Weather

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

Adds significant behavioral context beyond annotations: default model is free, Anthropic requires BYO key and incurs direct costs. Describes return structure (score, confidence, signals, raw_response + combined). Annotations already indicate read-only, idempotent, non-destructive – no contradiction.

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

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

Four sentences, each adding distinct value: main action, model options, return format, use cases. No redundancy, perfectly 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?

Description covers the tool's purpose, parameters, return format, and use cases well. Lacks explanation of what 'signals' are, but given high schema coverage and annotations, it's sufficiently complete for a probing 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. Description adds context about default model and API key requirement but doesn't significantly enhance understanding beyond 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?

Description clearly states it probes LLMs for knowledge about brands/topics and scores visibility (0-100). Specific verb 'probe' and resource 'LLMs' with clear output. Distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on scoring and per-model analysis.

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

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

Explains default model (Workers AI) and how to add Anthropic with API key. Provides use cases (AI-marketing audits, brand checks, competitive monitoring). Missing explicit when-not-to-use or alternative tools, but context is clear.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that Pipeworx 'picks the right tool, fills the arguments, and returns the result,' which reveals the tool's intelligent routing behavior. However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions, leaving some behavioral aspects unspecified.

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 efficiently structured with a clear purpose statement, operational explanation, and three relevant examples. Every sentence adds value without redundancy, and the information is front-loaded with the core functionality explained first.

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 single-parameter tool with no annotations and no output schema, the description provides good context about the tool's intelligent routing behavior and natural language interface. However, it doesn't describe what the output looks like (though there's no output schema to document this), and doesn't mention potential limitations or error cases that would be helpful for an 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?

The schema description coverage is 100%, with the single parameter 'question' well-documented in the schema. The description adds minimal additional context beyond what the schema provides, mentioning 'plain English' and 'natural language' which reinforces but doesn't significantly expand on the schema's 'question' parameter 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: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer from data source'), and distinguishes from siblings by emphasizing natural language processing instead of requiring tool-specific knowledge. The examples further clarify the scope.

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

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

The description explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It contrasts with sibling tools that likely require specific parameters or schemas, providing clear guidance on using this as a natural language alternative to more structured 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 key behaviors: market resolution, bet classification, fan-out to relevant packs, and return of evidence packet with market-vs-model comparison. This adds significant context beyond the annotations (readOnlyHint, openWorldHint) 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 a single, dense paragraph that is front-loaded with the core action. Every sentence adds value, covering input, process, output, and use cases. It is slightly long but appropriate given the tool's complexity.

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 (multiple input types, fan-out logic, classification), the description covers inputs, processing, outputs, and usage scenarios comprehensively. No output schema exists, but the description clearly explains return values.

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 already describes parameters with 100% coverage. The description adds practical nuance: 'market' can be slug, URL, or question text; 'depth' values ('quick' vs 'thorough') are explained. This enriches the semantic understanding for selection.

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, market-vs-model comparison). It distinguishes from siblings by implying this is a consolidated research tool for betting edge.

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

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

The description explicitly lists use cases: 'should I bet on X?', 'what does the data say?', 'is there edge in this bet?' It also positions the tool as the core demo product. However, it does not explicitly state when not to use it or compare to alternative tools on the server.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool is a read-like operation (no side effects mentioned), and it explains the return includes 'paired data + pipeworx:// resource URIs'. Specific data fields are detailed per entity type. It does not mention permissions or rate limits, which would improve transparency.

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

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

The description is tightly written in three sentences: purpose, type-specific details, and efficiency statement. Every sentence earns its place with no redundancy or filler. It front-loads the core action and constraints immediately.

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 returned data (paired data, URIs). It covers entity range (2-5), required parameters, and efficiency gains. However, it could briefly mention the data format (e.g., JSON) or pagination if applicable. Still, it is largely complete for comparison tasks.

