jikan

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

Annotations already declare read-only and idempotent; description adds cost implications (BYO key) and return format, enhancing transparency beyond 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?

Three sentences: purpose and output, model options and cost, return format and use cases. No redundant text, front-loaded with key info.

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

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

Covers purpose, parameters, return format, and use cases. Slightly missing error behavior if Anthropic model requested without API key, but minor given good overall description.

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. Description explains default model, API key pass-through, and context disambiguation, adding 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?

Clearly states it probes LLMs for visibility scoring, with specific verb and resource. Does not explicitly differentiate from sibling 'scan_competitor_ai_presence', but purpose is otherwise distinct.

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

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

Provides default model and instructions for Anthropic usage, but no direct when-to-use vs alternatives or 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 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?

Annotations already indicate readOnly, idempotent, non-destructive. Description significantly adds: resolver contract with confidence levels, parent event extraction, news fallback fields, safety short-circuits, and closed market handling. No contradiction with 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?

Description is very long with multiple paragraphs of examples and details. While well-structured with section headers (CLASSIFIERS, FAN-OUT EXAMPLES, etc.), it would benefit from trimming to be more concise.

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

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

Despite no output schema, the description thoroughly explains response shapes (market, analysis, evidence), resolver contract, parent event fields, news details, and safety behaviors. It covers all aspects of the tool's behavior.

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

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

Schema covers all 3 parameters with descriptions (100% coverage). Description reiterates market input formats but adds little new beyond schema for depth and include_raw.

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 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It also specifies input formats (slug, URL, question text) and output purpose, distinguishing it from sibling tools like ask_pipeworx or polymarket_edges.

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

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

Description explicitly says 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also covers when the tool blocks (low confidence, closed markets). However, it doesn't explicitly contrast with sibling tools or say when NOT to use it.

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

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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds detailed behavior: fans out across SEC, XBRL, USPTO, news, GLEIF; returns specific fields; mentions USPTO API sunset soft-fail. 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?

Front-loaded with example queries and clear purpose. Somewhat verbose but all sentences add value (e.g., listing returned fields, API status). Could be slightly tighter but overall 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?

Tool is complex with no output schema. Description comprehensively covers all returned fields (cik, filings with URIs, fundamentals, patents, news, LEI) and limitations (USPTO sunset, name unsupported). Adequate for the 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%, so baseline is 3. Description mostly repeats schema info (type must be 'company', value is ticker or CIK, names not supported). No additional semantic insight beyond what 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's purpose: 'full cross-source profile of a US public company in ONE parallel call.' It provides example queries showing when to use it, and distinguishes it from chaining individual lookups and sibling tools like resolve_entity.

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 guidance: 'ALWAYS PREFER over chaining single-pack... when the user asks for a holistic view.' Also states when not to use: 'names not supported (use resolve_entity first if you only have a name).'

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

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

No annotations are provided, so the description carries 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?

The description discloses that the tool fetches a page, extracts title/description/key links, and emits markdown, adding context beyond the annotations (which already declare it read-only and idempotent). It provides useful behavioral details like the output being a single text blob for site-root placement.

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

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

The description is two sentences long, with the first sentence front-loading the core purpose and the second listing use cases. Every word serves a purpose, no redundancy, perfectly concise for an AI agent.

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 only 2 parameters, no output schema, and rich annotations, the description provides sufficient context: it explains the output format (markdown), content (title/description/links), and destination (site-root/llms.txt). It also covers use cases, making it complete for agent selection and invocation.

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

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

The input schema has 100% coverage with descriptions for both parameters. The description does not add significant new meaning beyond the schema; it mentions 'max_links' but the schema already specifies default and max values. Thus, 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 generates a production-ready llms.txt file for any URL, specifying the verb ('generate'), resource ('llms.txt file'), and scope ('for any URL'). It also distinguishes itself from sibling tools by focusing on a unique output format and use cases.

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 use cases (getting indexed, drafting, auditing) and context for when to use the tool. However, it lacks explicit guidance on when not to use it or mention of alternatives, which would elevate it to 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?

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?

Annotations already declare readOnly, idempotent, non-destructive. Description adds that data is derived from CF analytics-engine, no PII, and cached 5min-1h, providing valuable behavioral context beyond 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?

Front-loaded with main result, then uses cases, then technical notes. All sentences are informative and non-redundant. Slightly verbose but not wasteful.

