Rebrickable

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

The description discloses key behaviors: it routes to 2,520 tools, fills arguments, and returns structured answers with stable citation URIs. Annotations confirm read-only and open-world nature, and the description adds context beyond annotations without 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 front-loaded with the key message and uses examples efficiently. It is slightly long but every sentence adds value, earning 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 simple input schema and presence of annotations, the description adequately explains the tool's purpose, routing behavior, and citation format. No output schema exists, so the description sufficiently covers what the agent needs to know.

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?

With only one parameter (question) and 100% schema description coverage, the description adds value by specifying the types of questions appropriate (e.g., 'what is', 'look up'), enhancing the schema's minimal 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 it is a question-routing tool covering many domains (SEC filings, FDA data, etc.) and explicitly says 'PREFER OVER WEB SEARCH', distinguishing itself from general search tools.

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

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

It provides explicit guidance to prefer this tool over web search for factual questions, with example query types and trigger phrases. However, it doesn't explicitly state when not to use it, missing a full exclusion criterion.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false, which the description aligns with by describing it as a research tool. The description adds value by explaining the fan-out behavior ('fans out to the right packs') and the output ('evidence packet plus... market-vs-model comparison'). No contradictions.

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

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

The description is two paragraphs: the first explains functionality, the second provides usage examples. It is concise and front-loaded with the action verb 'Research'. Every sentence earns its place, though it could be slightly tighter without losing clarity.

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 explains the return value ('evidence packet plus a simple market-vs-model comparison') and the fan-out logic. For a tool with two parameters, the description covers what the agent needs to know about inputs, behavior, and outputs.

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 semantic value beyond the schema. For the 'depth' parameter, it explains 'quick = 2-3 evidence sources, thorough = full fan-out.' For 'market', it reiterates the input formats but also clarifies that the tool resolves the market. The added context for depth is valuable.

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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question text) and output (evidence packet plus market-vs-model comparison), distinguishing it from siblings like polymarket_arbitrage and polymarket_edges that focus on different aspects.

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

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

Explicit usage contexts are given: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?"' The description also highlights that this is the core demo product where agents convert better. However, it does not explicitly list when not to use it or mention alternatives.

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

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

Annotations already declare the tool as read-only and non-destructive, but the description adds no additional behavioral context (e.g., what 'colors' refers to, whether results are cached, etc.). It does not contradict annotations but adds no value beyond them.

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

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

The description is highly concise with a single sentence that front-loads the purpose. However, it is so minimal that it could benefit from slightly more context without losing 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?

Given the low complexity (no parameters, no output schema) and rich annotations, the description is minimally adequate. However, it does not clarify what 'colors' means (e.g., names, codes) or the scope of the list, leaving room for ambiguity.

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 zero parameters, so schema description coverage is 100%. Per the rubric, the baseline is 4 for no parameters. The description does not add anything about parameters, but none are needed.

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 'List colors,' which is a specific verb ('list') and resource ('colors'). It distinctly identifies the tool's function, and given the sibling tools, it is unique and 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 no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or context. It lacks any explicit usage direction beyond the bare action.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context on data sources (SEC EDGAR, FAERS, etc.), return format (paired data + citation URIs), and efficiency gain (replaces 8-15 calls), going beyond the annotations without contradicting them.

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

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

The description is five sentences, front-loaded with the main purpose, and each sentence adds information. No redundant or vague language; it uses clear examples and synonyms.

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 two-type complexity and no output schema, the description covers purpose, type-specific behavior, data sources, and output mentions. It could specify that results are tabular, but the agent can infer from 'tables/rankings'. Sufficient for decision-making.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds examples (tickers/CIKs for company, drug names) and clarifies the format for the 'values' array, which is helpful beyond the schema.

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

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

The description clearly states the tool compares 2-5 companies or drugs side by side. It provides specific verb 'compare' and resource 'entities', and distinguishes from sibling tools like entity_profile by focusing on multi-entity comparison.

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

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

The description explicitly says 'Use when a user says ...' and lists example queries. It explains what each type does and notes it replaces many sequential calls, but does not explicitly state when not to use it or name alternative tools.

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

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

Annotations already indicate read-only and non-destructive nature. Description adds that it returns top-N relevant tools with names and descriptions, and clarifies it's for discovering options, not obtaining final answers.

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 purpose and usage, followed by examples. It is concise but could be slightly more succinct; however, it remains clear and effective.