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 significant value by explaining that the 'type' parameter determines which data fields are returned (e.g., revenue for companies, adverse-event counts for drugs). This meaningfully supplements the schema's enum and array 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 function: comparing 2-5 entities (companies or drugs) side by side in one call. It specifies the exact data fields for each type (e.g., revenue, net income for companies; adverse-event counts for drugs). This uniquely distinguishes it from sibling tools like get_forecast 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 indicates when to use this tool: when needing to compare multiple entities efficiently, as it 'replaces 8-15 sequential agent calls'. However, it does not explicitly state when not to use it or mention alternatives for single-entity queries. The context is clear but lacks 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'most relevant tools' and has a default/max limit (implied from schema), but lacks details on ranking criteria, error handling, or performance characteristics. It adds some context about the catalog size ('500+ tools') but doesn't fully compensate for missing annotation coverage.

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

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

The description is front-loaded with the core purpose, followed by usage guidance. Both sentences earn their place by providing essential information without redundancy. It's appropriately sized for a tool with two parameters and clear use case.

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

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

Given the tool's moderate complexity (search function with two parameters) and lack of annotations/output schema, the description is reasonably complete. It covers purpose, usage context, and outcome, but could benefit from more behavioral details (e.g., search algorithm, error cases). The 500+ tools context helps, but some 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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain query formatting or limit implications). Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), and distinguishes it from siblings by emphasizing its discovery function. It explicitly mentions returning 'most relevant tools with names and descriptions', making the outcome concrete.

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

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

The description provides explicit guidance on when to use this tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' It also implies an alternative context (not using it when fewer tools are available) and distinguishes from siblings like get_forecast and get_weather by focusing on tool discovery rather than data 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?

Although no annotations are provided, the description discloses that the tool replaces 10-15 sequential calls and returns pipeworx:// citation URIs. It could add more about potential performance or rate limits, but overall it provides adequate behavioral context for a read-only profile tool.

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

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

The description is two sentences, front-loading the key information and using efficient, packed language without 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 no output schema, the description explains what data is returned (SEC filings, revenue, patents, news, LEI) and the format (pipeworx:// URIs). It could mention response size limits or pagination, but the description is reasonably complete for the tool's complexity (2 required params, no nesting).

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 coverage is 100% and the description adds extra context beyond the schema: it specifies that only 'company' type is supported (others coming soon), explains that value can be a ticker or CIK (not a name), and provides examples. This significantly aids correct parameter 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 clearly states that the tool returns a full profile of an entity, lists specific data sources (SEC, XBRL, USPTO, GDELT, LEI), and mentions citation URIs. It differentiates from siblings like resolve_entity by noting that names are not supported.

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 when to use this tool (for company profiles) and when not to (for federal contracts, use usa_recipient_profile). It also advises to use resolve_entity first if only a name is available, providing clear alternative 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool deletes a memory, implying a destructive mutation, but lacks critical details: whether deletion is permanent or reversible, what permissions are required, if there are side effects (e.g., affecting other tools), or what happens on success/failure. For a destructive tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence with zero wasted words. It is front-loaded with the core action ('Delete') and resource ('stored memory'), making it immediately scannable. Every word earns its place, achieving optimal conciseness for such a simple tool.

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

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

Given the tool's destructive nature, lack of annotations, and absence of an output schema, the description is incomplete. It doesn't cover behavioral aspects like permanence, error handling, or return values, nor does it provide usage context relative to siblings. For a mutation tool with no structured safety or output information, the description should do more to guide safe and effective use.

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

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

The schema description coverage is 100%, with the single parameter 'key' documented as 'Memory key to delete'. The description adds minimal value beyond this, merely restating 'by key' without explaining key format, constraints, or examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, though the description could enhance understanding with contextual details.

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 target resource ('a stored memory by key'), making the purpose immediately understandable. It distinguishes from sibling tools like 'recall' (retrieve) and 'remember' (store), though it doesn't explicitly name these alternatives. The purpose is specific but could be slightly more precise about what 'stored memory' refers to in this context.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing memory key), exclusions, or comparisons to sibling tools like 'recall' or 'remember'. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds details: fetches page, extracts title/description/key links, emits standard markdown. 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?

