jikan

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 explains key behaviors: Pipeworx 'picks the right tool, fills the arguments, and returns the result,' which clarifies the tool's role as an orchestrator. However, it lacks details on potential limitations, such as rate limits, error handling, or data source reliability, which would be helpful for a tool with no 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 front-loaded with the core functionality in the first sentence, followed by clarifying details and examples. Every sentence earns its place by explaining the tool's value proposition, contrasting it with alternatives, and illustrating usage. It is appropriately sized without unnecessary elaboration, 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 complexity (orchestrating multiple data sources) and lack of annotations or output schema, the description does a good job of explaining the tool's behavior and use cases. However, it could be more complete by addressing potential constraints or output formats, as the agent has no structured information about what the tool returns. The examples help but do not fully compensate for the absence of an output schema.

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, with the 'question' parameter documented as 'Your question or request in natural language.' The description adds minimal value beyond this, only reinforcing that questions should be in 'plain English' and providing examples. Since the schema already covers the parameter well, the baseline score of 3 is appropriate, as the description does not significantly enhance understanding of the parameter.

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

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

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 from data source'), and distinguishes from siblings by emphasizing natural language input without needing to browse tools or learn schemas, unlike other tools like 'discover_tools' or 'search_anime' that imply more structured interactions.

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 this tool: 'No need to browse tools or learn schemas — just describe what you need.' It contrasts with sibling tools by positioning this as a high-level, natural language alternative to more specific tools like 'search_anime' or 'get_anime', and includes examples ('What is the US trade deficit with China?') to 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?

No annotations provided, so description carries full burden. It discloses return type (paired data + pipeworx:// URIs), data sources (SEC EDGAR, FDA), and data fields for each type. It does not mention error conditions or limitations, but covers key behavioral aspects for a comparison tool.

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

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

The description is three sentences long, front-loaded with core purpose, and every sentence adds value without redundancy or fluff.

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

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

Given the tool's complexity (two entity types, multiple data fields), the description adequately covers purpose, parameters, and return type. No output schema is provided, but return description is sufficient. Could include an example of paired data format, but overall complete for its use case.

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 meaning beyond schema by explaining how to format values for company (tickers/CIKs) and drug (names), and gives examples. This enhances parameter understanding.

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 'Compare 2–5 entities side by side in one call' with specific verb and resource, and distinguishes from sibling tools like resolve_entity (single entity) and search_* (search) by focusing on side-by-side comparison.

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 when to use (comparing 2-5 entities) and mentions efficiency (replaces 8-15 sequential calls), but does not provide explicit when-not-to-use or alternative tool comparisons. However, context signals with sibling tools make usage clear.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it's a search operation that returns relevant tools based on natural language queries, with implied ranking ('most relevant'). However, it doesn't mention rate limits, authentication requirements, or error conditions, leaving some behavioral aspects uncovered.

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 and front-loaded: the first sentence states the core purpose, the second explains the return value, and the third provides crucial usage guidance. Every sentence earns its place with no wasted 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?

Given the tool's moderate complexity (search functionality with ranking), no annotations, and no output schema, the description does well by explaining the purpose, return format, and critical usage context. However, it doesn't describe the output structure (e.g., what fields are returned beyond names/descriptions) or potential limitations, leaving some gaps in completeness.

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

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

The schema description coverage is 100%, so the schema already fully documents both parameters (query and limit). The description adds no additional parameter semantics beyond what's in the schema, such as query formatting examples or limit usage context. 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 resource ('tool catalog'), distinguishing it from sibling tools like get_anime or search_anime by focusing on tool discovery rather than anime data. It explicitly mentions returning 'most relevant tools with names and descriptions'.

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 this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task') and includes a clear alternative scenario (implicitly suggesting other tools for smaller catalogs or specific tasks like anime searching). This gives strong contextual direction.

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

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

Discloses that it returns pipeworx:// citation URIs and replaces 10-15 sequential calls. While no annotations are provided, the description adequately conveys the tool's bundling behavior and return structure, though it does not explicitly state read-only nature.

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

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

Description is a single paragraph with clear, front-loaded sentences. It efficiently conveys all necessary information, though it could be slightly more structured for easier scanning.

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

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

Given no output schema, the description explains the returned data types and sources, as well as the performance benefit. Could optionally mention error handling or rate limits, but overall sufficiently complete for a complex tool.

