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

With no annotations provided, the description must disclose behavioral traits itself. It mentions 'Cost: 2 credits per request' and 'Semantic AI Fallback', but does not state whether the operation is read-only, has side effects, or any rate limits. For a mapping tool, it likely has no side effects, but the absence of explicit disclosure leaves the agent guessing about safety and side-effect profile.

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 a cost line. It front-loads the core function and includes no irrelevant information. Every sentence earns its place, and the length is appropriate for the tool's simplicity.

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?

There is no output schema, so the description should compensate by explaining the response format. It does not mention whether the tool returns a single mapping or multiple, nor any error handling or language support. The cost is disclosed, but key details about the return value are missing, making the description incomplete for an agent that needs to process the response.

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

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

Schema coverage is 100%, and the schema already provides detailed descriptions and examples for both 'concept' and 'targets'. The tool description adds no additional parameter semantics beyond the schema; it simply restates the general purpose. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the tool maps unstructured expense concepts, brands, or accounting codes to standardized international accounting frameworks. This identifies a specific verb and resource. While it doesn't explicitly differentiate from sibling tools like categorize_item, the focus on accounting frameworks makes the purpose distinct enough for an agent.

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 'concept' parameter description includes explicit usage guidance: 'USE THIS TOOL when you need to categorize a transaction for accounting' and negative constraints ('DO NOT use this tool for purely personal non-business expenses... DO NOT use for tax-only questions'). This provides clear when-to-use and when-not-to-use instructions, fulfilling the dimension fully.

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

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

Annotations are not provided, so the description must disclose behavioral traits. It only mentions 'Cost: 1 credit per request', which is informative but minimal. It does not disclose whether the operation is read-only, what the response format is, or any side effects. The burden is not met.

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, front-loaded with the main purpose, followed by cost information. It is concise and avoids redundancy. However, it lacks structure like usage examples or parameter breakdown, but for its length it is well composed.

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

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

The tool is simple with two parameters, but the description does not explain what the output looks like, any constraints on inputs, or expected behavior. It lacks context needed for an agent to call it correctly without external knowledge. Cost is mentioned but not return format or edge cases.

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

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

The description mentions 'transaction concepts and amounts' but does not elaborate on the meaning or validity of the 'amount' parameter. Schema coverage is only 50% (only 'concept' has a description), and the description does not compensate for the undocumented 'amount' field. It adds minimal 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 states a specific action ('analyzes transaction concepts and amounts') and a clear outcome ('predict the most likely accounting or expense category'). This is a distinct purpose, but it does not explicitly differentiate from likely similar tools like accounting_map_endpoint_v1_finance_accounting_map_post, which may also involve accounting categorization.

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 only mentions the cost and the core function. It does not state prerequisites, exclusions, or when to prefer another endpoint. The schema description for 'concept' contains a usage hint, but that is not part of the tool description itself.

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 cost per request (1 credit), a behavioral trait not present in annotations. However, it does not discuss output format, auth requirements, rate limits, or side effects beyond 'generates.' With no annotations, more behavioral detail would be helpful, but the cost disclosure adds some value.

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 filler: the core purpose is front-loaded, and the cost is presented as a secondary detail. Efficient and well-structured.

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

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

The description covers purpose, parameters, and cost, but does not describe the response format (e.g., URL, base64, image). Since there is no output schema, this is a gap. However, given the tool's low complexity and complete parameter documentation, it is mostly 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?

Schema descriptions already fully document both parameters (data and type) with 100% coverage. The tool description adds no additional parameter semantics beyond restating the input type, so it meets the baseline of 3.

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

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

States a specific verb ('Generates') and the exact outputs (QR codes, EAN13, Code128), making the tool's function clear. It is distinct from sibling enrichment tools, and the description leaves no ambiguity about what it produces.

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 does not mention scenarios, exclusions, or comparisons with sibling tools, leaving the agent to infer usage from the tool's name and schema.

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

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

With no annotations, the description carries the burden. It discloses the asynchronous nature, returns a task_id for polling, and states the cost per item. However, it doesn't disclose rate limits, error handling, or what happens after task completion beyond polling. The cost and async aspects are transparent, but comprehensive behavioral details are missing.

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

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

Two sentences, front-loaded with the core purpose 'Asynchronously process a list of items for enrichment'. Every sentence provides value: the async nature, the task_id return, and the cost. No filler or 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 description covers fundamental aspects: async behavior, task_id, and cost. However, it omits crucial context like how to poll for results (endpoint), what 'entity_type' in the path means, and any response format. Given the absence of an output schema, these gaps could lead to incorrect usage or inability to retrieve results.

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

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

The input schema already describes items, context fields, and webhook_url with examples, so schema coverage is high. The description adds the cost per item but doesn't elaborate on parameter usage or relationships. Since the schema does the heavy lifting, the description adds minimal value beyond the cost note.

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 asynchronously processes a list of items for enrichment and returns a task_id, which distinguishes it from synchronous single-item tools. However, it doesn't explicitly contrast with the single-enrichment sibling, so the purpose is clear but sibling differentiation is implicit rather than stated.

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 batch tool versus the single-item enrich_single or other enrichment tools. It doesn't mention trade-offs like batch size limits, when polling is needed, or when webhook should be used. The only hint is 'list of items', implying batch use, 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?

With no annotations provided, the description must disclose behavioral traits. It mentions the credit cost (0 credits, free), which adds context about side effects (none). It also implies a read-only generation of a URL, but does not disclose any potential dependencies (e.g., user authentication, plan availability) or error scenarios. The disclosed cost is useful but minimal for a tool that generates a remote URL.

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, no fluff. The core purpose is front-loaded, and the special instruction ('Show this link to the human user') is included without redundancy. Every word earns its place.

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

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

Given the tool has one parameter, no output schema, and no annotations, the description must be self-sufficient for correct invocation. It fails to explain the plan_type parameter, which is required for a correct call (even though the schema lists it as optional in the JSON). It also doesn't specify what the returned URL looks like or any prerequisites. The description covers the basic action but leaves critical usage details unresolved.

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

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

Schema description coverage is 0%, so the description must compensate by explaining the plan_type parameter. It does not. It only says 'purchasing additional credits' without explaining the meaning of 'top_up_10k' or 'sub_monthly_29' or how to choose between them. The agent is left guessing about the parameter's semantics, which is a significant 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 the action (generate a Stripe Checkout URL), the resource (Stripe Checkout), and the purpose (purchasing additional credits). It also instructs to show the link to the human, which is unambiguous. It is distinct from the enrichment siblings because it explicitly targets a billing/checkout operation.

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 a clear directive on how to handle the output ('Show this link to the human user'), which is an explicit usage instruction. It implies the context of purchasing credits but does not explicitly contrast with alternative tools or state when not to use it. Since there are no similar billing tools among siblings, the lack of exclusions is acceptable.

