translate

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 effectively describes key behaviors: it accepts natural language questions, automatically selects and invokes tools, and returns results. However, it lacks details on limitations (e.g., rate limits, error handling, or data source constraints), which prevents a perfect 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 front-loaded with the core functionality in the first sentence, followed by clarifying details and examples. Every sentence adds value: the first explains the tool's purpose, the second describes its automation, and the examples illustrate usage. There is no wasted text.

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

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

Given the tool's complexity (automated tool selection and execution) and lack of annotations/output schema, the description does well by explaining the high-level behavior and providing examples. However, it could be more complete by mentioning potential limitations or the types of data sources available, which would help an agent anticipate 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?

Schema description coverage is 100%, so the schema already documents the single 'question' parameter. The description adds minimal value beyond the schema by emphasizing 'plain English' and 'natural language,' but doesn't provide additional syntax, format, or constraint details. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'). It distinguishes itself from siblings by emphasizing natural language input rather than structured tool invocation.

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

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

The description explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear alternatives (implicitly, use other tools if you want structured tool selection) and includes three concrete examples ('What is the US trade deficit with China?', etc.) that illustrate appropriate use cases.

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

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

With no annotations, the description discloses that it replaces many sequential API calls and returns paired data with URIs, but lacks details on authentication, rate limits, or safety guarantees.

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

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

The description is four sentences, front-loads purpose and type-specific details, and contains no redundant or irrelevant 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 tool with simple parameters and no output schema, the description provides sufficient context on return values and use cases, though it omits error handling 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?

Input schema covers 100% of parameter descriptions, and the description adds context about per-type data fields, but does not significantly enhance parameter meaning beyond the schema.

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

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

The description clearly states the tool compares 2-5 entities side by side, differentiates between company and drug types, and lists specific data points for each, making its purpose unmistakable.

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

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

It explains when to use the tool (for multi-entity comparisons) and highlights efficiency gains over sequential calls, but does not explicitly state when not to use it or suggest alternatives.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns an array of detected languages with confidence scores, which is useful behavioral context. However, it does not mention error conditions, rate limits, or other operational traits.

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

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

The description is two sentences, front-loaded with the purpose and followed by return value information. Every sentence adds value without redundancy, making it appropriately sized 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?

Given the tool's low complexity, one parameter with full schema coverage, and no output schema, the description is mostly complete. It explains the purpose and return format, but lacks details on error handling or performance characteristics, which could enhance completeness.

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

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

Schema description coverage is 100%, so the schema already documents the 'text' parameter. The description adds no additional parameter details beyond what the schema provides, such as text length constraints or format requirements, meeting the baseline for high schema coverage.

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

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

The description clearly states the specific verb ('detect') and resource ('language of a text string'), and distinguishes from siblings by focusing on detection rather than listing languages (list_languages) or translation (translate).

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

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

The description implies usage for language detection of text strings, but does not explicitly state when to use this tool versus alternatives like list_languages or translate, nor does it provide exclusions or prerequisites.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it performs a search based on natural language queries, returns the most relevant tools with names and descriptions, and has a default/max limit (implied by the schema). However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions.

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 appropriately sized and front-loaded: the first sentence states the core functionality, and the second provides crucial usage guidance. Every sentence earns its place with no redundant or vague information, making it efficient and easy to parse.

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

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

Given the tool's moderate complexity (search functionality with 2 parameters), no annotations, and no output schema, the description is reasonably complete. It covers purpose, usage context, and behavioral aspects, but lacks details on output format (beyond 'names and descriptions') and error handling, which could be helpful for an agent.

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

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

The schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'describing what you need' for the query parameter, but doesn't provide additional semantic context or examples not already in the schema. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resources ('tool catalog'), and explicitly distinguishes it from siblings by specifying its unique role in discovering tools among 500+ options. It goes beyond the name/title by explaining the search functionality and return format.

