Codewars

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

Annotations already indicate readOnlyHint=true and openWorldHint=true. Description adds valuable context: routes to 2,353 tools, returns structured answers with stable URIs, and covers authoritative data with citations. 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 front-loaded with the key preference statement. It lists many examples which, while slightly long, are useful for an agent. Every sentence contributes to understanding. Could be slightly shorter but still 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 no output schema, description explains return format (structured answer with citation URIs). Covers when to use, what it does, and examples. For a single-parameter query tool, it is sufficiently complete.

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

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

Only one parameter 'question' with schema description 'in natural language'. Description adds value by providing examples of valid questions and specifying the scope (current/historical data, factual entities). Schema coverage is 100%, baseline 3, plus useful extra context.

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: routing natural language questions to authoritative structured data sources, with specific examples and contrast to web search. Verb 'routes' plus resource '2,353 tools' and output 'structured answer with citation URIs' is unambiguous.

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

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

Explicitly says 'PREFER OVER WEB SEARCH' and lists many query types (SEC filings, FDA data, etc.). Provides concrete examples and signals when to use: 'what is', 'look up', etc. Gives clear context for agent decision.

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, destructiveHint false, and openWorldHint true. The description adds significant behavioral context: it resolves markets, classifies bets, fans out to appropriate data packs, and returns a comparison. No side effects are mentioned, which is consistent with read-only 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 well-structured: purpose first, then input, process, output, use cases, and a promotional note. Every sentence adds value, but it is slightly longer than necessary. However, it remains clear and front-loaded.

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

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

Given the complexity of the tool (market resolution, classification, fan-out, comparison), the description covers all major steps and output. It lacks an output schema but describes the output at a high level. The description is sufficiently complete for an agent to understand what the tool does and what it returns.

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 clear descriptions for both parameters (market accepts slug/URL/question; depth has enum quick/thorough with defaults). The description reiterates the market input options but adds no new semantic information beyond what the schema already provides.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data, specifying input types (slug, URL, question) and output (evidence packet + market-vs-model comparison). It effectively distinguishes from sibling tools like validate_claim or ask_pipeworx by emphasizing it is the specialized core demo product.

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 ('should I bet on X?', 'what does the data say?', 'is there edge?') and notes that agents using this tool convert better than those that discover packs themselves. It implies when to use but does not explicitly state when not to use or mention alternatives like ask_pipeworx.

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 and non-destructive behavior. The description adds valuable behavioral context: it replaces many sequential calls (efficiency), details data sources (SEC EDGAR, FAERS, etc.), and mentions output format (paired data with citation URIs). There is 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 with two paragraphs: first introducing the tool and use cases, second detailing types and outputs. Every sentence is informative, no redundancy or filler. It is front-loaded with the core purpose.

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

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

The description covers both entity types, data sources, output format, and example use cases. It lacks details on error handling or edge cases (e.g., invalid tickers), but given the presence of annotations and schema, it is sufficiently complete for most agentic scenarios.

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

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

Input schema has 100% coverage, so the description doesn't need to compensate heavily. However, the description adds significant meaning: it explains the mapping between type and data source, gives examples of valid values (tickers for company, drug names), and clarifies the range (2-5 items). This goes 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's purpose: comparing 2-5 companies or drugs side-by-side. It provides specific use cases ('compare X and Y', 'X vs Y', 'how do X, Y, Z stack up') and details what data is retrieved for each type (financials from SEC, adverse events from FAERS, etc.). It also distinguishes from sibling tools by noting it replaces 8-15 sequential 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 explicitly tells when to use the tool for comparison queries and explains the two types (company/drug). It does not explicitly state when not to use it or mention alternative tools, but the context is clear enough that it's for multi-entity comparison, not single-entity queries (which belong to entity_profile).

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 mark it as read-only and non-destructive. The description confirms this by stating it returns tool names and descriptions without side effects. No contradictions, and it adds context about the return 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?