Three sentences, front-loaded with purpose. Each sentence adds value: purpose, output, use cases. No fluff.

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

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

Given no output schema and simple 2-param tool, description covers input, process, output format, and use cases sufficiently. Missing edge case handling but not critical.

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 3. Description adds little beyond schema for 'url' and nothing for 'max_links'. Does not enhance understanding of parameter purpose or constraints.

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

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

Clearly states the tool generates a production-ready llms.txt file for any URL, specifying the output format and target consumers (AI crawlers). The verb 'generate' and resource 'llms.txt' are explicit. No sibling conflict.

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?

Lists three explicit use cases (indexing client sites, drafting for own project, auditing competitor AI presence). Lacks 'when not to use' but context is clear enough.

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

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

No annotations are provided, so the description carries the full burden. It mentions the return data types but lacks critical behavioral details such as data sources, update frequency, rate limits, error handling, or authentication requirements. For a tool with no annotations, this is a significant gap in transparency.

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

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

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

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

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

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is partially complete. It covers the basic purpose and output but lacks details on behavioral aspects like data freshness or limitations. Without annotations or output schema, more context on return format or errors would improve completeness.

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

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

Schema description coverage is 100%, so the input schema fully documents all parameters (latitude, longitude, days). The description adds no additional parameter semantics beyond what the schema provides, such as format details or usage examples. Baseline score of 3 is appropriate as the schema handles the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('Get') and resources ('multi-day weather forecast for a location'), and distinguishes it from the sibling tool 'get_weather' by specifying it returns forecast data rather than current conditions. It explicitly lists what information is returned (daily high/low temperatures, precipitation, and conditions).

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 context by specifying it's for 'multi-day weather forecast,' suggesting when to use it versus alternatives like current weather tools. However, it doesn't explicitly state when not to use it or name the sibling tool 'get_weather' as an alternative, leaving some ambiguity about tool selection.

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

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

No annotations; description mentions ERA5 reanalysis data source and time range. However, does not disclose API key requirement (implied by examples but not required) or any rate limits.

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 key info, no superfluous text.

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 output schema present, description covers purpose, usage, and data range. Missing minor behavioral aspects, but overall adequate.

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

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

Schema coverage is 100% with clear descriptions. Description adds context about returned data but no additional parameter 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?

Description clearly states it provides daily historical weather from 1940 onward, with specific data types (high/low temp, precipitation). Distinguishes from siblings like get_forecast and get_weather.

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 use cases: climate baselines, historical queries, comparing to current readings. Lacks explicit when-not-to-use or alternatives, but context is clear.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return data structure (temperature, humidity, wind speed, conditions) which is helpful, but doesn't address important behavioral aspects like rate limits, error conditions, authentication requirements, or data freshness. The description adds some value but leaves significant gaps.

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

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

The description is perfectly concise - two sentences that efficiently communicate the tool's purpose and return values without any wasted words. It's front-loaded with the core functionality and follows with essential output information, making it easy for an agent to quickly understand what the tool does.

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 read-only weather tool with no annotations and no output schema, the description provides basic functionality and return format information. However, it lacks important context about error handling, data sources, units of measurement, or temporal aspects of 'current' weather. Given the simplicity of the tool, the description is adequate but could be more 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 schema description coverage is 100%, with both latitude and longitude parameters fully documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline expectation but doesn't provide additional semantic context about coordinate formats, valid ranges, or special cases.

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

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

The description clearly states the specific action ('Get current weather conditions') and resource ('for a location'), distinguishing it from the sibling tool 'get_forecast' which likely provides future predictions rather than current conditions. It uses precise language that leaves no ambiguity about the tool's function.

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

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

The description implies usage for obtaining current weather data, but doesn't explicitly state when to use this tool versus the 'get_forecast' sibling. There's no guidance about alternative scenarios or exclusions, leaving the agent to infer the distinction based on the 'current' versus 'forecast' terminology.

