F1

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds beyond this: it explains the return structure (per-model results including score, confidence, signals, raw_response), the cost implication of using Anthropic (BYO key), and that the default model is free. No contradictions with annotations.

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

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

The description is concise (4 sentences) and front-loaded with the core purpose. Every sentence adds value: purpose, return type, cost/clarification, and use cases. No redundancy or filler.

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 (multi-model probing, optional API key, structured return), the description covers the return format and parameter nuances. No output schema exists, but the description provides sufficient detail. Minor missing aspects like rate limits or error handling are acceptable for a read-only, idempotent 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 for all 4 parameters. The description adds extra meaning beyond the schema: it explains that the default model is Workers AI Llama-3.3-70b (free), that `_apiKey` is only needed for Anthropic, and that `context` helps disambiguate. This elevates understanding beyond the schema alone.

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

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

The description clearly states the tool's purpose: probe LLMs for knowledge about a brand/product/topic and provide a visibility score. It uses a specific verb ('probe') and resource ('one or more LLMs'), and distinguishes from siblings like 'scan_competitor_ai_presence' by emphasizing multi-model probing with optional BYO key.

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 the tool: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also clarifies the default model and the need for an API key for Anthropic. While it does not mention alternatives, the context is clear enough for the 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?

The description discloses key behavioral traits: it returns the result from the best available data source, and it automatically selects the appropriate tool and fills arguments. This goes beyond what annotations (none provided) would reveal. However, it does not specify what happens if the question cannot be answered, or if multiple sources are available, but given the lack of annotations, the description is quite 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?

The description is concise, with three sentences and three examples. It front-loads the core action and ends with examples. No unnecessary words. However, the examples are somewhat lengthy, but they serve a useful purpose.

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

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

Given the tool's simplicity (one parameter, no output schema, no annotations), the description is nearly complete. It explains what the tool does, how to use it, and what to expect. The only minor gap is that it does not explain what happens if the question cannot be answered, but that is acceptable for a query 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% for the single parameter 'question', and the description provides additional context by stating it should be in 'plain English' and giving examples of natural language requests. This adds meaning beyond the schema's generic 'Your question or request in natural language' 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 explicitly distinguishes itself from sibling tools by saying it 'picks the right tool, fills the arguments, and returns the result,' which implies it abstracts away the need to directly use other tools like get_driver or get_race_results. The verb 'ask' and resource 'answer' are specific and action-oriented.

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

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

The description explicitly says 'No need to browse tools or learn schemas — just describe what you need,' which guides the agent to use this tool when the user provides a natural language question without specifying which sibling tool to use. It also provides three concrete examples that illustrate typical usage scenarios, covering data lookups across different domains.

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, destructiveHint), the description details the tool's internal process: resolving market, classifying bet type, fanning out to data packs, and returning an evidence packet with comparison. This adds significant behavioral context.

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

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

The description is thorough but could be slightly more concise. However, it is well-structured with front-loaded purpose and logical flow.

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 output (evidence packet + comparison) and covers the tool's multi-step behavior completely.

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%, providing clear definitions for both 'market' and 'depth'. The description reiterates but does not add new meaning beyond the schema examples.

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

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

The description clearly states it researches Polymarket bets using Pipeworx data. It distinguishes itself from sibling tools like ask_pipeworx by being specialized for betting 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?

Provides explicit use cases like 'should I bet on X?' and states it's the core demo product. Does not explicitly exclude scenarios or name alternatives among siblings.

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

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

With no annotations, the description must cover behavior. It discloses the return data (paired metrics and URIs) and entity-specific outputs, but does not state whether the operation is read-only, idempotent, or has any side effects. The behavioral coverage is functional but lacks safety details.

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

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

The description is three sentences long, front-loading the core action and purpose. Every sentence adds distinct information: what it does, what it returns, and its efficiency advantage. No redundancy or waste.

