fully compensates by explaining the exact format: 'number-fromBase-toBase' with an example. This adds complete meaning beyond the schema.

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

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

The description clearly states it performs number base conversion, specifies the input format 'number-fromBase-toBase', supports bases 2-36, and gives an example (255-10-16). This distinguishes it from sibling calc tools like calc_convert (unit conversion) and other calculators.

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 dev/encoding agents,' giving a clear context of use. It implies this is for base conversion rather than other calculations, but does not explicitly state when not to use or list alternatives. Still, context is clear.

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

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

No annotations are present, so the description must disclose behavioral traits. It mentions cost but does not explain what happens on invalid input, the exact format required, or any side effects. This is insufficient for an unannotated tool.

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

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

The description is relatively short and front-loaded with the core purpose, but it is cut off mid-sentence and includes cost information that could be considered extraneous. It could be more structurally sound by completing the thought and separating cost.

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 complete context for the string-based input. It does not specify valid values, examples, or error handling. The sibling list includes many similar calc tools, but no differentiation is provided.

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

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

The single parameter 'arg' has no schema description (0% coverage). The description hints at the format 'weight-height[-system]' and gives example units (lb/in, kg/cm) but does not specify the exact structure, delimiters, or order. This leaves significant ambiguity for the agent.

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 calculates BMI with weight, height, and system (imperial or metric), and returns a category. It distinguishes itself from other calc_* tools by specifying the resource. However, the description is cut off ('and in') which slightly reduces 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?

The description mentions 'For health, fitness, and in...' but is incomplete and provides no guidance on when to use this tool versus alternatives like calc_bmr or calc_base. No exclusions or context for selection 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 provided, so description must disclose behaviors. It reveals cost ($0.005–$0.05 per call) but omits whether it is read-only, what side effects exist, or data persistence. The cost info is useful, but overall behavioral transparency is low.

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 truncated ('Fo...'), indicating incompleteness. The cost note is appended but not integrated. While few sentences are used, the truncation harms structural integrity.

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 calculator with one string parameter and no output schema, the description provides the core formula and input pattern. However, it lacks output format, error handling, or limitations. Given the low complexity, it is moderately complete but not fully sufficient.

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

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

Schema has one string parameter 'arg' with no description. The description adds a pattern: 'weightKg-heightCm-age-sex[-activity]', which clarifies expected format. However, it does not explain individual components (e.g., units for weight, valid sex values). Schema coverage is 0%, so description provides partial but not complete 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 states it computes basal metabolic rate and TDEE using the Mifflin-St Jeor formula, and provides an input pattern. However, the description is truncated ('Fo...'), which partially obscures the purpose. Among siblings like calc_bmi, the name and description give enough differentiation, but truncation reduces 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 vs. alternatives (e.g., calc_bmi, calc_age). No mention of prerequisites or exclusions. The description solely explains what the tool does without contextual usage advice.

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

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

No annotations are provided, so the description must carry the burden. It mentions the input format and return values (future value, total interest) but lacks details on determinism, error handling, or data source. The cost information adds some 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?

The description is extremely concise with two sentences: one for purpose and usage pattern, one for cost. No extraneous information, and the key information is front-loaded.

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

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

For a simple calculator with one parameter and no output schema, the description covers the essential purpose and return values. However, the parameter syntax could be more explicit to avoid user confusion about input formatting.

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 one parameter 'arg' with no description (0% coverage). The description hints at the expected format 'principal-rate-years[-timesPerYear]' but does not specify delimiters, data types, or constraints, leaving significant ambiguity.

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

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

The description clearly identifies the tool as a 'Compound-interest / future-value calculator' and provides a usage pattern ('principal-rate-years[-timesPerYear]'). This distinguishes it from sibling calc_* tools like calc_loan or calc_salary.

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 compound interest calculations but does not explicitly state when to use it over alternatives, nor does it provide exclusions or context for other financial calculations.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently states the data source (ECB reference rate), the output (converted amount), and the cost range ($0.005–$0.05). It does not disclose error handling or rate limits, but for a simple conversion tool, this is adequate.

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, concise and front-loaded with the core purpose and format. The second sentence adds cost info without unnecessary words. Every clause adds value, and there is 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 one-parameter tool with no output schema, the description is largely complete: it explains the input format, the data source, and the cost. It could optionally mention the return type (e.g., a number), but that is not essential for an agent to use it 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 a single required parameter 'arg' with no description (0% coverage). The description adds meaning by specifying the expected format: 'amount-FROM-TO' with an example. This compensates for the missing schema documentation, although it does not detail valid currency codes or case sensitivity.

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 currency converter with a specific format 'amount-FROM-TO' and an example '100-USD-EUR'. It distinguishes itself from sibling tools like calc_unit (general unit conversion) and lookup_currency_historical (historical rates) by specifying it uses the latest ECB reference rate.

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 currency conversion using ECB rates but does not explicitly state when to use this tool versus alternatives like lookup_currency_historical or calc_unit. No exclusions or prerequisites are mentioned, making the guidance implied rather than explicit.

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

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

No annotations provided. Description fails to disclose behavioral traits such as read-only nature, authentication requirements, or side effects beyond the cost, leaving the agent with limited understanding of 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?

Extremely concise: two sentences and a cost line, front-loaded with the core purpose, with zero extraneous 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?

No output schema, yet description does not clarify return format (e.g., whether it returns days only or separate values for weeks/years). Also lacks details on error handling or edge cases, making it incomplete for a one-parameter 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?

With 0% schema description coverage, the description adds the format constraint 'YYYY-MM-DD_YYYY-MM-DD', which is critical for correct invocation. However, it does not explain the separator or any other parameter nuances.

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 calculates date differences in days, weeks, or years, specifies the input format 'YYYY-MM-DD_YYYY-MM-DD', and distinguishes from sibling calc_* tools like calc_age and calc_due_date.

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 mentions use cases (scheduling, billing, logistics) but lacks guidance on when not to use or when to prefer alternative date-related tools like calc_age or calc_due_date.

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 discloses the cost per call and the input format, but does not explicitly state side effects (none expected) or error handling. It is adequate but lacks detail on behavior for invalid inputs or edge cases.

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 short sentences plus cost note), front-loaded with the core purpose, and contains 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?

While it tells what the tool returns (sale price, savings, effective %), it does not specify the output format (e.g., JSON object keys) or mention validation. Given no output schema, this is a notable gap, but for a simple calculator it is minimally sufficient.

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

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

Schema coverage is 0%, but the description explains the single parameter 'arg' must be in the format 'price-percentOff[-percentOff2…]', adding essential context beyond the schema. It could benefit from examples or value 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 computes 'Sale price, savings, and effective %' for a discount format, distinguishing it from sibling calculators like calc_percentage or calc_tip. It specifies the exact input pattern (price-percentOff[-percentOff2…]) and explicitly mentions e-commerce use.

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 (e.g., calc_percentage). The description implies it's for e-commerce discounts but does not provide when-not-to-use or compare with 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?

With no annotations, the description must disclose behavior. It mentions the great-circle formula and input format but omits output format, units (km/miles), error handling, and edge cases (e.g., invalid coordinates). This leaves significant behavioral ambiguity.

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 for the core behavior plus a cost note. No unnecessary words, and the most critical information (format) is front-loaded.

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

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

Given the lack of output schema, the description should clarify return values (e.g., distance in km). It also lacks usage guidelines. For a simple tool, it's partially complete but misses key contextual details like output and error handling.

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

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

The single 'arg' parameter lacks schema description (0% coverage). The description compensates well by specifying the exact comma-separated format and providing an example, which adds essential meaning beyond the schema's type definition.

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

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

The description clearly states it calculates the great-circle distance between two lat/lon points, which is a specific verb and resource. It distinguishes itself from sibling calc_ tools (e.g., calc_age, calc_bmi) by being the only one for geographic distance.

