artic

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool automatically selects data sources and fills arguments, handles natural language questions, and returns results. However, it doesn't mention potential limitations like response time, accuracy, or data source constraints. It adds value beyond the schema but could be more comprehensive.

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

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

The description is well-structured and front-loaded with the core functionality. Every sentence adds value: the first explains the purpose, the second describes the mechanism, and the third provides concrete examples. There's no wasted text, and it efficiently communicates key information.

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

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

Given the tool's complexity (natural language processing, automatic tool selection) and lack of annotations/output schema, the description does a good job explaining what the tool does. However, it doesn't detail the return format or potential error cases. For a tool with no output schema, more information about results would improve completeness.

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

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

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

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

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

The description clearly states the tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'). It distinguishes from sibling tools like search_artworks or get_artist by emphasizing natural language queries rather than structured 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 explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear examples of appropriate questions and implicitly suggests alternatives (e.g., use sibling tools for specific structured queries). This gives strong guidance on its intended use case.

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. The description adds context: it resolves the market, classifies the bet, fans out to packs, and returns a comparison. This goes beyond annotations, providing a clear behavior model.

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

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

The description is three sentences, all informative. It is front-loaded with the main action and use cases, though slightly dense. No redundant words.

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

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

Given no output schema, the description explains return values (evidence packet + comparison) and the fan-out logic. It covers the main workflow but omits exact output format. For a tool of this complexity, 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?

Schema coverage is 100%, but the description adds meaning: it explains that 'market' can be slug, URL, or question text, and describes 'depth' as quick vs thorough with source counts. This enriches the schema definitions.

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

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

The description clearly states the tool's purpose: to research Polymarket bets by pulling Pipeworx data. It specifies inputs (slug, URL, question text) and outputs (evidence packet, market-vs-model comparison). It distinguishes from siblings like 'ask_pipeworx' by focusing solely on Polymarket bets.

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

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

The description explicitly lists use cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. It implies this is superior to discovering packs manually. However, it does not provide explicit when-not-to-use or alternatives, so slightly deduct.

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, so description carries full burden. Discloses return format (paired data + URIs) and specific data fields for each type. Does not mention auth, error conditions, or side effects, but for a read-like operation this is adequate.

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

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

Three sentences, front-loaded with purpose, no unnecessary 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?

Despite no output schema and no annotations, description fully explains purpose, usage, data returned, and efficiency benefit. Covers both entity types adequately.

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 type and values fully, but description adds rich context: for type=company it lists revenue, net income, cash, long-term debt; for type=drug it lists adverse-event reports, FDA approvals, active trials. This significantly augments schema meaning.

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 verb 'compare', resource 'entities', specifies range 2-5, and distinguishes between two entity types with different metrics. Stands out from siblings as a unique comparison tool.

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?

Explains when to use (side-by-side comparison of 2-5 entities) and highlights efficiency by replacing 8-15 sequential calls. Does not explicitly mention when not to use or alternative tools for single entities, but context is strong.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it's a search operation that returns relevant tools based on natural language queries. However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions, which would be helpful for a search tool.

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

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

The description is perfectly concise and front-loaded: two sentences that each earn their place. The first sentence explains what the tool does, and the second provides crucial usage guidance. No wasted words or redundant information.

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

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

Given the tool's moderate complexity (search functionality with 2 parameters) and no output schema, the description is mostly complete. It explains the purpose, usage context, and what it returns. However, it doesn't describe the format of returned results (e.g., list structure, fields included beyond names/descriptions), which would be helpful since there's no output schema.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't elaborate on query formatting or limit implications). This meets the baseline expectation when schema coverage is high.

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

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

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resources ('tool catalog'), distinguishing it from sibling tools that focus on art-related data (get_artist, get_artwork, etc.). It explicitly mentions what it returns ('most relevant tools with names and descriptions').

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

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

The description provides explicit usage guidelines: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context about when to use it (large tool catalog scenarios) and implies when not to use it (smaller catalogs or when you already know the 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?