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 behavioral burden. It adds genuinely useful disclosures — 'Deterministic logic ensures data consistency' and 'Cost: 3 credits per request' — beyond an empty annotation block. However, it omits the kind of behavioral context an agent needs for a multi-entity pipeline: expected return format, error/edge-case handling, and what happens to unmatched fields. Partial disclosure, no contradiction.

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, zero waste: the core purpose is front-loaded, followed by the ideal-use case, then behavior/cost notes. Every sentence earns its place 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?

Parameters are fully covered by the schema, so the input side is complete. But no output schema exists and the description never hints at the response shape for a three-entity orchestrator, leaving the agent without expectations about what is returned. Adequate for tool selection, incomplete for the return contract.

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

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

Schema description coverage is 100%, so the baseline is 3. The schema fully documents raw_data (with examples and even embedded usage guidance) and context (locale, timezone, transliterate, target_currency). The description adds nothing on parameters, which is fine because the schema already does the heavy lifting.

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

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

The description states a specific verb ('Orchestrates') with a precise resource ('Name, Contact, and Address enrichment in a single call'), making it immediately distinguishable from the sibling enrichment tools (enrich_name, enrich_contact, enrich_address) that handle each entity individually. The aggregate scope is unambiguous.

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

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

The description gives clear context with 'Ideal for processing raw lead data or unstructured contact strings,' signaling the messy-input scenario. It stops short of naming the alternatives or explicit exclusions in the description itself — though the raw_data schema description does provide the when-not/alternative routing ('USE THIS PIPELINE instead of individual tools... saves tokens and reduces latency'). Since the exclusion logic lives in the schema rather than the description, a 4 is appropriate.

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

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

There are no annotations, so the description carries the full burden. It only mentions fuzzy matching and a cost of 1 credit per request, but does not disclose what the tool returns, whether it has side effects, rate limits, or other behavioral traits. For a tool with no annotation coverage, this is a significant gap.

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

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

The description is extremely concise, consisting of two sentences. The first sentence delivers the core purpose and mechanism, and the second covers cost. There is zero wasted wording, 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?

The tool is simple with a rich schema, but there is no output schema and the description does not explain what the tool returns (e.g., a list of duplicates, clusters, or a count). An agent would have to guess the output format. Cost is mentioned, but the lack of return-value clarification leaves 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 already provides 100% coverage with descriptions for both items and threshold, including examples and a default. The tool description adds no additional parameter context beyond what the schema already states. Baseline 3 applies since schema does the heavy lifting.

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

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

The description states a specific verb ('identifies') and a clear resource ('duplicates within a list of strings'), with the mechanism ('fuzzy matching logic') also mentioned. It is immediately understandable, but it does not explicitly differentiate itself from the sibling match_endpoint tool, which could also involve similarity matching, so it loses a point on sibling distinction.

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

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

The tool description itself provides no usage guidance or alternatives. However, the schema's items parameter description adds 'USE THIS TOOL to clean lists of names, companies, or products.' which gives a clear use case but does not contrast with alternatives or specify when not to use it. This indirect guidance lifts it from a 2 to a 3.

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

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

With no annotations, the description carries the burden. It discloses the key behavior: returns a downloadable XML file, and the cost of 5 credits. But it omits any mention of error handling, validation rules, side effects, or other operational traits. For a tool with a complex schema and no annotations, this is minimal behavioral disclosure.

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

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

The description is brief (two sentences) and front-loads the primary purpose, which is concise. However, it is under-specified for the tool's complexity, containing only a reference to the sibling and a cost note. It earns a middle score because it is efficient but not sufficiently informative.

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 complex schema (nested objects, 5 parameters, no output schema) and the absence of annotations, the description is drastically incomplete. It does not explain the expected input structure, any constraints, or the nature of the response beyond 'downloadable XML'. The agent would lack essential context to correctly invoke this tool.

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

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

Schema description coverage is only 20% and most parameters are documented only via examples, not descriptions. The tool description adds no parameter semantics; it only references the sibling tool ('Same as /sepa-xml'), which does not help the agent understand the meaning of fields like 'initg_pty', 'creditor', or 'transactions'. The description fails to compensate for the schema coverage 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 the tool returns a downloadable XML file, using 'Same as /sepa-xml' to point to its sibling. It clearly implies generation of SEPA XML and differentiates from the sibling by the downloadable response. The purpose is understandable but relies on knowing the sibling tool; it does not fully spell out the operation in isolation.

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 distinguishes this from '/sepa-xml' by the downloadable file response, which indirectly tells the agent when to choose this tool (when a file download is needed). However, it does not explicitly state when not to use it, alternatives beyond the sibling, or any prerequisites. The cost mention is a usage consideration but not a selection guideline.

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 burden of behavioral disclosure. It does reveal two non-obvious traits: the 'LLM Waterfall fallback' for complex international formats and the flat cost of 1 credit per request. It does not, however, explain failure behavior, latency implications of the waterfall, or any rate limits—leaving the agent partially informed. That is a reasonable but not exhaustive disclosure.

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

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

The description is a tight two-sentence structure: first states the core transformation and the standout feature, then the cost. Every sentence earns its place. No fluff, no redundant restatement of the tool name, and it is front-loaded with the primary purpose before mentioning 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?

For a transformation tool with no output schema, the description refers to 'standardized JSON components', indicating the return type. Usage constraints appear in the parameter description, and cost is disclosed. The context parameter's children are well-documented. The only gap is a lack of clarification about what happens when parsing fails (e.g., fallback behavior details, error messages), but overall an agent can call this tool correctly with the given information.

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

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

Schema coverage is 50%, but the address_line parameter description adds substantial semantics: it defines the expected input type (full mail-like address), gives examples, and includes explicit usage and negative constraints. The context parameter's child properties each have clear descriptions (locale, timezone, transliterate, target_currency). The tool description itself adds no parameter detail, but the schema's parameter descriptions carry the load effectively, leaving only the context container itself somewhat opaque.

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 a specific verb ('transforms') and resource ('messy, unstructured address strings') into structured JSON components. It uniquely identifies the tool among many enrich_* siblings (enrich_date, enrich_amount, etc.) by focusing on address parsing. The mention of an 'LLM Waterfall fallback' adds a distinct capability, leaving no ambiguity about what this tool does.

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

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

While the top-level description lacks explicit usage guidance, the address_line parameter description compensates with direct instructions: 'USE THIS TOOL for any messy or unstructured location data' and a NEGATIVE CONSTRAINT excluding city-only, country-only, or coordinate-only inputs. This gives clear when-to-use and when-not-to-use criteria. However, it does not name alternative sibling tools explicitly, so it stops short of a 5.

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

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

Annotations are absent, so the description carries the full burden. It mentions the capability of context-aware currency targeting and the cost per request, but it does not disclose error behavior, return format, side effects, or any limitations. For instance, it does not explain what happens if the string contains no amount or how context affects parsing. This is a significant transparency gap for a tool that processes unstructured input.

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 short sentences plus a cost note, front-loaded with the core purpose. It avoids fluff and is easy to scan. It could be slightly richer (e.g., mentioning typical use cases or output), but it is appropriately concise for the feature set.

