colorapi

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

The description adds beyond annotations by detailing the default model (Workers AI Llama-3.3-70b free), the BYO key mechanism for Anthropic, and the return structure (per-model and combined view). It aligns with readOnlyHint and idempotentHint by describing a read-only probe.

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 concise sentences, front-loaded with the primary action and output. Every sentence adds information: purpose, defaults, return structure, example use cases. No wasted words.

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

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

Given the complexity (multiple models, optional API key, per-model scoring) and no output schema, the description fully explains inputs and returns. It provides sufficient context for an agent to decide use, though potential overlaps with siblings like 'scan_competitor_ai_presence' are not addressed.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining default model, optional key requirement, and context use for disambiguation, e.g., 'pass `_apiKey` to also probe Anthropic' and 'helps disambiguate common names'.

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

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

The description uses a specific verb ('Probe') and resource ('LLMs for what they know about a business/brand/product/topic') and clearly states the scoring output (0-100 per model). It distinguishes from siblings like 'ask_pipeworx' and 'entity_profile' by focusing on multi-LLM visibility auditing.

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

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

The description explicitly lists use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. It implies when to use (visibility probing) but does not explicitly say when not to use or name alternatives, though the sibling list provides implicit contrast.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it picks the right tool, fills arguments automatically, and returns results. However, it doesn't mention limitations like rate limits, response formats, or error conditions that would be helpful for complete transparency.

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

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

The description is perfectly concise and well-structured: it starts with the core functionality, explains the value proposition, provides clear examples, and every sentence earns its place. The information is front-loaded with no wasted words.

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

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

Given the tool's complexity (natural language processing with automatic tool selection) and lack of annotations/output schema, the description does a good job explaining what the tool does. However, it could benefit from mentioning what types of results to expect or any limitations, making it slightly incomplete for full contextual understanding.

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

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

The input schema has 100% description coverage, so the schema already documents the single 'question' parameter adequately. The description adds minimal value beyond the schema by reinforcing that questions should be in 'plain English' or 'natural language,' but doesn't provide additional syntax or format details.

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

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

The description clearly states the tool's purpose: to answer questions in plain English by selecting the best data source and returning results. It provides specific examples that illustrate the verb+resource combination and distinguishes it from sibling tools by emphasizing natural language processing rather than structured operations.

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

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

The description explicitly states when to use this tool: for asking questions in plain English when you don't want to browse tools or learn schemas. It provides clear examples of appropriate use cases and implicitly distinguishes it from sibling tools that require more structured inputs.

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?

Goes beyond annotations (readOnlyHint, openWorldHint, destructiveHint) by detailing internal fan-out logic (crypto+fred+gdelt for BTC bet, etc.), market resolution, bet classification, and return of comparison. 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?

Single paragraph, well-structured: starts with verb, then input types, then fan-out logic, then example use cases. Every sentence adds value, no waste. Effectively front-loaded with 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?

Covers main functionality and output (evidence packet + comparison). No output schema, but description gives enough for an agent to understand expected result. Lacks explicit return structure format, but sufficient given tool 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%, but description adds value: for 'market' explains three input formats (slug, URL, question text); for 'depth' specifies default (thorough) and clarifies 'quick' vs 'thorough' meaning (2-3 sources vs full fan-out). This adds beyond the schema enum.

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

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

Clearly states the main function: research a Polymarket bet by pulling Pipeworx data. Specifies input types (slug, URL, question text) and outputs (evidence packet + market-vs-model comparison). Differentiates from siblings by positioning as the core demo product that saves agents from discovering packs.

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 use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. Implies it's better than alternatives that require discovering packs themselves, but does not give explicit when-not-to-use.

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

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

No annotations are provided, so the description must carry full burden. It explains the data returned for each type and mentions URIs, but does not disclose whether the operation is read-only or if it has side effects. For a tool that likely reads data, this is acceptable but not comprehensive.

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

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

Three sentences front-load the purpose, then detail type-specific behavior, and finally state efficiency gains. Every sentence adds unique 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 no output schema, the description adequately explains the return includes paired data and URIs. It covers the data per type and the entity range. Minor lack of error handling or prerequisites but sufficient for an agent to invoke.

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

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

Schema coverage is 100%. The description adds specific meaning by listing the fields retrieved per type (e.g., revenue, adverse events), which goes beyond the schema's enum and array descriptions.