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 adequately explains the result format (paired data + URIs) and covers both modes. It does not address error conditions or edge cases like missing entities, but the core functionality is sufficiently described given the tool's complexity.

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

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

Schema coverage is 100%, so parameters are already documented. The description adds value by explaining the different required input formats (tickers/CIKs for company, names for drug) and providing example inputs, which helps the agent correctly construct the values array.

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 entities side by side, specifying two entity types (company and drug) with distinct metrics. It distinguishes from sibling tools by highlighting that it replaces 8–15 sequential agent calls, abstracting a complex workflow.

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 side-by-side comparisons of companies or drugs) and implies efficiency gains over sequential calls. It does not explicitly state when not to use or name alternatives, but the sibling tools are dissimilar, so the context 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 provided, so description carries full burden. It states the tool searches and returns tools with names and descriptions, but does not disclose behavior such as whether it performs semantic search, how results are ordered, or any limitations (e.g., indexing delays). Adequate but not rich.

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

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

Three concise sentences, front-loaded with purpose, includes example usage and explicit directive to use first. Every sentence adds value with no redundancy.

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

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

Given no output schema, description explains return value (tools with names and descriptions). With 2 simple parameters and no nested objects, the description is complete enough for an agent to understand what it does and how to use it. Missing details about result ordering or filtering capabilities, but sufficient.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by explaining the 'query' parameter with example natural language descriptions, and clarifies 'limit' default and max. This goes beyond the schema's minimal 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 tool searches the tool catalog by description, returns relevant tools with names and descriptions, and is to be called first when needing to find the right tools among many. Verb 'search' and resource 'Pipeworx tool catalog' are specific, and it distinguishes from siblings by positioning as the discovery tool.

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

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

Explicitly states when to use: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' Implies alternative is to not use it when tools are few or already known. Provides clear context for its 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?

Without annotations, the description must disclose behavior. It reveals that the tool bundles multiple data sources into one call, returns pipeworx:// URIs, and replaces many sequential calls. It mentions a limitation (federal contracts too slow). It does not explicitly state if the operation is read-only, but the context implies it is. The description adds significant behavioral context beyond the schema.

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

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

The description is relatively concise, front-loading the main purpose in one sentence. It uses a list format for data types, making it easy to scan. There is no fluff, though a more structured layout could improve readability. It earns its length by packing useful 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?

The description lists the types of data returned but does not specify the output structure or field names. Without an output schema, the agent lacks details on parsing the response. It also does not mention error handling or entity-not-found cases. For a tool that returns a complex profile, more detail on the return format would be beneficial.

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

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

The input schema has 100% coverage with descriptions. The description adds value by explaining the type enum only supports 'company' and that value can be a ticker or CIK, explicitly stating that names are not supported and directing to resolve_entity. This provides meaningful guidance beyond the schema alone.

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

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

The description clearly states the tool provides a 'full profile of an entity' across multiple data sources, listing specific types (SEC filings, XBRL, patents, news, LEI). It distinguishes from siblings like resolve_entity and compare_entities by focusing on aggregating data from various packs. The verb 'profile' and resource description are specific, and it explicitly contrasts with sequential calls.

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

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

The description provides explicit guidance: for federal contracts, use usa_recipient_profile directly, and for names, use resolve_entity first. It implies the tool is for company entities only. However, it does not mention when not to use it for other entity types or if there are usage 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?

No annotations are provided, so the description carries full burden. It states deletion but does not disclose whether the action is reversible, requires confirmation, or has side effects. The description is minimal and does not add behavioral context beyond the verb 'Delete'.

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?

Single sentence, no wasted words. It is front-loaded with the action. However, it could be slightly expanded to include usage guidance without losing conciseness.

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

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

Given the tool is a simple deletion with one parameter and no output schema or annotations, the description is barely adequate. It does not explain what happens after deletion (e.g., success response, error on missing key). Completeness is low.

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% (1 parameter, described). The description adds no additional meaning beyond the schema's parameter description. Baseline of 3 is appropriate since the schema already documents the parameter adequately.