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 computing distance from coordinates but lacks explicit guidance on when to use or alternatives. For example, it doesn't mention that lookup_geocode could provide coordinates or that this tool is for direct coordinate distance.

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 states that the tool returns due date, current gestational age, and trimester, giving some behavioral insight. However, it does not disclose error handling, input validation, or other behavioral traits. No annotations are present, so the description partially compensates but is incomplete.

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 includes extraneous cost information. It is truncated, which may confuse. It is front-loaded with purpose but could be more concise by omitting cost (which might belong elsewhere) and completing the sentence.

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 covers the main output fields (due date, gestational age, trimester) and input format. However, without output schema or annotations, it lacks details on potential errors, range limitations, or exact output structure. It is minimally adequate for a straightforward calculator.

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

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

The single parameter 'arg' has no schema description (0% coverage). The tool description specifies that the input is a date in YYYY-MM-DD format representing the last menstrual period. This adds critical meaning beyond the bare schema, helping the agent format the parameter 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 indicates the tool calculates pregnancy due date, gestational age, and trimester from the last menstrual period date. It is specific to pregnancy, distinguishing it from sibling calculators like 'calc_age' or 'calc_bmi'. However, the description is cut off, which slightly reduces 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 is provided on when to use this tool versus alternatives. There is no mention of prerequisites, caveats, or context for usage. Among many sibling calculators, the description does not help differentiate usage 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?

No annotations are provided, so the description must disclose behavioral traits. It only states it calculates a cost and mentions a cost per call (pricing). It does not disclose whether the tool is read-only, idempotent, or requires authentication. Since annotations are absent, the description fails to adequately describe behavioral aspects.

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 (three sentences) but truncated, ending with 'and fleet ag' which suggests incomplete thought. The first sentence is front-loaded with purpose and format. Despite conciseness, the truncation harms structure and completeness. Not all sentences earn their place due to the incomplete final word.

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

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

Given the tool's low complexity (one parameter, no output schema), the description provides the basic purpose and input format hint. However, it omits return value details, error handling, unit conversion constants, and valid input ranges. The truncation further reduces completeness. An agent would lack sufficient context for reliable 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 one string parameter 'arg' with no description (0% coverage). The description adds meaning by indicating the expected format: 'distance-efficiency-pricePerUnit' with unit options (mi+mpg+$/gal or km+km/L+$/L). This provides partial semantics, but the format is vague and not fully structured. Baseline for zero coverage is low, so this extra info raises the score to 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 states 'Trip fuel cost for distance-efficiency-pricePerUnit' which clearly indicates the tool calculates fuel cost based on distance, efficiency, and price. It also specifies the unit formats (mi+mpg+$/gal or km+km/L+$/L). However, the description is truncated ('and fleet ag') and does not fully complete the sentence, which slightly reduces 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?

The description mentions 'For logistics, travel, and fleet agents' but provides no explicit guidance on when to use this tool versus alternatives. Among sibling tools, there are many other calc_* tools (e.g., calc_distance, calc_convert) but no differentiation or when-not-to-use advice. The context is too broad to be helpful.

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

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

No annotations are provided, so the description carries the full burden. It mentions cost ($0.005–$0.05 per call) and the data source (official US CPI), but does not disclose behavioral traits like read-only nature, response format, or update frequency. The cost info adds some 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?

The description is very concise: two sentences. The first sentence front-loads the purpose and usage pattern, and the second adds cost information. 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 tool has a single parameter and no output schema, the description covers the input format and data source. It lacks details on return format, error handling, or edge cases, but for a simple calculator, it is mostly complete. Minor gaps remain.

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 one parameter 'arg' with no description (0% coverage). The description adds comprehensive meaning by explaining the expected format: 'amount-fromYear[-toYear]'. This fully compensates for the lack of schema documentation and clarifies how to specify the amount and years.

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 adjusts for inflation using US CPI, with a specific syntax pattern. It is distinct from sibling calculator tools like calc_convert, calc_compound, etc., which handle other types of conversions.

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 inflation adjustment and provides a syntax pattern, but does not explicitly state when to use this tool versus alternatives (e.g., calc_convert for currency conversion). No prerequisites or limitations 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?

With no annotations, the description carries full burden. It discloses the input format, output fields, and cost range. It implies a deterministic, read-only calculation with no side effects. While it lacks details on error handling or edge cases, it provides sufficient transparency for typical use.

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 with two sentences, efficiently stating purpose and cost. It is front-loaded with the core functionality. The truncation of 'tota' is minor but slightly detracts from completeness. Overall, it is concise without unnecessary 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?

For a simple calculation tool with one parameter and no output schema, the description covers the essential aspects: input format, output fields, and cost. It does not explain underlying math or error handling, but these are not critical for basic usage. The description is adequate for the tool's 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?

The schema has 0% coverage for the single parameter 'arg'. The description adds meaning by providing an example format 'principal-rate-years' and explaining that it represents principal, rate, and years. However, it does not formally document the parameter or specify constraints (e.g., allowed ranges, separators), leaving some ambiguity.

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

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

The description clearly identifies the tool as a loan/mortgage payment calculator. It specifies the input format 'principal-rate-years' and states it returns monthly payment, total paid, and total interest, making the purpose unambiguous and distinct from sibling calculator tools.

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

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

The description provides no guidance on when to use this tool versus alternatives like calc_compound or calc_percentage. It does not mention exclusions or context for use, leaving the agent to infer based solely on the name.

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 discloses the cost per call but lacks details on error handling, input validation, or whether the tool is read-only. The behavioral profile is incomplete.

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

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

The description is two sentences long, with the core functionality in the first sentence and cost information in the second. It is front-loaded and contains no filler, efficiently conveying essential 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?

While the tool is simple (one parameter, basic calculation), the description does not include examples or output structure. The cost disclosure is a nice addition. It is adequate but leaves room for improvement in 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 only parameter 'arg' has no schema description (0% coverage). The description hints the expected format ('cost-price') but does not fully specify the exact string pattern, units, or examples. It adds some meaning but insufficiently compensates for the 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 clearly states the tool computes profit margin and markup from a 'cost-price' input, specifying the outputs (profit, margin %, markup %). It also indicates the target users (retail, pricing, e-commerce agents), distinguishing it from other calculator tools.

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

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

The description mentions the intended use case ('For retail, pricing, e-commerce agents') but does not provide explicit guidance on when to use this tool versus other calculator tools or any 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?

No annotations exist, so the description must convey behavioral traits. It does not state that the operation is read-only, deterministic, or has no side effects. Cost is mentioned but that is not a behavioral trait. Lacks safety guarantees.

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

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

The description is short and front-loaded with purpose. The cost information is extra but not excessive. No unnecessary words, though the format hint could be clearer.

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 a simple calculator with one string input and no output schema, the description should specify the exact input format and the nature of the output (e.g., numeric value, currency). It is incomplete: the output is not described, and the input format is ambiguous.

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 coverage, the description hints at input format 'gross-totalTaxPercent[-deductions]' but does not fully explain the syntax or allowed values. It adds some meaning beyond the schema's bare 'Arg' name.

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

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

The description indicates it calculates net pay from gross income, but the format string 'gross-totalTaxPercent[-deductions]' is somewhat cryptic. It clearly distinguishes from sibling calc_* tools as a net paycheck calculator.

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 only mentions 'for payroll, budgeting, and personal' but provides no explicit guidance on when to use this vs alternatives like calc_salary or calc_tax. No exclusions or when-not-to-use 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?

With no annotations, the description must fully disclose behavior. It mentions three calculation modes and a cost range, but omits details like input precision limits, error handling, or output format. The truncation further obscures 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 concise (one sentence plus cost), but the apparent truncation harms structure and completeness. The information provided is front-loaded, but missing a critical final phrase.

