chucknorris

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it processes natural language questions, automatically selects tools and fills arguments, and returns results. However, it lacks details on limitations (e.g., data source availability, error handling, or rate limits), which prevents a perfect score.

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

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

The description is front-loaded with the core functionality, followed by supportive details and examples. Every sentence earns its place by clarifying usage, differentiating from alternatives, or illustrating with examples. It is appropriately sized and avoids redundancy, 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 (natural language processing with automatic tool selection), no annotations, and no output schema, the description does well by explaining the process and providing examples. However, it could improve by mentioning potential limitations or the types of data sources available, leaving some gaps in completeness for such a sophisticated tool.

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

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

The schema description coverage is 100%, so the baseline is 3. The description adds value by explaining the parameter's purpose beyond the schema: it specifies that the question should be in 'plain English' or 'natural language,' and provides examples like trade deficits or adverse events. This enhances understanding but doesn't fully detail constraints or formats.

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') and resource ('answer from data source'), and distinguishes itself from sibling tools by emphasizing natural language processing rather than browsing tools or learning schemas. The examples further clarify its unique function.

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

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

The description explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It contrasts with alternatives by implying that other tools might require manual tool selection or schema knowledge. The examples provide concrete scenarios for usage, making guidelines clear and actionable.

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, openWorldHint, destructiveHint. The description adds behavioral details: resolves market, classifies bet, fans out to appropriate packs, and returns evidence with model comparison. No contradiction; description enriches understanding.

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 moderately long but each sentence adds value. It front-loads the main verb and resource, then explains inputs, process, and output. Could trim redundant phrasing like 'so the caller can see', but overall 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?

No output schema exists, so description must explain returns. It does: 'evidence packet plus a simple market-vs-model comparison'. It also explains the classification and fan-out logic. Given the tool's complexity (multiple packs and bet types), the description provides sufficient completion.

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% (both parameters described). The description adds meaning: 'market' can be slug, URL, or question text; 'depth' has quick vs thorough with default thorough. This goes beyond schema definitions, providing practical usage 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 researches a Polymarket bet by pulling Pipeworx data. It specifies inputs (slug, URL, question) and outputs (evidence packet, model comparison). It distinguishes itself from sibling tools by being the 'core demo product' that combines multiple data sources, thus an agent knows exactly what it does.

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

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

Explicitly states when to use: 'should I bet on X?', 'what does the data say about this Polymarket market?', or 'is there edge in this bet?'. It also contrasts with agents that have to discover packs themselves, implying not to use when raw pack data is needed (for sibling tools 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?

Discloses data sources (SEC EDGAR for companies, adverse-event/FDA/trial data for drugs) and output format (paired data + URIs). However, no annotations are provided and the description does not address potential errors, rate limits, or the read-only nature of the operation.

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

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

Three sentences with no redundancy. Front-loaded with the main action and scope, then efficiently covers parameters and result. Every sentence contributes 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?

Covers input, behavior, output data fields, and performance advantage. Lacks details on error handling or edge cases, but for a straightforward comparison tool the description is nearly complete.

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

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

Schema already provides 100% description coverage, but the description adds valuable context by listing exactly which fields are returned for each 'type' option, going beyond the enum values and array constraints.

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 compares 2-5 entities of type company or drug, enumerates specific financial and clinical data fields per type, and distinguishes from other tools by offering a batch comparison that replaces many 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?

Explicitly says it replaces 8-15 sequential calls, implying efficiency for multi-entity tasks, but does not mention when to prefer single-entity calls or other alternatives available in the server.

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 tool returns 'the most relevant tools' and has a default/max limit implied by the schema, but lacks details on how relevance is determined (e.g., ranking algorithm), error handling, or performance characteristics. It adds some context (e.g., 'first' call recommendation) but is not comprehensive for a search 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 highly concise and well-structured in two sentences: the first states the purpose and output, the second provides usage guidelines. Every sentence earns its place by adding critical information without redundancy, and it is front-loaded with the core functionality.