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

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

Without an output schema, the description adequately explains what is returned (names and descriptions of top-N tools) and when to call it. Complete for a tool discovery function.

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

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

Schema coverage is 100% with descriptions for both parameters. The description repeats the default and max limit, but adds no new meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool finds tools based on a description of data or task, and lists many examples. It distinguishes from siblings by focusing on tool discovery rather than specific data retrieval.

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 instructs to call this tool first when many tools are available, and provides contexts like browsing, searching, or looking up tools. Contrasts with getting a single answer.

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 substantial behavioral context: it returns recent SEC filings, fundamentals, patents, news, and LEI with pipeworx:// citation URIs. This goes beyond the annotations to explain what data is aggregated. No contradictions.

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

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

The description is a single efficient paragraph that front-loads the core purpose and usage cues, followed by a concise list of returned data types and a parameter note. Every sentence earns its place, though it could be slightly more structured (e.g., bullet points) for readability.

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

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

Given the complexity of the tool (aggregates multiple data sources) and the absence of an output schema, the description does a good job summarizing the returned information (filings, fundamentals, patents, news, LEI). It covers the key domains and mentions citation URIs. However, it doesn't mention any limits on the number of results or pagination.

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

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

Schema coverage is 100% with descriptions. The description adds meaning by clarifying that 'type' is limited to 'company' and that 'value' accepts ticker or zero-padded CIK, explicitly excluding names. It also ties the parameters to usage: if you have a name, resolve first. This adds value beyond the schema.

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

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

The description clearly states the purpose: 'Get everything about a company in one call.' It uses a specific verb ('Get') and resource ('everything about a company'), and distinguishes itself from siblings like 'compare_entities' and 'resolve_entity' by being a compound call aggregating multiple data sources.

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 lists concrete query patterns that trigger this tool (e.g., 'tell me about X', 'research Microsoft'). It also provides a when-not-to-use: names not supported, recommending 'resolve_entity' first if only a name is available. This helps the agent choose correctly.

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 confirms the destructive nature ('Delete') and adds behavioral context like 'clear sensitive data the agent saved earlier'. Annotations already set destructiveHint=true, so the description adds extra clarity.

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 short, front-loaded sentences with no redundant information. Every sentence serves a purpose: stating the action and providing usage guidance.

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 simplicity (1 parameter, no output schema), the description sufficiently covers purpose, when to use, and pairing with related tools. No gaps remain.

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 a clear description for the 'key' parameter. The description only says 'by key', adding no new semantic details beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description explicitly states 'Delete a previously stored memory by key' using a specific verb and resource. It clearly distinguishes from siblings like 'remember' and 'recall' by focusing on deletion.

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 contexts: 'when context is stale, the task is done, or you want to clear sensitive data'. It also suggests pairing with 'remember' and 'recall', but does not explicitly state when not to use it.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description does not need to restate safety. However, it offers no additional behavioral context such as response structure or error conditions.

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

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

The description is extremely concise (two words) but at the cost of clarity. It is not front-loaded with useful information; instead it is overly terse and fails to earn its brevity.

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 tool with one parameter and no output schema, the description should at minimum state what the tool returns. It is completely inadequate, leaving the agent without critical usage details.

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

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

Schema description coverage is 0%, and the description 'Single part.' provides no explanation of the 'part_num' parameter or its expected format. The description adds zero meaning beyond the schema itself.

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

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

The description 'Single part.' is too vague. It does not specify the action (e.g., get, retrieve) and merely states a noun phrase. The tool's purpose is implied by its name and sibling 'parts' but not clearly conveyed.

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

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

No guidance is provided on when to use this tool versus alternatives like 'parts' or 'set_parts'. The description lacks context for appropriate use cases 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?

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description adds no new behavioral traits. It doesn't mention pagination, ordering, or result limits. Acceptable given annotations, but lacks extra 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?

Extremely short at two words, sacrificing essential information. Not a model of conciseness—it's under-specified.

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 tool has 6 parameters, no output schema, and moderate complexity (search with filters and pagination). The description provides none of the context needed for correct invocation, making it severely incomplete.

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 6 parameters with zero descriptions in schema or description. The description 'Search parts' provides no hints about 'query', 'page', 'page_size', 'color_id', 'ordering', or 'part_cat_id'. Complete failure to add meaning beyond the schema.

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

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

Description 'Search parts' is clear as a search operation, but lacks specificity on what parts (e.g., LEGO parts) and does not differentiate from sibling tools like 'part' which likely retrieves a single part. It meets the minimum but could be more precise.

