Climatiq

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

The description discloses behavioral traits beyond annotations: it clarifies that Workers AI is the free default, explains the cost implication for Anthropic (BYO key), and describes the return structure (per-model score, confidence, signals, raw_response + combined). This aligns with annotations (readOnly, openWorld, idempotent) and adds actionable detail.

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 and front-loaded with the core action. The first sentence is somewhat lengthy but packs necessary information. It earns its place by covering purpose, defaults, and return format.

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 absence of an output schema, the description adequately summarizes the response structure and use cases. It provides enough context for an agent to understand input requirements and expected output, though it could mention pagination or error handling if relevant.

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 each parameter already has a description. The tool description adds value by explaining defaults (models default to workers-ai), cost context (_apiKey), and the purpose of 'context' for disambiguation. This goes beyond the schema without replacing it.

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: probing LLMs for knowledge about a business/brand/product/topic and scoring visibility. It uses specific verbs ('probe', 'score') and resources ('LLMs', 'visibility'). It distinguishes itself from siblings like 'scan_competitor_ai_presence' by focusing on individual entity probing.

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 (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to use additional parameters like _apiKey for Anthropic. It implies defaults but does not explicitly contrast with sibling tools or state when not to use.

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

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

With no annotations, the description carries full burden. It explains routing, automatic tool selection, argument filling, and result returning. It does not disclose limitations (e.g., rate limits, authenticity of responses), but covers core behavior adequately.

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 purpose and usage, followed by examples. It is slightly verbose (multiple examples, sources), but every sentence adds value. No wasted words, though could be slightly more compact.

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 output schema, the description is comprehensive. It explains what the tool does, how it routes, scope of data sources, and provides concrete examples. It covers all necessary context for the agent to invoke correctly.

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

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

Only one parameter 'question' with schema description 'Your question or request in natural language'. The tool description adds valuable context with examples and data source scope, going beyond the schema. With 100% schema coverage, baseline is 3; the added examples and scope justify a 4.

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

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

The description clearly states the tool answers natural-language questions by automatically selecting the right data source. It lists specific examples and 300+ sources, distinguishing it from siblings like `discover_tools` or `entity_profile` which require explicit tool 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?

Explicitly says to use when the user asks 'What is X?' or similar queries and 'you don't want to figure out which Pipeworx pack/tool to call'. This gives clear when-to-use and implies alternatives (other tools) when specific selection is desired.

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

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

Annotations provide readOnlyHint=true and openWorldHint=true. The description adds behavioral details: it resolves the market, classifies the bet, fans out to packs, and returns an evidence packet with comparison. It also notes it's the core demo product and agents using it convert better. No contradiction with annotations.

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

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

The description is a single paragraph that front-loads the core action. It is efficient but slightly verbose with phrases like 'the core demo product'. Every sentence adds value, but it could be 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?

Without an output schema, the description explains the return format: 'evidence packet plus a simple market-vs-model comparison.' It covers input, processing, and output. However, it does not detail what the evidence packet contains, leaving some ambiguity for a complex 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 descriptions for both 'market' and 'depth' parameters. The description adds context: 'market' can be slug, URL, or question; 'depth' options are 'quick' (2-3 sources) and 'thorough' (full fan-out), with default 'thorough'. This adds value beyond the schema descriptions.

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

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

The description clearly states the tool's action: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question), processing steps (resolve, classify, fan out), and output (evidence packet + comparison). It distinguishes itself from siblings by being the core demo product for bet research.

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

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

The description explicitly says when to use the tool: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?".' It implies exclusive use for bet research but does not explicitly list alternatives or when not to use it.

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

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

No annotations provided, but the description adequately discloses the tool's behavior: it fetches financial data from SEC EDGAR/XBRL for companies and adverse event/trial data from FAERS for drugs, and returns paired data with citation URIs. It is clear this is a read-only operation.

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

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

The description is relatively concise, front-loads the purpose, and uses bullet-like structure. Some redundancy exists in the use-case examples, but overall it is efficient and easy to parse.

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

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

With no output schema, the description gives a clear idea of what is returned (paired data + citation URIs). It covers both entity types and data sources, though it omits details on error handling or edge cases. Complete enough for a typical tool.

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

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

Schema coverage is 100%, and the description adds significant meaning beyond the schema by explaining the 'type' parameter values and the format for 'values', including examples for companies (tickers) and drugs (names).

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

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

The description clearly states the tool compares 2-5 companies or drugs side by side, with specific use case examples and data sources for each type. It distinguishes itself from siblings by noting it replaces sequential entity queries.