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

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

Given the tool's complexity (search functionality with 2 parameters), no annotations, and no output schema, the description is reasonably complete. It covers the purpose, usage context, and output format, but lacks details on behavioral aspects like search mechanics or error handling. It compensates well for the absence of structured data but has minor gaps in transparency.

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

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

The schema description coverage is 100%, so the schema already documents both parameters ('query' and 'limit') thoroughly. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain query formatting nuances or limit implications). Baseline 3 is appropriate as the schema does the heavy lifting, but no extra value is added.

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'), and distinguishes it from sibling tools by emphasizing its role in discovery among 500+ tools. It explicitly mentions what it returns ('most relevant tools with names and descriptions'), making the purpose unambiguous and distinct from joke-related siblings.

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.' This clearly defines the context (large tool catalog) and priority (first step), distinguishing it from alternatives like direct tool invocation or other search methods, with no misleading or missing exclusions.

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

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

The description discloses the return format (pipeworx:// citation URIs) and lists the data types included. Though annotations are absent, it clearly indicates a read-only retrieval operation with no destructive hints. It could mention rate limits or authentication but is sufficient for a profile 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?

At ~80 words, the description is concise and front-loaded. Every sentence adds value: main purpose, contents, return format, efficiency claim, exclusion for federal contracts. No redundancy.

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

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

The description explains the tool's purpose, inputs, outputs, and usage boundaries. Without an output schema, it could provide an example response structure, but the mention of citation URIs gives a clear idea. It adequately covers the tool's functionality for its 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. The description adds no new parameter-level detail beyond the schema's descriptions (which already explain type and value). The tool-level description lists returned data but does not enhance 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 the tool returns a consolidated entity profile across multiple data sources, listing specific data types for companies (SEC filings, revenue, patents, news, LEI) and mentions it replaces many sequential calls. It distinguishes from siblings by advising use of a different tool for federal contracts.

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 not to use this tool (federal contracts - use usa_recipient_profile) and when to use a sibling (resolve_entity for names). It also implies use this for efficient batch retrieval instead of 10-15 calls.

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 deletes a memory, which implies a destructive mutation, but doesn't address critical aspects like whether deletion is permanent, if it requires specific permissions, what happens on invalid keys, or what the response looks like. This leaves significant gaps for a destructive operation.

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

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

The description is a single, efficient sentence with zero wasted words. It front-loads the core action ('Delete') and resource ('a stored memory'), making it immediately scannable 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?

For a destructive tool with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes a 'stored memory', how keys are structured, what happens on success/failure, or return values. Given the complexity of a delete operation and lack of structured metadata, more context is needed 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' fully documented in the schema as 'Memory key to delete'. The description adds no additional semantic context beyond what the schema provides, such as key format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the action ('Delete') and the resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'recall' or 'remember', but the verb 'Delete' provides enough distinction from their likely read/write functions.

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 prerequisites (e.g., needing an existing memory key), exclusions, or relationships with sibling tools like 'recall' (which likely retrieves memories) or 'remember' (which likely stores them).

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool fetches a 'random' joke, implying non-deterministic behavior, but doesn't disclose other traits like error handling (e.g., what happens if the category is invalid), rate limits, authentication needs, or response format. The description is minimal and lacks essential operational context.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It is appropriately sized and front-loaded, with every word contributing to clarity, making it highly concise 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 (a fetch operation with a parameter), no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., error handling, randomness), output format, or usage distinctions from siblings. The description alone is insufficient for an agent to fully understand how to invoke and interpret results from 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 100%, with the parameter 'category' fully documented in the schema, including its type and a note to use 'list_categories' for valid values. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without adding value.

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

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

The description clearly states the verb ('Get') and resource ('a random Chuck Norris joke') with specific scope ('from a specific category'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'random_joke' (which might fetch jokes without category filtering) or 'search_jokes' (which might allow keyword searches).

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 when a joke from a specific category is needed, and the input schema references 'list_categories' to see valid values, providing some contextual guidance. However, it doesn't explicitly state when to use this tool versus alternatives like 'random_joke' (for any random joke) or 'search_jokes' (for keyword-based searches), nor does it mention any exclusions or prerequisites.

