Cfpb

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

The description discloses that the tool picks the best data source and fills arguments, indicating autonomous behavior. No annotations provided, so the description carries the full burden; it could mention that the tool may call other tools internally or have latency.

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, well-structured, and front-loaded with the purpose. Every sentence adds value, including examples.

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 could mention the format of the answer or potential limitations. However, it is sufficient for the tool's simplicity.

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 single 'question' parameter. The description adds value by explaining how to use it (plain English) and providing examples, going beyond 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 the tool answers questions in plain English by selecting the best data source, filling arguments, and returning results. This distinguishes it from sibling tools that are specific to CFPB complaints 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 says 'No need to browse tools or learn schemas' and provides examples, making it clear when to use this tool (for any question) and when not to (it's a one-stop answer tool).

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds internal behavior details (resolves market, classifies bet, fans out to packs) without contradicting annotations.

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

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

Three sentences front-load the main purpose, though sentences are somewhat long and could be more concise. Overall well-structured.

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

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

Despite no output schema, description fully explains return values ('evidence packet plus market-vs-model comparison') and covers inputs, behavior, classification, and use cases.

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

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

Parameter descriptions in schema already cover market and depth fully (100% coverage). Description restates input formats for market but does not add meaningful new semantics 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 uses specific verb 'Research' with clear resource 'Polymarket bet', lists input formats (slug, URL, question text), and distinguishes itself from siblings by noting it is the 'core demo product' that fans out to packs, unlike lower-level tools like ask_pipeworx.

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 ('should I bet on X?', 'what does the data say...', 'is there edge...?'), but lacks explicit when-not-to-use or comparison to 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?

No annotations are present, so the description carries full burden. It discloses the tool is read-only ("Get") and returns sorted data, but does not mention potential rate limits, data freshness, or whether the company parameter is case-sensitive or requires exact matching. The description adds value but lacks depth on 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?

The description is a single sentence of 17 words, concise and to the point. It front-loads the purpose and result type, though it could mention the sorting behavior earlier. No wasted words.

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

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

Given the tool has 2 params, no output schema, and no annotations, the description provides a minimal functional overview. It explains input (company) and output (complaint details, response info) but omits pagination, error handling, and the sorting detail (though it does say 'sorted by newest first'). It is adequate but not thorough.

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 baseline is 3. The description does not add parameter-level detail beyond the schema. It mentions 'company' implicitly but does not clarify that company names should be uppercase as shown in the schema example. The limit parameter is not discussed in the 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 retrieves consumer complaints for a specific company, sorted newest first, and returns details and company response information. This distinguishes it from siblings like cfpb_search_complaints (which likely allows broader search) and cfpb_get_complaint (probably a single complaint lookup).

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 does not provide when to use this tool vs alternatives, nor does it mention when not to use it. It implies usage for company-specific complaints but lacks guidance on choosing between this, cfpb_search_complaints, or cfpb_top_companies.

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

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

No annotations are provided, so the description carries full burden. It clearly indicates this is a read operation (no side effects) and requires a complaint ID. However, it doesn't mention rate limits, error conditions, or data freshness.

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, concise sentence that conveys all essential information with no wasted words. It front-loads the action and resource.

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 is simple (one parameter, no output schema), the description adequately covers purpose and usage. It could mention the return format implicitly, but the output schema is absent, so some completeness is lost.

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

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

The schema already has 100% description coverage for the single parameter, so baseline is 3. The description adds context by specifying that the ID is a 'CFPB complaint ID number', reinforcing the parameter's 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?

The description uses a specific verb 'Get' and clearly identifies the resource: 'full details for a single consumer complaint'. It uniquely distinguishes this tool from siblings like cfpb_search_complaints and cfpb_company_complaints by emphasizing a single complaint identified by ID.

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

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

The description implies usage when you need full details of one specific complaint, contrasting with search tools. However, it doesn't explicitly say when not to use it or mention alternatives for batch retrieval.