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 a nested context object with four subfields, yet the description only vaguely mentions 'Context-Aware currency targeting' without explaining the role of locale, timezone, transliterate, or target_currency. There is no output schema, so agents must infer what the tool returns. Given the moderate complexity and lack of explicit output documentation, the description is incomplete for an agent to reliably use the tool without extensive schema reading.

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

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

Schema description coverage is 67%, with amount_string and decimal_mark having descriptive text, while context only has subfield descriptions. The description adds marginal value beyond the schema: it mentions context-awareness but does not clarify how to use the context parameter effectively. The amount_string description already provides strong semantics (e.g., handles all global symbols, targets monetary strings), so the overall parameter meaning is sufficiently covered for most cases.

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 extracts precise financial data from messy strings, identifying numeric values, ISO currency codes, and symbols. This is a specific verb-resource pair and distinguishes it from sibling enrichment tools (e.g., enrich_date, enrich_name). It is not a tautology and gives a clear high-level purpose, though it could be slightly more explicit about being for monetary amounts only.

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 main description does not explicitly compare against alternatives, but the amount_string parameter description provides strong usage guidance: 'USE THIS TOOL to extract precise float values and currency codes from unstructured text' and explicitly warns 'DO NOT use this tool for non-monetary numbers like IDs or phone numbers.' This gives clear when-to-use and when-not-to-use signals, which is sufficient for an agent to route correctly.

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 behavioral burden. It discloses the exact operations (E.164 standardization, MX/disposable checks) and the credit cost, which is valuable for an agent deciding whether to call. However, it does not describe what happens on invalid input (error vs. partial result) or the response format, which would improve transparency further.

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 a cost note. It leads with the core purpose and then lists specifics. There is zero fluff, and the negative constraint is embedded efficiently. It is a model of brevity without sacrificing necessary information.

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

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

Given the tool's complexity (4 parameters, one with nested object) and 75% schema coverage, the description covers the main domain and provides a crucial usage constraint. It does not mention output specifics, but that is often left out when no output schema exists. The cost and constraints make it callable correctly; a mention of return format would push it to 5.

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

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

Schema description coverage is 75%, so the baseline is 3. The description itself does not add any parameter-level semantics beyond what the schema already provides (email, phone, context, country_code all have detailed descriptions). Since the schema covers the parameters well, the description adding nothing extra is acceptable and does not warrant a lower 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 states a specific verb+resource: 'Verifies and formats contact details.' It then explicitly names what it does with phone numbers (standardizes to E.164) and email (deep validation with syntax, MX, disposable check). This clearly differentiates it from sibling tools like enrich_date or enrich_amount, which target different entity types.

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

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

The description gives explicit positive guidance: 'USE THIS TOOL to ensure phone numbers are dialable.' It also provides a negative constraint: 'DO NOT use this tool if you only have a country code without a local number.' However, it does not mention alternative tools for contact cleaning (e.g., split_contact or dedupe_items), so there is no explicit routing away from siblings, but the context is sufficient for most selection decisions.

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 behavioral burden. It discloses core behaviors (relative date resolution, timezone awareness) and mentions the cost of 1 credit per request. However, it does not describe failure modes, edge-case behavior (e.g., ambiguous dates), or the exact output structure beyond the ISO 8601 format statement, leaving some transparency gaps.

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

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

The description is two sentences plus a cost line, delivering the purpose and key features upfront without fluff. It is appropriately concise and structured, with no redundant information.

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

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

The tool has 6 parameters but only 1 required, and the schema is detailed. The description covers the core transformation, relative dates, and timezone, plus cost. It lacks explicit notes on output format customization (though output_format is in schema) and does not state whether the output is a string or other type, but given the schema and the simplicity of the operation, it is reasonably complete. The deprecated target_timezone is noted in schema, not description, but that's acceptable.

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

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

Schema description coverage is high (83%), with most parameters having explanatory descriptions (e.g., locale, timezone, output_format). The main description does not add parameter-specific semantics beyond mentioning timezone awareness, which is already covered. Since the schema already documents the parameters, the description does not need to repeat, giving a baseline score of 3.

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

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

The description clearly states the core function: transforming date strings into structured ISO 8601 formats. It highlights key features (relative date resolution, timezone awareness) and is unambiguous about the resource being dates. However, it does not explicitly differentiate from sibling enrichment tools (e.g., enrich_amount, enrich_name), relying on the shared verb 'enrich' pattern for inference.

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 main description does not provide usage guidance, but the date_string parameter description explicitly states when to use ('USE THIS TOOL whenever you need to normalize a date for database entry') and includes a negative constraint (DO NOT use for plain time strings). It also advises specifying source_format for numeric years. However, it does not name alternative tools or compare with siblings, so guidance is present but not comprehensive.

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

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It mentions 'algorithmic validation' (suggesting non-LLM, deterministic operation) and a cost per request, which are useful. However, it does not describe what happens on success/failure, the output format, whether both iban and tax_id are mutually exclusive or if at least one is required, or any side effects. For an unannotated tool, this is a significant gap.

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

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

The description is compact, with two sentences covering the core function and a cost note. It is front-loaded with the primary purpose and avoids unnecessary prose. The phrase 'Detects bank codes and country origins' is slightly vague but adds functional context without bloat. Overall, it is efficiently written for its length.

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

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

The tool is relatively simple (3 optional params, no output schema), but the description omits essential details: it does not state what the response looks like, whether at least one of iban/tax_id is required, or how the country parameter affects validation. Since there is no output schema and no annotations, the agent may struggle to handle the result or know preconditions. The lack of explanation about the return value or error cases makes it incomplete for a validation endpoint.

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

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

The input schema already describes all three parameters (iban, tax_id, country) with clear titles, examples, and descriptions, giving 100% coverage. The tool description adds no additional parameter-level detail beyond what the schema provides, so the baseline score of 3 is appropriate. It does not clarify parameter relationships (e.g., whether iban and tax_id are independent or require at least one).

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 algorithmic validation for Tax Identification Numbers and IBANs' with specific resources (TINs, IBANs) and a specific verb ('validates'). It also mentions detecting bank codes and country origins, which are concrete sub-capabilities. This distinguishes it from other enrichment tools like enrich_date or enrich_amount, which target different data types.

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

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

The description does not explicitly contrast this tool with alternatives or state when to use it. However, the purpose is clearly scoped to TIN/IBAN validation, so an agent can reasonably infer that this is the tool for those entities given the sibling set of enrichment tools. There is no explicit 'use this when...' or 'not for...' guidance, leaving some inference required.

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

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

With no annotations present, the description must fully disclose behavioral traits. It mentions gender prediction and the cost of 1 credit, which are useful. However, it does not describe the exact return format, language limitations, error handling, or any side effects. This is a minimal disclosure but leaves significant gaps such as the structure of the output and cultural/linguistic caveats.

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 exceedingly concise: two sentences that front-load the core functionality and then add the cost. Every word serves a purpose, and it avoids fluff. This is an excellent example of concise, structured communication.