Discloses return format (pipeworx:// URIs), data sources, and efficiency gain (replaces 10-15 calls). No annotations provided, so description carries burden; lacks error behavior but otherwise 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?

Three sentences, front-loaded overview, efficient listing of contents, and clear alternative guidance. 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, description adequately covers return format, sources, and usage context. Sufficient for agent to understand tool's capabilities and integration.

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 detailed parameter descriptions. Description reinforces but does not add significant new semantic information 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?

Clear verb+resource: retrieves a full entity profile across multiple packs. Distinguishes from siblings like resolve_entity (name prerequisite) and usa_recipient_profile (federal contracts alternative).

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 alternative (usa_recipient_profile for federal contracts) and prerequisite (use resolve_entity if only have a name). Provides clear context without ambiguity.

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, implying a destructive mutation, but doesn't clarify whether deletion is permanent, reversible, requires specific permissions, or has side effects (e.g., affecting related data). This is a significant gap for a mutation tool with zero 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 wasted words. It front-loads the core action ('Delete') and resource ('a stored memory'), making it immediately understandable. Every word earns its place, achieving optimal conciseness.

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 mutation tool with no annotations and no output schema, the description is incomplete. It lacks critical context such as deletion behavior (permanent vs. soft), error handling (e.g., what happens if the key doesn't exist), and response format. The agent must guess these aspects, reducing reliability.

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 'key' documented as 'Memory key to delete'. The description adds minimal value beyond this, merely restating that deletion is 'by key' without explaining key format, scope, or examples. 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 specific action ('Delete') and the target resource ('a stored memory by key'), distinguishing it from sibling tools like 'recall' (likely retrieval) and 'remember' (likely storage). It uses precise language that directly communicates the tool's function without ambiguity.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'recall' (which likely retrieves memories) or other deletion-related tools if they existed. It doesn't mention prerequisites (e.g., needing an existing memory key) or contextual constraints, leaving the agent to infer usage scenarios.

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

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

No annotations are provided, so the description carries the full burden. It discloses the return format (name, birth/death dates, description, artwork IDs), which adds value beyond the input schema, but does not cover behavioral traits like error handling, permissions, or rate limits. The description is consistent but lacks depth for a tool with no annotations.

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

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

The description is front-loaded with the core purpose and efficiently lists return details in a single, well-structured sentence. Every part earns its place without redundancy, making it highly concise and clear.

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

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

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is reasonably complete: it states the action, input requirement, and return values. However, it could improve by mentioning error cases or output structure more explicitly, but it's adequate for this 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 description coverage is 100%, so the schema already documents the 'id' parameter fully. The description adds no additional meaning beyond the schema, such as format examples or constraints, but does not need to compensate. Baseline 3 is appropriate as the schema handles parameter documentation.

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 ('Get an artist record'), resource ('by numeric ID'), and distinguishes from siblings by focusing on artist data rather than artwork or exhibitions. It provides concrete details about what is retrieved, making the purpose unambiguous.

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

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

The description implies usage by specifying 'by numeric ID', suggesting it's for known artist IDs, but does not explicitly state when to use this tool versus alternatives like search_artworks or provide exclusions. Usage is inferred rather than explicitly guided.

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 that the tool returns specific artwork details (title, artist, etc.) and image ID, which adds context beyond the input schema. However, it lacks information on behavioral traits such as error handling, rate limits, authentication needs, or whether it's a read-only operation, which are important for a tool with no annotations.

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

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

The description is appropriately sized and front-loaded, consisting of two concise sentences that directly state the tool's purpose and return values without any wasted words. Every sentence earns its place by providing essential information efficiently.

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

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

Given the tool's low complexity (1 parameter, no nested objects, no output schema) and lack of annotations, the description is fairly complete: it explains what the tool does, what it returns, and the parameter's role. However, it could be more complete by addressing behavioral aspects like error cases or read-only nature, which would help an agent use it correctly in varied contexts.

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

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

The input schema has 100% description coverage for the single parameter 'id', clearly documenting it as the ARTIC artwork ID. The description adds value by specifying that it's for 'a single artwork by its numeric ID' and implies the scope of retrieval, but does not provide additional syntax or format details beyond the schema. With high schema coverage and only one parameter, a baseline above 3 is appropriate as the description complements without redundancy.