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

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

No annotations are provided, so the description carries the full burden. It mentions 'List all available' but doesn't disclose behavioral traits such as whether this is a read-only operation, if it requires authentication, rate limits, or the format of the returned categories (e.g., list of strings). For a tool with zero annotation coverage, this is a significant gap in 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 a single, efficient sentence that front-loads the purpose ('List all available Chuck Norris joke categories') with zero wasted words. It's appropriately sized for a simple tool, making it easy to parse quickly.

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 (0 parameters, no output schema, no annotations), the description is minimally complete—it states what it does. However, it lacks details on return values (since no output schema) and behavioral context, which could help an agent understand the result format or usage constraints, leaving room for improvement.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add param info, which is appropriate, earning a baseline score of 4 since it doesn't need to compensate for any schema gaps.

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

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

The description clearly states the specific action ('List') and resource ('all available Chuck Norris joke categories'), distinguishing it from siblings like joke_by_category (fetches jokes in a category), random_joke (gets a random joke), and search_jokes (filters jokes). It precisely defines what the tool does without redundancy.

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 stating it lists categories, suggesting it's for retrieving category options before using tools like joke_by_category. However, it lacks explicit guidance on when to use this versus alternatives (e.g., if you need categories for filtering) or any exclusions, leaving usage context inferred rather than stated.

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

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

No annotations are provided, so the description carries the full burden. It discloses rate limiting ('5 messages per identifier per day') and states 'Free' suggesting no cost. It does not contradict any annotations. The description is transparent about behavior beyond what schema alone provides.

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 paragraph with all key information front-loaded: purpose, use cases, content guidelines, and constraints (rate limit, free). Every sentence contributes meaning 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?

Given the simplicity of the tool (feedback submission) and no output schema, the description covers all necessary context: purpose, when to use, content guidelines, and limitations. It is fully adequate for an agent to correctly invoke this tool.

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

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

The input schema has 100% coverage, so the baseline is 3. The description adds value by advising to 'Describe what you tried in terms of Pipeworx tools/data' for the message parameter, and reinforces the type enum without repeating schema. This extra guidance elevates the score to 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 function: 'Send feedback to the Pipeworx team.' It enumerates specific use cases (bug reports, feature requests, missing data, praise) which fully defines its purpose and distinguishes it from sibling tools like ask_pipeworx or joke_by_category.

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 (feedback) and what not to include ('do not include the end-user's prompt verbatim'). It also mentions rate limits. However, it does not explicitly contrast with other tools, but the context is clear enough.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds that it checks monotonicity and returns ranked opportunities with reasoning. No contradiction; adds context but not extensive behavioral detail 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?

7 sentences, well-structured with modes in bold. Front-loaded with purpose. Each sentence adds value. Slightly verbose on cross-event explanation but overall 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?

Tool is read-only analysis. No output schema, but description specifies return type: 'ranked opportunities with suggested trade direction + reasoning.' Annotations cover safety and open-world nature. Sufficient for an arbitrage-finding 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?

Both parameters (event, topic) fully documented in schema (100% coverage). Description adds meaning: event can be slug or URL, topic is a seed question. Explains how each parameter maps to a mode.

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 verb+resource: 'Find arbitrage opportunities on Polymarket by checking for monotonicity violations.' Distinct from siblings like polymarket_edges. Explains two modes (event/topic) with specific scopes.

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 event mode (single event slug) vs topic mode (cross-event, e.g., 'Strait of Hormuz traffic returns to normal'). Provides rationale for cross-event mode. No explicit when-not, but modes cover main 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?

The description details the internal process: scans top markets, groups by asset, fetches price history once, computes model probability, ranks by edge. It also notes the model source (FRED+coinpaprika) and output (edge magnitude + direction). No annotation 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?

Concise yet comprehensive: first sentence states purpose, then process steps, then output. No filler; 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?

Given no output schema, the description adequately describes return format (top N edges ranked with direction). It mentions limitations (V1 crypto-price bets) but could be more explicit about data freshness or error cases.