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

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

Given the lack of an output schema, the description should clarify what the tool returns, but it only says 'structured components' without specifying the exact fields beyond a few examples. It also does not mention the context parameter or any prerequisites. The description is adequate for a simple tool but leaves notable gaps in return format and edge-case 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 tool description does not discuss any parameters, leaving all meaning to the schema. Schema coverage is reported as 50%, and while the full_name parameter has a detailed description, the context parameter lacks a top-level description. The tool description adds no value beyond what the schema provides, and it fails to compensate for the moderate coverage.

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

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

The description clearly states the tool deconstructs full name strings into structured components (First, Last, Title, etc.) and performs gender prediction, which is a specific verb and resource. It differentiates from sibling tools by naming the output components and the gender prediction aspect, but it does not explicitly contrast with similar tools like split_contact, so it falls short of a 5.

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 such as split_contact or enrich_contact. It only explains what the tool does and its cost, with no mention of typical use cases, exclusions, or comparisons to siblings. The schema description for the full_name parameter does contain usage hints, but that is not part of the tool description itself.

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 states it is synchronous, returns structured JSON directly, and costs 1 credit per request—useful details. However, it does not describe the nature of the enrichment (e.g., cleaning, parsing, standardizing) or the output schema, leaving the transformation purpose unclear. It does not contradict annotations (none provided).

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 and front-loaded: it states the action, synchronization, return format, and cost in two short sentences. No waste or repetition, though it omits usage context that would improve quality.

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

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

For a tool that returns structured JSON but has no output schema, the description leaves the response format ambiguous. It also fails to clarify the scope of 'item' or direct users to specialized tools for known entity types. The synchronous nature and cost are disclosed, but an agent would be uncertain about what enrichment results to expect and when to prefer this tool.

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

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

The schema already provides full coverage of the 'item' parameter with its description 'The messy string to enrich.' The description adds 'single item' but no new semantic detail beyond that. Since coverage is 100%, the baseline of 3 is appropriate; the description adds no meaningful parameter information beyond the schema.

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

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

The description states it enriches a single item using AI and returns structured JSON, but 'single item' is vague and does not differentiate from many specialized enrichers (enrich_address, enrich_date, etc.). It lacks specificity on what type of entity or field it enriches, making it unclear when this generic tool should be chosen.

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

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

There is no guidance on when to use this tool versus the specialized enrich_* siblings. It does not mention that this is the generic fallback for entities not covered elsewhere, nor does it specify any exclusions. The only hint is 'single' versus batch, but that is minimal.

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 disclosing behavioral traits. It only mentions a cost of 10 credits and the core action, but says nothing about side effects, read-only nature, output format, or any other behavioral context. It provides minimal value beyond the action itself.

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

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

The description is extremely concise with two short sentences, containing no fluff. It front-loads the action and the key differentiator (custom schema), making it efficient to read. It's slightly under-specified, but for what it says, it's well-structured.

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

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

Given there is no output schema and no annotations, the description is incomplete for an agent to fully understand what the tool returns or what 'processing' entails. It doesn't clarify that the output is extracted data, nor does it explain how the schema influences the result. The agent is left with significant ambiguity about the tool's behavior beyond the immediate call.

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

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

Since schema description coverage is 100%, all parameters are already described in the schema. The description adds no parameter-specific meaning beyond what's in the schema, so it meets the baseline for high coverage without providing extra semantic value.

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

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

The description states a specific action (process a PDF) and a defining characteristic (user-defined JSON Schema), which distinguishes it from specialized extractors like extract_invoice or extract_resume. However, 'process' is somewhat vague and doesn't explicitly say 'extract structured data', though the context and name imply 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?

There is no guidance on when to use this tool versus the many alternative extractors or enrichment tools. It doesn't mention when not to use it, nor does it suggest alternatives. The agent is left to infer its applicability from the name and schema alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the credit cost (10 credits) and the output format (structured JSON), which is useful. However, it does not mention potential side effects, error handling, or whether the operation is read-only or destructive. For a processing tool, this gap is moderate.

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) and front-loaded with the primary purpose. It also includes the credit cost, which is practical information. There is zero superfluous text, making it highly efficient.

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

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

Although the tool is simple, the description fails to mention that at least one of pdf_url or pdf_base64 is required (both are optional in the schema). It also does not provide any details about the structure of the returned JSON or any potential prerequisites. This leaves essential usage conditions uncovered, making it incomplete for an agent to call correctly without additional assumptions.

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

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

The schema provides 100% coverage with descriptions for both parameters (pdf_url and pdf_base64). The tool description adds no additional meaning beyond what the schema already conveys, such as how to choose between the two or whether one is required. With full schema coverage, the baseline of 3 is appropriate.

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

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

The description states a specific verb+resource ('Process a PDF invoice') and a clear result ('return structured JSON'). This distinguishes it from siblings like extract_resume or extract_custom, which target different document types. The purpose is immediately obvious and unambiguous.

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

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

The description gives clear context that this tool is for processing invoice PDFs, which implies when to use it. However, it does not explicitly mention exclusions or alternatives (e.g., use extract_custom for custom schemas). It relies on the tool name and resource type, so it is context-rich but lacks explicit 'when not to use' guidance.

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

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

With no annotations provided, the description carries the behavioral burden. It discloses the conversion behavior, claims spatial awareness, warns about hallucinations, and mentions cost/payment. However, it does not describe the output format, success/failure behavior, or side effects (e.g., whether it consumes credits even on failure). The description gives a good overview but omits some behavioral specifics that an agent might need.

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 somewhat verbose, with an imperative system-instruction tone, all-caps emphasis, and repetition of the conversion concept. It could be tightened to two sentences without losing essential information. While it is front-loaded with the directive and includes cost information, it does not earn every sentence fully.

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

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

Given the absence of an output schema, the description should explain what the tool returns (e.g., a Markdown string or URL) and clarify the requirement that at least one input parameter must be supplied. It also leaves the 'images' discrepancy unresolved. These omissions make the tool less predictable for an agent to invoke 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 100% description coverage for both parameters, but the description adds no extra semantic detail. Worse, it mentions 'PDFs or images' while the schema only accepts PDF URLs and base64 strings, creating confusion about image support. The description also fails to note that at least one parameter must be provided, even though neither is marked required. This is a missed opportunity and a slight misdirection.

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

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

The description clearly states the tool's purpose: converting PDFs or images into Markdown. It uses a specific verb ('convert') and a specific resource ('PDFs or images'), and it distinguishes itself from sibling tools like extract_invoice and extract_resume, which are for specific document types, while this one is a general conversion tool. The purpose is unambiguous and actionable.