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

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

The description clearly states the tool compares 2–5 entities of type 'company' or 'drug' side by side, specifying the data fields retrieved for each type. It distinguishes itself from siblings by being the only comparison tool on this server.

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 says when to use it (to compare entities and replace sequential calls) but does not explicitly state when not to use it or provide alternatives. The context suggests it is for batch comparison, which is helpful.

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

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

No annotations are provided, so the description carries the full burden. It mentions the conversion outputs (hex, HSL, HSV, CMYK, color name) but does not disclose behavioral traits such as error handling for invalid inputs, performance characteristics, or any limitations (e.g., precision of conversions). This leaves gaps in understanding how the tool behaves beyond its basic function.

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 front-loads the core purpose (conversion) and lists all outputs without unnecessary details. Every word contributes to understanding the tool's function, making it appropriately sized and well-structured.

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

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

Given the tool's moderate complexity (conversion with multiple outputs), no annotations, and no output schema, the description is somewhat complete but lacks details on return values, error cases, or behavioral context. It covers what the tool does but not how it operates or what results to expect, 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?

Schema description coverage is 100%, with clear documentation of r, g, b parameters as RGB channels with range 0-255. The description adds no additional parameter semantics beyond what the schema provides, such as format details or constraints. Baseline 3 is appropriate since the schema adequately covers parameter information.

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 ('convert an RGB color') and the comprehensive outcome ('to all other color formats... and get its closest color name'). It distinguishes from sibling tools like 'generate_scheme' and 'identify_color' by focusing on format conversion rather than scheme generation or color identification.

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

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

The description implies usage when RGB input is available and conversion to multiple formats is needed, but it does not explicitly state when to use this tool versus alternatives like 'generate_scheme' or 'identify_color'. No exclusions or prerequisites are mentioned, leaving usage context somewhat vague.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it's a search operation that returns relevant tools with names and descriptions, and specifies it should be called first in large tool catalogs. However, it lacks details on rate limits, error handling, or authentication needs, which would be beneficial for a discovery tool.

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

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

The description is front-loaded with the core purpose, followed by usage guidance, all in two concise sentences. Every sentence adds value: the first explains what the tool does, and the second provides critical context on when to use it, with no wasted words.

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

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

Given the tool's moderate complexity (search functionality with 2 parameters), no annotations, and no output schema, the description is largely complete. It covers purpose, usage context, and behavioral aspects, but could improve by hinting at return format or error cases, though not strictly required since output schema is absent.

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%, so the schema already fully documents the parameters (query and limit). The description adds minimal value beyond the schema by mentioning 'describing what you need' for the query, but does not provide additional syntax, format, or usage context for parameters. This meets 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 resources ('tool catalog'), and explicitly distinguishes it from sibling tools by emphasizing its role in discovering tools among 500+ options, unlike the color-related siblings (convert_color, generate_scheme, identify_color).

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

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

The description provides explicit guidance on when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task') and includes a clear alternative scenario (implicitly suggesting not to use it for simple tasks with fewer tools). This directly addresses when and why to choose this tool over others.

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

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

The description discloses the bundled nature of the call, the return format (pipeworx:// citation URIs), the supported entity type (company only), and the limitation that names are not supported. With no annotations provided, the description fully reveals behavioral traits.

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

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

The description is concise (5 sentences) and front-loaded with the core purpose. Every sentence earns its place: listing data sources, noting the bundling advantage, and providing an alternative for federal contracts.

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 return format (citation URIs) and the included data. It could be slightly more specific about the structure of the profile, but overall it is sufficient for an AI 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?

Input schema coverage is 100%, with detailed descriptions for 'type' and 'value' already explaining supported values and format. The tool description reinforces these points but does not add new parameter-level meaning 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: fetching a full profile of an entity across multiple Pipeworx packs. It lists specific data sources (SEC filings, XBRL, USPTO patents, news, LEI) and distinguishes itself from siblings like resolve_entity and usa_recipient_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?

Explicit guidance is provided: the tool replaces multiple sequential calls, and for federal contracts, 'usa_recipient_profile' should be used instead. Parameter descriptions also advise using 'resolve_entity' when only a name is available.

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 but only states the basic action ('Delete'). It doesn't disclose behavioral traits like whether deletion is permanent/reversible, permission requirements, error handling for non-existent keys, or side effects. This is inadequate for a destructive operation 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 front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place 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 destructive tool with no annotations and no output schema, the description is incomplete. It lacks crucial context: what 'stored memory' means in this system, confirmation of deletion, error responses, or impact on related data. The agent must guess these behavioral aspects.