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

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

Schema has 100% description coverage for all three parameters. The description adds context about model coverage (crypto-price bets) but does not further clarify parameter behavior 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's function: scan Polymarket markets and return those with largest disagreement between Pipeworx model and market price. It distinguishes from siblings like polymarket_arbitrage by specifying the unique Pipeworx model 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 targets the 'what should I bet on today' use case and mentions it avoids manual paging, but does not provide when-not-to-use or compare to related tools like bet_research or 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?

No annotations are provided, so the description carries full burden. It states what the tool does but fails to disclose behavioral traits such as whether it requires authentication, rate limits, or what the output format looks like (e.g., text string, structured data). This is a significant gap for a tool with no annotation coverage.

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 with zero waste. It is front-loaded with the core purpose and appropriately sized for a simple tool, making it easy to parse quickly.

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 annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., joke text, metadata), behavioral aspects like error handling, or how it differs from siblings beyond the 'random' hint. For a tool in this context, more detail is needed to be fully helpful.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't mention parameters, aligning with the schema. Baseline is 4 for zero parameters, as it avoids unnecessary detail.

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

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

The description clearly states the verb ('Get') and resource ('a random Chuck Norris joke'), making the purpose immediately understandable. It distinguishes from sibling tools like 'joke_by_category' and 'search_jokes' by specifying 'random' selection, though it doesn't explicitly contrast with 'list_categories'.

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 when a random joke is needed, but provides no explicit guidance on when to choose this tool over alternatives like 'joke_by_category' or 'search_jokes'. It lacks any mention of prerequisites, exclusions, or comparative contexts with siblings.

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

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

With no annotations provided, the description carries the full burden. It effectively describes the tool's behavior: retrieving stored memories by key or listing all keys, and clarifies that memories persist across sessions. However, it doesn't mention potential limitations like memory size constraints or retrieval errors.

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

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

The description is perfectly concise with two sentences that each serve distinct purposes: the first explains the core functionality, the second provides usage context. There is no wasted language, and information is front-loaded effectively.

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 retrieval tool with no annotations and no output schema, the description provides adequate context about what the tool does and when to use it. However, it doesn't describe the format of returned memories or potential error conditions, 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 has 100% description coverage, so the baseline is 3. The description adds value by explaining the semantic meaning of omitting the key parameter ('omit to list all keys'), which clarifies the tool's dual functionality beyond what the schema alone 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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings like 'remember' (which stores) and 'forget' (which deletes) by focusing on retrieval operations.

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

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

The description provides explicit guidance on when to use this tool: 'to retrieve context you saved earlier in the session or in previous sessions.' It also specifies when to omit the key parameter ('omit key to list all keys'), giving clear operational 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?

No annotations provided, so description carries full burden. It explains the fan-out behavior, supported entity types, input formats, and return structure (changes + count + URIs). Lacks explicit statement of read-only nature or potential side effects, but the description is reasonably thorough.

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

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

Four sentences, each with distinct purpose: first defines overall function, second details behavior for company type, third explains since parameter, fourth describes return and use cases. No redundant information.

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

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

With 3 parameters, no annotations, and no output schema, the description covers purpose, behavior, parameter formats, and return structure. It could be more explicit about error handling or unsupported types, but overall it is sufficient for correct tool 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?

Schema coverage is 100%, so baseline is 3. Description adds value by explaining relative date formats for 'since', providing examples for 'value', and clarifying that only 'company' is supported for 'type'. This goes beyond 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?

Clearly states the tool fetches recent changes for an entity since a given time. Specifies supported types (company) and data sources (SEC EDGAR, GDELT, USPTO). Distinguishes from siblings like entity_profile and compare_entities by focusing on updates over time.

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 suggests use cases: 'brief me on what happened with X' and change-monitoring workflows. Does not explicitly list when not to use, but the purpose is clear and different from siblings.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context beyond basic functionality: it explains persistence differences ('authenticated users get persistent memory; anonymous sessions last 24 hours'), which is critical for understanding data retention. However, it does not cover error conditions, rate limits, or specific permissions required.