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

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

No guidance on when to use this tool versus alternatives like 'part', 'set_parts', or 'colors'. The agent has no context for selecting this tool over siblings in a complex 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?

Annotations have readOnlyHint=false and destructiveHint=false; the description adds behavioral context: it is free, does not count against tool-call quota, rate-limited to 5 per identifier per day, and the team reads digests daily. No contradiction with annotations.

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

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

The description is a single, well-structured paragraph that is front-loaded with the core action and purpose. Every sentence adds value, and it is appropriately concise for the complexity of the 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?

The description covers all aspects needed: feedback types, message guidelines, context usage, rate limits, quota, and team impact. Given no output schema, it adequately explains what happens. No 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%, but the description adds significant meaning beyond the schema: explains each enum value with examples, advises on message content (be specific, 1-2 sentences, 2000 chars max), and clarifies optional context fields. This extra detail justifies a 4.

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

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

The description clearly states the tool's purpose: to send feedback about bugs, features, data gaps, or praise to the Pipeworx team. It uses specific verbs and resources, and distinguishes from sibling tools by focusing on reporting issues rather than querying data.

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

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

Explicitly tells when to use the tool for each feedback type (bug, feature/data_gap, praise) and provides a clear 'don't' (do not paste end-user prompt). Also mentions rate limit and quota, giving complete guidance.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds behavioral details: it walks child markets, searches across events, checks monotonicity, and returns ranked opportunities with trade direction and reasoning. No contradiction with annotations.

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

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

The description is a single paragraph that front-loads the purpose and mode details. It is informative but slightly lengthy; however, every sentence contributes value. Minor improvement could be breaking into bullet points, but current structure is 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 no output schema, the description adequately describes return value (ranked opportunities with trade direction and reasoning). With only two optional parameters and full schema coverage, the description covers all necessary context for an agent to use the tool correctly.

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

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

With 100% schema description coverage, the baseline is 3. However, the description adds significant meaning beyond the schema: it explains that the 'event' parameter processes child markets and 'topic' parameter performs cross-event search, including an example. This provides context the schema alone does not.

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

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

The description clearly states the tool's purpose: find arbitrage opportunities by checking monotonicity violations. It specifies two distinct modes (event and topic), which distinguishes it from sibling tools like polymarket_edges and bet_research.

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 explains when to use each mode: event mode for a single event's child markets, topic mode for cross-event comparisons. It gives an example where topic mode catches cases that event mode misses, providing clear exclusion guidance.

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

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

The description extensively details behavioral traits beyond annotations: V1 scope, lognormal model from FRED + coinpaprika, scanning top markets, grouping by asset, fetching price history once, computing model probability, ranking by edge magnitude, and returning top N with suggested trade direction. Annotations already indicate read-only and non-destructive; description aligns and adds depth.

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 well-structured sentences, each serving a purpose: purpose and domain, process details, and intended use case. No redundancy, and key information is front-loaded.

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

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

The description explains the algorithm, data sources, and output (ranked list with edge magnitude and direction). It mentions V1 which hints at limitations. Could be slightly improved by noting any assumptions or conditions for the model, but overall complete for a read-only tool with simple parameters.

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 does not add significant meaning beyond the schema descriptions for limit, window, and min_edge_pp. Mentions 'returns top N' which maps to limit, but no additional semantics for parameters.

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

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

The description clearly states it scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price, with specific mention of crypto-price bets. It distinguishes itself from sibling tools like polymarket_arbitrage and bet_research by focusing on edge detection for betting opportunities.

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 it is built for the 'what should I bet on today' question, indicating context of use. It does not provide explicit when-not-to-use guidance or mention alternatives, but the context is clear and implies it is for crypto-price bets.

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?

Adds scoping detail and integration with remember/forget beyond annotations. No contradiction. Annotations already provide readOnlyHint and destructiveHint.

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 front-loaded with action. No superfluous 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 single optional parameter and no output schema, description fully covers input, behavior, and sibling relationships.

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% coverage. Description adds examples of key values and clarifies behavior when omitted, 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?

Description states 'Retrieve a value previously saved via remember, or list all saved keys'. Clear verb and resource, distinguishes from siblings like remember and forget.

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 advises use for looking up stored context and avoids re-deriving. Mentions scoping and pairing with siblings. Missing explicit when-not-to-use.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds value by detailing the parallel fan-out behavior and the return structure (structured changes + count + URIs), going beyond what annotations provide.