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

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

No annotations are provided, so the description must carry the full burden. It correctly indicates this is a read operation (getting counts) with optional filtering, but does not disclose return format, pagination, rate limits, or any side effects. The description is adequate but lacks detail for a non-annotated 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 a single sentence that is concise and front-loaded with the core purpose. It mentions optional filters efficiently. It earns its place without redundancy, though it could be slightly more structured (e.g., listing parameters explicitly).

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

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

Given no output schema and no annotations, the description is minimally viable for a tool with 3 optional parameters. It states the purpose and filtering options, but lacks details on return structure, pagination, or use cases. It is complete enough for a basic understanding but not 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%, meaning the schema already describes each parameter well (company name, start/end date format). The description adds context by stating 'optional' filtering and grouping by product category, but does not elaborate on parameter constraints beyond what the schema provides. 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 it 'Get[s] complaint counts broken down by product category', which is a specific verb and resource. It distinguishes itself from sibling tools like cfpb_get_complaint (retrieves individual complaints) and cfpb_search_complaints (searches complaints), but does not explicitly differentiate from cfpb_top_companies, which may overlap in providing aggregated counts.

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 mentions optional filters ('by company and/or date range'), implying when to use them, but does not provide guidance on when NOT to use this tool or alternatives. No sibling differentiation or usage constraints are given.

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?

There are no annotations provided, so the description carries full burden. It describes the tool as a search that returns specific data, implying it is read-only and non-destructive, which is correct. It does not disclose any behavioral traits like rate limits, pagination behavior, or whether it returns raw or processed data, but it is adequate given the straightforward nature.

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 concise sentences. The first sentence states the action and key filters. The second sentence clarifies the return values. 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 search tool with 6 parameters all documented in the schema and no output schema, the description provides enough context on what it does and what it returns. It could mention that results are paginated (limit parameter is implied) or that start_date/end_date are required together, but overall it is fairly complete.

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

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

The input schema has 100% coverage, with all parameters having descriptions. The tool description does not add new meaning beyond listing filter types, but it does summarize the filters (keyword, company, product, date range) in a more accessible way. Given high schema coverage, 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 it searches a specific database (CFPB consumer complaint database) with a specific verb 'Search', and lists the resources returned (complaint narratives, company responses, resolution status). It also distinguishes itself from sibling tools like cfpb_get_complaint (single complaint) and cfpb_company_complaints (company-specific) by being a general search 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?

The description lists filter options (keyword, company, product, date range), which gives context on when to use this tool. However, it does not explicitly state when not to use it or point to alternative sibling tools for more specific queries, so it misses some 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?

No annotations are provided, so the description carries the full burden. It states it returns the top companies in a date range, implying a read-only, aggregated query. However, it does not disclose behavioral traits such as rate limits, pagination, or whether the results are sorted by complaint count. The description adds context beyond the schema but lacks 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 two sentences long, with the purpose front-loaded. It is concise and avoids unnecessary detail, though it could be slightly more specific about the output nature without becoming verbose.

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

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

Given the tool has 4 parameters, no output schema, and no annotations, the description is adequate but not complete. It explains the basic purpose and usage context (identifying top-complaint companies), but does not describe the return format (e.g., list of company names with counts) or behavior like whether the limit parameter affects 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?

The input schema has 100% description coverage, so the baseline is 3. The description adds no additional meaning beyond what the schema already provides for parameters. It does not explain the format of start_date/end_date or provide examples for product filter, relying solely on 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 it retrieves companies with the most consumer complaints in a date range, specifying the resource ('companies') and action ('get the companies with the most consumer complaints'). It distinguishes from siblings like cfpb_search_complaints (which likely searches individual complaints) and cfpb_company_complaints (which may be per-company details), but does not explicitly contrast 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 mentions it is useful for identifying companies with the most complaints, implying a top-N ranking use case. However, it does not provide explicit guidance on when to use this tool versus alternatives like cfpb_product_breakdown or cfpb_company_complaints, nor does it mention prerequisites or limitations.