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 ('Get full details') and resource ('a single artwork by its numeric ID'), distinguishing it from sibling tools like get_artist, get_exhibitions, and search_artworks which target different resources or operations. It precisely defines what the tool does without being vague or tautological.

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 specifying 'by its numeric ID' and listing return fields, which suggests it's for retrieving detailed information on a known artwork. However, it does not explicitly state when to use this tool versus alternatives like search_artworks (e.g., for unknown IDs or broader queries), nor does it provide exclusions or prerequisites, leaving some ambiguity in context.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return format (title, short description, status) but lacks details on pagination, rate limits, authentication needs, or error handling. For a read operation tool, this leaves significant gaps in understanding its operational 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 front-loaded with the core purpose in the first sentence and adds return details concisely in the second. Both sentences earn their place by providing essential information without redundancy or fluff, making it efficient and well-structured for quick understanding.

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

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

Given the tool's low complexity (one optional parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return data but lacks depth on usage context, behavioral traits, or integration with siblings, leaving room for improvement in guiding the agent effectively.

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

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

The input schema has 100% description coverage, with the 'limit' parameter fully documented in the schema. The description does not add any parameter-specific information beyond what the schema provides, such as default behavior or usage tips, so it meets the baseline for high schema coverage without compensating 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 tool's purpose with specific verb ('List') and resource ('current and recent exhibitions at the Art Institute of Chicago'), and distinguishes it from sibling tools (get_artist, get_artwork, search_artworks) by focusing on exhibitions rather than artists or artworks. The inclusion of return data (title, short description, status) further clarifies what it provides.

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 search_artworks, which might also return exhibition-related data. It mentions the scope ('current and recent') but does not specify timeframes, exclusions, or prerequisites, leaving the agent to infer usage context without explicit direction.

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

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

No annotations are provided, so the description must convey behavior. It discloses the rate limit (5 per day per identifier) and a privacy guideline (omit user's prompt). However, it does not mention whether the feedback is sent immediately, if there is a confirmation response, or if it is processed asynchronously.

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 (3 sentences) and well-structured, with clear enumeration of use cases and constraints. It efficiently conveys all necessary 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?

Given that the tool is a simple feedback submission with 3 parameters (one nested), the description adequately covers purpose, parameter meanings, usage tips, and rate limits. No output schema exists, but for a feedback tool, no return value is expected.

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 the baseline is 3. The description adds value by explaining the enum values for the type field, the message length constraints (1-2 sentences, 2000 chars max), and the optional context object. This goes beyond the schema descriptions.

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

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

The description clearly states the tool sends feedback to the Pipeworx team and enumerates specific use cases (bug reports, feature requests, etc.). This distinctly separates it from sibling tools that handle data retrieval or memory operations.

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

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

The description explicitly lists when to use the tool (bug reports, feature requests, missing data, praise) and provides constraints (do not include verbatim user prompt, rate limit of 5 per day). It does not explicitly state when not to use it, but the use cases are 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, destructiveHint=false. Description adds value by explaining the two detection modes and the monotonicity checking logic, as well as indicating the output includes ranked opportunities. No contradictions with annotations.

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

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

Description is well-organized: overall purpose, then two modes explained in a clear structure, and finally output description. Every sentence contributes value, though it could be slightly more concise. 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?

For a tool with no output schema, the description sufficiently covers inputs (two modes with examples), logic (monotonicity checking), and output (ranked opportunities with reasoning). It does not mention prerequisites or limitations, but given the annotations and simplicity, it 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 coverage is 100% with each parameter already described. The tool description enhances these by providing context on why topic mode is useful and giving concrete examples (e.g., 'Strait of Hormuz traffic returns to normal'). This adds meaning beyond the schema alone.

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 explicitly states 'Find arbitrage opportunities on Polymarket by checking for monotonicity violations' and distinguishes two modes (event and topic). This is a specific verb+resource combination that clearly differentiates the tool's purpose from siblings like 'polymarket_edges' which likely serves a different 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?