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 the action (Delete) and the resource (a stored memory by key). It is clear and concise, though it could be more specific about what kind of memory (e.g., user-generated or system). Among sibling tools, 'forget' is distinct from 'recall' and 'remember', which are likely retrieval and storage 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 does not provide guidance on when to use this tool versus alternatives like 'recall' or 'remember'. It lacks context on prerequisites (e.g., key must exist) or consequences (e.g., irreversible). No mention of when not to use it.

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

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

Annotations already indicate read-only, idempotent, non-destructive. The description adds context: it fetches the page, extracts title/description/key links, and emits standard markdown format. 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 fairly concise but contains useful details and use cases. It is front-loaded with the core function, then adds extra context. Only minor redundancy exists.

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

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

Given the tool's simplicity (2 params, no output schema, good annotations), the description covers purpose, behavior, output format, and use cases. It lacks details on error handling or rate limits but is sufficient for a 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% with clear parameter descriptions in the schema. The description does not add additional meaning beyond what the schema provides, so baseline score of 3 is appropriate.

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

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

The description clearly states the tool generates an llms.txt file for a given URL, explicitly mentioning the output format and use cases. It distinguishes from sibling tools like 'ai_visibility_check' by focusing on file generation rather than visibility scanning.

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 concrete use cases (client indexing, personal project, competitor audit) which imply appropriate scenarios, but does not explicitly state when not to use or compare to alternatives like 'scan_competitor_ai_presence'.

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 bears full burden. It explicitly states it returns current standings with specified fields, which is sufficient. However, it does not disclose if the data is cached, how often it updates, or any potential latency. Still, for a read-only tool, this is reasonably 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?

The description is a single concise sentence that front-loads the core purpose and lists returned data. Every word is informative with no redundancy.

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

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

Given zero parameters and no output schema, the description fully specifies what the tool does and what it returns. It is complete for its complexity level.

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 tool has no parameters (schema coverage 100%), so the description does not need to elaborate. It adds value by listing the returned fields (position, points, wins, driver, constructor), which compensates for the lack of output 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 current F1 driver championship standings, specifying the data fields (position, points, wins, driver name, constructor). This verb+resource combination is unambiguous and distinguishes it from sibling tools like get_driver or get_race_results.

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 the tool is for retrieving current standings, but does not explicitly state when to use it versus alternatives like get_driver or get_race_results. However, the context is clear enough for an agent to infer its 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 provided, the description carries the full burden. It explains the output (name, number, nationality, date of birth) but does not disclose any side effects, authorization needs, or limitations (e.g., whether it always returns data or can fail). This is adequate but not thorough.

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: two sentences that front-load the purpose and immediately list the returned fields. Every word adds value, with no redundancy.

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

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

Given the tool's low complexity (one parameter, simple retrieval) and no output schema, the description adequately covers what the tool returns. It could optionally mention that it expects a valid driver ID, but overall 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% (the single parameter driverId is well-described with examples). The description adds no further parameter meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.

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

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

The description clearly states the tool retrieves profile information for an F1 driver using their Ergast driver ID, listing specific fields (name, number, nationality, date of birth). It distinguishes itself from sibling tools like get_race_results and get_current_standings, which serve different purposes.

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

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

The description provides clear context for when to use this tool (to get driver profile info by ID) and implicitly distinguishes it from sibling tools. However, it does not explicitly state when not to use it or mention alternatives, leaving room for improvement.

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 are empty, so the description carries the full burden. It accurately describes a read-only operation (no side effects), but doesn't disclose any potential limits, caching behavior, or data freshness. The description is truthful but could add more behavioral context.

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

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

Two sentences, front-loaded with the main purpose, no wasted words. Efficiently conveys the tool's function and output.

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

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

Given the tool's simplicity (2 required string params, no output schema), the description is nearly complete. It states what it returns, but could optionally mention the data format or ordering.