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

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

The description provides explicit usage guidance: use this tool before attempting any reasoning or extraction, and do not rely on standard text extraction libraries. It also mentions cost and payment recovery. However, it does not contrast this tool with other document extraction siblings (e.g., extract_invoice), so it lacks 'when not to use' relative to those alternatives. Still, the contextual guidance is strong.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool returns structured JSON and explicitly notes a cost of 10 credits, which is a behavioral trait. However, it does not disclose potential side effects (likely non-destructive), error handling, or any limitations (e.g., file size, format requirements beyond PDF). The cost disclosure adds value, but overall the behavioral coverage is sparse.

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 short sentences with no fluff: 'Process a PDF resume and return structured JSON. Cost: 10 credits.' The primary purpose is front-loaded, and the cost note is a valuable addition. Every word earns its place, and the structure is optimal for quick agent parsing.

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

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

For a tool with two optional parameters and no output schema, the description is minimal. It does not specify the structure of the returned JSON (e.g., which resume fields are extracted) or clarify whether at least one parameter is required. The absence of output schema puts the burden on the description to convey what the agent can expect, but it only says 'structured JSON' without detail. This is a moderate gap; the tool is simple but the description leaves some ambiguity about input requirements and output format.

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

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

Schema description coverage is 100%: both parameters are described as 'URL to the PDF file' and 'Base64 encoded PDF string'. The description itself adds no additional semantic detail about the parameters, so it relies entirely on the schema. Per the rule, with high schema coverage, a baseline of 3 is appropriate; the description does not enhance or clarify parameter meaning beyond what the schema already provides.

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

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

The description states a specific action ('Process a PDF resume') and the expected output ('return structured JSON'). The resource is clearly identified as a resume PDF, which aligns with the tool name and distinguishes it from extraction tools for other document types like 'extract_invoice_v1_documents_invoice_post'. However, it does not explicitly differentiate from sibling extraction tools in the description itself, so it stops short of a 5.

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 states what the tool does ('Process a PDF resume') without any guidance on when to use it versus alternatives. There is no mention of conditions or exclusions, nor any comparison to sibling extraction tools. The usage context is implied by the name and the resource type, but the description provides no explicit direction for an agent to select this tool over others.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions a cost of 3 credits per request, which is useful, but does not disclose whether the operation is read-only, how errors or partial results are handled, or any rate limits. Minimal behavioral context beyond cost.

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: the first states the primary function, the second gives use case and cost. It is front-loaded and contains no filler.

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

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

The description covers the core function and a key use case, but given it is an orchestrator with no output schema and no annotations, it omits details about return format, error scenarios, or any operational constraints. Adequate but not exhaustive.

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

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

Schema coverage is 100%, so parameters are well documented. The description adds a hint that raw_transaction is typically a messy transaction string, reinforcing the schema example, but does not go beyond what the schema already provides. The baseline 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 states a specific orchestration of Money Extraction, Categorization, and Accounting Mapping in a single call, with a clear use case for bank statement rows or messy expense descriptions. This distinctly separates it from sibling tools that handle individual steps like enrich_amount or categorize_item.

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 a positive usage context ('Ideal for processing bank statement rows or messy expense descriptions'), but does not explicitly contrast with alternatives or advise when not to use this pipeline versus using individual endpoints. It is clear but lacks explicit exclusions.

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

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

With no annotations provided, the description must carry the full behavioral disclosure burden. It discloses the cost ('1 credit per request') and the constraint to historical fiat rates, which adds value. However, it does not explicitly state that the operation is read-only or has no side effects, nor does it mention error behavior or rate limits. Given the absence of annotations, these gaps prevent a higher score.

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 efficiently front-loaded with the core purpose in the first sentence, followed by a use-case justification and a cost note. It is three short sentences with no waste, and the most critical information (what it does and when to use) appears first.

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

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

The description covers the purpose, use cases, cost, and key constraints, while the schema fully documents parameters. The main missing piece is the response format or return value, which is not hinted at and there is no output schema. For a conversion tool, an agent might need to know whether it returns the numeric amount or a richer object. This is a minor but non-trivial gap.

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

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

Schema description coverage is 100%, so the baseline is 3. The tool description itself adds no additional parameter semantics beyond what the schema already provides. The schema descriptions are thorough with examples and constraints, but the description does not go beyond them, so no bonus is warranted.

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

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

The description states a clear verb ('converts'), a specific resource (amount between currencies), and a critical qualifier ('historical exchange rates'). It immediately distinguishes itself from any real-time or future rate tools, and the use case ('tax declarations and international accounting') adds purpose context. Though no sibling is explicitly named, the clarity and constraints make the tool's intent unmistakable.

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

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

Explicit positive and negative usage guidance is embedded in the date parameter description: 'USE THIS TOOL for historical conversions' and 'DO NOT use for real-time crypto rates or future exchange rate predictions. It only supports historical data for fiat currencies.' This tells the agent exactly when to select this tool and when to avoid it, with clear constraints.

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 (5 credits per request), which is a behavioral trait, and implies a transformation from JSON to XML. However, it does not mention side effects, authorization requirements, rate limits, or whether the operation is synchronous or asynchronous. Given the lack of annotations, the description provides some transparency but leaves significant gaps.

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

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

The description is concise, with exactly two sentences: the first states the core purpose, the second gives the cost. It is front-loaded and free of fluff. However, given the tool's complexity, a slightly longer description that hints at input structure would improve clarity without harming 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?

The tool has nested objects (creditor, payment_info, transactions) and five input parameters, yet the description provides no overview of the required input shape. There is no output schema, so the description should at least mention that the result is an XML file with a specific format. The cost detail is helpful but does not address the structural complexity. The description is far from complete for how to call the tool correctly.

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

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

Parameter description coverage is only 20%, meaning most fields lack descriptive text in the schema. The tool description adds no parameter-level meaning at all—it simply says 'JSON input' without outlining the required structure (creditor, initg_pty, payment_info, transactions) or any field semantics. With such low coverage, the description must compensate, and it fails to do so.

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 generates a SEPA Direct Debit XML file in PAIN.008.001.02 format from JSON input. It includes a specific verb (generates), a resource (SEPA Direct Debit XML), and a format identifier. It also distinguishes from the sibling download_sepa_endpoint_v1_finance_sepa_xml_download_post by focusing on generation rather than downloading.

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 offers no guidance on when to use this tool versus alternatives. While the sibling download tool is implicitly different, there is no explicit statement about the intended workflow (e.g., generate first, then download) or any conditions or exclusions. The only extra is the cost note, which is not usage guidance.

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

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

With no annotations present, the description carries the full burden. It discloses the action (fetching metadata) and the cost (2 credits per request), but does not mention error handling, rate limits, authentication requirements, or the exact structure of the response. Since the tool is a POST endpoint with no output schema, more behavioral details would help the agent anticipate results.

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

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

The description is exceptionally concise: two sentences, no fluff, and the primary purpose is front-loaded. The cost information is clearly stated. 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?

The description covers the core purpose and lists expected output items, but without an output schema it leaves ambiguity about the exact response format. It also omits details such as error cases, URL validation, and any rate limiting. For a tool with a single parameter, this is adequate but not comprehensive.

