chess

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

Adds significant context beyond annotations: explains cost implication of Anthropic key ('you pay Anthropic directly'), details return structure per model and combined view. Annotations already indicate idempotent and read-only, so description enriches without redundancy.

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?

Description is well-organized with key info upfront, but slightly verbose with multiple sentences. Could be trimmed without losing clarity; still effective and not excessively long.

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 4 parameters, no output schema, but rich annotations, the description covers all essential aspects: what it does, how to configure models and API key, what returns, and typical use cases. No gaps identified.

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 parameter purpose in context: e.g., 'BYO key' for _apiKey, 'disambiguates common names' for context. This extra nuance pushes 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 action (probe LLMs), resource (knowledge about brand/product/topic), and output (visibility score per model). It distinguishes from similar tools like 'scan_competitor_ai_presence' by specifying generic entity probing and return structure.

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 clear context for when to use, mentioning default model and optional Anthropic API key for paid probing. Includes use cases (AI-marketing audits, brand checks) but does not explicitly exclude alternatives or contrast with 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 provided, the description carries the full burden of behavioral disclosure. It explains the tool's core behavior (natural language processing, automated tool selection, argument filling) and provides examples, but doesn't address important aspects like rate limits, authentication requirements, error handling, or what happens when no suitable data source is found.

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

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

The description is efficiently structured with a clear purpose statement, key behavioral explanation, and illustrative examples. Every sentence adds value, and the information is front-loaded with the most important details first.

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

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

For a tool with no annotations and no output schema, the description provides good coverage of the tool's purpose and usage but lacks details about behavioral constraints, error conditions, and response format. The examples help but don't fully compensate for the missing structural information about what the tool returns.

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

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

With 100% schema description coverage for the single parameter, the schema already documents the 'question' parameter adequately. The description adds value by providing concrete examples of appropriate question formats and emphasizing natural language input, though it doesn't add significant semantic detail beyond what the schema provides.

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

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

The description clearly states the tool's purpose with specific verbs ('Ask a question', 'get an answer') and resources ('from the best available data source'). It distinguishes itself from sibling tools by emphasizing natural language processing and automated tool selection, unlike the more specific data retrieval tools in the sibling 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 provides explicit guidance on when to use this tool ('No need to browse tools or learn schemas — just describe what you need') and includes concrete examples that illustrate appropriate use cases. It implicitly suggests alternatives by contrasting with manual tool selection approaches.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds useful behavioral context: it fans out to specific data packs based on bet type, returns an evidence packet plus comparison, and is the 'core demo product'. It does not disclose potential rate limits or failure modes, but given the annotations, it sufficiently informs an agent about what to expect.

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

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

The description is three sentences, each conveying important information. It is front-loaded with the core action. While slightly verbose in the last sentence about conversion, it still earns its place by emphasizing the value. Could be slightly tightened but remains effective.

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

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

Given no output schema, the description adequately hints at the output (evidence packet and market-vs-model comparison). It covers the major steps: market resolution, classification, fan-out to packs. It omits specifics about error scenarios or response structure, but for the level of detail needed by an agent, it 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 beyond schema: for 'market', it elaborates on the three input forms (slug, URL, question text) which matches the schema description. For 'depth', it explains 'quick = 2-3 evidence sources, thorough = full fan-out. Default thorough.' which adds context beyond the enum values. This helps the agent choose appropriately.

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: researching a Polymarket bet by pulling Pipeworx data, resolving the market, classifying bet type, fanning out to relevant data packs, and returning an evidence packet with market-vs-model comparison. It uses specific verbs ('Research', 'pulling', 'resolves', 'classifies', 'fans out', 'returns') and identifies the resource ('Polymarket bet', 'Pipeworx data'). This distinguishes it from siblings like 'ask_pipeworx' or 'validate_claim'.

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: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It also implies when not to use it (if you don't need Polymarket-specific context) and contrasts with alternative approaches ('agents that get bet-relevant context here convert better than ones that have to discover the packs themselves'). This provides clear usage 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 fully discloses behavior: it returns paired data and pipeworx:// URIs for each entity type, listing specific metrics (revenue, net income, etc. for companies; adverse-event reports, FDA approvals, trials for drugs). 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?

Four sentences with no fluff. Front-loads the core action ('Compare 2–5 entities side by side in one call') and efficiently covers purpose, metrics, and benefits. Every sentence is informative.