Three well-structured sentences: purpose, domains, output and guidance. No wasted words, and key information is front-loaded.

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

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

For a simple discovery tool with no output schema, the description adequately explains the return value (top-N tools with names+descriptions). Input parameters are well-covered by schema and 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% with descriptions for both 'query' and 'limit'. The description adds examples for 'query' and mentions default/max for 'limit' (already in schema). Baseline 3 is appropriate as 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: 'Find tools by describing the data or task.' It lists specific domains and explains the output (top-N relevant tools). This distinguishes it from sibling tools, which are domain-specific or task-specific.

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

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

Explicitly says 'Use when you need to browse, search, look up, or discover' and instructs to 'Call this FIRST when you have many tools available and want to see the option set.' This provides clear when-to-use and context relative to other tools.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds that results include pipeworx:// citation URIs and that person/place entity types are coming soon, providing useful 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?

The description is a single paragraph that is efficient, front-loading purpose and usage. It is concise without unnecessary words, though slightly dense for quick 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?

No output schema, so the description lists return categories: SEC filings, financials, patents, news, LEI, and citation URIs. This provides a good overview, though detailed format is not specified.

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

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

Input schema coverage is 100%, with descriptions for both type and value. The description reinforces that value accepts ticker or CIK and that names need resolve_entity, but adds no new parameter semantics 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 begins with 'Get everything about a company in one call,' clearly stating the verb and resource. It distinguishes from siblings by mentioning it replaces calling multiple tools (SEC EDGAR, USPTO, etc.), and lists specific data returned.

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

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

Explicit when-to-use examples: 'when a user asks tell me about X...' and provides clear exclusion: 'Names not supported — use resolve_entity first if you only have a name.' This gives unambiguous guidance and an alternative tool.

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 provide destructiveHint=true. The description adds usage context but does not expand on behavioral traits beyond what annotations convey.

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, front-loaded with core purpose, 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?

Simple tool with one parameter and no output schema. Description fully covers purpose and usage 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?

Schema covers 100% with a clear description for the key parameter. The description adds no additional parameter details 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 action ('Delete'), the resource ('memory'), and the mechanism ('by key'). It also distinguishes itself from sibling tools like 'remember' and 'recall'.

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: 'when context is stale, the task is done, or you want to clear sensitive data', and provides pairing advice with 'remember' and 'recall'.

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 and destructiveHint=false, so the safety profile is clear. However, the description adds no behavioral context beyond that—e.g., what side effects occur, what the output looks like, or any constraints. For a tool with no output schema, the description should provide more behavioral detail.

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

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

The description is extremely short but not concisely effective—it is under-specified. A single phrase 'Single kata.' does not earn its place as it conveys no actionable information. Conciseness must be balanced with informativeness.

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

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

With one required parameter having no schema description, no output schema, and a single-word description, the tool definition is severely incomplete. An agent cannot determine how to invoke the tool correctly or interpret its output. The description fails to compensate for missing structured data.

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

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

The schema has 0% description coverage, and the description does not explain the single parameter 'id_or_slug'. The agent has no idea what valid values are (e.g., format, example) or how to construct the slug, making the tool unusable.

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

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

The description 'Single kata.' is a tautology that restates the tool name without explaining what a kata is or what the tool does (e.g., retrieve, view, search). It provides no verb or specific resource, making it impossible for an agent to understand the tool's purpose.

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

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

There is no guidance on when to use this tool instead of alternatives like entity_profile or resolve_entity. The description offers no context, prerequisites, or exclusions, leaving the agent without decision support.

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

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

Annotations are minimal (readOnlyHint false, destructiveHint false), but the description adds significant behavioral context: rate-limited to 5 per day, free, doesn't count against quota, and that the team reads digests daily affecting roadmap.

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?

Single paragraph, front-loaded with purpose, then usage, then formatting restrictions, then process/limitations. Every sentence adds value without redundancy.