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

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

The parameter is fully described in the schema (100% coverage), so the description adds limited new meaning. It does reinforce that the URL is used to retrieve metadata and hints at the output fields, but this is already implied. The schema already says 'USE THIS TOOL to get page titles and social preview images,' so the description provides no significant extra semantic value beyond what the schema already conveys.

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

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

The description states a specific verb and resource: fetches website metadata, listing the key items (title, description, favicon, social preview images). This clearly distinguishes it from the sibling enrichment tools, which target different data types. The tool's name reinforces its purpose.

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 explicit guidance on when to use this tool versus alternatives. It does not mention exclusions or contexts where another tool would be more appropriate. The only usage hint appears in the parameter description ('USE THIS TOOL to get page titles and social preview images'), but the main description lacks such direction.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the core function and the cost, but does not disclose what the response looks like (e.g., whether it returns just the matched string, a score, or null if no match reaches min_score), nor does it note any error behavior or side effects. For a POST endpoint with no annotations, this is a significant gap.

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

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

The description is exceptionally concise: two sentences with no fluff. The core purpose is front-loaded, and the cost note is a useful practical detail. Every word earns its place; 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?

The tool has no output schema, so the description is responsible for explaining return values and failure modes. It only says 'Finds the most similar string' without specifying the exact return format, what happens if no choice meets the min_score, or any error conditions. Given the low complexity and no annotations, the description leaves essential operational details unspecified.

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 100% coverage of all three parameters (value, choices, min_score) with clear descriptions and examples. The description adds no additional meaning about the parameters beyond what the schema already states, so it meets the baseline of 3 without adding extra value.

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

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

The description clearly states the tool's purpose: 'Finds the most similar string from a list of choices' – a specific verb and resource. It also mentions the method ('fuzzy matching algorithms'), which distinctively separates it from sibling enrichment tools that handle names, addresses, or legal data. The purpose is unambiguous and easily distinguishable.

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 a clear context for when to use this tool (when you need fuzzy string matching against a list). However, it does not explicitly mention any alternative tools or conditions when not to use it. The cost note ('1 credit per request') implies it should be used when needed, but there is no explicit guidance on alternatives 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 the burden of behavioral disclosure. It mentions the transformation and extraction capability, plus the cost of 5 credits per request, which is helpful. However, it does not state whether the operation is read-only, what happens on invalid input, or any error behavior. For a non-annotated parse tool, this is adequate but not exhaustive.

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, and every sentence earns its place. The primary action and input format are front-loaded. The cost is a useful extra that is not redundant with any structured field.

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 one-parameter tool with no output schema and no annotations, the description covers the core essentials: input format, what it extracts, and cost. It lacks details about the output structure or error handling, but an agent can reasonably infer how to call it and what to expect. Given the simplicity, it is close to complete but could be enhanced with response shape hints.

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

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

Schema description coverage is 100% for the single parameter 'raw_text', which is clearly documented as 'The raw content of the Norma 43 (N43) bank statement file.' The description adds no new semantic detail beyond that; it merely restates what the schema already conveys. With full schema coverage, 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?

States a specific verb ('Transforms'), a specific resource ('Spanish Norma 43 (N43) raw bank statement files'), and an explicit output ('structured JSON'). It also lists extracted data types. This clearly distinguishes it from sibling parse tools like parse_xml_flatten or parse_edi_endpoint, which target different formats.

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 clearly implies when to use it: when you have raw N43 bank statement text. It does not name alternatives or exclusions, but given the niche format and lack of direct sibling competitors, the context is sufficiently clear. A higher score would require explicitly stating 'use this for N43, other parsers for other formats'.

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 responsibly discloses the 5-credit cost and the current 850-optimization limitation, setting expectation about scope. However, it does not disclose behavior on malformed/non-850 input, failure modes, or output shape, which would matter for a parsing tool with no annotation safety profile.

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 short, dense sentences with zero waste. The purpose is front-loaded, followed by the optimization caveat and the cost disclosure. 'Costs 5 credits' is an extra detail but genuinely useful for a billing-qualified endpoint and 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?

With no output schema present, the description does not describe the structure of the returned JSON, which an agent might want when deciding to call this tool. However, the single parameter is fully covered by the schema and the tool's scope is clearly stated. Adequate but the missing return-shape guidance is a gap.

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

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

Schema coverage is 100% and the single parameter 'raw_edi' is already fully described in the schema ('Raw ANSI X12 EDI string to parse'). The description adds no new meaning beyond what the schema provides, so the high-coverage baseline of 3 applies. No extra format or validation detail is given.

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

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

States a specific verb ('Parses'), a precise resource ('raw ANSI X12 EDI string'), and a clear output ('structured JSON'). It also names the document type (850 Purchase Order) which distinguishes this from the many other parse siblings (parse_bank_statement, parse_xml_flatten, parse_trade_history). Purpose is unambiguous.

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

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

The note 'Currently optimized for 850 (Purchase Order) documents' gives useful context about when this tool is best suited, implying it may underperform on other EDI document types. However, it does not name any alternative tools for non-850 EDI or explicitly state when NOT to use it, leaving routing to inference.

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 states that extraction is '100% deterministic' and outputs 'standard JSON', and it explicitly disclaims that it does not calculate capital gains. This sets expectations and clarifies what the tool does and does not do, though it does not mention error handling, size limits, or other potential 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 two sentences with no filler. The system instruction style front-loads the key directive, and every sentence earns its place: the warning, the purpose, the output format, and the call to action. It is exceptionally concise and well-structured for agent consumption.

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 indicates the output is 'standard JSON' but does not specify the structure or fields of that JSON, and there is no output schema to rely on. It also omits any mention of limitations (e.g., supported file sizes, encoding, or format variations). For a tool with a single input and no output schema, more detail about the expected result shape would be helpful, so it falls short of being fully complete.

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

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

The schema provides 100% coverage for the single parameter 'text' with a clear description. The tool description adds context about broker export variability ('formats vary wildly') that reinforces the input's nature, but it does not add new parameter-specific details beyond what the schema already states. Thus, it meets the baseline but does not enrich the parameter meaning significantly.

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

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

The description clearly identifies the tool's function: parsing broker trade history into standard JSON. It uses a specific verb ('extract') and resource ('trades from raw CSV/PDFs'), and it distinguishes itself from sibling parsers (e.g., parse_bank_statement) by focusing on trade history and broker exports. The mention of '100% deterministic' adds clarity about expected behavior.

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

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

The description explicitly instructs agents not to attempt direct extraction or capital gains calculation from raw broker files, and to route the raw text/file to this tool instead. This provides a clear when-to-use directive. However, it does not name specific alternative tools or state when not to use it beyond the direct-parsing warning, so it leaves some room for interpretation.

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

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

With no annotations, the description must disclose behavioral traits. It does mention the cost ('Cost: 2 credits per request'), which is a usage cost, and the output format (flat JSON). However, it does not disclose behavior around invalid XML, size limits, or the exact key-flattening convention (e.g., dot notation). This is a minimal but not comprehensive disclosure.