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 a single string parameter with no schema description, no output schema, and no annotations, the description should fully explain input format and output. It explains the three modes but breaks down on syntax and return value. Sibling tools share similar calculators but no differentiation is provided.

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

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

The single parameter 'arg' has no schema description (0% coverage). The tool's description hints that the argument encodes one of three operations, but does not specify the expected syntax or format (e.g., '50% of 200' vs '50 of 200'). This leaves the agent uncertain how to construct valid input.

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 is a percentage calculator and lists three specific modes: 'X-of-Y', 'X-is-Y', and 'A-change-B'. However, the description appears truncated (ends with 'Exact for any numbe'), 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?

The description does not provide any guidance on when to use this tool versus other calculators (e.g., calc_discount, calc_tip) or alternative methods. No explicit 'when to use' or 'when not to use' information.

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

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

No annotations are provided, so the description carries the burden. It only mentions cost, which is extra but doesn't disclose behavioral traits like idempotency, side effects, or authorization requirements. The tool appears to be a simple calculator, but that is assumed.

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 brief and front-loads the key output 'ROI & CAGR'. The cost note is a minor extra. However, it could be more concise while adding critical details about the parameter.

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 one undocumented parameter and no output schema, the description should provide complete guidance. It lacks detailed input specification and output format, making it insufficient for accurate tool invocation.

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

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

The input schema has one string parameter with no description (0% coverage). The description gives a format hint 'initial-final[-years]' but does not explain the meaning of the values, validation rules, or exact syntax. This leaves the agent guessing how to construct valid input.

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 calculates ROI and CAGR, and provides a hint of the input format. It targets investing/finance agents, which differentiates from generic calculators. However, it could be more explicit about what it computes (e.g., return on investment from initial and final values).

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 specifies its domain as investing/finance, giving some context. But it does not provide when to use this tool over other calc tools (e.g., calc_compound) or any 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?

No annotations provided. Description includes cost per call, which is useful behavioral info, but lacks details on side effects, rate limits, or data handling.

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 short but appears truncated (ends with 'p'), making it less polished. The cost line is extra but acceptable.

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?

Provides purpose and cost, but lacks parameter format examples and clear return value description. For a simple tool, it is partially 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 has one string parameter with no description. The description provides a format hint ('amount-mode[-hoursPerWeek]') adding some meaning, but it is incomplete and doesn't fully specify allowed modes or examples.

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 converts between hourly and annual salary and provides full breakdown. However, sentence is truncated, and it does not explicitly differentiate from sibling tools like calc_paycheck.

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. other calculator tools. Only hint 'For HR' is vague and incomplete.

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 does not disclose behavioral traits beyond what is implied (pure calculation). No annotations are provided, so the burden is on the description. It does not explicitly state idempotency, side effects, or authentication needs, though the cost note hints at a paid call.

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 plus a cost note, all front-loaded. No extraneous information, every part serves a 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?

With no output schema, the description lists the computed statistics but does not specify the output format (e.g., JSON structure) or error handling. It partially compensates for the missing schema but leaves gaps.

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

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

The input schema has 0% coverage for the single parameter 'arg'. The description adds the meaning that it expects a comma-separated number list, but does not specify exact format, constraints, or examples. It provides moderate value beyond the schema.

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

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

The description clearly states it computes descriptive statistics (count, sum, mean, median, etc.) for a comma-separated number list. It distinguishes itself from sibling calc_* tools by covering general statistics rather than specific calculations like BMI or age.

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

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

The description does not provide any guidance on when to use this tool versus alternatives. It lacks context such as prerequisites, limitations, or comparison to other calc_* tools, 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?

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It reveals the cost per call ($0.005–$0.05 USDC on Base) but does not indicate whether the tool is read-only, idempotent, or has any side effects. Critical details like error handling or auth requirements are absent.

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, using two sentences plus cost information. It front-loads the core function, then context, then cost. Every sentence adds value without 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?

Despite the tool's simplicity, the description lacks completeness. It does not explain the return format (e.g., plain text or JSON), possible error cases, or the meaning of 'rate'. Without an output schema, more detail is needed for an agent to correctly 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?

With 0% schema description coverage, the description must explain the parameter 'arg' thoroughly. It provides only a vague example 'amount-rate%' without specifying the exact format, allowed values, or whether the rate includes a percent sign. This is insufficient for precise usage.

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

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

The description clearly states the tool calculates sales tax/VAT, specifies the input format 'amount-rate%', and describes output as 'tax + total'. It also indicates use cases like e-commerce, invoicing, and POS agents, distinguishing it from other calc_* tools such as calc_discount or calc_percentage.

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

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

The description implies when to use the tool by listing e-commerce, invoicing, and POS agents but does not explicitly state when not to use it or mention alternatives. No comparison to other calculator tools is provided, leaving the agent to infer suitability.

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. It discloses the tool's cost and indicates it is a calculation (no side effects). While it lacks details on authentication or idempotency, the simple nature of a calculator makes this sufficient.

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

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

The description is extremely concise: two sentences with no fluff. It front-loads the core function and format, then adds context and cost. 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?

While the tool is simple, the description lacks explanation of the output format or what the tool returns. Given no output schema, the agent may be unsure of the result structure. The input format is hinted but not fully described.

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 sole parameter 'arg' has no schema description (0% coverage). The description adds critical meaning by specifying the expected format 'bill-tipPercent[-split]', which compensates for the schema gap. However, it could elaborate on the meaning of each component.

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 calculates tip and bill split, specifies the input format 'bill-tipPercent[-split]', and identifies target agents (dining, expense, POS). This is a specific verb+resource, distinct from other calc_ siblings.

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 use-case context ('For dining, expense, and POS agents'), helping the agent decide when to invoke it. However, it does not explicitly state when not to use or mention alternatives, which is acceptable given no similar 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?

The description explicitly mentions cost ($0.005–$0.05 USDC per call), which is a behavioral trait. But with no annotations provided, it fails to disclose other behavioral aspects like idempotency, side effects, or error handling. The cost information adds some transparency but 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 brief but cut off abruptly, indicating incompleteness. It front-loads the purpose and cost, but the truncated sentence undermines clarity. Conciseness is present at the expense of completeness.

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 schema coverage and no output schema, the description should provide more guidance. It lacks details on return format, accepted unit abbreviations, and error behavior. The tool is simple but the description leaves important gaps.

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

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

The input schema has a single parameter 'arg' with 0% description coverage, leaving it entirely unexplained. The description's 'value-FROM-TO' hint and category list add meaning, but it still lacks precise formatting instructions or examples. This compensates partially for the schema gap.

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

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

The description states it's a universal unit converter and specifies the parameter format 'value-FROM-TO' across multiple unit categories. However, the description is cut off and doesn't provide a concrete example, which slightly reduces clarity. It distinguishes itself from other calc_ tools by being a generic converter.

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 calc_convert. No mention of prerequisites, exclusions, or typical use cases. The description only gives a cost range, not usage 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 provided. The description does not disclose any behavioral traits like whether the call is read-only, destructive, or requires authentication. The cost hint is present but insufficient for full 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 concise sentences: first describes the core function, second provides cost. No fluff, front-loaded with 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?

With one parameter and no output schema, the description covers the input and general output but lacks details on return structure, pagination, or how to query multiple counties. 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 single parameter 'arg' lacks schema description. The description indicates it is a 5-digit county FIPS code, adding some meaning but no format, validation, or examples. Schema coverage 0% requires more compensation.

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 US county business statistics (establishments, employment, annual payroll, industry-sector breakdown) identified by 5-digit county FIPS. This distinguishes it from siblings like census_business_metro and census_business_state.

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

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

No explicit guidance on when to use this tool versus alternatives (e.g., metro or state level). Only implies input is a FIPS code, but no prerequisites or use cases are mentioned.