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 descriptions already explain the two parameters well. The description adds no additional meaning beyond what the schema provides, which meets the baseline for high coverage.

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

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

The description clearly states the tool retrieves finishing results for a specific F1 race using season and round, and lists the returned fields (position, driver, constructor, status, points). This specific verb+resource combination distinguishes it from siblings like get_current_standings or get_schedule.

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 getting results by season/round but provides no explicit guidance on when to use this versus alternatives (e.g., get_current_standings). No exclusion criteria or prerequisites are mentioned.

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 are empty, so description bears full burden. It correctly identifies the tool as a read operation (no destructive hint) and lists the returned data fields, which is transparent. However, it does not mention pagination, response format, or if the data is cached.

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

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

The description is a single sentence of appropriate length, front-loading the core purpose. Every word contributes meaning, with no filler or redundancy.

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

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

Given the tool has only one parameter, no output schema, and no annotations, the description adequately covers the basics: what it returns and what input is needed. It could be slightly improved by mentioning if the data is for a specific season range or if it includes future races.

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% (season parameter is described). The description adds no extra meaning beyond the schema: it restates that season is a year string. It does not elaborate on accepted formats (e.g., '2025' vs '25') or range limitations.

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 a full race calendar/schedule for an F1 season, listing specific fields (round number, race name, circuit, location, date). It uses a specific verb ('Get') and resource ('full race calendar/schedule'), and distinguishes from siblings like get_race_results and get_current_standings by focusing on schedule 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 implies the tool is for retrieving the schedule for a given season, but does not explicitly state when to use this tool versus alternatives like get_race_results (which likely returns results for a specific race) or get_current_standings (standings). It provides no guidance on season selection (e.g., only historical seasons? future?) or constraints.

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

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

No annotations are provided, so the description must carry the behavioral information. It discloses the rate limit (5 per identifier per day) and mentions 'Free'. It does not specify side effects or data handling, but for a feedback tool this is adequate.

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

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

Three sentences, no redundant words. The core purpose and key rules are front-loaded. Every sentence provides essential information.

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

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

For a feedback tool with no output schema, the description covers purpose, usage, content restrictions, rate limits, and cost ('Free'). The nested context parameter is fully described in the schema, so the description does not need to repeat it. No gaps are evident.

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 all parameters. The description adds value by advising to 'Describe what you tried in terms of Pipeworx tools/data' for the message parameter, and hints at typical message length (1-2 sentences, 2000 chars max). 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 specifies the verb 'send feedback' and the resource 'Pipeworx team', and lists concrete use cases (bug reports, feature requests, missing data, praise). This clearly distinguishes it from sibling tools like get_driver or discover_tools.

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

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

The description explicitly states when to use the tool ('Use for bug reports...') and gives a negative instruction ('do not include the end-user's prompt verbatim'). It also mentions rate limits. It lacks explicit alternatives among siblings, but the unique purpose makes this less critical.

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 (readOnlyHint=true, destructiveHint=false) already indicate non-destructive read-only behavior. The description adds valuable context: it walks child markets, extracts dates/thresholds, sorts, and reports violations. This transparently explains the underlying logic and what the tool does 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: it begins with the core purpose, provides an illustrative example, explains usage, and lists the return format. Every sentence adds information. It is slightly verbose but not excessively so, earning a score of 4 for maintaining clarity without waste.

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 lacking an output schema, the description fully describes the return format (list of {market_a, market_b, gap_pp, suggested_trade}). It explains the condition for arbitrage and the tool's logic comprehensively. Given the tool's complexity (arbitrage detection), the description is complete and self-contained.

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

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

The input schema has 100% coverage, describing the 'event' parameter as a slug or URL. The description builds on this by clarifying what constitutes a valid input ('e.g. "when-will-bitcoin-hit-150k"') and how the tool processes it (extracts dates/thresholds from child markets).