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

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

The description is concise and front-loaded: it opens with the core transformation, then gives a usage context, then the cost note. Every sentence serves a purpose with no redundancy.

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

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

For a single-parameter tool with no output schema, the description provides the essential information: purpose, output structure, and ideal usage. However, it omits details on how keys are flattened (e.g., dot vs underscore notation) and error handling for malformed XML, which could matter to an agent deciding on downstream parsing. Still, it is largely complete for a simple transformation 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 already fully describes the single parameter xml_string with 'The raw XML content to flatten into Key-Value pairs.' With 100% schema coverage, the description adds no additional semantic value beyond what the schema provides. Baseline score of 3 is appropriate.

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

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

The description explicitly states 'Converts complex, deeply nested XML strings into a flat Key-Value JSON structure', which names the specific verb (converts), resource (XML strings), and output (flat Key-Value JSON). It clearly distinguishes itself from sibling tools like parse_edi or parse_bank_statement.

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: 'Ideal for data ingestion pipelines and ETL processes.' This implies when to use the tool. While it does not explicitly list alternatives or when-not-to-use conditions, this is adequate given the unambiguous purpose and the sibling 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 of behavioral disclosure. It mentions the cost ('1 credit per request') and the nature of the analysis, but does not disclose what the output will look like (e.g., score, label, risk level) or any side effects (e.g., whether the password is transmitted to a third party). The 'deep security analysis' phrase is vague; additional details on return format or processing would enhance 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 with no fluff. The purpose is front-loaded, followed by the cost. Every word earns its place, and the structure is efficient.

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

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

The tool has a simple interface (2 params, no nested objects) and schema covers parameters well. However, without an output schema, the description should explain what results the agent can expect. It does not mention the return value or any additional behavior, making the context incomplete for an agent deciding whether to use it and how to interpret results.

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

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

Schema description coverage is 100%, so the parameters are already documented. The description adds no new meaning to 'password' or 'user_inputs'; it only mentions 'checking for complexity and common patterns' which loosely relates to the password parameter but doesn't clarify how user_inputs are used. With full schema coverage, a baseline of 3 is appropriate, and the description adds marginal 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 the tool's purpose with a specific verb ('performs deep security analysis') and resource ('passwords'), and adds detail about what it checks ('complexity and common patterns'). The sibling tools are all clearly different (finance, documents, enrichment of names/dates, etc.), so this tool is easily distinguished without 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?

The description implies usage context by its clear purpose and distinct tool name. However, it does not explicitly state when to use this tool over alternatives or mention any exclusions. The context signals indicate this is one of many enrich-* tools, but the description relies on the agent to infer that this is specifically for password evaluation rather than stating it.

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

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

With no annotations, the description must carry the behavioral burden. It does disclose the auth-free nature and the return values (poll_id, checkout_url), which is useful, but it omits the side effect of creating a checkout session or how the polling flow progresses. The agent cannot anticipate state changes beyond the return.

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

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

Two sentences, no filler, with the purpose and key facts (auth, returns) front-loaded. Every word earns its place; the structure is exemplary for such 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?

While the description gives a purpose and return values, it is incomplete for a tool that initiates a billing flow. It fails to explain the plan_type parameter, what the poll_id is for, or how the checkout_url should be used. An agent cannot confidently execute the full flow based solely on this text.

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

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

Schema description coverage is 0%, so the description must explain the one parameter (plan_type), but it does not. The enum values (top_up_10k, sub_monthly_29) are visible in the schema but their practical implications (prepaid vs subscription) are unexplained, leaving the agent unable to choose wisely.

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

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

States a specific trigger ('programmatic flow for an AI agent to buy its first API key') and a clear verb ('Start'), which conveys the tool's role. It does not explicitly contrast itself with the sibling create_checkout_v1_system_billing_checkout_post, but the mention of 'first API key' and 'agent' gives a distinct identity.

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 a clear condition for use (when an AI agent needs to buy its first API key) and notes that no authentication is required. However, it offers no guidance on when to prefer this over the related checkout sibling or what to do after receiving the checkout_url, leaving alternatives implicit.

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

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does add useful context: it runs locally in RAM, uses no external LLMs, and claims GDPR/HIPAA compliance. However, it omits what the tool returns (e.g., redacted text), error behavior, or any side effects. It adds some value but not comprehensive 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 with no filler. It opens with the primary purpose and follows with compliance details. Every sentence earns its place; the structure is front-loaded and efficient.

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

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

For a tool with three parameters and no output schema, the description leaves gaps. It doesn't specify what the tool returns (e.g., the redacted text), how it handles unsupported languages, or what 'all supported entities' includes. It's adequate but not fully complete for an agent to call it correctly without further inference.

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

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

All three parameters have descriptions in the schema (100% coverage), so the description doesn't need to add parameter-level detail. It doesn't, but the baseline of 3 is appropriate because the schema handles the semantic explanation.

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 'PII Anonymizer', which conveys a specific verb and resource. It doesn't explicitly mention 'redact' but 'anonymize' is synonymous in this context. The purpose is distinct from sibling tools like enrich_name or dedupe_items, but the description doesn't call out those differences, so it doesn't earn a 5.

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

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

There is no guidance on when to use this tool versus alternatives. The description doesn't mention any conditions, prerequisites, or alternatives, leaving the agent to infer usage solely from the tool name and schema.

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 must carry the full burden. It mentions the credit cost and the outputs (polarity, subjectivity, sentiment label) but does not disclose error handling, rate limits, input length constraints, or output formatting. It is not misleading but is minimal.

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

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

The description is two sentences, front-loading the core purpose and adding the cost note. There are no unnecessary words or redundant information.

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

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

For a single-parameter tool with no output schema, the description covers the core behavior and cost but omits output structure (e.g., types of polarity, possible sentiment labels) and constraints like maximum text length. While not critical for calling the tool, richer context would improve agent understanding.

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

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

The single parameter 'text' is fully documented in the schema with an example and a usage hint. The tool description adds no additional parameter semantics, but since schema coverage is 100%, the baseline of 3 is appropriate.

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

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

The description explicitly states it evaluates emotional tone and lists specific outputs (polarity, subjectivity, sentiment label), clearly distinguishing it from sibling enrichment tools like date, amount, or address analyzers. The parameter description reinforces the purpose by saying 'USE THIS TOOL to gauge user feedback or emotional tone.'

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 parameter description explicitly instructs to use this tool for emotional tone analysis, making the intended use case clear. While it does not name alternatives or exclusion criteria, it is the only sentiment tool among many enrichment siblings, so the guidance is sufficient.

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

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

With no annotations at all, the description carries the full burden of behavioral disclosure. It adds one useful non-schema detail (billing context: 'Cost: 1 credit per request') and implies the output components, but it does not disclose error behavior, return format, or whether the operation is read-only or has side effects. Some value added, but significant gaps remain 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?

Two tight sentences front-load the core action and output, with zero filler. The cost note is concise but slightly unconventional inside a tool description; it earns its place as useful billing context, so this rates just below a perfect 5.