Description provides clear guidance on when to use each mode: event mode for a single Polymarket event, topic mode for cross-event scenarios. It explains the limitation of single-event mode (misses May≤June rule) and how topic mode addresses it. However, it does not explicitly compare to sibling tools or state when not to use this 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 indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds substantial behavioral details: internal logic (groups by asset, fetches price history once, computes model probability, ranks by |edge|), return value (top N with suggested trade direction), and data sources (FRED, coinpaprika). 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?

The description is a single, well-structured paragraph that front-loads the core purpose. Every sentence adds value—no fluff. It efficiently conveys purpose, method, and output.

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

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

Despite no output schema, the description explains the return value (top N ranked by edge magnitude with suggested trade direction) and the process. For a tool with 3 parameters and no output schema, this is thorough and leaves no major gaps.

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

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

Schema coverage is 100% with good parameter descriptions. The tool description adds overall context but does not significantly elaborate on parameters beyond what the schema already provides. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price. It specifies the verb 'scan', the resource 'Polymarket markets', and the outcome ranked by edge magnitude. This differentiates it from siblings like polymorph_arbitrage which focuses on arbitrage.

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

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

The description explicitly says it's built for the 'what should I bet on today' question, providing clear context. It doesn't explicitly state when not to use it or name alternatives, but the context implies it's for opportunity discovery rather than in-depth market analysis.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool retrieves or lists memories, including cross-session persistence, but lacks details on error handling (e.g., if key doesn't exist), performance limits, or authentication needs. It adds some context but not rich behavioral traits.

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

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

Two sentences with zero waste: the first states the dual functionality, and the second provides usage context. It is front-loaded with the core purpose and appropriately sized for a simple tool.

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

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

Given the tool's low complexity (1 optional parameter, no output schema, no annotations), the description is nearly complete. It covers purpose, usage, and parameter semantics well, but lacks output details (e.g., format of returned memories) which would be needed for full completeness.

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

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

Schema coverage is 100%, so the schema already documents the optional 'key' parameter. The description adds meaning by explaining the semantic effect of omitting the key ('list all stored memories') and linking it to retrieval of saved context, which compensates beyond the schema's technical description.

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

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

The description clearly states the specific verb ('retrieve') and resource ('previously stored memory'), distinguishing it from siblings like 'remember' (store) and 'forget' (delete). It explicitly mentions retrieving by key or listing all, which establishes its dual functionality.

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

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

It provides explicit guidance on when to use this tool: 'to retrieve context you saved earlier in the session or in previous sessions.' It also specifies when to omit the key ('omit key to list all keys'), offering clear alternatives within the tool itself.

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

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

No annotations provided, so description carries full burden. It discloses parallel fan-out to multiple sources, return format (structured changes, count, URIs), and that only 'company' type is supported. Lacks mention of rate limits or authentication, 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?

Two well-structured sentences, front-loaded with purpose, no redundant information. Every sentence adds value.

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

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

Given 3 params with full schema coverage and no output schema, the description covers entity type, parameter formats, sources, return structure, and use cases. Complete for the tool's complexity.

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

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

Schema coverage is 100% with parameter descriptions, but the description adds significant context: explains accepted formats for 'since' (ISO, relative), typical values ('30d', '1m'), and that 'type' is restricted to 'company'. Also clarifies that 'value' can be ticker or CIK.

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's purpose: retrieving recent changes for an entity since a given time. It specifies supported entity type (company), sources (SEC EDGAR, GDELT, USPTO), and differentiates from siblings like entity_profile by focusing on temporal changes.

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 use cases ('brief me on what happened with X', 'change-monitoring workflows') and explains parameter formats (ISO date, relative times). Does not explicitly state when not to use or list alternatives, but context is clear.

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

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

With no annotations provided, the description carries the full burden and does so effectively by disclosing key behavioral traits: it explains persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours') and the tool's role in session management. It does not cover rate limits or error handling, but provides sufficient context for safe use.