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

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

The description provides explicit usage guidelines: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context on when to use it (large tool catalogs) and implies alternatives are not needed in such scenarios, though it doesn't name specific sibling tools for comparison.

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?

Describes return data and citation URIs, notes it replaces 10-15 sequential calls. No annotations provided, but description is adequate. Could mention rate limits or performance, but not required.

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?

Four sentences, well-structured, front-loaded purpose. Efficient but could be slightly more concise. No waste.

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

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

Given no output schema and no annotations, description is highly complete. Explains data, usage, and limitations, providing sufficient context for an agent to use the tool correctly.

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

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

Input schema has 100% coverage with descriptions for both parameters. Description adds context: explains value accepts ticker or CIK, clarifies name not supported, and suggests resolve_entity for names.

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

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

Description clearly states tool returns a full profile of an entity across multiple packs, lists specific data sources (SEC, XBRL, patents, news, LEI), and distinguishes from sibling tools like resolve_entity and usa_recipient_profile.

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

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

Explicitly states when to use (for comprehensive profile) and when not (for federal contracts, use usa_recipient_profile). Also explains input requirements (ticker or CIK) and suggests name resolution via resolve_entity.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Delete' implies a destructive mutation, it doesn't specify whether this action is reversible, what permissions are required, what happens if the key doesn't exist, or any rate limits. This leaves significant behavioral gaps for a destructive operation.

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

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

The description is perfectly concise at just 6 words, front-loading the essential information with zero wasted language. Every word earns its place in communicating the core functionality.

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 destructive tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what constitutes a 'stored memory', what format the key follows, what happens on success/failure, or how this tool relates to sibling memory tools. The agent lacks necessary context for safe and effective use.

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

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

The schema description coverage is 100% with the single parameter 'key' well-documented as 'Memory key to delete'. The description adds no additional parameter semantics beyond what the schema already provides, so it meets the baseline for adequate but not exceptional 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 action ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential siblings like 'recall' or 'remember' that might also interact with stored memories, so it doesn't reach the highest score.

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. With sibling tools like 'recall' and 'remember' that likely interact with stored memories, there's no indication of when deletion is appropriate versus retrieval or storage, leaving the agent without usage context.

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

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

With no annotations provided, the description carries full burden. It discloses the return format ('language codes and names'), which is helpful behavioral context. However, it doesn't mention other behavioral traits like whether this is a cached list, requires authentication, or has rate limits.

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

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

The description is perfectly concise with two sentences that each earn their place: the first states the action and scope, the second describes the return format. No wasted words, front-loaded with the core purpose.

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

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

Given the tool's simplicity (no parameters, no annotations, no output schema), the description is reasonably complete. It explains what the tool does and what it returns. However, without an output schema, it could provide more detail about the return structure (e.g., array format, specific fields).

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

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

The tool has 0 parameters with 100% schema description coverage. The description appropriately doesn't discuss parameters since none exist, maintaining focus on the tool's purpose and output. This meets the baseline expectation for zero-parameter tools.

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 specific action ('List all languages') and resource ('supported by the translation API'), distinguishing it from sibling tools like detect_language and translate. It provides a complete picture of what the tool does.

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

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

The description implies usage context (when you need to know available languages for translation), but doesn't explicitly state when to use this versus alternatives like detect_language or translate. It provides clear context but lacks explicit exclusions or comparisons.

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

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

No annotations are provided, so the description bears the full burden. It discloses the rate-limiting behavior and the instruction to avoid verbatim user prompts. It does not describe what response the tool returns (e.g., confirmation or error), but for a feedback tool this is acceptable. No contradictions.

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

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

The description is concise with four sentences, each adding unique value: purpose, use cases, content guidelines, and rate limit. It is well-structured and front-loaded, no wasted words.

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

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

The description is complete for a feedback tool with no output schema. It covers purpose, usage, content rules, rate limits, and parameter details via the schema. The optional context parameter is explained. No additional information is needed.

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 context beyond the schema, such as the instruction to not include the end-user's prompt verbatim and the note about the 'type' enum values. However, it does not add significant new meaning to the parameters themselves.