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

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

Schema coverage is 100%, but the description adds significant value by explaining that 'value' accepts tickers or CIKs, not names, and cross-referencing resolve_entity for name resolution, going beyond the schema's basic 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 provides a full profile of an entity across multiple Pipeworx packs in one call, specifying the data sources for type 'company', distinguishing it 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 company profiles) and when not to (for federal contracts, use usa_recipient_profile). Also advises using resolve_entity if only a name is available, providing clear guidance on prerequisites and alternatives.

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

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

No annotations are provided, so the description carries full burden. While 'Delete' implies a destructive mutation, it doesn't disclose whether this operation is reversible, requires specific permissions, has side effects, or provides confirmation feedback. For a destructive tool with zero annotation coverage, this is a significant behavioral information gap.

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

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

The description is a single, efficient sentence that states the core functionality without any wasted words. It's appropriately sized for a simple tool and front-loads the essential information.

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

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

For a destructive tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address what happens after deletion (success/failure indicators), whether the operation is idempotent, or how it relates to sibling memory tools. The agent lacks critical 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' already documented as 'Memory key to delete'. The description adds no additional meaning beyond what the schema provides, such as key format examples or deletion constraints. With complete schema coverage, the baseline 3 is appropriate.

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

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

The description clearly states the action ('Delete') and resource ('a stored memory by key'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'recall' or 'remember', which appear related to memory operations, so it doesn't achieve full sibling differentiation.

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 the same memory system, the absence of when/when-not instructions or explicit alternatives leaves the agent without contextual 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool as a read operation ('Get full details'), which is clear, but lacks information on error handling, rate limits, authentication needs, or response format. For a tool with no annotations, this leaves significant behavioral gaps.

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

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

The description is front-loaded with the core purpose in the first sentence and efficiently lists included details. It avoids unnecessary words, but could be slightly more structured by explicitly separating usage guidance from details. Overall, it's concise with minimal waste.

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

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

Given the tool's low complexity (1 parameter, no nested objects) and high schema coverage, the description is adequate for basic use. However, with no annotations and no output schema, it lacks details on behavioral traits and return values, which are important for a read operation. It meets minimum viability but has clear gaps.

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

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

Schema description coverage is 100%, with the parameter 'id' fully documented in the schema. The description adds minimal value beyond the schema by mentioning 'MyAnimeList ID' and giving an example (Fullmetal Alchemist: Brotherhood), but doesn't provide additional syntax or format details. 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 specific verb ('Get full details') and resource ('for a specific anime'), and distinguishes it from siblings by specifying it requires a MyAnimeList ID rather than search terms or rankings. It explicitly lists the types of details included (score, synopsis, genres, etc.), making the purpose unambiguous.

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

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

The description implies usage context by stating it's for 'a specific anime by its MyAnimeList ID,' which suggests it should be used when the ID is known. However, it doesn't explicitly state when to use alternatives like search_anime (for unknown IDs) or top_anime (for rankings), leaving some ambiguity in sibling differentiation.

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

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

With no annotations, the description carries full burden. It discloses the rate limit ('5 messages per identifier per day. Free.') and provides a usage constraint (excluding end-user prompts). For a feedback tool, this is adequate, though it omits details like auth or response format.

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

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

The description is two sentences plus one for rate limit, concise and front-loaded. No redundant or extraneous information. 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?

For a simple feedback tool with full schema coverage and no output schema needed, the description is complete. It covers purpose, content guidelines, rate limits, and optional context. No additional details are necessary.

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

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

Schema coverage is 100% with good descriptions. The description adds context beyond schema: it clarifies the 'type' field with inline examples and provides behavioral guidance (e.g., specificity in message, no verbatim prompts). This elevates above the baseline 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 'Send feedback to the Pipeworx team' with specific use cases (bug reports, feature requests, missing data, praise). This distinguishes it from all sibling tools, which are unrelated (e.g., ask_pipeworx, discover_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 explicitly lists usage cases and provides content guidelines (e.g., 'Describe what you tried in terms of Pipeworx tools/data — do not include the end-user's prompt verbatim'). It also mentions rate limits. It does not explicitly state when not to use, but clarity is high given sibling distinctness.

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 describes the tool's behavior: retrieving or listing memories based on key presence, and mentions persistence across sessions. However, it lacks details on error handling (e.g., what happens if a key doesn't exist), performance traits (e.g., speed, limits), or authentication needs, leaving gaps for a mutation-free tool.

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

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

The description is front-loaded with the core functionality in the first sentence, followed by a usage guideline. Both sentences are essential: the first defines the tool's actions, and the second provides context for when to use it. There is no redundant or verbose language, making it highly 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?