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

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

With no annotations, the description carries full burden for behavioral disclosure. It discloses rate limiting and content policy. However, it does not specify the outcome of sending feedback (e.g., confirmation, storage) or whether the message is sent immediately. The 'Free' note is ambiguous.

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. The first sentence states the core purpose, the second adds usage rules, and the third specifies the rate limit. 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 low complexity (simple feedback tool with no output schema), the description covers purpose, usage guidelines, rate limiting, and content policy. It lacks an explicit statement about return value or confirmation, but for a feedback tool, the provided information 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 description coverage is 100%, so the schema itself documents all parameters with detailed enum descriptions and property explanations. The tool description adds minimal parameter-specific value beyond reinforcing the 'type' choices and the 'context' structure. The instruction about not including user prompt is helpful but not about parameter syntax.

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: 'Send feedback to the Pipeworx team.' It enumerates specific use cases (bug reports, feature requests, missing data, praise) and provides instructions on what to include, distinguishing it from sibling tools like ask_pipeworx 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 mentions a rate limit (5 messages per identifier per day) and gives guidance on what to include ('describe what you tried in terms of Pipeworx tools/data — do not include the end-user's prompt verbatim'). It does not explicitly state when not to use the tool, but the sibling context implies it is the only feedback channel.

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?

Beyond annotations (readOnly, idempotent, non-destructive), the description adds specifics: derived from CF analytics-engine, no PII, cached 5min-1h. This enhances transparency 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?

Concise, front-loaded with core result, then use cases, then technical notes. 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?

Despite no output schema, description sufficiently describes return shape (top tools, packs, volume). Also covers caching and data source, making it complete for informed 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 coverage is 100% with enum description for window. Description adds practical guidance (shorter windows for hot topics, longer for steady-state), which adds moderate value beyond schema.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a window, with a specific verb and resource. It distinguishes from siblings like discover_tools by focusing on current AI agent usage.

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

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

Explicitly lists three concrete use cases: discovering hot data sources, confirming canonical choices, and checking alignment. Although it doesn't state when not to use, the use cases are comprehensive and the open-world annotation implies broad applicability.

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 context like search behavior for topic mode, grouping of related markets, and return of ranked opportunities with reasoning. No contradiction.

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

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

The description is slightly long but well-structured with bullet points for modes. Front-loaded with purpose. Each sentence adds value, though it could be marginally more 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?

No output schema exists, but the description mentions return value. Given tool complexity (two modes, cross-event search), it provides sufficient context for an agent to understand behavior and output format.

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%. The description adds meaning by associating parameters with modes, providing examples (event slug vs. topic), and explaining cross-event behavior for the topic 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?

The description uses specific verb 'Find arbitrage opportunities' and resource 'Polymarket', clearly distinguishing two modes. It contrasts with sibling 'polymarket_edges' by focusing on monotonicity violations across related markets.

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 each mode ('event' or 'topic') and explains the advantage of cross-event mode. It lacks explicit 'when not to use' statements but provides 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?

Beyond annotations (readOnlyHint, openWorldHint), the description details the methodology (lognormal model from FRED and coinpaprika, groups by asset, ranks by edge). This provides significant behavioral context beyond what annotations offer.

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 somewhat verbose but front-loads the purpose and is organized logically. It could be more concise, but every sentence adds value.

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

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

Given no output schema, the description adequately explains the return format (top N ranked by edge magnitude with suggested trade direction). It covers data sources, model, and use case, making it complete for a read-only discovery 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 description adds marginal value by framing parameters in the tool's pipeline (e.g., 'after ranking') but does not substantially enhance understanding 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 scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price, using specific verbs like 'scan', 'return', and 'ranks'. It distinguishes itself from siblings like polymarket_arbitrage by focusing on edge detection.