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 purpose: sending feedback to the Pipeworx team. It lists specific use cases (bug reports, feature requests, missing data, praise) and distinguishes from sibling tools like ask_pipeworx by focusing on feedback rather than Q&A or data operations.

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 guidance on when to use (bug reports, feature requests, etc.) and what content to include (describe what you tried in terms of Pipeworx tools/data). It also mentions the rate limit (5 per day). However, it does not explicitly state when not to use or name alternatives, though the sibling tools are distinct enough.

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

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool can retrieve individual memories or list all keys, and memories persist across sessions. However, it doesn't mention error behavior (what happens if key doesn't exist), performance characteristics, or format of returned data. The description adds value but doesn't provide comprehensive behavioral context.

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

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

The description is perfectly concise with two sentences that each earn their place. The first sentence states the dual functionality clearly, and the second provides essential usage context. No wasted words, and the most important information (what the tool does) is front-loaded.

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

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

Given the tool's moderate complexity (retrieval/listing operations), no annotations, and no output schema, the description is adequate but has gaps. It explains the basic functionality well but doesn't describe the return format (what a 'memory' contains), error conditions, or any limitations. For a tool with no structured output documentation, more detail about return values would be helpful.

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

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

The schema description coverage is 100%, so the schema already documents the single parameter. However, the description adds important semantic context: it explains that omitting the key triggers listing all memories, which clarifies the optional parameter's meaning beyond the schema's 'omit to list all keys'. This adds meaningful value despite the high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings by mentioning 'context you saved earlier' which differentiates it from tools like 'detect_language' or 'translate' that don't involve memory storage/retrieval.

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: 'Retrieve a previously stored memory by key, or list all stored memories (omit key)' tells exactly when to use each mode. It also provides context with 'Use this to retrieve context you saved earlier in the session or in previous sessions' which helps distinguish it from other memory-related tools like 'remember' (for storage) and 'forget' (for deletion).

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

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

No annotations are provided, so the description carries full burden. It discloses that it fans out to multiple data sources in parallel, returns structured changes, total_changes count, and pipeworx:// URIs. It does not mention whether it modifies data or auth requirements, but as a read operation this is acceptable.

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

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

The description is a single paragraph with clear front-loading of purpose. It is concise and each sentence adds information, though it could be broken into bullet points for better scanability.

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

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

Given no output schema, the description covers return values and use cases. It explains the since parameter formats and entity types. It lacks error handling details but is otherwise complete for the tool's complexity.

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

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

Schema coverage is 100%, but the description adds significant value: it explains the since parameter accepts ISO dates and relative strings like '7d' or '1y', notes that value can be a ticker or CIK, and clarifies type only supports 'company'. This goes well 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: 'What's new about an entity since a given point in time.' It specifies the entity type (company) and details the fan-out to SEC, GDELT, and USPTO. This differentiates it from sibling tools like entity_profile and compare_entities.

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 usage context: 'brief me on what happened with X' or change-monitoring workflows. It does not explicitly mention when not to use or list alternatives, but the context is clear enough for an agent to infer appropriate use cases.

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

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

With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it's a write operation (implied by 'store'), has persistence differences based on authentication, and includes session lifetime details (24 hours for anonymous). It doesn't cover error cases or rate limits, but provides substantial context.

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

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

Two sentences with zero waste: the first states the purpose and usage, the second adds critical behavioral context. Every phrase adds value, and it's front-loaded with the core function.

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 no annotations and no output schema, the description provides good coverage of purpose, usage, and key behavioral aspects (persistence, session limits). It doesn't explain return values or error handling, but given the tool's simplicity and full parameter documentation, it's largely 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 schema already fully documents both parameters. The description adds no additional parameter semantics beyond what's in the schema (e.g., no format constraints or examples not already covered), meeting the baseline for high schema coverage.