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 parameter 'key' fully documented in the schema. The description adds no additional meaning beyond implying the key identifies a memory, which the schema already covers. Baseline 3 is appropriate when the schema does all the work.

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'), making the purpose immediately understandable. It distinguishes from sibling 'recall' (which retrieves) and 'remember' (which stores), but doesn't explicitly contrast with other siblings like 'convert_color' or 'identify_color', keeping it at 4 rather than 5.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing memory key), exclusions, or compare it to other deletion-related tools (none in siblings), leaving the agent to infer usage from context alone.

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

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

Annotations declare readOnly, openWorld, idempotent, non-destructive. Description adds that it fetches and extracts, outputs markdown ready for site-root/llms.txt. No contradictions. Minor missing detail about rate limits or robots.txt, but acceptable.

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

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

Three sentences: purpose, process, use cases. No redundancy, front-loaded, each 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 annotations cover safety, and no output schema exists, description explains output format and placement. Parameters are fully documented. Adequate for agent to invoke correctly.

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

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

Schema coverage is 100% with detailed descriptions for both parameters. Description restates the same info (e.g., URL example, link count limits). Baseline 3 per instructions.

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 generates a production-ready llms.txt file for any URL, with specific verb 'generate', resource 'llms.txt file', and scope. It distinguishes from siblings as no other tool does this.

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

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

Explains three explicit use cases (indexing client site, drafting own project, auditing competitor). Lacks explicit when-not-to-use or alternatives, but context is clear given no sibling overlap.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns 'a set of harmonious colors' but doesn't describe the return format (e.g., array of hex values), potential errors (e.g., invalid hex input), or any side effects. For a tool with zero annotation coverage, this leaves significant behavioral gaps, though it does mention the output is based on the chosen mode.

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 core functionality: generating a color scheme from a seed hex with mode-based harmony. It's front-loaded with the main purpose and avoids unnecessary details, making it highly concise with zero 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?

Given the tool has no annotations and no output schema, the description is incomplete. It doesn't explain what the returned 'set of harmonious colors' looks like (e.g., format, structure), potential constraints, or error handling. For a tool with 3 parameters and no structured output information, the description should provide more context to be fully helpful.

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 (hex, mode, count) with details like format, enums, and defaults. The description adds minimal value beyond the schema by implying the hex is a seed and mode influences harmony, but doesn't provide additional syntax or usage context. This meets the baseline of 3 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: 'Generate a color scheme from a seed hex color' specifies the verb (generate) and resource (color scheme). It distinguishes from sibling tools like 'convert_color' and 'identify_color' by focusing on scheme generation rather than conversion or identification. However, it doesn't explicitly differentiate from potential overlapping functionality, keeping it at a 4 rather than a 5.

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 'convert_color' or 'identify_color'. It mentions 'based on the chosen mode' but doesn't explain when to choose different modes or what contexts suit this tool. There are no explicit when/when-not statements or prerequisites, resulting in minimal 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return values (color name, format representations, contrast info), which is helpful, but it doesn't describe error handling (e.g., invalid hex values), performance characteristics, rate limits, or authentication needs. For a tool with zero annotation coverage, this leaves significant behavioral 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 extremely concise and front-loaded, consisting of just two sentences that directly state the tool's purpose and output. Every sentence earns its place by providing essential information without redundancy or fluff, 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 low complexity (one parameter, no annotations, no output schema), the description is somewhat complete but has gaps. It explains what the tool does and what it returns, which is adequate for basic use. However, without annotations or an output schema, it lacks details on error cases, behavioral traits, and exact return structure, making it minimally viable but not fully comprehensive.

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

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

Schema description coverage is 100%, so the schema already documents the 'hex' parameter thoroughly. The description adds marginal value by reinforcing the purpose ('identify a color by its hex value'), but it doesn't provide additional syntax, format details, or constraints beyond what the schema specifies. With only one parameter and high schema coverage, a baseline of 3 is appropriate, but the description's clarity elevates it slightly.

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 ('identify a color by its hex value') and the resource (color information). It distinguishes from sibling tools like 'convert_color' (which likely transforms between formats) and 'generate_scheme' (which likely creates color palettes) by focusing on identification and comprehensive representation extraction rather than conversion or generation.