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 trigger phrases like 'compare X and Y' and states when to use, but does not mention when not to use or alternative tools. However, the description implies that for single entity queries, other tools like entity_profile should be used.

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. The description states it returns 'top-N most relevant tools with names + descriptions.' It mentions the default limit and max, giving good transparency. It does not claim any destructive or modifying behavior. Could be improved by explicitly stating read-only nature, but overall 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 moderately long but every sentence adds value: purpose, usage guidance, examples, and return format. It is front-loaded with the core purpose. Minor redundancy could be trimmed, but overall it is effective and not 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?

For a discovery tool with no output schema, the description sufficiently covers input parameters and return format (tool names + descriptions). It lacks details on ranking or pagination, but for the intended use case of browsing tools, it is reasonably 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% with descriptions for both parameters. The description adds value by explaining the query parameter with examples (e.g., 'analyze housing market trends') and clarifying the 'limit' parameter's default and max. It also states the return format, which complements 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?

Clearly states 'Find tools by describing the data or task.' The verb 'find' and resource 'tools' are explicit. It distinguishes from siblings by positioning itself as a discovery tool to be called first when exploring available options, contrasting with the other specific tools like 'entity_profile' or 'search_factors'.

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 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set.' Provides clear context but does not explicitly list exclusions or alternatives beyond general 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, the description carries full burden. It enumerates returned data (SEC filings, fundamentals, patents, news, LEI) and mentions citation URIs. However, it does not mention any limitations like data freshness, rate limits, or size 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 front-loaded with the main purpose. It has 3 sentences that each add useful context, but could be slightly more concise without losing key information.

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

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

No output schema exists, so description must explain return values. It covers the core data types (SEC, USPTO, news, LEI) but omits details like whether historical data is included or response structure.

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 that only 'company' type is supported and that value must be a ticker or zero-padded CIK (not names), directing to resolve_entity for name resolution.

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 'Get everything about a company in one call' and lists specific data sources (SEC, USPTO, news, LEI). It contrasts with calling multiple pack tools, distinguishing it from siblings like resolve_entity and 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?

Explicitly lists example user queries ('tell me about X', 'brief me on Tesla') and instructs to use resolve_entity if only a name is provided, providing clear when-to-use and when-not-to 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 provided, so description carries full burden. It describes the calculation operation and required inputs, implying no side effects. Does not mention rate limits or permissions, but for a read-only calculation this is acceptable.

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 purpose and quickly cover input requirements and output. 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's simplicity (calculation, no output schema needed), the description fully covers what the agent needs: inputs, unit hints, and output summary. No gaps.

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

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

Schema coverage is 100%, but description adds value by explaining the relationship between emission_factor and parameters, giving an example with energy/kWh. This helps the agent understand how to construct the parameter object.

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 'Calculate kgCO₂e for an activity.' The verb 'Calculate' and resource 'kgCO₂e' are specific. Differentiates from sibling tools like search_factors.

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 instructions: 'Pass an emission_factor with at minimum activity_id (from search_factors) or id, plus the required parameters.' Implicitly tells when to use this tool after retrieving a factor. Lacks explicit when-not-to-use but is adequate.

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 exist, so description must bear the burden. It states 'Delete', implying destructiveness, but does not explicitly confirm irreversibility or describe behavior on missing keys.

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

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

Three sentences with no redundancy; first sentence delivers the core purpose, making it efficient 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?

For a simple single-parameter tool with no output schema, the description covers purpose and usage adequately, though it could mention error handling for nonexistent keys.

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 one parameter 'key' described as 'Memory key to delete'. The description adds no further meaning beyond the schema, 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 action ('Delete') and the resource ('previously stored memory by key'), distinguishing it from siblings like 'remember' 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?

Provides explicit when-to-use scenarios ('context is stale, task done, clear sensitive data') and recommends pairing with 'remember' and 'recall', offering clear guidance.

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

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

Annotations already indicate read-only, idempotent, and open-world behavior. The description adds value by detailing the process (fetches page, extracts title/description/key links) and output format (standard llms.txt markdown, single text blob). No contradiction with annotations.

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

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

The description is three sentences with a front-loaded main action and a bullet-style use case list. Every sentence adds value, and there is no redundant or extraneous information.

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

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

Given no output schema, the description explains the return value as a single text blob. It covers purpose, process, output format, and use cases. Minor gaps: no mention of error handling or URL validation, but acceptable for a straightforward read-only 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% (both parameters have descriptions). The description adds slight context by mentioning 'key links' in relation to max_links, but does not provide novel semantics beyond the schema's own descriptions (e.g., defaults, format). Baseline 3 is appropriate.