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

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

The description clearly states the verb ('store') and resource ('key-value pair in your session memory'), distinguishing it from sibling tools like 'recall' (retrieval) and 'forget' (deletion). It specifies the exact operation with no 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?

It provides explicit guidance on when to use this tool ('save intermediate findings, user preferences, or context across tool calls') and distinguishes usage contexts (authenticated vs. anonymous sessions). This helps the agent choose between 'remember', 'recall', and 'forget' appropriately.

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

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

No annotations are provided, so the description carries full burden. It explains input formats and output fields but does not disclose if the operation is read-only, side effects, or authentication needs.

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 purpose, and no extraneous words. Every sentence adds value.

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

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

The description explains return values (ticker, CIK, name, URIs) despite no output schema. It is sufficient for the tool's simplicity, though it could mention error handling for unknown entities.

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

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

Schema coverage is 100%, so baseline is 3. The description adds examples (e.g., 'AAPL', '0000320193') and clarifies accepted formats, which adds value beyond the schema descriptions.

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

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

The description clearly states the tool resolves entities to canonical IDs across Pipeworx data sources. It specifies verb, resource, and outcome, but does not explicitly differentiate from sibling tools.

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

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

The description explains that it accepts ticker, CIK, or name and returns canonical IDs, implying usage scenarios. However, it does not explicitly state when not to use this tool or mention alternative tools.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Returns the translated text,' which covers the basic output but lacks details on error handling, rate limits, supported language codes, or performance characteristics. For a tool with no annotations, this is insufficient to fully inform agent behavior.

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

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

The description is extremely concise and front-loaded: two sentences that directly state the tool's function and output. Every word earns its place, with no redundant or vague phrasing, making it efficient for quick comprehension.

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

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

Given the tool's moderate complexity (translation with three parameters) and no annotations or output schema, the description is minimally adequate. It covers the core purpose and return value but lacks details on language support, errors, or integration with siblings. It meets basic needs but leaves gaps for informed usage.

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

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

The schema description coverage is 100%, with clear descriptions for all three parameters ('text', 'source', 'target'). The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Translate text from a source language to a target language.' It specifies the verb ('translate') and resource ('text'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'detect_language' or 'list_languages', which would require 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. It doesn't mention sibling tools ('detect_language', 'list_languages') or any contextual prerequisites, such as when translation is needed over language detection. This leaves usage decisions entirely to the agent's 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?

No annotations are provided, so the description bears full responsibility. It discloses the tool returns a verdict, extracted structured form, actual value with citation, and percent delta. It also notes it is read-only (no destructive actions mentioned). The domain limitation and v1 status are transparent. It could include more details on rate limits or latency, but the provided information is comprehensive.

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 well-structured: first sentence states purpose, second details capabilities and scope, third lists return fields, fourth highlights efficiency. It is concise without being overly brief, packing necessary information into three sentences. A slight improvement could be removing the parentheses or shortening examples, but it is effective.

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

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

Given one parameter, no output schema, and no annotations, the description provides all essential information: what the tool does, what inputs it expects, what outputs it produces, and its limitations. An agent can confidently invoke the tool with appropriate claims and understand the result format. No gaps are apparent.

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 describes the single 'claim' parameter with examples. The description adds significant context by specifying the claim should be a natural-language factual claim about company finances, and it gives example formats (e.g., 'Apple's FY2024 revenue was $400 billion'). This goes beyond the schema definition, clarifying the expected input domain.

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: 'Fact-check a natural-language claim against authoritative sources.' It specifies the domain (company-financial claims for US public companies) and supported metrics (revenue, net income, cash). It also distinguishes itself from siblings by noting it replaces 4-6 sequential agent calls, a clear differentiator from tools like 'compare_entities' or 'entity_profile'.

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

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

The description explicitly says it supports company-financial claims and lists the return values. It implies when to use (for financial claims) and its limitations (v1). However, it does not explicitly state when not to use or provide alternatives, though it does mention that it replaces other calls. The context is clear enough for an agent to infer appropriate usage.

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