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

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

Covers tool purpose, usage conditions, parameter semantics, and limitations. Could mention expected response or post-submission behavior, but not essential for a feedback 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 with descriptions. Description adds extra context on message formatting (don't paste user prompt) and type categories, going 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 the tool sends feedback to the Pipeworx team about bugs, missing features, or praise. Differentiates from sibling tools like ask_pipeworx or discover_tools with specific categories and usage.

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

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

Provides explicit when-to-use scenarios (bug, feature, data_gap, praise) and what not to do (don't paste user prompt). Mentions rate limits and free usage. Lacks explicit mention of when NOT to use it, 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 already declare readOnlyHint=true and destructiveHint=false, so the agent knows it is safe. The description adds behavioral context beyond annotations: it explains that the tool walks child markets, checks ordering within an event (event mode), or searches across separate events and groups them (topic mode), and returns 'ranked opportunities with suggested trade direction + reasoning.' This provides operational detail without contradicting 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 concise (~150 words) and well-structured. It starts with the core purpose, then explains the two modes in a bullet-like fashion, and ends with the output. Every sentence adds value; there is no repetition or fluff. It is front-loaded with the main action and then provides necessary details.

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 modes, no output schema, no required parameters), the description covers the functionality well. It explains the data flow (searches, groups, checks monotonicity), the output format (ranked opportunities with direction and reasoning), and the distinction between modes. A slight gap is the lack of mention of potential error cases or empty results, but it is largely complete.

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

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

The input schema covers both parameters with descriptions (100% coverage). The description adds meaning by providing examples and context: for 'event' it says 'Polymarket event slug (e.g. "when-will-bitcoin-hit-150k") or full URL,' and for 'topic' it gives a concrete example ('Strait of Hormuz traffic returns to normal') and explains how the tool uses it. This goes beyond the schema, though the schema already suffices.

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: 'Find arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets.' It specifies the verb (find), resource (Polymarket markets), and method (monotonicity violations). It distinguishes itself from sibling tools like 'polymarket_edges' by focusing on arbitrage and detailing two distinct modes (event and topic).

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 guidance on when to use each mode: 'Cross-event mode catches the cases where Polymarket lists each cutoff as its own event... single-event mode misses the May≤June rule.' This helps the agent choose between event and topic modes. However, it does not explicitly compare this tool to sibling tools or mention when not to use it, so it is not a perfect 5.

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

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

Annotations indicate readOnlyHint=true and openWorldHint=true, so the description carries a moderate burden. It adds significant value by detailing the workflow: scanning top markets, grouping by asset, fetching price history once, computing model probability, and ranking by |edge|. It also mentions the data sources (FRED, coinpaprika) and output directions. Missing details like rate limits or external API calls, but overall transparent.

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

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

The description is about six sentences, each providing essential information. It front-loads the primary action in the first sentence and avoids redundancy. While efficient, it could be slightly more structured (e.g., using bullet points for steps), but it is not verbose.

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 compensates by clearly stating the output (top N ranked by edge magnitude with suggested trade direction). It covers the input parameters (via schema) and the workflow. It doesn't mention error conditions or data freshness, but for a scanning tool with openWorldHint, this is adequate.

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

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

Schema description coverage is 100% (all three parameters have clear descriptions). The tool description does not add additional meaning beyond the schema (e.g., it doesn't discuss parameter interactions or defaults in more depth). Baseline score of 3 is appropriate since the schema already covers parameter semantics well.

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

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

Description clearly states the tool scans Polymarket markets, identifies where Pipeworx data disagrees with market prices, and returns top edges with trade direction. It specifies the model (lognormal from FRED + coinpaprika), scope (crypto-price bets), and output (ranked by edge magnitude). This distinguishes it from the sibling polymarket_arbitrage, which likely focuses on arbitrage rather than edge detection.

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 frames the tool for the 'what should I bet on today' question, indicating it helps users discover opportunities without manual browsing. However, it does not explicitly state when not to use it or provide alternative tools from the sibling list (e.g., ask_pipeworx, bet_research), which would clarify context further.