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

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

Given no output schema, the description adequately explains the return format (paired data + URIs). It covers both entity types with specific metrics. It doesn't mention error handling or invalid inputs, but the schema's enum and min/max constraints cover validation.

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 how each entity type maps to metrics, which is not in the schema. This helps the agent understand what data to expect for each 'type' value.

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 the metrics for 'company' and 'drug' types. It distinguishes itself from sibling tools by emphasizing multi-entity comparison, which is not offered by other tools like 'resolve_entity' (single entity).

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

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

The description explicitly says to use it for comparing 2-5 entities and claims it replaces 8-15 sequential calls, implying efficiency. While it doesn't explicitly list when not to use it, the context and mention of being a replacement for sequential calls guide appropriate usage.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the search functionality and return format ('most relevant tools with names and descriptions'), but lacks details on error handling, performance expectations, or authentication requirements, leaving some behavioral aspects unclear.

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

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

The description is front-loaded with the core purpose, followed by usage guidance, all in two efficient sentences with zero wasted words. Each sentence adds clear 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?

Given the tool's moderate complexity (search function with 2 parameters) and lack of annotations/output schema, the description provides good context on purpose and usage but could better cover behavioral aspects like result ordering or limitations, leaving minor 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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter semantics beyond what the schema provides, such as examples or usage nuances, meeting the baseline for high schema 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's purpose with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), and distinguishes it from siblings by emphasizing its discovery function rather than direct data retrieval like the sibling tools (get_games, get_leaderboards, etc.).

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

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

Explicit guidance is provided on when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task'), including a specific threshold (500+ tools) and context (finding tools for a task), with no misleading or contradictory advice.

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 bundling of multiple data sources, returns URI citations, and replaces many calls. However, it does not mention latency or any potential rate limits, but the read-only nature is 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?

The description is concise, well-structured, and front-loaded with the main action. It includes examples and alternatives without being 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 (bundles many data sources), the description covers the main types of returned data (SEC filings, revenue, patents, news, LEI) and mentions citation URIs. No output schema, but the description explains what is included. Could add more detail on return format, 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?

Input schema has 2 parameters with full description coverage. Description adds context: explains that type is only 'company' currently, value can be ticker or CIK with examples, and clarifies that names are not supported, directing to resolve_entity. This adds significant value beyond the schema.

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

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

The description clearly states it returns a full entity profile across multiple Pipeworx packs in one call. It specifies the supported type (company) and lists the data included (SEC filings, revenue, patents, news, LEI). It also distinguishes from sibling tools by noting it replaces 10-15 sequential calls and directs to usa_recipient_profile for federal contracts.

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 when to use (for a full entity profile) and when not to (for federal contracts, use usa_recipient_profile directly). It also hints at prerequisite steps: if only a name is available, use resolve_entity first.

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. While 'Delete' implies a destructive operation, the description doesn't disclose whether deletion is permanent, reversible, requires specific permissions, or has side effects. It also doesn't mention what happens if the key doesn't exist or any rate limits. This is inadequate for a mutation tool with zero annotation coverage.

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

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

The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary elaboration.

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

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

Given this is a destructive operation with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes a 'stored memory', what format the key uses, what happens on success/failure, or any behavioral traits. For a mutation tool with zero structured coverage, more context is needed.

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

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

Schema description coverage is 100%, with the single parameter 'key' fully documented in the schema. The description adds no additional meaning beyond what the schema provides ('Memory key to delete'), so it meets the baseline of 3 where the schema does the heavy lifting.

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

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

The description clearly states the action ('Delete') and resource ('a stored memory by key'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'recall' (which likely retrieves memories) or 'remember' (which likely stores memories), so it doesn't reach the highest score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when deletion is appropriate, what happens after deletion, or how it differs from sibling tools like 'recall' or 'remember'. This leaves the agent with minimal context for tool selection.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context beyond annotations, such as the process of fetching the page and extracting title/description/key links, and that the output is a single text blob. 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 of 4-5 sentences, front-loading the purpose, then process, output, and use cases. Every sentence is valuable 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, the description explains the output format ('single text blob ready to drop at site-root/llms.txt'). It covers the effect of fetching external URLs (relevant for openWorldHint) and provides use cases, making it fully complete for this tool's simplicity.