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

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

The description implies usage when you have a hex value and need detailed color information, but it doesn't explicitly state when to use this tool versus alternatives like 'convert_color' or 'generate_scheme'. There's no guidance on prerequisites, exclusions, or specific scenarios where this tool is preferred over others, leaving usage context somewhat ambiguous.

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 discloses important behavior: rate limiting ('5 messages per identifier per day') and the fact that it is free. It also implies asynchronous handling (no immediate response). The transparency is good but could be improved by stating what happens after sending (e.g., whether a confirmation 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?

The description is concise (6 sentences) and well-structured: it starts with the primary action, followed by use cases, content guidelines, and rate limit. 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 the tool has three parameters, no output schema, and no annotations, the description adequately covers purpose, usage guidelines, and behavioral constraints (rate limit). It lacks information about what the user can expect after sending feedback (e.g., no response or acknowledgment), but for a feedback tool this is often acceptable. Overall, it provides sufficient context for correct 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 coverage is 100% with detailed descriptions for all three parameters (type, context, message). The description adds general guidance on message content but does not significantly enhance parameter understanding beyond what the schema already provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool's action ('Send feedback'), the target ('Pipeworx team'), and enumerates specific use cases (bug reports, feature requests, missing data, praise). It distinguishes the tool from siblings since no other sibling serves a feedback purpose.

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 various feedback types and provides concrete guidelines on what to include ('Describe what you tried in terms of Pipeworx tools/data') and what to avoid ('do not include end-user prompt verbatim'). It also mentions a rate limit. However, it does not explicitly state when not to use the tool, but no alternative feedback tool exists among siblings, so this is acceptable.

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 adds context beyond annotations by disclosing caching behavior (5min-1h), data source (Cloudflare analytics engine), aggregation nature (no PII), and that it is self-aggregating. This enriches the readOnly, idempotent, and non-destructive hints already 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?

The description is concise with seven sentences, each adding meaningful information. The first sentence front-loads the core purpose, followed by use cases and behavioral details with no wasted words.

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

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

Given the simplicity of a single optional parameter with full schema coverage and annotations, the description adequately covers purpose, usage, and behavior. However, it does not specify the return structure (e.g., format of top tools and packs), which is a minor 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 description adds meaning to the 'window' parameter by explaining the behavioral difference between shorter and longer windows. While the schema already provides an enum, the description clarifies the rationale for choosing among options.

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 top tools, packs, and call volume over recent windows. It specifies the verb 'returns' and the resource 'top tools, top packs, total call volume'. It distinguishes from sibling tools like 'discover_tools' by focusing on aggregated trend 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 lists three explicit use cases: discovering hot data sources, confirming popular tools, and aligning use cases. It also explains the trade-off between window lengths. However, it omits explicit guidance on when not to use or 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true. The description adds context on how the tool searches and checks monotonicity across modes, with no contradictions. It fully discloses behavioral traits 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 concisely structured with clear mode separation. Every sentence earns its place, providing necessary details without repetition or fluff.

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

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

Given the moderate complexity and lack of output schema, the description is complete: it covers both modes, explains return values (ranked opportunities with suggestions), and provides sufficient context for correct tool invocation.

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

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

Schema coverage is 100%, so baseline 3. The description adds significant value by explaining the two modes, providing examples, and detailing how each parameter controls behavior, far exceeding schema documentation.

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 finds arbitrage opportunities on Polymarket by checking monotonicity violations. It distinguishes two modes (event and topic) with specific behaviors, and differentiates from siblings by focusing on arbitrage detection.

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

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

The description explicitly explains when to use each mode: event for single-event checks, topic for cross-event searches. It provides examples and reasoning for why cross-event mode is necessary, giving clear guidance on 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?

Annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false) are consistent. Description adds behavioral details: scans top markets, groups by asset, fetches price history once, computes model probability from FRED and coinpaprika, ranks by edge. 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?

Description is moderately concise and front-loaded with the core action. It includes technical details (V1, lognormal model) that may not be essential but are not overly verbose. Structure is logical.

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 3 optional parameters, no output schema, and simple effects, the description adequately explains its purpose and behavior. It could mention output format briefly, but current content is sufficient for an agent to select and invoke correctly.

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

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

Schema coverage is 100% with descriptions for each parameter. Description adds default values (limit=10, window=1wk, min_edge_pp=0.5) not present in schema, and explains the role of each parameter in the ranking process.