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 1 param, no output schema, and rich annotations, the description fully covers purpose, usage, behavior, and parameter meaning. No notable 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?

Single parameter window has enum and schema description. Further explains semantics: shorter windows for 'what's hot', longer for 'steady-state'. Adds meaning beyond schema.

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

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

Clearly states it returns top tools, packs, and call volume over a window. Lists three specific use cases, making 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?

Explicitly provides three scenarios where the tool is useful (discovering hot data, confirming canonical choice, aligning use case). Lacks explicit when-not-to-use or alternatives, but context is clear.

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

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

Annotations provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: that a parameter is mandatory, the two modes and their internal logic (monotonicity checks, partition sums, Jaccard similarity threshold, placeholder filtering), and the response structure. 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 relatively long but well-structured with clear sections. It front-loads the requirement and main purpose. Minor redundancy (e.g., repeating 'event' and 'topic' descriptions) but overall efficient for the complexity.

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

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

Despite no output schema, the description thoroughly explains the response fields (opportunities array with gap_pp, suggested_trade, reasoning, monotonicity context; event mode includes partition_check details). This is highly 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 covers 100% of parameters. The description adds value by giving concrete examples for both 'event' (e.g., 'fed-decision-may-2026') and 'topic' (e.g., 'Fed rate decision'), and notes that full URLs are accepted for 'event'. This aids understanding beyond the schema.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It explicitly distinguishes two modes (event and topic) with specific examples of slugs and seed questions, 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 explicitly requires one of 'event' or 'topic' and warns that no args fails. It recommends 'event' for specific markets and 'topic' for cross-event scanning, and explains what each mode does. However, it does not explicitly differentiate from sibling tools like polymarket_edges or polymarket_kalshi_spread.

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 already indicate read-only, idempotent, and non-destructive behavior. The description adds extensive behavioral details: three model families, edge components (edge_pp_net, kelly fractions, 24h-move warning), caching policy, diagnostics, and tradeable-edge knobs. No contradictions with 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 relatively long but well-structured, with clear sections for model segments, knobs, and response format. It front-loads the purpose. Some redundancy exists (e.g., explaining segments in both purpose and detail), but overall the length is justified by the tool's complexity.

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

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

Despite no output schema, the description thoroughly explains the response structure (by_segment, fed_candidates, _diagnostics) and the meaning of fields like edge_pp_net and 24h-move warning. It also covers edge cases like Fed bets being excluded from ranking. This ensures an agent can fully understand and use the output.

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

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

Schema coverage is 100%, but the description adds significant semantic value beyond parameter names and brief descriptions. For example, it explains how min_partition_leg_kelly applies to per-leg Kelly inside top_legs because partition arbs have zero parent-level Kelly, and it provides real-world context for slippage (Polymarket fees, bid/ask spreads).

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 scans Polymarket markets for opportunities where Pipeworx data disagrees with market price, with an explicit use case: 'what should I bet on today — agents discover opportunities without paging hundreds of markets.' It distinguishes itself through detailed internal segmentation but does not explicitly compare to sibling tools; however, the purpose is highly specific 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 clear context for when to use the tool (daily edge discovery) and explains knobs like min_kelly, slippage_pp, and min_liquidity for tuning. However, it lacks explicit guidance on when not to use this tool or references to alternative sibling tools like polymarket_arbitrage.

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 extensive behavioral details beyond annotations: compatibility_warning conditions, temporal alignment, skipped cross types/subtypes, and response structure. It explains when spreads are meaningful and when they are not, which is exceptional 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 well-structured with clear sections for modes and response fields. It is somewhat verbose (around 200 words) but front-loads the purpose effectively. Minor redundancy could be trimmed.

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 thoroughly explains the response structure including leg-by-leg prices, matched spread list, top_spreads_pp, safety fields, temporal_alignment, and skipped types. It leaves no significant gap for the agent to infer.

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

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

The schema has 100% description coverage, and the description adds meaning by explaining the two modes, providing examples for 'topic', and clarifying that explicit parameters override topic-mapped sides. This adds value beyond the schema, though not extremely detailed for each 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 as calculating cross-venue spreads between Kalshi and Polymarket. It distinguishes two modes and explains the concept. However, it does not explicitly differentiate from sibling tools like 'polymarket_arbitrage' or 'polymarket_edges', which could be related.