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

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

No annotations are provided, so the description carries the full burden. It only mentions a cost range but does not disclose behavioral traits like read-only nature, rate limits, data freshness, or error behavior. The cost information is useful but insufficient for full 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?

The description is two sentences plus a cost line, front-loading the core purpose. Every word serves a purpose; no fluff or repetition. It is appropriately sized for a simple tool.

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

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

Given the tool has only one parameter and no output schema, the description adequately explains input (CBSA code) and output scope (establishments, employment, payroll, sector breakdown). However, it lacks details on return format, sector breakdown example, and data source completeness ('from Census County B' seems truncated).

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 parameter 'arg' with 0% description coverage. The description vaguely indicates it takes a CBSA code, but does not explicitly state that 'arg' is the CBSA code or provide format examples. Schema coverage is low and description adds only partial meaning.

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 'US metro-area business stats (establishments, employment, payroll + sector breakdown) by CBSA code', which distinguishes it from sibling tools like census_business_county (county-level) and census_business_state (state-level). The verb 'business stats' and specific resource 'metro-area' make the purpose explicit.

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 metro-area data by CBSA code but provides no explicit guidance on when to use this tool versus alternatives such as census_industry_metros or other census tools. No when-not-to-use or prerequisite information is given.

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

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

With no annotations, the description is expected to disclose behavioral traits. It mentions pricing, which is helpful, but does not cover data freshness, update frequency, response size, or any side effects. This is inadequate for a tool with zero 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 concise with two sentences, but the lack of parameter explanation means it sacrifices completeness for brevity. It is not optimally structured 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 minimal schema coverage, no output schema, and no annotations, the description is insufficient. It fails to explain input format, output structure, or any constraints, making it incomplete for a tool with only one 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 required parameter 'arg' with 0% description coverage. The description does not clarify what value to provide (e.g., state name, abbreviation, FIPS code). This is a critical gap that leaves the agent unable 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 it provides US state business landscape data including establishments, employment, and payroll from Census County Business Patterns. The purpose is clear and specific, but it does not explain the expected input argument, which is necessary to fully understand how to invoke it.

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 census_business_county or census_business_metro. The description lacks any context about use cases 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?

No annotations provided, so description carries full burden. It mentions cost range, which is a behavioral aspect, but lacks details on read-only nature, authentication, rate limits, or data freshness.

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

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

Description is concise with front-loaded purpose and cost note, but appears truncated ('per-c'), which undermines clarity. It could be more complete without sacrificing brevity.

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

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

Given no output schema and multiple census siblings, the description lacks details on return fields, data interpretation, and limitations. The list of example fields is helpful but incomplete for a data retrieval 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 0%, but description states the 'arg' parameter accepts US state or 5-digit ZIP, adding meaning. However, it does not specify expected formats (e.g., full state name, abbreviation, ZIP as string) which is insufficient for precise usage.

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

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

Description clearly states it provides a full demographic profile for US states or 5-digit ZIPs from the Census ACS, listing specific data fields. This distinguishes it from siblings like census_demographics_county and census_demographics_place.

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 census_demographics_county or census_demographics_place. The description does not mention when not to use or provide context for choosing.

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

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

No annotations are provided, so the description must cover behavioral traits. It mentions cost but fails to disclose rate limits, authentication needs, data freshness, or whether the call is read-only. The description focuses on what it does, not how it behaves.

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 (one sentence plus cost line) but appears truncated ('educatio'). While brevity is good, the cut-off reduces clarity. The cost information is an extra detail that might be better placed elsewhere.

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

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

Given the simple input and no output schema, the description is adequate but incomplete. It explains the parameter and high-level output but does not describe the response format, error handling, or limitations (e.g., data update frequency). For a tool with siblings, it could clarify which Census ACS year is used.

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 parameter 'arg' with no description (0% coverage). The description adds crucial meaning by specifying it expects a 5-digit FIPS code for a US county. This compensates for the lack of schema documentation, though it could clarify the code format (e.g., leading zeros).

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 a full demographic profile for any US county using a 5-digit FIPS code from the Census ACS, listing key data points (population, income, home value, education). The purpose is specific to county-level demographics, distinguishing it from siblings like census_demographics_place. However, the description is cut off and could be more complete.

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 county-level demographic data but provides no explicit guidance on when to use this tool versus alternatives like census_demographics (general) or census_business_county. No when-not-to-use or prerequisite information is given.

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

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

No annotations provided; description does not disclose if tool is read-only, safe, or any side effects. It mentions ACS data but lacks explicit behavioral traits. Since the description carries the full burden, 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?

Description is brief with two sentences and cost info, front-loaded with purpose. No fluff, but truncated ending ('edu' suggests cutoff) and could be more complete 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?

No output schema; description lists only a few data fields (population, income, home value, edu) but claims 'full demographic profile', leaving agents uncertain about the complete response. Missing details on return format and comprehensive field 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% with a single vague parameter 'arg'. Description clarifies arg is a 7-digit GEOID for US city/place, adding context beyond the schema. However, lacks format validation or examples.

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 provides full demographic profile for US cities/places using 7-digit GEOID from Census ACS, distinguishing it from similar sibling tools like census_demographics_county by specifying the geographic level.

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

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

Description implies use for city/place demographic data but offers no explicit guidance on when to use this vs alternatives like census_demographics or county-level tools. No when-not-to-use or prerequisite info.

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

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

No annotations are provided, so the description bears full burden. It discloses the output (national totals and state breakdowns) and cost, but lacks details on data freshness, authentication needs, rate limits, or side effects. The description is adequate but not rich.

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

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

The description is reasonably short but contains a sentence fragment ending with 'for', suggesting truncation. Cost info is included, which is useful but not core. Structure is poor due to incomplete thought.

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, no annotations, and a single undocumented parameter, the description is incomplete. It fails to specify how to indicate the industry (parameter format) or provide examples, leaving high ambiguity for a tool with numerous siblings.

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 required parameter 'arg' with no description and 0% coverage. The description does not clarify what the parameter expects (e.g., NAICS code, industry name). It says 'any US industry' but provides no format or validation details, leaving the parameter entirely 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 states the tool provides national establishment/employment/payroll totals and all-state ranked breakdown for any US industry. The verb is implied (query), and the mention of 'all-state' vs county/metro in sibling names distinguishes it from census_industry_counties and census_industry_metros.

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 census_industry_counties or census_industry_metros. No exclusions or prerequisites mentioned.

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

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

No annotations provided. The description mentions cost but lacks behavioral disclosure such as data freshness, error handling, or whether usage is cumulative. It does not explain that this is a read-only operation.

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

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

The description is two sentences: first states the function clearly, second notes cost. Every word is purposeful, and it is front-loaded with the core action.

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

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

Given no output schema and minimal parameter info, the description is incomplete. It lacks details on output structure, example usage, and how to specify the industry code.

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 input parameter 'arg' is not explained. The description references 'given NAI' but does not confirm that 'arg' is the NAICS code or provide format details.

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 county-level establishment, employment, and payroll counts for a US industry using a given NAI code. It distinguishes from sibling census tools by specifying county-level scope, though 'NAI' is not 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 use when county-level industry data is needed but offers no explicit guidance on when to use this tool versus alternatives like census_industry_metros or census_business_county.

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 mentions the data types (establishment/employment/payroll counts) but does not disclose whether the tool is read-only, the response format, or any constraints like rate limits or authentication. With no annotations, the description carries full burden, and it fails to provide sufficient behavioral context.

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

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

The description is brief but truncated, and includes extraneous cost information that is better placed elsewhere. It could be more concise by focusing on core functionality. The structure is acceptable for a short 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 complexity of census data, one parameter, no output schema, and no annotations, the description is inadequate. It fails to clarify what the tool returns or how to interpret the results, leaving significant gaps 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?