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 the burden. It discloses data sources (SEC EDGAR, FDA reports) and return type (paired data + URIs). However, it omits potential constraints like data freshness, rate limits, or authentication requirements.

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, front-loaded with purpose, no superfluous words. Each sentence adds necessary context.

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 lack of output schema, the description explains return format (paired data + URIs) and expected fields per type. Could benefit from stating the response structure is JSON, but is otherwise sufficient for an agent to use the 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%, but the description adds value by explaining what data is returned per type (revenue, net income for company; adverse event counts for drug), which aids the agent in selecting appropriate 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 compares 2–5 entities side by side in one call, specifying two distinct types (company, drug) with concrete data fields. It distinguishes itself from sibling tools which focus on complaints, memory, etc.

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

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

Explicitly says when to use: for comparing entities. Mentions efficiency gains (replacing 8–15 sequential calls). While it doesn't state when not to use, the context is clear and sibling tools are unrelated.

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 states the tool returns 'the most relevant tools with names and descriptions,' but doesn't disclose whether it modifies state or requires special permissions. Since it's a search tool, it's likely read-only, but this is not explicitly stated.

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, each providing essential information: what it does, what it returns, and when to use it. No unnecessary words, and the key action 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?

Given that this is a simple search tool with no output schema and only two parameters, the description covers the main aspects: purpose, input format, and usage context. It could mention if results are ranked or if there are any limitations, but overall it's 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 description coverage is 100%, so the baseline is 3. The description adds value by explaining that 'limit' controls the maximum number of tools (default 20, max 50) and 'query' expects a natural language description, which enhances the schema's brief descriptions.

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

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

The description clearly states the tool's purpose: 'Search the Pipeworx tool catalog by describing what you need.' It specifies the verb 'search' and the resource 'tool catalog', and differentiates from siblings by mentioning it returns tool names and descriptions for selection.

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

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

The description provides explicit guidance: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This tells the agent when to use it and implies it should be used before other tools.

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

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

With no annotations, the description carries the full burden. It discloses that the tool returns pipeworx:// citation URIs and replaces multiple sequential calls, but does not mention any side effects or potential delays. However, it is reasonably transparent for a read 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?

Every sentence adds value: main purpose, detailed data sources, return format, efficiency gain, and explicit alternative. No redundant words, well-structured and front-loaded.

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

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

The description covers what the tool does, what data it returns, and what to avoid. It mentions an alternative for contracts, which covers a likely use case. Given no output schema, the description still provides enough context to understand the tool's output.

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

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

Schema coverage is 100%, so baseline is 3. The description adds extra meaning: specifies that type only supports 'company' (though schema enum already does), and elaborates that value can be ticker or CIK, and warns against using names. This provides context 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 it provides a full entity profile, lists specific data sources (SEC filings, XBRL, patents, news, LEI), and distinguishes from alternatives by mentioning usa_recipient_profile for federal contracts. The verb 'profile' and resource 'entity' are specific.

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

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

Explicitly states when to use: for a full profile across packs. Provides an explicit alternative for federal contracts and implies name resolution via resolve_entity. This gives clear guidance on 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?

No annotations are provided, so the description must cover behavioral aspects. It does not mention idempotency (e.g., deleting a non-existent key), error behavior, or authorization needs.

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 sentence with no wasted words. It is front-loaded with the action and resource.

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 (one required parameter, no output schema, no annotations), the description is adequate but could be improved by noting behavior for missing keys or idempotency.

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

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

The schema covers the single parameter with 100% description coverage. The description does not add additional semantic value 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 clearly states the action ('Delete') and the resource ('a stored memory by key'). It distinguishes the tool from siblings like 'recall' and 'remember' 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?

No guidance on when to use this tool versus alternatives like 'remember' (create) or 'recall' (retrieve). The description does not mention any prerequisites or restrictions.

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

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

With no annotations, the description discloses key behaviors: a rate limit of 5 messages per identifier per day and a privacy constraint (omit user prompts). Adequate for a simple feedback 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?