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

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

Schema coverage is 100%, so the baseline is 3. The description does not add significant parameter-specific meaning beyond the schema; it mentions 'url' implicitly but does not elaborate on semantics of 'url' or 'max_links'.

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 'a production-ready llms.txt file for any URL', specifying verb, resource, and scope. It distinguishes itself from siblings like 'ai_visibility_check' and 'scan_competitor_ai_presence' by focusing on outputting the standard llms.txt format.

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 such as getting a client's site indexed, drafting for own project, or auditing competitors. However, it does not mention when not to use this tool or compare it to alternatives, 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 are provided, so the description carries the full burden. It states the tool returns game data (URLs, time controls, results, ratings) but doesn't disclose behavioral traits like whether it's read-only (implied by 'Get'), rate limits, authentication needs, error conditions, pagination, or data freshness. For a tool with zero annotation coverage, this leaves significant gaps.

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

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

The description is two sentences, front-loaded with the core purpose and followed by return details. Every word earns its place with zero waste, making it highly 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?

Given the tool's moderate complexity (3 required parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and return data but lacks behavioral context (e.g., rate limits, errors) and usage guidelines. Without annotations or output schema, more detail would be helpful for safe and effective use.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters (username, year, month) with clear descriptions. The description adds no additional parameter semantics beyond implying the temporal filtering context, which is already covered by the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the specific action ('Get'), resource ('player's completed games'), and scope ('for a specific month'), distinguishing it from siblings like get_leaderboards (leaderboard data), get_player (player profile), and get_stats (statistics). It provides a complete picture of what the tool does.

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 'for a specific month' which implies temporal context, but provides no explicit guidance on when to use this tool versus alternatives like get_stats (which might include game data) or get_player (which might include game history). There's no mention of prerequisites, limitations, or comparative use cases.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes a read operation ('Get'), implying it's non-destructive, but doesn't mention any behavioral traits such as rate limits, authentication needs, pagination, or what happens if no data is available. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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

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

The description is a single, efficient sentence that states the tool's purpose clearly without unnecessary details. It's front-loaded with the core action and resource, and every word earns its place by specifying the scope (game formats). There's no waste or redundancy.

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

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

Given the tool's complexity (simple read operation with no parameters) and lack of annotations/output schema, the description is minimally adequate. It explains what the tool does but lacks details on behavioral aspects and usage context. Without an output schema, it doesn't describe return values, which could be a gap, but the simplicity of the tool makes this less critical.

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

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

The tool has 0 parameters, and the schema description coverage is 100% (since there are no parameters to describe). The description adds no parameter information, which is appropriate here. Baseline for 0 parameters is 4, as there's nothing to compensate for, and the description doesn't need to cover parameters.

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

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

The description clearly states the tool's purpose: 'Get the top-ranked Chess.com players across game formats including daily, rapid, blitz, and bullet.' It specifies the verb ('Get') and resource ('top-ranked Chess.com players'), and mentions the scope of game formats. However, it doesn't explicitly differentiate from sibling tools like get_stats (which might provide statistical data rather than rankings).

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

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

The description provides no guidance on when to use this tool versus alternatives like get_player or get_stats. It mentions the types of rankings (daily, rapid, etc.) but doesn't specify use cases, prerequisites, or exclusions. Without this context, an agent might struggle to choose between this and 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 provided, the description carries the full burden of behavioral disclosure. It indicates this is a read operation for public data, which implies no destructive actions or authentication needs, but does not mention rate limits, error conditions, or response format details. It adds basic context about what data is returned, but lacks depth on operational traits.

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

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

The description is a single, well-structured sentence that efficiently conveys the tool's purpose and key data points without unnecessary words. It is front-loaded with the main action and resource, making it easy to parse and understand quickly.

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, no output schema, no annotations), the description is adequate but has gaps. It specifies what data is returned, which helps compensate for the lack of output schema, but does not cover behavioral aspects like error handling or performance constraints, leaving room for improvement in 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 schema description coverage is 100%, with the single parameter 'username' fully documented in the schema. The description does not add extra parameter details beyond what the schema provides, but since there is only one parameter and the schema covers it well, a baseline of 4 is appropriate as the description doesn't need to compensate for gaps.