The input schema has a single required parameter 'arg' with 0% description coverage, and the description provides no clue about its expected format (e.g., NAICS code, industry name). The agent has no way to correctly populate 'arg' without external knowledge.

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 'establishment/employment/payroll counts for every metro/micro area' for US industries, indicating a specific verb (map?) and resource (metro-area industry data). However, the description is truncated, leaving some ambiguity about the exact output, and it does not explicitly differentiate from sibling tools like census_business_metro or census_industry.

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 census_industry_counties or census_business_metro. There is no mention of prerequisites, data freshness, or limitations, leaving the agent unprepared to choose appropriately among many similar census 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; the description adds cost ($0.005–$0.05 USDC on Base per call) and 'fast' implying low latency, but fails to disclose behavior on invalid slugs, rate limits, or error handling.

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

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

Two short sentences plus cost line – efficiently front-loaded with essential info, 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?

For a simple single-parameter tool without output schema, the description covers the purpose and cost but lacks details on return format, required slug format, and what 'TVL' includes.

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 coverage, the description mentions 'by DefiLlama slug' suggesting 'arg' is the slug, but does not provide format, examples, or clarification on what constitutes a valid slug.

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 'Current DeFi protocol TVL (USD) by DefiLlama slug' – a specific verb+resource (get TVL) and distinguishes from siblings like crypto_dex or crypto_price by focusing on TVL.

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

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

The description says 'Fast DeFi health signal for crypto agents' implying usage for quick health checks, but provides no exclusions or when-not-to-use compared to other crypto 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?

While there are no annotations, the description discloses the cost range ($0.005–$0.05 USDC on Base per call), which is transparent about pricing. However, it does not mention rate limits, authentication needs, or whether the data is cached.

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 to the point, but it is truncated ('The data cryp...'), which reduces clarity. It earns a middle score because conciseness is compromised by incompleteness.

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 incomplete parameter documentation, the description fails to provide enough context for the agent to correctly use the tool. It lacks return format, parameter constraints, and behavioral traits.

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

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

The single required parameter 'arg' has no description in the schema (0% coverage) and the description does not explain what should be passed (e.g., token address, symbol). The cost mention does not clarify parameter usage.

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

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

The description clearly states the tool provides live DEX market data (price, liquidity, 24h volume, FDV, market cap, chain) via DexScreener. It identifies the resource (any token) and action (get data), distinguishing it from generic crypto price 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?

No guidance on when to use this tool versus alternatives like crypto_price or crypto_market. There is no mention of prerequisites or exclusions, leaving the agent without context for selection.

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

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

No annotations are provided, so the description must fully disclose behavior. It mentions the data source and cost per call, which is useful. However, it fails to explain what the required 'arg' parameter expects, nor does it describe any rate limits, authentication needs, or response structure. This leaves significant behavioral ambiguity.

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 brief and front-loaded with the core purpose, but it is cut off mid-sentence ('gate e') which hinders readability. It could be more structured and complete.

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, parameter description, and annotations, the description falls short. It explains what data is returned (index + 7-day history) and notes cost, but omits critical details about the parameter and how to format the call, making it insufficient for reliable usage.

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 required parameter 'arg' with no description (0% coverage). The tool's description does not explain what value should be passed for 'arg', leaving the agent without any guidance on how to invoke 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 returns the Crypto Fear & Greed Index (0-100 sentiment) along with a 7-day history from Alternative.me. This differentiates it from price or market tools among siblings. However, the sentence appears cut off ('The sentiment signal trading bots gate e') which slightly reduces 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?

The description does not provide any guidance on when to use this tool versus alternatives like crypto_price or crypto_global. No scenarios, prerequisites, or when-not-to-use are mentioned. The cost is noted but does not inform usage 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 provided, so the description is the sole source. It mentions the on-chain source and cost but fails to disclose the return format, possible error behavior, or whether it is a read-only operation (likely). Limited 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?

The description is short and front-loaded, but the second sentence is truncated ('What bots check before subm'). It includes useful cost information. Slight reduction due to incompleteness.

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 should fully explain its behavior. It lists supported chains but does not clarify the parameter mapping, return value, or usage example. 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?

The input schema has one required parameter 'arg' with 0% description coverage. The description does not explain what 'arg' represents (likely the chain name, but not stated). 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 states it provides live gas prices in gwei for multiple Ethereum-compatible chains via on-chain eth_gasPrice, and mentions the cost. However, the sentence 'What bots check before subm' is truncated, 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 explicit guidance on when to use this tool versus alternatives. The distinct purpose is implied by the tool name and description, but there is no 'when not to use' or comparison with sibling tools like crypto_price or crypto_market.

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 adds cost information ($0.005–$0.05 per call), which is a useful behavioral trait beyond a read-only hint. However, it does not explicitly state the tool is read-only, mention data freshness, or any other behavioral details. With no annotations, the description carries the full burden and partially meets it.

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 and front-loaded, efficiently conveying the purpose. However, it omits critical parameter information, which reduces its effectiveness. Still, it earns a place for brevity.

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

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

Given the complexity of one undocumented parameter, no output schema, and no annotations, the description is severely incomplete. The missing parameter explanation alone makes it insufficient for correct invocation.

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

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

The sole required parameter 'arg' has no description in the schema (0% coverage) and is not mentioned in the tool description. The agent has no way to know what value to pass, making the tool nearly unusable without external knowledge.

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 a global crypto market snapshot with specific metrics (total market cap, 24h volume, BTC/ETH dominance, active-coin count) via CoinGecko. However, it does not explicitly distinguish from sibling crypto tools like crypto_market or crypto_price, which offer related but different data.

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

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

No guidance is given on when to use this tool versus alternatives. The description implies it is for a broad overview, but does not specify when to choose it over other crypto tools or provide any exclusion 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?

No annotations are provided, so the description carries full responsibility for disclosing behavioral traits. It mentions cost but does not state that the operation is read-only, whether authentication is needed, or any side effects. The description lacks transparency for a tool that appears to be a simple data lookup.

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, listing key data points in a single sentence. The cost note is additional but relevant. It is front-loaded with the purpose. However, it could be structured more cleanly (e.g., bullet points).

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 covers many returned fields but does not mention error handling, rate limits, or pagination. With many crypto-focused sibling tools, it could clarify that this is the comprehensive market data endpoint. Still, it provides a reasonable overview of return data.

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 required string parameter 'arg' with no schema description (0% coverage). The description says 'by CoinGecko id,' implying arg should be a CoinGecko ID, which adds essential semantic meaning beyond the schema. However, it does not provide format examples or list valid IDs.

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 rich crypto market data by CoinGecko id, listing specific data points (price, market cap, rank, changes, volume, supply, ATH/ATL, categories). It's specific about the resource and what data is returned, but does not differentiate from sibling tools like crypto_price or lookup_coingecko.

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 mentions cost but does not specify prerequisites, access requirements, or recommend when to use it over similar crypto tools. Usage context is entirely implied.

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

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

The description discloses the data returned (spot price, 24h change, market cap) and the source (CoinGecko). However, it lacks details on parameter format, rate limits, authentication, or whether multiple IDs are supported. With no annotations, the description carries full burden but is incomplete.

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 only two sentences plus cost info, no unnecessary text. The key purpose is front-loaded. Could be slightly more structured but is efficient for the detail provided.

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 annotations or output schema, the description covers the basic function and cost, but lacks parameter format, supported IDs, and output structure. It is adequate but leaves gaps that could lead to incorrect 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 only parameter 'arg' has no schema description. The description says 'any coin id(s)' which adds some meaning, but the format (e.g., comma-separated, single ID) is unclear. With 0% schema coverage, the description should compensate more but only provides a vague hint.