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

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

Three sentences, front-loaded with purpose, then usage examples, then technical details. No wasted words; every sentence earns its place.

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

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

Despite no output schema, the description explains return values (structured changes + total_changes count + citation URIs). All 3 required parameters are covered, and the fan-out behavior is fully disclosed.

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 practical guidance: 'since accepts ISO date or relative shorthand... Use '30d' or '1m' for typical monitoring.' This enhances the schema's generic 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 tool's purpose: 'What's new with a company in the last N days/months?' using specific verbs like 'fan out' and listing example queries. However, it does not explicitly differentiate from siblings like 'entity_profile' or 'compare_entities'.

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

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

The description provides explicit example queries ('what's happening with X?', 'any updates on Y?') and explains the tool fans out to multiple sources (SEC EDGAR, GDELT, USPTO). It lacks explicit when-not-to-use guidance but gives clear 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?

The description adds behavioral details beyond annotations: key-value scoping by identifier, persistence differences for authenticated users vs anonymous, and 24-hour retention. Annotations already indicate write and non-destructive, but description enriches 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?

Three efficient sentences with no wasted words. Each sentence serves a distinct purpose: purpose, usage guidance, storage details. Front-loaded with 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?

For a simple save tool with no output schema, the description covers purpose, storage behavior, and pairing with recall/forget. Lacks return value info, but not critical for this operation. Context is 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?

Schema coverage is 100%, baseline 3. The description adds useful examples for key (e.g., 'subject_property', 'target_ticker') and explains value as any text, providing more context than schema descriptions 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?

The description clearly states the tool saves data for reuse, with specific verb 'Save' and resource 'data'. It distinguishes from sibling tools 'recall' and 'forget' by explicitly pairing with them.

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 when-to-use guidance ('when you discover something worth carrying forward') and mentions alternatives ('Pair with recall to retrieve later, forget to delete'). It lacks explicit when-not-to-use or differentiation from other tools like 'set', but the guidance is clear.

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

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

Annotations already mark it as read-only and non-destructive. The description adds value by explaining the output includes IDs and pipeworx:// citation URIs, and states it replaces multiple lookup calls—disclosing composite behavior. No contradictions with annotations.

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

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

The description is concise (5 sentences) and front-loaded with the primary purpose. Every sentence adds value—examples, ID system listing, usage instruction, efficiency claim. No fluff or redundancy.

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

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

Given the tool's simplicity (2 params, no output schema), the description is nearly complete. It explains input semantics, usage context, and output content (IDs + URIs). A brief note on output format (e.g., JSON structure) would push it to 5, but current coverage is adequate for an AI agent.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning beyond the schema by providing examples of acceptable values for the 'value' parameter (e.g., ticker, CIK, name for companies; brand or generic name for drugs) and clarifying the 'type' enum with examples.

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

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

The description clearly states the tool's purpose: looking up canonical/official identifiers (CIK, ticker, RxCUI, LEI) for companies or drugs. It gives concrete examples (Apple, Ozempic) and explicitly names the ID systems, distinguishing it from sibling tools by noting it replaces 2-3 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?

The description explicitly instructs to use this tool BEFORE other tools that need official identifiers, providing clear usage context. It does not mention when not to use it or list specific alternative tools, but the instruction is sufficiently directive for an AI agent.

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

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

Annotations indicate read-only, non-destructive behavior. The description adds no additional behavioral information (e.g., return format, permissions, or side effects), relying entirely on 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?

Extremely short but under-specified. While front-loaded, the two-word description fails to convey meaningful information, making it ineffective rather than concise.

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

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

Given the availability of multiple related sibling tools ('sets', 'set_parts'), the description is insufficient to understand the tool's purpose or output. No output schema further limits 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?

Input schema has 0% description coverage for the single parameter 'set_num'. The description does not compensate by explaining the expected format or purpose of the parameter.

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

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

Description 'Single set.' is a tautology restating the name without specifying an action. It does not clarify whether the tool retrieves, updates, or describes a set, and fails to distinguish from sibling tool 'sets'.

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

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

No guidance on when to use this tool versus alternatives like 'sets', 'set_parts', or 'set_minifigs'. The description lacks any usage context or prerequisites.

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

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

Annotations indicate readOnlyHint=true, destructiveHint=false, and openWorldHint=true, but the description adds no further behavioral context, such as error handling or result format.

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

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

Extremely short but under-specified. The single sentence fails to convey the tool's action or constraints, making it insufficiently informative.