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, so the read-only nature is covered. The description adds valuable context: scoping to identifier, and avoiding re-deriving information. 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?

Two sentences: first sentence states core action, second provides use-case context. No unnecessary words; every sentence earns its place.

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

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

For a simple read tool with complete annotations and schema, the description fully covers purpose, usage, behavior, and parameters. The agent has all needed information.

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

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

Schema coverage is 100%, and the description adds meaning beyond the schema by explaining the effect of omitting the key (list all) versus providing a key (retrieve specific value).

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

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

The description uses specific verbs ('retrieve', 'list') and clearly identifies the resource (values saved via remember). It distinguishes the tool from siblings (remember, forget) by naming them and explaining how recall differs.

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

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

The description explicitly states when to use recall (look up stored context) and when to omit the key to list all keys. It also advises pairing with remember and forget, providing clear context for 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?

Annotations already indicate read-only and non-destructive. Description goes further by detailing the fan-out to SEC EDGAR, GDELT, and USPTO, the accepted date formats, and the return structure including pipeworx:// URIs, providing full transparency.

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

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

Description is concise, using only 5 sentences. It's front-loaded with purpose and example queries, then covers parameter details and return structure. No fluff.

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

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

Given no output schema, the description adequately describes return values: 'structured changes + total_changes count + pipeworx:// citation URIs'. It also explains the parallel fan-out behavior, making the tool's behavior fully understandable.

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 value by explaining the 'since' parameter's format options (ISO date or relative shorthand) and suggesting typical values like '30d'. This helps agents use the parameter correctly.

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

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

Description clearly states 'What's new with a company in the last N days/months?' and provides multiple example query forms. It distinguishes itself from siblings like entity_profile by focusing on recent changes rather than static data.

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

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

Explicitly tells when to use: 'Use when a user asks ...' with examples. Implies alternatives by noting it fans out to specific sources, so agents know not to use for static queries.

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 key behaviors: key-value scoping, persistence for authenticated users, 24-hour memory for anonymous sessions. Annotations only declare read/write nature, so description adds significant value.

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

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

Four concise sentences, each providing distinct information: purpose, usage, scoping, and pairing. No unnecessary words.

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

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

For a simple two-parameter tool with no output schema, the description covers all necessary context: purpose, when to use, scoping, and relation to sibling tools.

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 adds example values but does not significantly enhance parameter understanding 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 'Save data' with specific examples, and distinguishes from siblings by mentioning 'recall' and 'forget' 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?

Explicitly describes when to use (when discovering something worth carrying forward) and provides alternatives (recall, forget). Also explains scoping differences.

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, destructiveHint=false, openWorldHint=true. Description adds that tool returns IDs plus pipeworx:// citation URIs, which goes beyond annotations. 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?

Description is reasonably concise with 4 sentences. Front-loaded with the main action and key ID systems. Every sentence adds value, though slightly longer than necessary; still efficient.

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

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

Given 2 simple parameters, good annotations, and no output schema, the description covers the tool's purpose, inputs, and return values (IDs + URIs). Complete enough for agents to use 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 for both parameters. The description adds significant context: for type enum it explains both values; for value field it explains acceptable inputs (ticker, CIK, name for company; brand/generic for drug) with examples. This adds meaning beyond the schema.

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

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

Description clearly states the tool looks up canonical identifiers for companies or drugs, provides specific ID systems (CIK, ticker, RxCUI, LEI), and gives concrete examples like "Apple" → AAPL / CIK 0000320193. It distinguishes from sibling tools by noting it replaces 2–3 lookup calls, implying efficiency.

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

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

Explicitly tells agents to use this BEFORE calling other tools needing official identifiers. Provides context on when to use (when a user mentions a name and needs an ID). No explicit when-not cases, but the examples and purpose 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?

The annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true. The description adds no behavioral context beyond these, failing to disclose what 'open world' implies or any other side effects. It does not contradict the annotations but also does not enhance them.

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?