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

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

The description provides guidance on when to use the tool (for finding spreads) and includes warnings that pre-mapped topics may not yield tradeable signals. It explains the two modes but does not explicitly state when to prefer this tool over siblings or specify non-usage scenarios.

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

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

No annotations are provided, so the description 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?

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds significant context: parallel fan-out to multiple APIs, fallback from GDELT to GNews, USPTO soft-fail due to API sunset, and return format details. No contradiction with 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 well-structured: front-loaded with user intent examples, then explains functionality, parameter, return, and alternative. Every sentence is informative and necessary; 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?

Given the tool's complexity (3 required params, no output schema), the description covers all essential aspects: inputs, multi-source fan-out, fallback, return format, and alternative tool. It is fully sufficient for an agent to select and use this 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?

Schema coverage is 100% with descriptions. The description adds value beyond schema by explaining the `since` parameter format with examples (ISO date and relative shorthand), and clarifies the `type` is currently limited to 'company'. This enriches the agent's 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 the tool as a change feed for a company in a time window, provides concrete user query examples ('What's new with X'), and distinguishes it from the sibling tool 'entity_profile'. It specifies the verb 'fans out' and the resource 'companies', 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 explicitly tells when to use the tool (for recent changes) and when to use the alternative 'entity_profile' (for static profile regardless of window). It also explains data sources and fallback behavior, giving clear context for proper usage.

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

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

With no annotations provided, the description carries the full burden 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?

Annotations already indicate safety (readOnly, idempotent, not destructive). The description adds useful behavioral details: each entity is probed, results are ranked, first entity is treated as subject, and context disambiguates names. 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 three sentences, front-loaded with the main purpose, and every sentence contributes meaningful information. There is no 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 no output schema, the description covers the return format (ranked list with score, confidence, signal density). It addresses parameters, use case, and an important detail (subject treatment). It is largely complete for a query tool, though missing error behaviors or limits.

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

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

Schema coverage is 100%, so baseline is 3. The description adds semantic value by explaining the first entity is the subject, models have specific requirements (workers-ai default, anthropic requires key), and context disambiguates common names. These go 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 compares AI visibility across multiple entities side-by-side, using specific verbs like 'Compare', 'Probes', 'ranks', and 'surfaces'. It distinguishes from sibling tools by mentioning it uses ai_visibility_check and focusing on multi-entity 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 gives a concrete use case ('competitive AI-marketing audits') and explains when to use it. However, it does not explicitly state when not to use it or name alternative tools for single-entity checks, though this is implied.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint. Description adds behavioral details like graceful partial degradation, bundlephobia's first measurement delay (5-30s), and that sources_failed will list timeouts. 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?

Description is packed with information and front-loaded with purpose. Each sentence adds value, though slightly verbose; still efficient for the complexity.

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

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

No output schema, but description lists all return fields (summary block, advisories, links, recent versions) and explains partial failure behavior. Complete for a composite tool with two data sources.

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 descriptions already cover package name and version defaults. Description adds minor context about scoped packages and default behavior but does not significantly extend beyond 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 performs a composite check for npm packages covering license, advisories, version history, and bundle information. It explicitly distinguishes from siblings by specifying NPM ecosystem 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?

Explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"' and notes when not to use it (PyPI/Maven/Cargo/Go fall under deps.dev:version directly).

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?

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds significant behavioral context: it uses SEC EDGAR + XBRL, returns specific fields (verdict, structured form, actual value with pipeworx:// citation, percent delta), and is limited to company-financial claims. This goes well beyond 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 well-structured: it starts with common query patterns, states the purpose, defines the scope, lists return fields, and explains efficiency benefits. Every sentence adds value and is front-loaded with the most important information.

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

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

Despite lacking an output schema, the description fully explains return values and behavior. It covers usage context, supported claim types, data sources, and output format. No gaps are apparent for this single-parameter tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds examples of valid natural-language claims and specifies the scope (company-financial), but the schema already describes the parameter adequately. The addition is helpful but not essential.

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

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

The description clearly states it performs 'natural-language claim verification against authoritative sources' and gives specific query examples. It distinguishes its function from siblings by specifying its scope (company-financial claims for public US companies) and efficiency benefits.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also notes that it replaces 4-6 sequential calls, implying efficiency. However, it does not explicitly mention when not to use or list alternative tools for non-financial claims.

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