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 uses specific verbs ('Find arbitrage opportunities', 'checks for monotonicity violations') and a concrete example to illustrate, effectively distinguishing it from sibling tools like polymarket_edges.

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 tells users to pass a Polymarket event slug or URL. It explains the condition for arbitrage (earlier market trading higher than later) and when to use the tool (events with multiple 'by date' or 'by threshold' markets). While it doesn't list alternatives among siblings, the guidance is clear and actionable for the intended use case.

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

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

The description provides rich behavioral detail beyond annotations: it explains the model (lognormal from FRED + live coinpaprika price), the process (scans top markets, groups by asset, fetches price history once, computes model probability, ranks by |edge|), and the output format (top N with trade direction). Annotations are readOnlyHint=true, openWorldHint=true, destructiveHint=false, all consistent with description.

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 concise, with 4 sentences that front-load the core purpose. While it packs in useful detail, it could be slightly tightened. Overall, 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?

Given no output schema, the description adequately explains the output (top N ranked by edge magnitude with suggested trade direction). It also covers the algorithm and data sources. The annotation set (readOnlyHint, openWorldHint) is complete. A slightly fuller explanation of the edge calculation could be added, but it's sufficient.

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

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

Schema coverage is 100%, so description adds limited value. The description mentions defaults (limit default 10, window default 1wk) but these are already in schema. The description does provide context that parameters control ranking and filtering, but this is marginal.

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 highest-volume Polymarket markets and returns those where Pipeworx data disagrees most with the market price. It specifies the model (V1, lognormal from FRED + coinpaprika) and the output (top N ranked by edge with suggested trade direction). This distinguishes it from siblings like polymarket_arbitrage and bet_research.

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

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

The description explicitly says it is built for the 'what should I bet on today' question and that agents/users can discover opportunities without paging through hundreds of markets. This provides clear usage context. It does not explicitly state when not to use it, but the purpose is specific enough to infer.

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 states the tool retrieves or lists memories but does not disclose behavioral traits like whether memory persists across sessions, or if there are limits on key length or number of memories. Since annotations are absent, more detail would be helpful.

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 core function, second provides usage guidance. Every word 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?

Given no output schema, description does not need to explain return values. It adequately explains behavior for a simple key-value retrieval. However, it could mention that keys are case-sensitive or note if listing all memories returns keys only or values too. Still, it is complete enough for most uses.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds context that omitting the key lists all memories, which is already implied by the schema (key not required) but clarified. 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?

The description clearly states the tool retrieves a memory by key or lists all memories, which distinguishes it from sibling tools like 'remember' (store) and 'forget' (delete). It explicitly specifies the resource (stored memory) and action (retrieve/list).

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 tells the agent when to use the tool: 'to retrieve context you saved earlier...' and implies when to omit key to list all. It differentiates from siblings: 'remember' and 'forget' are separate tools for different operations. No exclusion is needed as the 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?

No annotations provided, so description carries full burden. It discloses that the tool fans out to multiple sources in parallel, returns structured changes, total_changes count, and pipeworx:// URIs. Also explains the 'since' parameter formats. This provides a clear understanding of behavior without 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 at 4-5 sentences, front-loaded with the core purpose followed by functional details. Every sentence adds value, no fluff or repetition.

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 covers return structure (structured changes, count, URIs). It explains input parameters, use cases, and the parallel fan-out behavior. For a tool of this complexity, it is complete.

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

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

Schema coverage is 100%, but description adds significant value beyond schema: it explains the 'type' enum (only 'company'), provides examples for 'since' (ISO and relative), describes 'value' as ticker or CIK, and recommends typical values like '30d'. This enriches the schema meaning.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It specifies the supported entity type (company) and details the fan-out to multiple sources (SEC EDGAR, GDELT, USPTO). This distinguishes it from sibling tools like entity_profile or compare_entities.

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

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

Explicitly states when to use: 'Use for "brief me on what happened with X" or change-monitoring workflows.' It also provides guidance on the 'since' parameter with examples of ISO dates and relative formats, and suggests defaults like '30d' or '1m'. No explicit exclusion but the context is clear.