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 spot price, 24h change, and market cap for any coin id(s) via CoinGecko. It also differentiates as 'the cheapest high-frequency price call', which distinguishes it from other crypto tools, though overlap with 'lookup_coingecko' is possible.

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 cheap, high-frequency price calls but provides no explicit guidance on when to use vs alternatives or when not to use. No exclusions or prerequisites are mentioned.

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

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions output fields (price, rank, 24h change) but does not specify whether results are a single item or list, pagination, error handling, or any limitations. This is insufficient for a tool with zero 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 two sentences plus a cost note, which is concise. The cost line is slightly extraneous but does not detract significantly. Overall, it is well-structured and front-loaded with important information.

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

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

Given the lack of output schema, parameter documentation, and annotations, the description should provide more context. It fails to explain the input parameter or clarify the result format, leaving the agent guessing. The tool is simple but still insufficiently described.

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 sole parameter 'arg' is undocumented in the schema (0% coverage) and the description does not explain its purpose, format, or acceptable values. The agent has no guidance on what to provide, making the tool effectively unusable.

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

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

The description clearly states the tool returns trending coins with price, market-cap rank, and 24h change via CoinGecko. It uses a specific verb and resource ('get trending coins') and distinguishes itself from siblings like crypto_price (single coin) or crypto_global (market-wide data).

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

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

The phrase 'What crypto traders/agents are watching' gives a clear use case, but there is no explicit guidance on when not to use this tool or alternatives. The cost information is provided but does not offer usage boundaries or comparisons to other 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 full burden. It does not disclose whether the operation is read-only, any side effects, authentication needs, rate limits, or error conditions. Only cost is mentioned.

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 plus cost line. Front-loaded and efficient. The phrase 'What yield-farming agents shop for' adds flavor but is not strictly necessary. Overall good 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 tool with no output schema, description lists return fields (APY, TVL, protocol, chain) but lacks details on pagination, limits, or error handling. Adequate but not comprehensive.

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

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

One parameter 'arg' has no schema description (0% coverage). The description suggests it is a token identifier (symbol/address) but lacks format, examples, or validation rules. Does not compensate adequately for bare 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?

Clearly states it returns 'Top DeFi yield pools (APY, TVL, protocol, chain) for a token via DefiLlama'. Distinguishes from sibling crypto tools like crypto_defi_tvl, crypto_dex, crypto_price by focusing on yield pools.

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?

Implies usage for yield-farming agents and mentions cost, but does not explicitly state when to use this vs other crypto tools or any prerequisites for the token parameter.

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

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

No annotations provided; description adds cost disclosure but lacks details on side effects, authentication, rate limits, or error handling. The read-only nature is implied but not explicitly 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?

Three sentences: purpose, usage context, example with cost. No redundant text; every sentence adds value. Front-loaded with the verb and resource.

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 the metadata type (description, pricing, last build, recent runs) and cost, which is sufficient given the tool's simplicity and lack of output schema. No apparent 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?

Input schema has 0% description coverage, but the description compensates with an example ('apify~instagram-scraper') and clarifies the parameter is for a public Apify actor ID, adding necessary meaning.

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 specific verb 'Get metadata' and resource 'public Apify actor', listing exactly what metadata is retrieved. Distinguishes from sibling enrichment tools by specifying the Apify platform.

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

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

Explicitly tells when to use: 'when researching Apify scrapers or comparing actor coverage'. This contrasts with sibling tools like scrape_* which extract data from sites, providing clear 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?

Since no annotations are provided, the description carries the full burden. It discloses the headless browser behavior, full JS render, return of HTML and screenshot URL, and mentions cost. It does not detail error handling or limits, but adequately covers the core 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: two sentences, an example, and a cost note. Every sentence adds value, and the key information is front-loaded.

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

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

The tool has no output schema, so the description must cover return values. It states 'returning final HTML and screenshot URL', which is sufficient. It also includes cost and an example. Could mention error scenarios, but completeness is good for a simple fetch tool.

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

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

The schema has only one parameter 'url' with no description. The description adds the example and implies it is a URL string, but does not elaborate on format or constraints. With 0% schema coverage, it provides minimal extra meaning beyond the schema.

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

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

Clearly states that the tool performs a headless-browser fetch of a URL with full JS render, returning final HTML and screenshot URL. The verb 'enrich' and resource 'browserbase' are specific, and the description distinguishes it from plain fetch alternatives by mentioning JS rendering.

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

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

Explicitly says 'Use when the target page is SPA/JS-rendered and a plain fetch returns empty HTML', providing clear when-to-use guidance. It does not explicitly state when not to use, but the context is well-defined.

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 notes 'Headless-browser' and 'returning a CDN screenshot URL,' which indicates the tool runs a browser in the background and provides a hosted image URL. It also includes cost information ($0.005–$0.05 USDC per call) and an example. No annotations are provided, so the description carries the full burden and does so adequately.

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

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

The description is extremely concise: two sentences plus an example and cost note. It is front-loaded with the core purpose and adds no unnecessary words or 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 the tool has only one required parameter and no output schema, the description fully covers what the agent needs: input (URL), output (CDN screenshot URL), use case, cost, and an example. Nothing essential is 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?

The schema has 0% description coverage, so the description must compensate. It provides an example call with a URL and implies the parameter is a web address, but it does not add detail beyond what the schema already provides (type string, title 'Url'). The example helps clarify usage but is not extensive.

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 'screenshot of a URL, returning a CDN screenshot URL,' which is a specific verb and resource. It distinguishes itself from sibling enrich tools that target specific platforms (e.g., enrich_instagram) and from lookup/scrape tools by emphasizing visual snapshots.

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

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

The description explicitly says 'Use when you need a visual snapshot for a report, slide, or QA pipeline,' providing clear context for when to use the tool. However, it does not mention when not to use it or suggest alternatives, which would be helpful given the many 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 provided, so description must disclose behaviors. It mentions cost and lists return metadata, but does not disclose read/write nature, rate limits, or data freshness. It is adequate but not fully 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?

Description is brief (three sentences including example and cost). Each sentence adds value. Example call is provided. Could be slightly more structured, but no waste.

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

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

Given the simplicity (1 param, no output schema, no annotations), the description covers purpose, usage, cost, and partial behavior. Lacks explicit return structure but lists fields. Sufficient for a narrow enrichment 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 0% (no description in schema). Description only repeats 'given just a domain' and provides an example, but does not clarify format (e.g., protocol, TLD requirements) or any constraints. Minimal added value for the single parameter.

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?

Clear verb 'enrich' with specific resource 'company' and explicit output metadata fields (industry, employee size, etc.). Distinguishes from siblings by specifying input is a 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?

States when to use: 'Use whenever you need company context for a B2B lead, prospect, or competitor.' Does not provide explicit when-not or alternatives, but context combined with sibling names (many enrich_* tools) makes selection clear.

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

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

No annotations are provided, so the description must carry the full burden. It discloses pricing ($0.005–$0.05) and mentions data sources ('from public sources'), which implies read-only behavior. However, it does not discuss latency, authentication requirements, rate limits, or whether the tool modifies any data. The cost disclosure is positive but insufficient for full 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 with no wasted words. The first clearly states the purpose, and the second provides cost. Both are front-loaded and 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?

Given the minimal schema (1 param, no description, no output schema, no annotations), the description is incomplete. It lacks input format, output structure, and any caveats. The context of cost is helpful, but overall the agent cannot determine how to invoke the tool correctly without more 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?

The input schema has a single parameter 'arg' with zero description coverage (<50%). The description does not explain what 'arg' should be (e.g., company name, domain, ID). With such poor schema coverage, the description must compensate, but it provides no parameter semantics. 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 states that the tool enriches company data with a 'fused firmographic profile' including legal entity, HQ, funding signal, and gov-contractor signal. This provides a specific verb and resource, distinguishing it from generic enrichment tools like 'enrich_company'. However, it does not specify what input the tool requires (e.g., company name, domain, or ID), which slightly reduces 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 is provided on when to use this tool over sibling enrichment tools (e.g., 'enrich_company', 'enrich_domain'). There is no mention of prerequisites, alternatives, or when not to use it. The cost information is useful but does not cover usage context.