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 the tool's simplicity (1 parameter, no output schema), the description omits essential details like return type, expected behavior, or error conditions, leaving the agent unable to use it 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?

With 0% schema description coverage, the description should clarify the parameter, but only says 'Minifigs in a set,' implying set_num is a set identifier without specifying format or accepted values.

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 is a noun phrase ('Minifigs in a set') that doesn't specify the verb or action. Given the name and sibling tools, it likely retrieves minifigs for a set, but the description is ambiguous and fails to clearly state the purpose.

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

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

No guidance on when to use this tool versus alternatives like 'set_parts' or 'sets'. No context on prerequisites or typical use cases.

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

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

The description does not add behavioral information beyond annotations (readOnlyHint, openWorldHint, destructiveHint). It does not contradict them, but also does not provide additional context like data freshness or pagination.

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

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

The description is extremely short (4 words) but is under-specified. It lacks essential information, making it more of a tautology than a concise, informative statement.

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

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

Given the simple tool with one parameter and no output schema, the description should clarify what is returned (e.g., list of part IDs or names). It fails to provide minimal context for an agent to understand the output.

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

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

With 0% schema description coverage, the description should explain the sole required parameter 'set_num', but it only says 'Parts in a set.' without any details on format or usage.

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 'Parts in a set.' is a noun phrase that vaguely indicates the tool lists parts for a given set, but it lacks a verb and does not distinguish it from siblings like 'parts' or 'sets'.

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

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

No guidance is provided on when to use this tool versus alternatives such as 'parts' or 'set_minifigs'. There is no mention of prerequisites or 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds no additional behavioral context, such as pagination behavior or result filtering details.

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

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

The description is extremely short (2 words), but this is under-specification rather than conciseness. It does not front-load useful information and wastes no words because it contains almost no content.

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 9 parameters, no parameter descriptions, and no output schema, the description is completely inadequate. It does not provide enough context for an AI agent to use the tool correctly.

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

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

Schema description coverage is 0%, and the description does not explain any of the 9 parameters. The description should clarify parameter usage but fails to do so.

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 'Search sets' is a vague verb+noun. It does not specify what kind of sets are being searched, nor does it differentiate from siblings like 'set' (singular) or 'set_parts'.

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

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

No usage guidance is provided. The description lacks any indication of when to use this tool versus alternatives, and it does not mention prerequisites 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?

Annotations provide readOnlyHint=true, openWorldHint=true, destructiveHint=false, but the description adds no behavioral insight beyond what annotations already state. Behavior is implied as a read-only list but no detail on side effects or limitations.

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?

Extremely concise with no wasted words. Front-loaded and easy to parse. However, it lacks context that could be added without significant bloat.

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 listing tool with no parameters, the description is minimally adequate. However, given the sibling tools like 'colors' and 'parts', the description could benefit from mentioning what domain the themes belong to (e.g., LEGO).

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?

No parameters exist, and schema coverage is 100%. The description 'List themes' adds no meaning beyond the schema. Baseline score of 3 is appropriate as per guidelines.

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 'List themes' indicates the tool lists themes but lacks specificity about what themes (e.g., LEGO themes?). It doesn't distinguish from sibling tools like 'colors' or 'parts'. The purpose is minimally clear but vague.

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

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

No guidance on when to use this tool versus alternatives. No context about prerequisites or typical use cases.

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

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

Annotations indicate read-only, non-destructive, and open-world. The description adds context: it uses SEC EDGAR + XBRL data, returns a structured verdict with citation and delta, and replaces several sequential calls. 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?

Two concise sentences with no wasted words. The purpose is front-loaded, followed by usage guidance and a brief on return format. Every sentence adds value.

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

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

For a tool with one parameter, no output schema, and clear annotations, the description covers purpose, scope, data sources, return types, and justification. Slightly lacking on potential limitations or edge cases, but 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?

The single parameter 'claim' has full schema description coverage (100%). The description adds example claims but does not provide additional meaning beyond what the schema already offers. Baseline 3 applies.

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 fact-check natural-language factual claims against authoritative sources, specifically for company-financial claims. It uses a specific verb ('Fact-check, verify, validate, or confirm/refute') and resource ('natural-language factual claim'), distinguishing it from sibling tools like 'bet_research' or 'compare_entities'.

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

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

The description explicitly says 'Use when an agent needs to check whether something a user said is true' and provides example queries. It also notes the scope (company-financial claims for US public companies). It does not mention when not to use or list alternatives, but the context is clear.

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