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

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

The description clearly states the tool generates an llms.txt file for a given URL, specifying the verb 'generate' and the resource. It distinguishes from siblings by focusing on a specific output format (llms.txt) and use cases like AI indexing and competitor auditing, which no other sibling tool explicitly addresses.

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 three concrete use cases (getting a client's site indexed, drafting for own project, auditing competitors), providing context for when to use the tool. However, it does not explicitly exclude scenarios or compare to alternatives like 'scan_competitor_ai_presence', which is only implied.

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

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

No annotations are provided, so the description must convey behavioral traits. It accurately describes the read-only nature of listing data, but does not mention potential ordering or grouping of output. For a simple list without side effects, 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?

The description is a single, well-structured sentence that immediately conveys the tool's function. It includes examples in parentheses to clarify the families without unnecessary verbosity.

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 (no parameters, no annotations, no output schema), the description is fully complete. It tells the agent exactly what the tool returns: families and their units, sufficient 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 has zero parameters and 100% coverage. With no parameters to document, the baseline is 4. The description does not add parameter semantics because none exist, but it appropriately describes the output content.

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: listing unit-type families (e.g., Energy, Volume, Distance) and their valid units. It uses a specific verb 'list' and identifies the resource, distinguishing it from sibling tools which focus on queries, comparisons, or mutations.

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 one needs to see valid units or families, but it does not explicitly state when to use this tool versus alternatives. Since no sibling tool has a similar function, the omission is minor, but explicit guidance would improve clarity.

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

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

Discloses rate limits (5/day), free usage, no quota impact, and team digest reading. Since no annotations exist, this covers key behaviors well, though could mention if feedback is irreversible.

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

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

Concise yet comprehensive: starts with main purpose, then usage categories, constraints, and tips. No fluff, 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?

Covers all necessary aspects for agent usage: purpose, when to use, parameter details, rate limits, and what team does with feedback. No output schema needed for this tool.

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

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

Adds value beyond schema by explaining enum types, context fields, and message tips. Schema coverage is 100% so baseline 3, but description enriches 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?

Clearly states the tool is for providing feedback (bug, feature, etc.) and distinguishes from sibling tools like 'ask_pipeworx' and 'discover_tools' by focusing on reporting issues or praise.

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 specifies when to use each feedback type (bug, feature, data_gap, praise) and provides constraints like not pasting user prompts and rate limits.

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 a safe read operation. The description adds valuable behavioral context: no PII, derived from CF analytics-engine, cached 5min-1h, and returns (pack, tool, count) tuples. No contradictions.

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

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

The description is concise, well-structured with a clear opening, bullet-pointed use cases, and additional notes. Every sentence adds value 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?

For a read-only trending tool with one parameter, the description covers the return content (top tools, packs, call volume) and caching. It is mostly complete, though it could optionally mention any limits on top items, but this is minor.

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 description for the 'window' parameter. The description adds extra semantics by explaining that shorter windows show what's hot and longer windows show steady-state demand. This goes 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 states it returns top tools, top packs, and total call volume over a window, using specific verbs like 'returns'. It distinguishes from siblings like 'discover_tools' and 'ask_pipeworx' by focusing on trending signal.

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 three use cases with bullet points, providing clear context on when to use the tool. It does not explicitly state when not to use it, but the use cases are well-defined.

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 readOnlyHint and openWorldHint. The description adds value by explaining the internal process: walking child markets, extracting dates/thresholds, sorting, and reporting violations. No contradictions.

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

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

The description is fairly concise and well-structured, with the purpose front-loaded. While slightly verbose, 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 the complexity of arbitrage detection, the description is thorough: it explains the monotonicity rule, parameter usage, and return format (list of entries). No output schema exists, but the description covers the return type adequately.

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

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

Schema coverage is 100% and the description adds minimal extra meaning beyond the parameter description. The explanation of how 'event' is used is helpful but not substantial enough to raise the baseline.

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: finding arbitrage opportunities via monotonicity violations in Polymarket events. It provides a specific verb-resource pair and distinguishes itself from siblings like 'polymarket_edges' by detailing the logic.

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 (for events with multiple 'by [date]' or 'by [threshold]' markets) and provides a clear example. It lacks explicit when-not-to-use guidance but the context is sufficient.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds valuable detail: the underlying model (lognormal from FRED + coinpaprika), processing steps (group by asset, fetch price once, compute model probability), and output format (ranked by edge with suggested trade direction). No contradictions.

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

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

The description is a single paragraph but packs essential information without redundancy. It front-loads the core purpose, then details scope, model, and output. Could be slightly tighter, but overall efficient.