Given the tool's moderate complexity (retrieve/list operations), no annotations, and no output schema, the description is adequate but incomplete. It covers purpose and basic usage but omits details on return values (e.g., format of retrieved memories or list), error cases, or session-specific behaviors. This leaves the agent with gaps in understanding the full context.

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

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

The input schema has 100% description coverage, with the parameter 'key' documented as 'Memory key to retrieve (omit to list all keys).' The description adds semantic context by explaining the dual functionality: retrieving by key or listing all if omitted. This enhances understanding beyond the schema, but since schema coverage is high, the baseline is 3, and the added value justifies a 4.

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: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It specifies the verb ('retrieve'/'list') and resource ('stored memory'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'remember' or 'forget', which prevents a perfect 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 clear usage guidance: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It explains when to use the tool (for retrieving saved context) and implies when not to use it (e.g., for saving new memories, which might be 'remember'). However, it doesn't explicitly name alternatives or exclusions, such as contrasting with 'search_anime' for different data types.

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

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

No annotations provided, so description carries full burden. It discloses parallel fan-out, supported input formats (ISO and relative dates), output structure (structured changes, count, URIs), and the limitation that only 'company' is supported. Lacks error behavior or rate limits but is adequate.

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

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

A single dense paragraph that is well front-loaded with purpose and key details. Every sentence adds value; no redundancy or fluff. Efficiently communicates all 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 complexity (multi-source, multiple input formats, structured output), the description covers inputs, supported types, output format, and use case. Missing output schema is compensated by stating return values. Could mention pagination or limits but is largely complete for AI agent 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?

Schema coverage is 100%, but description adds significant value: explains 'since' accepts ISO or relative dates with examples and suggests '30d' or '1m', clarifies 'value' accepts ticker or CIK, and notes 'type' only supports 'company'. This goes well beyond schema definitions.

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

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

The description uses a specific verb+resource ('What's new about an entity since a given point in time') and details the fan-out to SEC EDGAR, GDELT, and USPTO. It clearly distinguishes from siblings like 'entity_profile' by emphasizing change monitoring over static profiling.

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

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

Explicitly states use cases: 'Use for brief me on what happened with X or change-monitoring workflows.' However, it does not explicitly mention when not to use or compare to alternatives like 'entity_profile' or 'compare_entities'.

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: storage persistence differences for authenticated vs. anonymous users (24-hour limit). It explains what gets stored and session context, though it could mention limitations like memory size or key constraints.

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

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

The description is appropriately sized and front-loaded, with two efficient sentences that each add value: the first states the core action, and the second provides important behavioral context. There is zero waste 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?

Given the tool's moderate complexity (storage with session-based persistence), no annotations, and no output schema, the description is mostly complete. It covers purpose, usage, and key behavioral traits, but could benefit from mentioning return values or error cases to fully compensate for the lack of structured output 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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter semantics beyond what the schema provides, such as examples or usage tips, 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 tool's purpose with specific verbs ('Store a key-value pair') and resources ('in your session memory'), distinguishing it from siblings like 'forget' (remove) and 'recall' (retrieve). It explicitly identifies what gets stored and where.

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

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

The description provides clear context on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but does not explicitly mention when not to use it or name alternatives (e.g., 'recall' for retrieval). The guidance is helpful but lacks sibling differentiation.

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

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

No annotations provided, so description carries full burden. It details output (ticker, CIK, name, pipeworx:// URIs) and gives example inputs. It does not mention error handling or auth, but behavior is well-scoped for a read/transform tool.

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

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

Two sentences, no wasted words. Front-loaded with purpose and key details. Efficiently communicates all essential information.

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

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

For a 2-parameter tool with no output schema, the description covers input formats, output contents, and usage context. It is comprehensive enough for an agent to understand how and when to invoke it.

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 description adds examples and accepted formats (ticker like AAPL, CIK with leading zeros, name like Apple) beyond the schema's descriptions. This clarifies the value parameter's acceptable patterns.

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 an entity to canonical IDs across Pipeworx data sources. It specifies the action (resolve), resource (entity), and scope (single call, across sources). It distinguishes from sibling tools that focus on anime or memory 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 explains when to use the tool (single call to resolve entities) and mentions it replaces 2–3 lookup calls. It does not explicitly say when not to use it or name alternatives on the server, but context implies its unique purpose among siblings.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the data source ('MyAnimeList data') and return fields, but doesn't cover important aspects like rate limits, authentication needs, pagination, error handling, or whether this is a read-only operation. For a search tool with zero annotation coverage, this is inadequate.