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

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

The description is appropriately sized and front-loaded, with the first sentence stating the core purpose and subsequent sentences adding valuable context without redundancy. Every sentence earns its place by clarifying usage and behavioral details efficiently.

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

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

Given the tool's moderate complexity (storage with persistence rules), no annotations, no output schema, and rich schema coverage, the description is largely complete. It covers purpose, usage, and key behavioral traits, but could benefit from mentioning return values or error cases to be fully comprehensive.

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 adds no additional parameter semantics beyond what the schema provides, such as constraints or usage tips, meeting the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('store a key-value pair') and resource ('in your session memory'), distinguishing it from siblings like 'recall' (retrieval) and 'forget' (deletion). It explicitly mentions what gets stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous and distinct.

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

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

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 state when not to use it or name alternatives. It implies usage for persistence needs but lacks exclusions or direct comparisons to siblings 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 provided, so description carries full burden. It explains inputs (ticker, CIK, name), outputs (ticker, CIK, name, URIs), and efficiency gain. Lacks error or rate limit details, but acceptable for a read-only tool.

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

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

Two sentences with no waste. First sentence states purpose clearly; second sentence provides key details on inputs and outputs.

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

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

Despite no output schema or annotations, description sufficiently covers return values and efficiency. Could mention more about error handling, but overall complete for a simple resolution tool.

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

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

Schema coverage is 100%, and description adds substantial value: clarifies acceptable formats for value (with examples) and that type is 'company' for v1, providing context beyond schema enums.

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?

Describes specific action (resolve entity to canonical IDs), resource (Pipeworx data sources), and distinguishes from siblings by stating it replaces multiple lookup 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?

Implies when to use (single call for entity resolution) and that it replaces multiple lookups, but does not explicitly list alternatives or 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?

With no annotations provided, the description carries the full burden. It discloses the return format ('list of artworks with title, artist, date, medium, and image ID'), which is useful behavioral context. However, it lacks details on permissions, rate limits, or pagination, leaving gaps for a mutation-free but potentially complex search 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 two sentences with zero waste: the first states the purpose and input, the second specifies the output. It is appropriately sized and front-loaded, making every sentence earn 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?

Given the tool's moderate complexity (search with two parameters), no annotations, and no output schema, the description is mostly complete—it covers purpose, usage, and return format. However, it lacks some behavioral details (e.g., error handling or result limits), preventing a perfect score.

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 fully documents both parameters (query and limit). The description adds no additional parameter semantics beyond what the schema provides, such as search syntax or result ordering, meeting the baseline for high coverage.

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

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

The description clearly states the specific action ('Search'), resource ('Art Institute of Chicago collection'), and scope ('by keyword'), distinguishing it from sibling tools like get_artist, get_artwork, and get_exhibitions which retrieve specific entities rather than performing 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 for keyword-based searches of the collection, providing clear context. However, it does not explicitly state when not to use this tool or name alternatives (e.g., using get_artwork for known IDs), which prevents a perfect 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?

No annotations provided, so description carries full burden. It discloses return values (verdict, structured form, actual value with citation) and notes it replaces multiple sequential calls. No mention of destructive actions (assumed read-only). Adds meaningful behavior beyond schema.

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 concise sentences with no filler. First sentence states the core function, second scopes the domain, third lists key outputs. 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 complexity (fact-checking) and lack of output schema, description covers return values and scope adequately. Could mention potential limitations (e.g., only public US companies) or confidence indicators, but overall sufficient.

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

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

Input schema has 100% coverage with a clear description for the only parameter 'claim'. The description reinforces this with examples and context (e.g., 'Apple's FY2024 revenue was $400 billion'). This adds value 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 it fact-checks natural-language claims against authoritative sources, specifically for company-financial claims. It lists the returned verdict types and other outputs, making the purpose precise. The sibling tools are unrelated (e.g., art tools, memory tools), so no ambiguity.

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

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

The description specifies the domain (company-financial claims) and version (v1), giving clear context. It implies when to use this tool (for financial fact-checking) but does not explicitly exclude other claim types or mention alternatives. Still, the context is strong.

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