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

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

No annotations are provided, so the description bears full responsibility. It discloses cost ($0.005–$0.05 per call), which is helpful. However, it omits other behaviors like rate limits, error handling, idempotency, or whether it modifies anything. The fused nature implies read-only, but not explicit.

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 and to the point, using a dash-separated list of provided data. It front-loads the purpose and includes cost info in a second sentence. The truncation is a minor issue but does not severely impact 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 no output schema, the description provides a reasonable overview of output constituents (DNS, WHOIS, TLS). However, it lacks detail on output format, completeness, or pagination. The cost mention adds practical context. Adequate for a simple tool but could be more precise.

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

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

The single parameter 'arg' has no description in the schema (0% coverage) and the tool description does not explicitly state that 'arg' should be the domain name. Although it's implied by context, the lack of explicit parameter documentation forces the agent to infer.

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

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

The description clearly states the tool provides domain intelligence by fusing DNS records, WHOIS registration, and TLS certificate info. It is distinct from sibling tools like lookup_dns or lookup_whois which offer individual lookups. However, the truncation 'Lead-enri' slightly detracts from completeness.

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 individual DNS, WHOIS, or SSL lookups. The description does not mention prerequisites, context, or exclusions, leaving the agent without decision support.

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

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

No annotations provided, so description carries full burden. It mentions cost and ranking method but lacks details on rate limits, accuracy, or failure behavior. The cost disclosure is helpful but incomplete.

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

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

Concise with two sentences covering purpose and cost. Efficient but omits critical input format details, slightly reducing 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?

Lacks output schema and does not describe what the tool returns (e.g., a list of ranked guesses). Given the enrichment complexity, the description is incomplete for an agent to understand expected 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?

Input schema has one required 'arg' with 0% description coverage. The description mentions 'name + company domain' but does not clarify how to combine them into the single argument. This ambiguity hinders correct invocation.

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

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

The description clearly states the tool finds a person's likely work email from name and company domain, distinguishing it from other enrich_ tools. However, the input schema is inconsistent, only showing a single 'arg' parameter without specifying the expected format.

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?

Implicitly suggests usage when needing to find a work email given name and domain, but provides no when-not-to-use instructions or comparisons to siblings like lookup_email_validate or enrich_company.

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 does not disclose whether the operation is read-only, requires authentication, has rate limits, or what happens on errors. The cost range is mentioned but insufficient for full 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?

The description is three sentences, each serving a clear purpose: what the tool does, when to use it, and an example with cost. No redundant information, and the most important information is front-loaded.

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

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

Given no output schema or annotations, the description covers the basic input (username) and output (enriched fields) but omits output format, error handling, and data freshness. It is adequate for a simple tool but has gaps.

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

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

The schema has a single 'username' parameter with no description (0% coverage). The description provides an example ('torvalds') but does not add constraints like case sensitivity or validity rules, so it adds minimal meaning beyond the schema.

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

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

The description explicitly states the tool 'Enrich a GitHub user profile' and lists specific fields (public repo count, followers, hireable flag, top languages, join date). This clearly distinguishes it from sibling tools like lookup_github_user (which likely returns basic profile info) and other enrich_* tools for different platforms.

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

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

The description says 'Use for developer-lead qualification or recruiter sourcing,' providing clear context. However, it does not explicitly contrast with alternatives like lookup_github_user or mention when not to use it, so it lacks 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?

The description includes pricing (cost per call), which is a behavioral trait, but lacks crucial details such as read-only nature, authentication needs, rate limits, data freshness, or what happens on errors. With no annotations, 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 very short (one line plus cost), achieving conciseness but sacrificing informativeness. It is front-loaded with the purpose, but critical details are missing.

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

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

Given the complexity of a Google Maps business review tool, the description is entirely insufficient. It lacks parameter explanation, output format, and usage context. The absence of an output schema and nested objects further limits 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 single parameter 'arg' has no description in the schema or the tool description. There is no indication of what value to provide (e.g., business name, place ID, URL). Schema coverage is 0%, and the description adds no meaning.

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 'Google Maps Business Review Aggregation' gives a vague purpose but lacks specificity. It does not state what the tool does with the input or what output to expect. The name 'enrich_googlemaps' suggests enrichment, but the description only mentions aggregation. Sibling tool 'enrich_googlereviews' could be overlapping, creating confusion.

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

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

No guidance is provided on when to use this tool versus alternatives like 'enrich_googlereviews', 'search_places', or scrape tools. There is no mention of prerequisites, context, 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?

No annotations are provided, so the description carries full responsibility. It mentions the cost per call, which is useful, but it does not disclose other behavioral traits like rate limits, data freshness, authentication requirements, or any side effects. The description is minimal in this regard.

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 four sentences covering purpose, usage, an example, and cost. Every sentence adds value, and there is no redundant or unnecessary text. It is well-structured and front-loaded.

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

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

For a simple tool with one parameter and no output schema, the description covers the basics: purpose, usage, example, and cost. However, it lacks details about the output structure (e.g., format of rating distribution, length of recent text) 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?

The only parameter, place_id, is explained in the description as a Google place_id, with a concrete example. Since schema description coverage is 0%, the description adds meaning, but it does not explain what a place_id is or how to obtain one, leaving some ambiguity.

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 aggregates Google Maps reviews by place_id, producing rating distribution and recent review text. While it identifies the specific resource and action, it could be more explicit about the exact output structure to fully distinguish from other review-related tools.

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

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

The description provides a clear use case: when you have a place_id and need structured review data. However, it does not mention when not to use this tool or suggest alternatives, such as if a place_id is unavailable or if a different review source is needed.

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

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

No annotations are provided, so the description carries full burden. It discloses the cost range but does not mention rate limits, authentication requirements, error handling for invalid usernames, or whether the operation is safe/read-only. This leaves significant behavioral 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 extremely concise: two sentences for purpose and usage, plus an example and cost. It is front-loaded with the core action, then use cases, then practical details. Every sentence adds value without redundancy.

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

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

Given the low complexity (1 parameter, no nested objects, no output schema), the description covers the main purpose and usage context. It lists the enriched fields and provides an example. However, it does not describe the return format or potential errors, which would enhance completeness 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?

The input schema has one required parameter (username) with no description (0% coverage). The description mitigates this by providing an example call ('{"username": "natgeo"}'), which implies the expected format (Instagram handle). However, it does not explicitly explain what constitutes a valid username or hint at format requirements.

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 what the tool does ('Enrich an Instagram profile with follower count, bio, business category, verified status, profile picture, and external links.') and provides specific use cases (qualifying leads, scoring creators). It distinguishes from other enrichment tools by focusing on Instagram, but 'enrich' is a verb shared among siblings.

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

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

The description explicitly states when to use the tool ('Use when you need to qualify an Instagram lead, score a creator... or pull live profile context'), and includes an example call. It does not specify when not to use it or mention alternatives, but the guidelines are clear enough for the stated purposes.

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 carries full burden. It mentions cost and public-only access, but omits authentication needs, rate limits, error handling, or side effects. Insufficient for a paid tool.

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

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

Three focused sentences: purpose, example, cost. No redundancy, front-loaded with key information. Excellent 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?

Description lists expected output fields (headline, company, experience snapshot) despite no output schema. Covers essential context for a simple 1-param tool, though format 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?

Schema has 0% description coverage, but description explains what 'slug' means (part after /in/) and provides an example. This adds essential meaning beyond the raw 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?