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

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

The description clearly states the specific action ('Get') and resource ('Chess.com player's public profile') with explicit details about what information is retrieved ('name, title, followers, country, join date, and last online time'). It distinguishes itself from sibling tools like 'get_games', 'get_leaderboards', and 'get_stats' by focusing on profile data rather than game history, rankings, or statistics.

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 retrieving public profile data, but does not explicitly state when to use this tool versus alternatives like 'get_stats' for performance metrics or 'get_games' for match history. No exclusions or prerequisites are mentioned, leaving some ambiguity about context-specific selection.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what data is returned but doesn't mention error handling (e.g., for invalid usernames), rate limits, authentication requirements, or whether the data is cached or real-time. This leaves significant gaps in understanding the tool's operational behavior.

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 efficiently conveys the tool's purpose and scope without unnecessary words. It front-loads the key action and resource, making it easy to parse quickly.

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

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

Given the tool's moderate complexity (retrieving multi-format statistics) and lack of annotations or output schema, the description adequately covers what data is returned but falls short on behavioral aspects like error handling and performance. It's complete enough for basic use but lacks depth for robust agent interaction.

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

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

The schema description coverage is 100%, with the single parameter 'username' clearly documented as 'Chess.com username'. The description adds no additional semantic context beyond this, such as format constraints or examples. Since the schema does the heavy lifting, the 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 specific action ('Get') and resource ('a player's game statistics'), listing the exact data points returned (rating, best rating, win/loss/draw record) and the formats covered (daily, rapid, blitz, bullet). It distinguishes from sibling tools like 'get_games' (which likely returns game details) and 'get_player' (which likely returns profile information).

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_player' or 'get_games'. It doesn't mention prerequisites, such as whether the username must exist or be valid, nor does it specify any context for when this tool is appropriate versus other statistical 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?

Although no annotations exist, the description discloses rate limiting, the free nature, and content constraints. It does not describe what happens after submission (e.g., storage or response), 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?

The description is three sentences, front-loaded with purpose and use cases, no extraneous words. 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 tool's simplicity (feedback submission), the description covers purpose, usage, parameter hints, and rate limiting. No output schema is 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?

Schema description coverage is 100%. The description adds value for the 'message' parameter by advising specificity and omitting verbatim prompts. For 'type' and 'context', the schema already covers their meanings.

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 sends feedback to the Pipeworx team, enumerates specific use cases (bug reports, feature requests, missing data, praise), and is distinct from sibling tools which are for data retrieval or memory.

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

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

The description provides explicit usage guidance: what to include (what was tried, omit verbatim prompts) and a rate limit (5 per day). It does not explicitly mention when not to use it, but the context implies it's for feedback only.

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 the annotations (readOnlyHint, idempotent, etc.), the description adds valuable behavioral context: data source, no PII, caching behavior. This fully informs the agent of important traits.

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

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

The description is concise (three sentences) and front-loaded with the main purpose. 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 simple tool with one optional parameter and no output schema, the description covers purpose, usage, behavior, and parameter meaning. It is complete and sufficient.

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

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

The parameter 'window' is fully described with enum values and guidance on when to use each. The description adds meaning beyond the schema's enum list.

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 trending tools, packs, and total call volume over a recent window, distinguishing it from sibling tools like discover_tools by focusing on what other AI agents are using.

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 specific use cases for when to use this tool, providing clear context. However, it does not explicitly mention when not to use it or name alternative tools for comparison.

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

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

The description fully discloses the behavioral traits: it is read-only (consistent with annotations), walks child markets, extracts dates/thresholds, sorts them, and reports violations. 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 detailed but well-structured, starting with purpose, then logic, then output format. It is slightly longer than necessary but remains clear 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 the tool's complexity (explaining monotonicity violations) and the presence of annotations and schema, the description provides complete context: what it does, how it works, and what it returns. No output schema exists, but the description fills that gap.

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

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

The schema covers the single parameter 'event' with a description. The tool description does not add extra meaning beyond the schema's description, so baseline score 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 specifies the tool's function: finding arbitrage opportunities via monotonicity violations in Polymarket events. It explains the underlying logic with an example, and the unique purpose distinguishes 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 states when to use the tool (for events with multiple 'by date' or 'by threshold' markets). However, it does not explicitly mention when not to use it or provide alternatives, which would strengthen guidance.