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 concise with two sentences: one stating the purpose and one listing return fields. It's front-loaded with the core functionality. The return field list could be slightly more structured, but overall it's efficient with minimal waste.

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

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

Given the simple single-parameter schema and no annotations or output schema, the description provides basic completeness by stating the purpose, data source, and return fields. However, it lacks important context about behavioral traits and usage guidelines that would be needed for optimal agent operation.

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, with the 'query' parameter clearly documented. The description adds no additional parameter information beyond what's in the schema, so it meets the baseline of 3 where 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 purpose: 'Search anime by title using MyAnimeList data.' It specifies the verb ('search'), resource ('anime'), and data source ('MyAnimeList'). However, it doesn't explicitly differentiate from sibling tools like 'get_anime' or 'search_characters', which would be needed for a perfect 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. It doesn't mention sibling tools like 'get_anime' (which might retrieve a specific anime by ID) or 'top_anime' (which might list popular anime), leaving the agent to guess based on tool names alone.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return fields (name, nicknames, favorites count, biography) but doesn't cover critical aspects like pagination, rate limits, authentication needs, error handling, or whether it's a read-only operation. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its 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 a single, efficient sentence that front-loads the core action ('Search anime and manga characters by name') and immediately specifies the return data. There is no wasted text, and every word contributes to understanding the tool's 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?

Given the tool's low complexity (1 parameter, no nested objects) and lack of annotations or output schema, the description is minimally adequate. It covers the basic purpose and return fields but misses behavioral details like result limits or error cases. For a simple search tool, it meets the minimum viable threshold but lacks depth for robust agent use.

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

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

The schema description coverage is 100%, with the single parameter 'query' well-documented in the schema as 'Character name to search for (e.g., "Naruto")'. The description adds no additional parameter semantics beyond what the schema provides, such as search syntax or limitations. 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: searching anime and manga characters by name and returning specific fields (name, nicknames, favorites count, biography). It uses specific verbs ('search', 'returns') and identifies the resource ('anime and manga characters'). However, it doesn't explicitly differentiate from sibling tools like 'search_anime' or 'get_anime', which likely search different 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'search_anime' or 'top_anime', nor does it specify contexts where character search is preferred over anime search. Usage is implied by the purpose but lacks explicit when/when-not instructions.

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 tool retrieves top-ranked anime, but it doesn't cover key behavioral traits such as rate limits, authentication needs, pagination, or what 'top-ranked' means (e.g., based on ratings, popularity). This leaves significant gaps in understanding how the tool behaves beyond basic functionality.

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, well-structured sentence that efficiently conveys the core functionality and optional filtering. It's front-loaded with the main purpose and includes necessary details without any redundant information, making it highly concise and effective.

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

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

Given the tool's low complexity (1 optional parameter, no output schema, no annotations), the description is somewhat complete but lacks depth. It covers what the tool does and the parameter, but without annotations or output schema, it fails to address behavioral aspects like response format or constraints, making it only adequate for basic use.

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

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

The schema description coverage is 100%, so the input schema already documents the single optional parameter 'type' with its description and allowed values. The description adds minimal value by reiterating the filtering option but doesn't provide additional semantics beyond what's in the schema, such as default behavior or implications of omitting the parameter.

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

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

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('top-ranked anime from MyAnimeList'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_anime' or 'search_anime', which might also retrieve anime data, 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 implies usage by mentioning optional filtering by type, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'search_anime' or 'get_anime'. There's no mention of specific contexts, exclusions, or comparative scenarios, leaving some ambiguity for the agent.

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

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

With no annotations, the description carries full burden. It discloses the data sources (SEC EDGAR + XBRL), the return values (verdict, structured form, actual value with citation, delta), and implies non-destructive read-only behavior. 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 two sentences with no fluff. It front-loads the purpose and efficiently covers scope, output, and benefits.

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

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

Given the single parameter and no output schema, the description fully explains the tool's scope, input, output, and data sources, leaving no obvious gaps for an agent to misuse.

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 description adds meaningful context for the sole parameter 'claim', including examples and the expectation of natural language, exceeding what the schema provides.

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

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

The description clearly states the tool fact-checks natural-language claims against authoritative sources, specifies the supported domain (company-financial for public US companies), lists the verdict types, and explains the output. It effectively distinguishes itself from sequential agent calls.

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 on when to use (company-financial claims) and notes it replaces multiple agent calls, but does not explicitly mention when not to use or provide direct alternatives among sibling tools.

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