At two words, the description is extremely short but not effectively concise; it lacks critical details. Though structurally small, it fails to earn its place by informing the agent meaningfully, crossing into under-specification.

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

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

Given the simple input schema and lack of output schema, the description is still incomplete. It does not explain what a 'user profile' entails (e.g., returned fields, pagination, error cases), and with siblings offering more specific user-related functions, the agent cannot fully understand this tool's scope.

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

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

The input schema has one parameter 'username' with no description (0% schema coverage). The tool description provides no additional meaning about the parameter's format, purpose, or constraints, leaving the agent without essential semantic information.

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 'User profile.' essentially restates the tool name 'user' without specifying any action or distinguishing it from siblings like 'user_authored' or 'user_completed'. It does not clarify whether the tool retrieves, displays, or searches profiles, making the purpose vague.

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

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

No information is provided about when to use this tool versus alternatives such as 'user_authored' or 'user_completed'. There is no mention of prerequisites, context, or exclusions, leaving the AI agent without guidance for selection.

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

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

Annotations already indicate readOnlyHint=true, destructiveHint=false. Description 'Authored kata.' adds no behavioral context beyond what annotations provide. Does not explain what 'authored' means or what happens.

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 only two words, but it is not concise; it is under-specified. It does not earn its place as it fails to convey 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 simplicity (1 param, no output schema), description is extremely minimal. It does not explain the tool's return value, the concept of 'authored kata', or how to use it properly.

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

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

Input schema has one parameter 'username' with 0% schema description coverage. Description does not explain the meaning or usage of 'username', leaving agents without guidance.

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

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

Description 'Authored kata.' is vague. It does not clearly state the verb or resource. 'Authored' could be a past participle or verb, and 'kata' is undefined. No differentiation from siblings like 'kata' or 'user'.

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

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

No guidance on when to use this tool vs alternatives. Sibling tools like 'kata' and 'user' exist, but no comparison or context provided.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds no additional behavioral context (e.g., what constitutes 'completed', data freshness, or filtering behavior).

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

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

The description is extremely concise (two words) but lacks essential context. It is under-specified rather than efficiently compact.

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

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

Given no output schema and simple annotations, the description does not sufficiently explain the tool's behavior (e.g., return format, pagination, or the nature of 'completed challenges'). An agent cannot reliably invoke this tool.

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

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

Schema description coverage is 0%, yet the description fails to explain the purpose of 'page' or 'username'. Without additional documentation, an agent cannot understand how to supply these parameters correctly.

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

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

The description 'Completed challenges.' is vague and lacks a specific verb or resource. It does not clarify whether this tool retrieves or manages completed challenges, nor does it distinguish from siblings like 'user' or 'user_authored'.

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

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

No guidance is provided on when to use this tool versus alternatives such as 'user' or 'user_authored'. There is no mention of typical use cases or prerequisites.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds value by detailing the return format (verdict, citation, delta) and internal pipeline (NL parsing to numeric comparison), which is beyond what annotations provide.

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

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

The description is concise and well-structured, starting with purpose, then usage, domain, output, and efficiency. Every sentence adds essential information without redundancy.

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

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

Despite lacking an output schema, the description fully explains the return values (verdict, extracted form, actual value, citation, delta) and the tool's limitation. It provides complete context for an agent to correctly invoke and interpret results.

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

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

The schema covers 100% of parameters with descriptions. The description reinforces the parameter's purpose and adds domain-specific guidance (e.g., 'v1 supports company-financial claims'), providing additional context beyond the schema's examples.

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

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

The description clearly defines the tool as a fact-checker for natural-language claims against authoritative sources, specifying the supported domain (company-financial claims via SEC EDGAR). It distinguishes itself from siblings by its unique function and efficiency claim, replacing multiple sequential 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?

Provides explicit usage context with examples like 'Is it true that…?' and implies when to use. It states the domain limitation (company-financial claims only), but lacks explicit comparison to alternative tools among siblings.

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