Description clearly states tool enriches a public LinkedIn profile using the vanity slug, listing specific data points (headline, company, experience). It distinguishes from siblings like enrich_company and enrich_github by focusing on LinkedIn, though it doesn't explicitly contrast.

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?

Provides use case ('B2B lead enrichment when you have a LinkedIn URL') and an example call, but no guidance on when not to use or alternatives. Lacks exclusion 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?

No annotations are provided, so the description carries the full burden. It implies a read operation ('aggregate') and discloses cost, but does not explain any side effects, privacy implications, or limits. The description could be more explicit about the tool being non-destructive.

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

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

The description is concise at three sentences, front-loads the purpose, and includes an example and cost without redundancy. 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?

Given the tool's simplicity (one parameter, no nested objects, no output schema), the description covers the return values (rating, review count, review snippets) and use cases. It could mention pagination or response format, but it is sufficiently complete for an agent to understand the tool.

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

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

The input schema has only one parameter 'query' with type string and no further description. The description adds an example ('blue bottle coffee oakland'), which clarifies the expected format and meaning. With 0% schema coverage, this compensation elevates 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?

The description clearly states it aggregates Google Maps reviews for a local business, listing specific outputs (rating, review count, recent review snippets). This is a specific verb+resource and distinguishes from sibling enrich_* tools by mentioning Google Maps.

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 use cases: local-SEO research, competitor monitoring, or restaurant/service intelligence. It gives context for when to use, though it does not explicitly state when not to use or mention alternatives. Still, the guidance is clear and helpful.

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

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

No annotations exist. Discloses cost ($0.005–$0.05 per call), which is helpful, but omits behavioral details like rate limits, authentication requirements, or error 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?

Three sentences: purpose, use case, cost. No unnecessary words, 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?

For a simple tool with one parameter and no output schema, the description covers purpose, use context, and cost. Missing details on return structure or error handling, but acceptable for its 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?

Only one parameter (artist_id) with 0% schema description coverage. The description provides an example call but does not explain the expected format or constraints of the artist ID.

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

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

Clearly states the verb 'Enrich', resource 'Spotify artist profile', and specific data fields (monthly listeners, follower count, top tracks, genres). Distinguishes from siblings targeting other platforms.

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?

Provides explicit use cases ('music marketing, A&R research, or playlist-pitching workflows') but does not mention when not to use or alternative tools.

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

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

No annotations are provided, so the description must cover behavioral traits. It includes cost information ($0.005–$0.05 per call) and an example call, but does not disclose rate limits, authentication requirements, data freshness, or error handling. This is adequate for a simple enrichment tool but incomplete.

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

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

The description is very concise: two sentences plus an example and cost. It front-loads the purpose, then provides a concrete example. No wasted words; every sentence earns its place.

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

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

Given the tool's simplicity (1 param, no output schema, no annotations), the description covers purpose, usage context, example, and cost. It lacks return format details, but the output is likely self-explanatory (the enriched data). Minor gap: no mention of what happens on error (e.g., username not found). Still, it is mostly complete for an enrichment tool.

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

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

The input schema (100% coverage) already defines the 'username' parameter. The description adds an example ('zuck') implying it expects a Threads handle. This adds marginal value beyond the schema. With 0% schema description coverage, the description provides minimal additional 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 the tool's action ('Enrich'), resource ('Threads (Meta) profile'), and the specific data added (follower count, bio, verified status). It also provides a use case ('cross-platform social-presence checks'), which differentiates it from sibling enrichment tools targeting other platforms.

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

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

The description explicitly states when to use the tool ('Use for cross-platform social-presence checks'), giving clear context. However, it does not provide explicit when-not-to-use scenarios or alternatives, though the sibling list implies the context.

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

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

No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits such as whether the tool is read-only, requires authentication, has rate limits, or how it handles errors. The cost is mentioned but does not substitute for operational behavior.

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

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

The description is extremely concise: one sentence for purpose, one for usage, plus an example and cost. It is front-loaded with the most important information and contains no redundant or extraneous text.

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 core aspects: what data is returned, when to use it, and cost. However, it lacks details on error handling, data freshness, or required permissions, which would make it fully complete. Still, it is sufficient for typical 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 schema has one parameter (username) with 0% description coverage. The description provides an example call ('{"username": "khaby.lame"}'), which adds slight meaning beyond the schema. However, it does not explicitly describe the parameter format or constraints, so the value added is minimal. A baseline of 3 is appropriate.

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

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

The description explicitly states the verb 'enrich' and the resource 'TikTok profile', listing specific data fields (follower count, total likes, etc.). It clearly differentiates from sibling tools like enrich_instagram or enrich_x by naming the platform and the type of data 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?

The description gives explicit use cases: 'qualify a TikTok creator for paid partnerships' and 'pull live audience data'. While it does not mention when not to use the tool or alternatives, the context signals show many sibling tools for other platforms, so the guidance is clear and sufficient.

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

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

No annotations provided; description adds cost information ($0.005–$0.05) and live nature, but lacks details on rate limits, data freshness, 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?

Three sentences plus an example; no wasted words, purpose is front-loaded.

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

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

Lists the fields returned implicitly, which is adequate for a single-param tool with no output schema, though explicit output structure would improve completeness.

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

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

Only one parameter (username) with no schema description; the description provides an example call ('{"username": "elonmusk"}'), adding value beyond the raw 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 an X/Twitter profile' and lists specific attributes (follower count, bio, etc.), distinguishing it from sibling enrich tools for other platforms.

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

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

Explicitly states when to use ('lead research or influencer scoring') but does not mention alternatives or when not to use.

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

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

With no annotations, the description should disclose behaviors such as authentication requirements, rate limits, or the meaning of 'AI-generation detection'. It only mentions cost, but not 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 brief but includes extraneous cost information that could be separate. It is concise but lacks necessary structure and clarity about parameters.

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 1 parameter with no schema description and no output schema, the description fails to provide essential context such as input format, return values, or how the AI-generation detection works, making 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?

The input schema has a single parameter 'arg' with 0% coverage in the description. The description does not explain what 'arg' should be (e.g., a YouTube video URL or ID), leaving the agent unable to correctly invoke the tool.

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 'YouTube Video Metadata & AI-Generation Detection', which is a specific verb+resource combination. It distinguishes from sibling enrich_ tools by specifying YouTube as the target platform.

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

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

The description does not provide any guidance on when to use this tool versus alternatives like lookup_youtube or search_youtube. No context is given regarding prerequisites or scenarios where this tool is preferred.

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

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

No annotations are provided, so the description must fully convey behavioral traits. It mentions cost ($0.005–$0.05 USDC per call) and supported formats, but omits critical details such as whether the operation is read-only, error handling behavior, rate limits, authentication requirements, or return value structure. The lack of output schema or description of what 'structured data' entails leaves significant gaps in 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?

The description is tightly written with no superfluous content. The purpose statement, supported formats, use cases, example call, and cost are each presented concisely and in logical order. Every sentence contributes necessary information without 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?

The tool involves document extraction with no output schema, so the description should ideally clarify output format, error handling, and limitations. It covers purpose, parameter usage, supported types, and cost. However, it does not describe the structure of returned data, what happens when extraction fails, or whether there are size or content constraints. This leaves some gaps for an AI agent to reason about tool behavior.

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

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

With schema description coverage at 0%, the description must compensate. It explains that the query_string parameter should include a URL (e.g., 'url=https://example.com/invoice.pdf') and shows an example call. While the parameter format (URL encoding) is not explicitly stated, the example provides sufficient guidance for an AI agent to construct valid input. This adds meaningful context beyond the bare 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's function: extracting structured data (tables, forms, invoice fields) from document URLs. It specifies supported formats (PDF/PNG/JPG) and use cases (invoice processing, form parsing, document AI). This distinguishes it from sibling tools, which are primarily web scrapers or lookups, making the purpose unambiguous.

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