Three sentences, each serving a distinct purpose: purpose, usage guidance, and rate limit. No wasted words. Information is front-loaded.

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

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

For a feedback submission tool, the description covers what feedback to include, how to describe it, and constraints. No output schema is needed as the tool is one-way. Complete and self-contained.

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 description adds minimal extra meaning. It suggests a typical message length (1-2 sentences) and clarifies purpose of the 'type' enum, but this is already in the schema. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: sending feedback to the Pipeworx team. It enumerates specific use cases (bug reports, feature requests, missing data, praise) and is distinct from all sibling tools, which focus on data retrieval or memory.

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 when to use the tool and provides behavioral guidance (describe what you tried in terms of Pipeworx tools/data, do not include end-user prompt). Mentions rate limiting. Lacks explicit 'when not to use' 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?

The description details the algorithm: walks child markets, extracts dates/thresholds, sorts, and reports violations. Annotations already mark it as read-only and non-destructive, so the description adds rich behavioral context 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 about 6 sentences, covering purpose, logic, and output. It is front-loaded with the main purpose. Slightly longer than the ideal two-sentence example but still 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 simple input (one parameter) and no output schema, the description adequately explains the output format and algorithm. It lacks explicit handling of edge cases like no arbitrage found or invalid events, but overall is sufficient for an AI agent to understand usage.

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 parameter 'event' is fully documented in the schema. The description echoes that information without adding new semantics, so 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: 'Find arbitrage opportunities within a Polymarket event by checking for monotonicity violations.' It uses a specific verb ('find') and resource ('arbitrage opportunities'), and explains the underlying concept, distinguishing it from generic 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?

The description provides clear context for when to use the tool (when an event has multiple by-date markets) but does not explicitly mention alternatives or when not to use it. Given the sibling tools exist, the lack of exclusion criteria prevents a top 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?

Annotations already declare readOnlyHint and openWorldHint. The description adds behavioral details like fetching price history once and computing model probability, which enriches the transparency beyond annotations.

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

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

The description is a single dense paragraph that effectively front-loads the main purpose. It could be slightly more structured (e.g., bullets) but remains informative without superfluous 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?

Even without an output schema, the description clearly states what is returned (top N ranked by edge magnitude with suggested trade direction), covering the return value adequately for an analysis 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% with clear descriptions. The tool description reinforces parameter roles but does not add significant new semantic information 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 specifies the verb 'scan' and the resource 'highest-volume Polymarket markets', and explains how it ranks by edge magnitude. It distinguishes from siblings like 'polymarket_arbitrage' by focusing on opportunity discovery based on model disagreement.

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 the tool is built for 'what should I bet on today' and helps avoid manual paging through markets, giving clear usage context. However, it does not explicitly describe when not to use it or suggest 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?

The description discloses the dual behavior (retrieve by key vs list all) and that memories persist across sessions. No annotations are provided, so the description carries the burden; it adequately covers the basic behaviors but doesn't detail edge cases (e.g., non-existent key) or performance.

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 that front-load the core functionality and add usage context. Every word is necessary; no redundancy.

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

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

Given the simple tool (one optional param, no output schema), the description is complete enough. It explains retrieval and listing behaviors and cross-session persistence. Could mention the output format briefly, but not essential.

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

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

The input schema has 100% coverage with a clear description for the 'key' parameter. The description adds the nuance that omitting the key lists all memories, which is not in the schema. Baseline 3 is appropriate as schema already covers the parameter well.

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

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

The description clearly states the tool retrieves memories by key or lists all when key is omitted. It distinguishes from siblings like 'remember' (store) and 'forget' (delete), and uses specific verbs 'retrieve' and 'list' with the resource 'memory'.

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 it (to retrieve context saved earlier) and when to omit key (to list all). It does not mention when not to use it or compare directly with siblings, 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.

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

With no annotations, the description discloses the fan-out behavior across multiple data sources (SEC, GDELT, USPTO) and the return format (structured changes, count, URIs). It does not mention potential latency or rate limits, but covers key behavioral aspects.