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

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

The description goes beyond the annotations (readOnlyHint, openWorldHint, destructiveHint) by detailing the methodology: uses lognormal model from FRED and live coinpaprika price, groups by asset, fetches price history once, computes model probability per market, and ranks by edge. It also notes that it returns suggested trade direction. 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 somewhat lengthy but well-structured. It front-loads the main purpose and then provides necessary details. All sentences are relevant and contribute to understanding the tool. Could be slightly more concise, but is not excessively verbose.

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

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

Given the tool's complexity and lack of output schema, the description adequately explains the process, return value (top N edges with direction), and model scope (crypto-price bets). It does not specify exact output format, but that is acceptable without an output schema.

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

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

Schema description coverage is 100%, so baseline is 3. The tool description adds context for why parameters exist (e.g., limit for top N, window for volume filter, min_edge_pp for threshold), but does not provide new semantics beyond what the schema already describes 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 clearly states that the tool scans high-volume Polymarket markets, computes edges using Pipeworx data and a model, and returns the top-ranked ones by edge magnitude. It distinguishes itself from sibling tools like polymarket_arbitrage by specifying its focus on Pipeworx disagreement and its use case for discovering betting opportunities.

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

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

The description explicitly says it is built for the 'what should I bet on today' question, providing a clear use case. It encourages agents to discover opportunities without manual browsing. However, it does not explicitly mention when not to use it or compare to alternatives like polymarket_arbitrage.

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 behavior. Description adds details on output (raw probabilities, spreads) and notes the arb signal nature, which goes 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?

Well-structured with clear mode separation and front-loaded purpose. Slightly verbose but every sentence adds value. Minor redundancy in repeating the list of topics already in schema.

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, modes, parameters, and return format despite lacking output schema. Complete for a read-only tool with good annotations and 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 description coverage is 100%. Description enriches by listing topic shortcuts, explaining override behavior, and clarifying that parameters are optional. Adds 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 computes cross-venue spreads between Kalshi and Polymarket, distinguishing it from siblings like polymarket_arbitrage (likely internal Polymarket). It specifies two modes and concrete resources.

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 describes two usage modes (topic shortcuts and explicit pairings) and when each applies. Lacks explicit when-not-to-use or alternatives, but context is clear.

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

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

With no annotations provided, the description carries the full burden. It discloses that memories can be retrieved from 'earlier in the session or in previous sessions' which adds useful context about persistence. However, it doesn't mention error handling, performance characteristics, or what happens when a non-existent key is provided.

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 perfectly front-loaded: the first sentence explains the core functionality, the second provides usage context. Every word earns its place with zero waste 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?

For a simple retrieval tool with 1 optional parameter and no output schema, the description is quite complete. It explains what the tool does, when to use it, and the parameter semantics. The main gap is lack of information about return format or error conditions, which would be helpful given no output schema.

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

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

The schema has 100% description coverage, so the baseline is 3. The description adds value by explaining the semantic meaning of omitting the key parameter: 'omit to list all keys' and connecting it to the tool's dual functionality. This goes beyond what the schema provides.

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

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

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes this tool from siblings like 'remember' (which stores) and 'forget' (which removes).

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

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

The description explicitly states when to use this tool ('to retrieve context you saved earlier') and provides clear usage guidance: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It also distinguishes between the two modes of operation.

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

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

With no annotations provided, the description fully discloses behavior: parallel fan-out to SEC EDGAR, GDELT, and USPTO; accepted 'since' formats (ISO date or relative); and return format (structured changes, count, pipeworx:// URIs). It is thorough and accurate.

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, behavior, parameter formats, and usage. It is front-loaded with the core function and avoids unnecessary details, making it easy for an agent to parse quickly.

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 lack of an output schema, the description adequately describes the return structure (structured changes, total_changes count, pipeworx:// URIs). It covers all parameters, explains the parallel fan-out, and notes the supported entity type. No aspect of the tool's functionality is left unexplained.

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?

Although the input schema already has 100% descriptive coverage, the description adds critical context: it explains the entity type limitation ('only company supported'), gives examples for 'since' (e.g., '7d', '30d', '3m', '1y'), and clarifies that 'value' can be a ticker or CIK. This enhances the agent's 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: retrieving what's new about an entity since a given time. It specifies the entity type ('company') and the multiple data sources (SEC EDGAR, GDELT, USPTO) it fans out to, distinguishing 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?