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

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

Despite no output schema, the description explains the return format (top N ranked by edge, with direction). It covers the model, data sources, and processing logic comprehensively, making the tool's behavior fully understandable 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?

Schema coverage is 100%, so the schema already explains all parameters. The description does not add new meaning beyond what the parameter descriptions provide. Baseline 3 is appropriate as the description neither enhances nor detracts.

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 action ('scan', 'return'), resource ('Polymarket markets'), and the unique value ('where Pipeworx data disagrees most with market price'). It distinguishes itself from siblings like 'bet_research' or 'polymarket_arbitrage' by focusing on edge discovery.

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 intended use case: 'Built for the "what should I bet on today" question'. It provides context that it automates discovery without manual paging. While it doesn't mention alternatives or exclusions, the purpose is clear, earning a 4.

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 complements annotations fully. Annotations (readOnlyHint, idempotentHint, etc.) already indicate safety and idempotency. The description adds the return format ('leg-by-leg prices, spread in pp'), the rationale for the delta, and the overriding behavior, providing transparency beyond structured fields.

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 long, front-loaded with purpose, and efficiently presents modes and return. Though slightly wordy in the second sentence, it earns its place by explaining the reason for the spread.

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 compensates by detailing the return structure (prices and spread). It covers both usage modes, overriding behavior, and the nature of the output, making it complete for an agent to use correctly.

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

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

Schema description coverage is 100%, but the description adds significant value: it explains the 'topic' parameter's shortcuts (fed, btc, etc.) and clarifies that explicit parameters override topic-mapped sides. This enables correct invocation beyond the bare 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 'Cross-venue spread between Kalshi and Polymarket for the same resolving question,' specifying the verb (compute spread), resource (two prediction markets), and the context (same event). It distinguishes from siblings like 'polymarket_arbitrage' by focusing on the specific spread between these two 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 provides clear usage context: to capture arbitrage signals due to price differences between venues. It explains two modes (topic shortcuts and explicit tickers) and how to use them. However, it lacks explicit 'when not to use' or alternative tool names, but the niche purpose makes this less crucial.

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 fully covers behavioral traits: retrieval is scoped to an identifier, omitting key lists all keys, and it's a read operation. It does not mention rate limits or error handling, but the basic behavior is well-disclosed.

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

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

The description is a single, well-structured paragraph. It starts with the main action, then provides examples, scoping, and pairing. 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?

For a simple tool with one optional parameter and no output schema, the description is complete: it explains the action, usage context, scoping, and relationships to siblings. It could mention return format or error cases, but the core usage is fully covered.

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

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

Input schema has 100% coverage with a description for 'key'. The description adds value by giving concrete examples of keys and clarifying that omission lists all keys, which the schema does not explicitly state.

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

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

Description clearly states the tool retrieves values saved via remember or lists keys if omitted. It specifies the verb 'retrieve' and resource 'saved memories,' and distinguishes from siblings 'remember' and 'forget' by describing complementary actions.

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 provides when to use: to look up context without re-deriving, with examples (ticker, address, notes). Mentions scoping and pairing with remember/forget. Lacks explicit when-not-to-use but 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?

With no annotations, the description fully discloses behavioral traits: it fans out to three sources in parallel, accepts specific date formats, and returns structured data with citation URIs. This is comprehensive and leaves no ambiguity.

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, using four sentences to convey purpose, usage, inputs, and outputs. Every sentence adds information without redundancy or 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 three required parameters and no output schema, the description covers the tool's behavior and output well. It explains the fan-out and return structure. Minor omission: no mention of potential errors or rate limits, but this is acceptable for a simple tool.

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

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

The schema covers all three parameters with descriptions. The description adds value by clarifying acceptable formats for 'since' (ISO date vs relative) and 'value' (ticker or CIK), and noting that 'type' only supports 'company'. This enhances 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's purpose: reporting what's new with a company. It provides specific example queries and explains the fan-out to multiple sources, distinguishing it from siblings which likely focus on different entity types or historical data.

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

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

The description explicitly lists when to use the tool, with multiple example user queries. It does not, however, mention when not to use it or suggest alternative siblings, which would improve 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 provided, so the description fully explains persistence behavior: authenticated users get persistent memory, anonymous sessions 24 hours. Could mention update behavior for existing keys, but otherwise strong.

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, well-structured with purpose, usage, and persistence details. No wasted sentences; front-loaded with core function.

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, but description doesn't specify return value. However, for a storage operation, success/failure is often implicit. Overall context sufficient for agent decision-making.

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

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

Input schema covers both parameters with descriptions (key examples, value as any text). Description reinforces key-value pair concept but adds minimal new semantic value beyond the schema.