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

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

Description explicitly states it scans high-volume Polymarket markets, finds discrepancies between Pipeworx data and market prices, and returns top edges. This is specific and distinguishes from sibling tools like polymarket_arbitrage which likely deals with cross-platform arbitrage.

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?

Description clearly states the tool is built for 'what should I bet on today' and helps discover opportunities without manual browsing. However, it does not explicitly contrast with alternatives like ask_pipeworx or bet_research, 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds valuable context about the two modes, how parameters interact (explicit overrides topic), and the return fields (leg prices and spread), going beyond what annotations provide.

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

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

The description is well-structured with labeled sections (TWO MODES, Returns) and front-loads the main purpose. It is concise yet informative, with each sentence contributing value. The background on price delta is relevant but could be slightly trimmed.

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

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

Given three optional parameters and no output schema, the description covers the return format (leg prices and spread) sufficiently. It explains the two modes and parameter interactions. However, the exact output structure (e.g., keys) is not specified, leaving some ambiguity for agents needing precise parsing.

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 significantly enhances meaning by explaining the two modes, the concept of pre-mapped topics, and how explicit parameters override them. It adds nuance not captured in the schema's brief descriptions.

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

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

The description clearly states the tool's function: computing the cross-venue spread between Kalshi and Polymarket for the same resolving question. It specifies two modes (topic and explicit tickers) and the output format, distinguishing it from sibling tools like polymarket_arbitrage and 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 explains when to use the tool (to capture arbitrage signals from price differences between venues) and describes two usage modes. However, it lacks explicit guidance on when not to use it or comparisons to specific alternatives, though the context signals list 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 dual behavior (retrieve by key vs. list all) and persistence across sessions, which is valuable. However, it doesn't address potential edge cases like what happens with invalid keys, whether there are size/rate limits, or authentication requirements. For a tool with no annotations, this leaves some behavioral aspects unspecified.

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

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

The description is perfectly concise with two sentences that each serve distinct purposes. The first sentence explains the dual functionality and parameter usage. The second sentence provides the usage context. There's no wasted text, repetition, or unnecessary elaboration. The information is front-loaded with the core functionality stated immediately.

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

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

For a single-parameter tool with good schema coverage but no annotations or output schema, the description does well. It explains the tool's purpose, usage guidelines, and behavioral context. The main gap is the lack of output information - we don't know what format memories are returned in or what the list output looks like. However, given the tool's relative simplicity, the description provides most of what an agent needs to use it correctly.

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

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

The schema has 100% description coverage, so the baseline is 3. The description adds meaningful context by explaining the semantic implication of omitting the key parameter: 'omit to list all keys' (not just 'omit key' as in the schema). This clarifies that omitting the parameter changes the tool's behavior from retrieval to listing, which is valuable guidance beyond the schema's technical specification.

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 from sibling tools like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations. The phrase 'by key' adds specificity about the retrieval mechanism.

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

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

The description provides explicit guidance on when to use this tool: 'to retrieve context you saved earlier in the session or in previous sessions.' It also gives clear parameter-based usage rules: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' This tells the agent exactly when to use each mode 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?

Without annotations, the description carries full burden. It explains the parallel fan-out to multiple sources, the accepted `since` format (ISO or relative), and the return structure (changes, total_changes, URIs). It notes the limitation 'type="company" only supported today.' Missing details on error handling or rate limits but sufficient for safe invocation.

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

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

The description is three sentences with no wasted words. It front-loads the core purpose, then details data sources, parameter formats, and return types. Each sentence serves a purpose, making it easy for an AI 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 3 parameters, no output schema, and moderate complexity (multi-source aggregation), the description is fairly complete. It covers input formats, behavior, and return fields (structured changes, total_changes, URIs). Minor gaps like pagination or specific error messages are acceptable at this 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?

Schema coverage is 100%, but the description adds value beyond the schema. For `since`, it provides examples ('7d', '30d', '1y') and suggests '30d or 1m for typical monitoring.' For `value`, it gives ticker and CIK examples. For `type`, it clarifies the current limitation. This aids correct parameter selection.

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

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

The description clearly states the tool's function: 'What's new about an entity since a given point in time.' It specifies the supported entity type (company) and lists the data sources (SEC, GDELT, USPTO). This clearly distinguishes it from siblings like entity_profile (static overview) or compare_entities (side-by-side comparison).