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

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

Discloses memory persistence behavior (authenticated users get persistent, anonymous sessions last 24 hours). No annotations provided, so description carries full burden; covers key behavioral traits without 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?

Two sentences, concise and front-loaded with core purpose, then usage guidance, then behavioral detail. 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 low complexity (2 required params, no output schema), the description adequately covers purpose, usage, and behavior. Lacks mention of return value, but return is likely trivial for a store operation.

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

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

Schema description coverage is 100%, so baseline is 3. Description does not add additional meaning beyond the schema's parameter descriptions, which already provide examples and context.

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 specifies verb 'store' and resource 'key-value pair in session memory', distinguishing it from sibling tools like recall (retrieve) and forget (delete).

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

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

Explicitly states when to use (save intermediate findings, user preferences, context) and hints at persistence differences between authenticated and anonymous sessions. Does not explicitly mention when not to use or 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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It indicates the tool is a read-only look-up by describing its return values and noting it replaces calls, but it does not detail auth requirements, rate limits, or side effects beyond mutation.

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-loaded with the core purpose, and every sentence adds information. No unnecessary words or repetitions.

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 simplicity of the tool (2 parameters, no output schema, no annotations), the description provides sufficient information: what the tool does, inputs, outputs, and benefits. It is complete for an agent to use effectively.

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

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

Schema coverage is 100%, but the description adds valuable meaning beyond the schema by providing concrete examples for 'value' (AAPL, 0000320193, Apple) and clarifying that 'type' currently only supports 'company'. This helps the agent understand valid input formats.

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: resolving an entity to canonical IDs across Pipeworx data sources in a single call. It specifies the entity type (company) and acceptable inputs (ticker, CIK, name), and distinguishes itself from alternatives by noting it replaces 2–3 lookup calls.

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

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

The description provides clear context for when to use this tool (for company entity resolution) and what inputs are acceptable. However, it does not explicitly state when not to use it or mention alternative sibling tools like ask_pipeworx or get_* functions.

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 that the tool probes each entity with ai_visibility_check, returns a ranked list with score/confidence/signal density, and treats the first entity as subject. The annotations already indicate read-only, idempotent, and non-destructive behavior, and the description adds context without contradiction.

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

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

Two sentences with a parenthetical example. Every sentence adds value, no waste. Front-loaded with the main action.

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 adequately describes the return format (ranked list with score, confidence, signal density). It also explains internal probing with another tool. For a 4-param tool, this is sufficient.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning: entities[0] is the subject, context disambiguates names, and it explains models and _apiKey usage. This extra context justifies a score above 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 compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks results, and surfaces most/least recognized. It distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (more general).

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 gives a specific use case (competitive AI-marketing audits) and implies alternatives (ai_visibility_check for single entities), but does not explicitly state when not to use it or list 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?

With no annotations provided, the description carries full burden. It discloses the input format, supported claim types, output components (verdict, structured form, actual value, citation, delta), and the underlying data sources. It lacks details on error handling or constraints but is generally transparent.

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

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

Three sentences with no wasted words: first states purpose, second adds version and scope, third lists outputs and benefit. Front-loaded and 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?

Given a simple single-parameter schema and no output schema, the description covers purpose, domain, output values, sources, and performance benefit. It omits edge cases but remains fairly complete.

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

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

The input schema already provides a clear description of the 'claim' parameter with examples (100% schema coverage). The description adds domain context and return details but does not significantly enhance parameter semantics 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: fact-check a natural-language claim against authoritative sources. It specifies the domain (company-financial claims for US public companies via SEC EDGAR + XBRL) and lists the output, making it distinct from sibling tools.

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

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

The description mentions that it replaces 4–6 sequential agent calls, implying a specific use case. It does not explicitly state when not to use it or mention alternatives, but the narrow domain provides clear context.

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