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

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

Description explicitly states the tool saves data for later reuse, provides concrete examples (resolved ticker, address, preference), and distinguishes it from siblings 'recall' and 'forget'.

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

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

Clearly advises when to use ('when you discover something worth carrying forward') and mentions complementary tools ('Pair with recall to retrieve later, forget to delete').

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

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

No annotations, so description fully carries the burden. It discloses returns include IDs and citation URIs, with examples. Missing details on error or no-match behavior, but safe lookup implied.

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, front-loaded with purpose, no filler. Each sentence adds critical context (purpose, usage, examples, output).

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

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

Covers purpose, usage, examples, and return format. Lacks handling of ambiguous or missing lookups, but sufficient for a medium-complexity tool with full schema coverage.

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 description adds value with concrete examples (e.g., 'Ozempic' → RxCUI 1991306) and clarifies input formats for both parameters 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 resolves entities to official identifiers (CIK, ticker, RxCUI, LEI) with concrete examples. It distinguishes from siblings like 'entity_profile' by focusing on ID lookup needed by other 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?

Explicitly says 'Use this BEFORE calling other tools that need official identifiers' and describes scenarios (user mentions a name). Lacks explicit when-not-to-use, but provides strong usage context.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, non-destructive. The description adds behavioral details: it probes entities, ranks by score, treats first entity as subject, and returns score, confidence, signal density. No contradiction with annotations.

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

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

The description is two concise sentences plus an illustrative example. It is front-loaded with the primary purpose and efficiently conveys all necessary information without redundancy.

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

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

For a tool with no output schema, the description provides a clear picture of the output (ranked list with score, confidence, signal density). It covers the main use case, optional parameters (models, _apiKey, context), and the significance of ordering entities. Given the complexity of comparing multiple AI probes, this is 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 coverage is 100% with descriptions for all parameters. The description reinforces that the first entity is the subject and that models defaults to 'workers-ai'. This adds contextual 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 clearly states the tool's purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies it probes each entity with ai_visibility_check, ranks results, and identifies most/least recognized. This distinguishes it from single-probe sibling ai_visibility_check and generic 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 explains when to use the tool: for competitive AI-marketing audits, with an example. It implies not to use for single-entity checks (use ai_visibility_check instead). However, it does not explicitly state alternatives or exclusions.

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

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

With no annotations, the description carries the full burden. It discloses that the tool filters by query, category, source, region, and year, but does not mention pagination, rate limits, or whether the operation is read-only. It is adequate but not deeply transparent.

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

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

Two sentences that are concise and front-loaded. Every sentence adds value, with 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 8 parameters and no output schema or annotations, the description covers the essential purpose and filters. It lacks details on pagination and response format, but is adequate for a search tool with a rich schema.

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

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

Schema coverage is 100%, so baseline is 3. The description adds workflow context by stating these are filters used to discover factor IDs, which adds value beyond the schema descriptions. However, it does not explain all 8 parameters in detail.

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

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

The description clearly states the verb 'search' and the resource 'emission-factor database'. It also explains the purpose: to discover factor IDs and required parameters before calling estimate_emissions, distinguishing it from siblings.

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

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

The description explicitly says 'Use to discover factor IDs and required parameters before calling estimate_emissions', providing clear context for when to use this tool. However, it does not explicitly exclude alternatives or specify when not to use it.

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

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

With no annotations, the description carries full responsibility for behavioral disclosure. It explains the returned verdict and components, the source (SEC EDGAR + XBRL), and notes it's v1 limited to company-financial claims. This provides good transparency about capabilities and limitations.

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

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

The description is compact and well-organized: first sentence defines purpose, followed by usage examples, scope, return details, and efficiency gain. Every sentence adds value 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?

For a tool with 1 parameter and no output schema, the description covers purpose, usage, behavior, and output adequately. It notes limitations (v1 scope). However, it omits potential errors, rate limits, or prerequisites, which 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?

The input schema has 100% coverage, so baseline is 3. The description adds value by providing natural-language examples and clarifying the claim's scope (financial data for public US companies), which goes beyond the schema's generic description.

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

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

The description clearly states the tool validates factual claims against authoritative sources, with a specific scope of company-financial data. It uses strong verbs like 'verify' and 'validate', and distinguishes its purpose from simple data retrieval, though it doesn't explicitly differentiate from siblings 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 provides clear examples of when to use the tool (e.g., checking truth of statements) and its scope (company-financial claims). However, it lacks explicit when-not-to-use guidance or alternatives, leaving the agent to infer from context.

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