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: 'Use for "brief me on what happened with X" or change-monitoring workflows.' This gives clear context for when to invoke the tool. However, it does not explicitly mention when not to use it or suggest alternatives like entity_profile for deeper analysis.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the persistence characteristics (persistent for authenticated users, 24-hour lifespan for anonymous sessions) and the cross-tool context capability ('across tool calls'). However, it doesn't mention potential limitations like storage capacity, key 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?

The description is perfectly concise and well-structured: two sentences that each earn their place. The first sentence states the core functionality and usage context, while the second provides crucial behavioral context about persistence. There's zero wasted text, and important information is front-loaded.

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

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

Given the tool's moderate complexity (storage with persistence rules), no annotations, and no output schema, the description does a good job covering key aspects: purpose, usage context, and persistence behavior. However, it doesn't mention what happens on success/failure or return values, which would be helpful since there's no output schema. The description compensates well but has 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 fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain key naming conventions or value formatting beyond the schema's examples). The baseline score of 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'forget' (likely for deletion) and 'recall' (likely for retrieval). It explicitly mentions what gets stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous and differentiated.

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

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

The description provides explicit guidance on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls') and includes important contextual distinctions ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), which helps the agent decide when this tool is appropriate versus alternatives like 'recall' for retrieval.

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

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

No annotations are provided, so the description carries the full burden. It transparently describes the accepted input formats (ticker, CIK, name) and output (ticker, CIK, company name, URIs). It does not claim any destructive behavior, and it clearly states the tool performs a lookup, implying read-only use.

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 with no superfluous words. Purpose is front-loaded, and each sentence adds value.

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

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

Given no output schema or annotations, the description covers the main aspects: input types, output fields, and version information. It could mention error handling or case sensitivity, but for a simple lookup tool 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 description coverage is 100% with both parameters described. The description adds concrete examples and clarifies the accepted value formats (e.g., 'AAPL', '0000320193', 'Apple'), which enhances understanding beyond the schema.

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

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

The description clearly states the tool resolves entities to canonical IDs, specifies the verb 'resolve', names the resource 'entity to canonical IDs', and provides examples for companies. It distinguishes from siblings by noting it replaces multiple 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 implies usage when needing canonical IDs for companies, and states it replaces 2-3 lookup calls, but does not explicitly list alternatives or when not to use. However, the context is sufficiently clear for an AI agent.

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 readOnly, idempotent, openWorld, and non-destructive. Description adds that it internally calls ai_visibility_check, returns scores/confidence/signal density, and requires optional API key. No contradictions. Good disclosure of behavioral traits 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?

Two clear sentences plus an illustrative quote. Front-loaded with the main purpose. No unnecessary 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 complexity (4 params, no output schema), description explains behavior (probes, ranks, lists), return fields, and internal dependency. Could mention pagination or limits, but sufficient for a comparative 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%. Description adds meaning by explaining that the first entity becomes the 'subject' and others are competitors, and by noting 'free default' for models. This goes beyond schema descriptions.

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

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

Description explicitly states 'Compare AI visibility across multiple entities side-by-side' with specific verb and resource. Differentiates from sibling ai_visibility_check (single entity) and compare_entities (generic compare) by targeting competitive AI-marketing audits.

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 usage context ('competitive AI-marketing audits') with an example. Implicitly suggests when to use (multiple entities) versus ai_visibility_check for single, but does not explicitly state when not to use or list alternatives.

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

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

No annotations are provided, so the description carries the full burden. It discloses what the tool returns (verdict, structured form, actual value with citation, delta), and its domain scope. It does not mention rate limits, auth, or error handling, but for a read-only validation tool the behavioral disclosure is adequate.

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

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

The description is four sentences with clear front-loading of purpose. Every sentence adds value: purpose, scope, output summary, and efficiency claim. No filler 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?

For a single-parameter tool without output schema or annotations, the description covers purpose, domain, output structure, and value proposition. Missing details about edge cases or errors, but overall sufficient for 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?

Only one parameter ('claim') with 100% schema description coverage. The description adds examples that mirror the schema's examples, but does not add new semantic information beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the verb (fact-check) and resource (natural-language claim against authoritative sources), and distinguishes from siblings by specifying the domain (company-financial via SEC EDGAR+XBRL) and the efficiency gain (replacing 4-6 agent calls). The output verdict types are listed.

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

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

The description explicitly says when to use the tool (fact-checking claims, specifically company-financial), but does not provide negative guidance (when not to use) or alternative tools beyond implying it replaces sequential calls. The domain limitation (v1 supports only certain types) is clearly stated.

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