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

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

The description is appropriately sized and front-loaded: the first sentence states the core purpose, and subsequent sentences add essential context without waste. Every sentence earns its place by clarifying usage and behavioral traits, making it 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 (2 parameters, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, and key behavioral aspects like persistence, but lacks details on return values (e.g., confirmation message) or error handling. With no output schema, some gaps remain in explaining what happens after 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?

Schema description coverage is 100%, so the schema already documents both parameters ('key' and 'value') with examples. The description does not add syntax or format details beyond what the schema provides, such as constraints on key naming or value size. Baseline 3 is appropriate when 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 with specific verbs ('store a key-value pair') and resource ('in your session memory'), distinguishing it from siblings like 'recall' (retrieval) and 'forget' (deletion). It explicitly mentions what can be stored ('intermediate findings, user preferences, or context across tool calls'), 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 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. It implies usage for persistence needs but lacks explicit exclusions or comparisons to sibling tools like 'recall' for retrieval.

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

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It mentions that the call is a read operation (returns data), describes inputs and outputs, and implies no destructive action. It lacks error handling or rate limit info but is sufficient for a simple lookup.

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 concise sentences: first states purpose and scope, second details parameters and returns. No superfluous words; every sentence adds value.

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

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

With no output schema, the description adequately describes return values (ticker, CIK, company name, URIs). It covers both parameters and explains the benefit of replacing multiple calls. Complete for the tool's functionality.

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%, but the description adds significant value by providing context for each parameter: the 'type' parameter is an enum with only 'company', and 'value' accepts ticker, CIK, or name with examples. This goes beyond the schema's 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 verb 'Resolve', the resource 'entity to canonical IDs', and the data source 'Pipeworx'. It specifies the supported type ('company') and what is returned, distinguishing it from sibling tools that are joke/memory/ask 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 indicates it should be used for a single call to get canonical IDs, replacing multiple lookups. It does not explicitly mention when not to use it, but the context of sibling tools makes it clear that this is for data resolution. A brief note on unsupported entity types would improve the score.

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 but offers minimal information. It states the tool searches jokes but doesn't cover aspects like rate limits, authentication needs, response format, or pagination. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

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 directly states the tool's function without unnecessary words. It is front-loaded with the core purpose, making it easy to parse quickly. Every part of the sentence contributes 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?

Given the lack of annotations and output schema, the description is incomplete for effective tool use. It doesn't explain what the search returns (e.g., list of jokes, metadata), how results are formatted, or any behavioral constraints. For a search tool with no structured context, this leaves critical 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?

The schema description coverage is 100%, with the single parameter 'query' fully documented in the schema. The description adds no additional parameter details beyond what the schema provides, such as search syntax or examples. This meets the baseline for high schema coverage but doesn't enhance 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 the action ('Search') and resource ('Chuck Norris jokes') with a specific mechanism ('by keyword'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'joke_by_category' or 'random_joke', 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 no guidance on when to use this tool versus alternatives like 'joke_by_category' or 'random_joke'. It mentions searching by keyword but doesn't clarify scenarios where this is preferred over other methods, leaving the agent without contextual usage 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, the description carries full burden. It lists return values (verdict, extracted form, actual value, citation, delta) and data sources (SEC EDGAR + XBRL). It does not disclose edge-case behavior for unsupported claims, but scope is clear.

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 with no waste. First sentence defines purpose and scope, second lists outputs, third explains efficiency benefit. Front-loaded core 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 no output schema, the description sufficiently explains return values and data sources. It is complete for tool selection and invocation, though it could mention handling of claims outside the supported 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?

Schema description coverage is 100% (baseline 3). The tool description adds context that the claim should be company-financial, but the parameter schema already describes the input format adequately. Additional value is marginal.

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

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

The description clearly states the tool fact-checks natural-language claims against authoritative sources, specifically company-financial claims for public US companies. It distinguishes from sibling tools by noting it replaces 4-6 sequential agent calls, showing a unique value proposition.

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 limits scope to 'company-financial claims (revenue / net income / cash for public US companies)' and explains that it replaces sequential calls. It does not list alternative tools but the context effectively tells when to use it.

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