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

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

The description explicitly frames the tool for the 'what should I bet on today' question, indicating when to use it. It does not mention alternatives or when not to use it, but the context is clear given sibling 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 declare read-only, idempotent, and non-destructive behavior. The description adds that the tool returns probabilities (0-1) and spreads in percentage points, and explains the source of the delta (different participant pools). This adds useful behavioral context beyond annotations.

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

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

The description is well-structured with an introduction, mode explanation, and output description. Every sentence contributes value, though it could be slightly more 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 no output schema, the description explains return format (prices and spreads). It covers usage modes and limitations (pre-mapped topics). For a tool relying on external data, it is sufficiently complete.

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

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

Schema coverage is 100%, and description adds meaning: it explains the two modes, lists pre-mapped topics, and clarifies that explicit parameters override topic-mapped ones. This adds value beyond schema descriptions.

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

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

The description clearly states it calculates a cross-venue spread between Kalshi and Polymarket for the same event, with two modes (topic shortcuts or explicit tickers). It distinguishes itself from siblings like polymarket_arbitrage and polymarket_edges by focusing on the spread between venues.

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 it (arbitrage signal) and how to use it (two modes), but does not explicitly state when not to use it or compare to sibling tools. It provides clear context for usage.

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

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: the tool can retrieve both individual memories and list all memories, works across sessions, and handles missing parameters gracefully. However, it doesn't mention error behavior (e.g., what happens if key doesn't exist) or performance characteristics.

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

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

Two sentences with zero waste. The first sentence states the core functionality with parameter semantics. The second sentence provides usage context. Every word earns its place, and the information is front-loaded appropriately.

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

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

For a simple retrieval tool with 1 optional parameter and 100% schema coverage, the description is quite complete. It explains what the tool does, when to use it, and parameter behavior. The main gap is lack of output information (no output schema provided), but the description compensates reasonably well for this simple use case.

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 meaningful context beyond the schema: it explains the semantic meaning of omitting the key ('omit to list all keys') and connects the parameter to the broader purpose ('retrieve context you saved earlier'). This provides valuable operational understanding.

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

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

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory by key', 'all stored memories'). It distinguishes from siblings like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations.

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

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

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

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

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

With no annotations provided, the description fully discloses the tool's behavior: it fans out to multiple sources in parallel, returns structured changes plus a count and URIs, and accepts ISO dates or relative times. It does not mention any destructive actions, which is consistent with a read-only monitoring 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, well-organized paragraph that front-loads the core purpose and follows with details on behavior, input formats, and use cases. It is concise (approx. 100 words) without unnecessary fluff.

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

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

Given the tool's moderate complexity (fan-out to three sources) and the lack of an output schema, the description covers the essential behavioral aspects, input requirements, and usage scenarios. Minor gaps remain (e.g., error handling or rate limits), but overall it provides sufficient context for correct invocation.

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

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

The input schema already describes all three parameters with 100% coverage. The description adds significant value by explaining the fan-out logic, the return structure, and the acceptable formats for 'since', which goes beyond the schema's basic descriptions.

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

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

The description clearly identifies the tool's purpose: reporting what is new about an entity since a given time. It explicitly mentions the supported entity type 'company' and the three data sources (SEC EDGAR, GDELT, USPTO), making the function distinct from sibling tools like 'entity_profile' or 'get_historical'.

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' or change-monitoring workflows') and explains the 'since' parameter with example values. Although it does not explicitly state when not to use the tool, the guidance is clear enough for an AI agent to decide.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool performs a write operation (store), explains persistence characteristics (authenticated vs. anonymous sessions), and mentions the 24-hour retention limit for anonymous sessions. However, it doesn't address potential limitations like storage capacity or key constraints.

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

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

The description is perfectly concise with two well-structured sentences. The first sentence states the core function, the second provides essential usage context and behavioral details. Every word earns its place with zero redundancy or unnecessary elaboration.

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 write operation tool with no annotations and no output schema, the description provides strong contextual coverage. It explains what the tool does, when to use it, and key behavioral characteristics. The main gap is the lack of information about return values or confirmation of successful storage, which would be helpful given the absence of an output schema.