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 very concise: three sentences that front-load the core purpose and then add detail. Every sentence serves a purpose without redundancy.

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

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

Given the tool's complexity (fan-out to multiple sources) and lack of output schema, the description provides sufficient context: explanation of types, supported formats, and return structure. It could mention error handling or limits, but is largely complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by specifying acceptable formats for 'since' (ISO date, relative) and recommending default values ('30d', '1m'). This enhances parameter understanding beyond schema.

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

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

The description clearly states the tool's function: retrieving recent changes about an entity since a given time. It uses specific verbs and resources, and distinguishes itself 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?

The description provides explicit use cases ('brief me on what happened with X', change-monitoring workflows). However, it does not explicitly mention when not to use this tool or suggest alternatives, which is acceptable given diverse siblings.

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

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

No annotations provided, so description carries full burden. It discloses behavioral traits: key-value storage, session memory, and persistence duration (persistent for authenticated users, 24 hours for anonymous). This is sufficient for a simple store operation.

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

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

Three sentences with no waste. First sentence defines action, second explains usage context, third adds behavioral nuance about persistence. Front-loaded with core purpose.

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

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

Given low complexity (2 string params, no output schema), the description is complete. It explains storage mechanism, usage, and persistence without requiring additional return value 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 100% with clear examples for key and value. The description adds context about what values can store (findings, addresses, preferences) but does not significantly augment the schema's meaning beyond 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 explicitly states the tool stores a key-value pair in session memory, with specific use cases like saving intermediate findings, user preferences, or context. It clearly distinguishes from siblings such as 'forget' and 'recall'.

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

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

The description explains when to use the tool (to save context across tool calls) and mentions persistence differences between authenticated users and anonymous sessions. However, it does not explicitly state when not to use it or compare to alternatives like 'recall'.

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

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

With no annotations provided, the description carries full responsibility. It discloses accepted input formats and return fields (ticker, CIK, name, URIs) but lacks details on error behavior, permissions, or limitations. Some behavioral context is added, but gaps remain.

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 with two front-loaded sentences. Every word adds value: purpose, input format, output, and efficiency claim. No redundant or extraneous 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?

Given the low complexity (2 params, no output schema), the description adequately covers input and output. It lacks error handling or edge-case details but is complete enough for a straightforward resolution tool without annotations.

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 covers both parameters with 100% description, so baseline is 3. The description adds concrete examples (e.g., AAPL, 0000320193) and clarifies that the tool returns canonical IDs and URIs, providing extra meaning beyond schema.

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

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

The description explicitly states the tool resolves an entity to canonical IDs across Pipeworx data sources in a single call, using a specific verb and resource. It distinguishes from siblings by highlighting efficiency (replaces 2-3 lookup calls) and provides concrete examples for the 'company' type.

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 a use case (efficient single-call resolution) but does not explicitly state when not to use or suggest alternatives. While it mentions replacing multiple calls, no specific alternative tools are named for comparison.

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 fields (verdict, extracted form, actual value, citation, delta) and notes the tool is v1 with limited support. It does not mention rate limits or auth, but as a read-only query tool, the disclosure 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?

The description is two sentences long, with the first sentence stating the core purpose and the second adding scope, returns, and efficiency. Every sentence adds value; 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?

The tool has a single parameter and no output schema. The description fully covers what the tool does, its domain, and what it returns. For a tool of this complexity, it is 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%, so baseline is 3. The description adds value by providing an example of the claim format (e.g., "Apple's FY2024 revenue was $400 billion"), helping the agent understand the expected input beyond the schema 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: fact-checking natural-language claims. It specifies the domain (company-financial claims for public US companies) and what it returns (verdict, structured form, actual value, citation, delta). This uniquely distinguishes it from sibling tools like compare_entities or resolve_entity.

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

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

The description defines the scope (company-financial claims) and provides an efficiency claim (replaces 4-6 agent calls). It does not explicitly state when not to use or list alternatives, but the clear domain guidance is sufficient for appropriate use.

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