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

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

For a single-parameter tool with 100% schema coverage and no output schema, the combination of description plus parameter guidance covers the essentials: what it does, what it produces, cost, and usage boundaries. Lacking is return-format detail and differentiation from the enrich_contact sibling, but complexity is low enough that this is not a critical gap.

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

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

Schema description coverage is 100%, and the schema parameter description already includes both semantic meaning and usage constraints, so the baseline of 3 applies. The description's list of output components adds marginal context beyond the schema but does not meaningfully compensate further.

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

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

The description states a specific verb (splits), the resource (messy contact string), and the structured output components (Name, Title, Phone, Email), making the tool's purpose clear. However, it does not distinguish this tool from its near-twin sibling enrich_contact_v1_enrich_contact_post — both appear to operate on contact data — so the 'distinguishes from siblings' bar for a 5 is not fully met.

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 parameter schema provides explicit guidance: 'USE THIS TOOL to extract structured components from a single line of contact info' and a NEGATIVE CONSTRAINT against paragraphs or multiple contacts, giving clear context and an exclusion. However, the description does not address when to prefer this over the similar enrich_contact sibling, so alternative-selection guidance is missing.

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 operational behavior. It mentions 'using LLM' and 'high-context accuracy' as behavioral traits, and notes the cost per request, but omits essential aspects like synchronous vs. asynchronous operation, response format, failure modes, and whether any side effects occur. The sparse disclosure is inadequate given the absent annotations.

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 the core purpose first and the cost second. It contains zero filler or redundant phrasing, making it highly efficient 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 translation tool with no output schema, the description is minimally adequate but incomplete. It does not specify source language handling (e.g., autodetection), response structure (e.g., whether the translated text is returned directly), or potential rate limits beyond cost. While the two parameters are fully covered, an agent might still have questions about the expected output format and operational constraints.

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

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

The input schema already documents both parameters with descriptions (text: 'The text to translate.', target_lang: 'Target ISO language code'). The description adds no parameter-specific meaning beyond the schema, so it does not elevate the baseline of 3 despite 100% schema coverage.

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

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

The description clearly states it is a 'Universal translator using LLM for high-context accuracy,' identifying the action (translation), the resource (text), and a distinguishing feature (high-context accuracy). This sets it apart from the many enrich_* siblings that handle dates, amounts, names, etc., leaving no ambiguity about its function.

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

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

The description states it is a 'Universal translator,' implying it handles all translation needs, but provides no explicit guidance on when to use it vs. alternatives (though no sibling is a translator). It also lacks notes on constraints such as text length limits or when not to use it, leaving usage context implicit 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 are provided, so the description carries the full burden. It states the cost ('1 credit per request') and the conversion nature, but it does not disclose output format, whether the operation is read-only, limitations on supported units, or error behavior. For a tool with no annotations, this is a significant gap – an agent cannot fully anticipate the tool's side effects or response structure.

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

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

The description is two sentences, with the core purpose front-loaded and the cost noted in the second sentence. There is zero fluff or repetition; every word earns its place. This is exemplary 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 two-parameter tool with no output schema, the description covers the essential purpose and cost. It does not describe the return value format, but this is a minor omission given the simplicity. The presence of examples in the schema compensates for missing details. Overall, the agent has enough to invoke it correctly, though a note on output shape would make it fully complete.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds no information about parameters beyond what the schema already provides (e.g., examples and defaults). It does not clarify the exact format of input_string beyond the schema's example, but the schema handles this adequately, so the description adds no extra semantic value.

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

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

The description clearly states a specific verb ('Normalizes and converts') and resource ('physical measurements (weight, length, volume)') and distinguishes it from sibling enrich tools (date, amount, name, etc.). The scope is explicit and actionable.

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 main description does not explicitly say when to use this tool vs. alternatives, but the input_string schema description includes 'USE THIS TOOL for automated unit normalization', which gives a direct call to action. However, it does not mention when not to use it or compare to tools like enrich_amount for currency or other conversions, leaving the routing mostly implicit.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds cost information ('1 credit per request') but omits other behavioral traits such as response format, rate limits, or side effects. The cost disclosure is useful but limited.

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 exceptionally concise—two sentences with no redundancy. The primary purpose is front-loaded, and the cost is stated in a separate sentence, making it efficient and scannable.

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

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

For a tool with a single optional parameter and no output schema, the description is adequate. It covers the core function and cost. It could mention the response structure, but given the simplicity and lack of output schema, this is a minor gap.

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

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

Schema coverage is 100%, and the schema already describes 'ua_string' thoroughly, including the fallback to the requester's UA. The description adds no additional parameter meaning beyond what the schema provides, aligning with the baseline of 3 for high coverage.

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

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

The description clearly states the tool extracts 'detailed browser, OS, and device information' from a 'User-Agent string,' using a specific verb and resource. This distinguishes it from sibling enrichment tools (e.g., enrich_date, enrich_name) without 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?

While not explicitly stating when to use it vs. alternatives, the description's focus on User-Agent parsing makes its intended use obvious. It provides clear context of the tool's function but lacks explicit exclusions or alternative routing.

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 is the sole source of behavioral information. It does disclose the cost ('2 credits per request'), which is a useful behavioral detail. However, it does not state whether the operation is read-only, side-effect free, or what the output format looks like. Given the tool's generative nature, the absence of explicit non-destructive statements and output details leaves transparency incomplete, but the cost disclosure earns a 3.

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 and front-loads the core purpose in the first sentence. The cost is a secondary detail placed after the purpose. While it is concise and well-structured, it could have included a brief note on expected output, but it is not overly verbose. A 4 reflects the efficient and purposeful structure.

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 three parameters (two required nested objects) and no output schema. The description explains what it does (generates combinations) but does not describe the return format, limits, or any prerequisites beyond the schema. For a tool that produces a potentially large Cartesian product, the absence of output details and any caveats (e.g., handling of empty option lists) makes it only minimally sufficient. Given the schema richness, a 3 is appropriate.

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

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

Input schema coverage is 100%, meaning every parameter (variants, base_product, sku_format) has a description and example. The tool description itself adds no parameter-level details beyond what the schema provides. Per the calibration baseline, with high schema coverage a 3 is appropriate because the schema does the heavy lifting.

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

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

The description clearly states the tool's function: 'Generates all possible combinations (Cartesian product) from a base product and lists of variant options.' This is a specific verb ('generates') and resource ('combinations'), and it distinguishes the tool from most siblings (e.g., enrich_* tools) by its exclusive focus on variant generation. The mention of E-commerce and ERPs further clarifies the 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?

The description says the tool is 'Useful for E-commerce platforms (Shopify, WooCommerce) and ERPs,' providing context for when it might be applied. The schema field for 'variants' explicitly says 'USE THIS TOOL to generate all possible SKU combinations,' which is strong usage guidance. However, it does not mention when not to use it or name alternative tools (none of the siblings appear directly comparable), so it falls short of a 5.

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