The description explicitly suggests use cases: 'brief me on what happened with X' or change-monitoring workflows. While it doesn't explicitly state when not to use it, the context is clear enough for an agent to decide. The sibling tools cover other purposes, reducing ambiguity.

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 and does well by disclosing key behavioral traits: it explains persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), which is crucial context beyond basic storage functionality. However, it doesn't mention rate limits, size constraints, or error conditions.

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

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

Two sentences with zero waste - the first states the core functionality with examples, the second adds crucial behavioral context about persistence. Every sentence earns its place and the description is appropriately sized for a simple storage tool.

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

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

For a 2-parameter tool with no annotations and no output schema, the description provides good context about persistence behavior and usage scenarios. However, it doesn't explain what happens on duplicate keys, return values, or error conditions, leaving some gaps for a mutation 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 description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions, but doesn't need to compensate for gaps. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verb ('store') and resource ('key-value pair in your session memory'), and distinguishes it from sibling 'recall' by focusing on storage rather than retrieval. It provides concrete examples of what to store ('intermediate findings, user preferences, or context across tool 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 ('save intermediate findings, user preferences, or context across tool calls'), but doesn't explicitly state when not to use it or name alternatives. It implies usage for memory persistence but doesn't contrast with other storage mechanisms or sibling tools like 'forget'.

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

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

With no annotations, the description carries full burden. It discloses that the tool returns ticker, CIK, company name, and pipeworx:// URIs, and implies it is a read-only lookup. No side effects or auth requirements are mentioned, but the core behavior is well-described.

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

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

The description is extremely concise: two sentences that front-load the purpose and then provide specific details. 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 has no output schema, the description gives a good idea of what is returned and how it simplifies workflow. It could mention error handling or limitations, but for a simple lookup 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% with descriptions for both parameters. The description adds context by explaining the accepted formats for 'value' (ticker, CIK, name) and the return values, going 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 an entity to canonical IDs, specifying the resource (entities like companies) and the operation. It includes examples and distinguishes from sibling tools like ask_pipeworx or get_games.

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 (single call replacing 2–3 lookup calls) and specifies accepted inputs (ticker, CIK, name). It does not explicitly state when not to use it, but the context is clear for company resolution.

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

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

Annotations already declare readOnly, idempotent, openWorld. The description adds that it internally calls ai_visibility_check, returns a ranked list with score/confidence/signal density, and surfaces comparisons. No contradiction.

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

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

The description is four sentences, front-loaded with the main action and purpose. 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?

Given 4 parameters (1 required) and no output schema, the description adequately explains that the return is a ranked list with per-entity score, confidence, and signal density. It could mention minimum entity count (2) but that is implied by 'multiple entities' and 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% with descriptions for all 4 parameters. The description does not add significant extra meaning beyond what the schema already provides for each parameter, so baseline 3 is appropriate.

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

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

The description clearly states it compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and identifies most/least recognized. It gives a concrete use case example, distinguishing it from single-entity probes like ai_visibility_check.

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's useful for competitive AI-marketing audits and implies use when comparing multiple entities. It does not list when not to use or name alternatives explicitly, but the context of sibling tools makes the distinction 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; description carries burden. Mentions v1 support limited to company-financial claims, returns specific verdicts and citation format. Does not disclose external API calls, rate limits, or error handling, but offers reasonable behavioral context for a read-only tool.

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

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

Three sentences, each earning its place: action/scope, limitations, output summary. Front-loaded with verb and context. No extraneous 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 one simple parameter, no output schema, and zero annotations, description covers core functionality, supported claims, and benefits. Missing edge-case or error behavior, but sufficient for v1.

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?

Single required parameter 'claim' with schema description and examples. Schema coverage 100% means description need not elaborate further; baseline 3 is appropriate. Description adds domain context but no additional syntax guidance.

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 specifies verb (fact-check), resource (natural-language claim against authoritative sources), domain (company-financial claims for US public companies via SEC EDGAR + XBRL), and return types (verdicts, values, citation). Distinguishes from siblings like compare_entities or entity_profile.

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?

States it replaces 4-6 sequential agent calls, implying it should be used when a single fact-check is needed instead of manual multi-step resolution. Does not explicitly list when not to use or alternative tools, but context is clear.

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