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

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

With 100% schema description coverage, the input schema already documents both parameters thoroughly. The description doesn't add meaningful parameter semantics beyond what's in the schema - it mentions 'key-value pair' but provides no additional context about parameter usage, constraints, or examples that aren't already in the schema descriptions.

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

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

The description clearly states the specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'forget' (delete) and 'recall' (retrieve). It explicitly identifies the tool's function as persistent storage for various data types.

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

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

The description provides explicit guidance on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls') and distinguishes it from alternatives by specifying the storage mechanism. It also clarifies the persistence differences between authenticated and anonymous users.

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

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

With no annotations, the description carries full burden. It discloses supported types, input formats, and return fields (ticker, CIK, name, URIs). Lacks error handling or limits, but for a simple lookup, this is adequate.

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

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

Two sentences, no fluff. First sentence states main purpose, second provides specific input/output details. 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?

Covers inputs, outputs, and use case benefit. Minor gaps in error handling and idempotency, but for a simple 2-parameter tool, it is fairly complete. No output schema needed given clear return description.

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

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

Schema coverage is 100%, baseline 3. Description adds concrete examples ('AAPL', '0000320193', 'Apple') and explains that type currently supports only 'company', providing context beyond the schema's enum and 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?

Description clearly states the action: 'Resolve an entity to canonical IDs across Pipeworx data sources in a single call.' Verb (Resolve) and resource (entity) are specific, and the benefit over alternatives ('Replaces 2–3 lookup calls') differentiates it from sibling tools like ask_pipeworx and get_forecast.

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 context on when to use (v1 for company type, accepts ticker/CIK/name) and efficiency gain. Lacks explicit exclusions or alternative conditions, but sibling tools are distinct, so usage 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 indicate safe, read-only, idempotent behavior. The description adds value by detailing that it probes each entity with ai_visibility_check and ranks results by score, surfacing most/least recognized. It also describes the return format (ranked list with score, confidence, signal density), which is not contradicted by 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 with two sentences. The first sentence front-loads the main action and result, and each sentence adds essential detail without redundancy.

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

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

Despite no output schema, the description fully explains the return value (ranked list with metrics). It covers purpose, usage scenario, parameter nuances, and the underlying process, making it complete for an agent to understand when and how 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 meaning beyond the schema by explaining that the first entity in the entities array is treated as the 'subject' for narrative and the rest as competitors. It also clarifies model usage (omitting models uses workers-ai) and conditional _apiKey requirement.

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 AI visibility across multiple entities side-by-side. It explicitly mentions probing with ai_visibility_check and ranking, distinguishing it from the sibling tool ai_visibility_check which likely handles single 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 a clear use case (competitive AI-marketing audits) and an example question. It implicitly suggests using ai_visibility_check for single entities by describing how this tool utilizes it. However, it does not explicitly state when not to use this tool or mention other 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 provided, so the description carries the full burden. It discloses supported data sources (SEC EDGAR + XBRL) and return fields (verdict, structured form, value with citation, delta). It does not cover limitations like time range or company scope beyond 'public US companies'.

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

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

The description is two sentences with no wasted words. The main purpose is front-loaded, and each sentence adds value.

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

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

Given one parameter and no output schema, the description adequately covers the purpose, return types, and domain. It could add details on supported time periods or edge cases, but is mostly 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 schema has 100% description coverage for the single 'claim' parameter, including a clear example. The description adds minimal extra semantics beyond what the schema already provides.

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

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

The description clearly states the tool's verb ('Fact-check'), resource ('natural-language claim against authoritative sources'), and domain ('company-financial claims'). It lists return types and distinguishes from sequential agent calls, though it does not explicitly differentiate from sibling 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?

The description indicates when to use (for financial claims of US public companies) and provides example claims. However, it does not explicitly state when not to use the tool or suggest alternatives among siblings.

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