countries

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds value by detailing the return structure ({score, confidence, signals, raw_response}) and disclosing that Anthropic probing requires a BYO key with direct billing. 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 four sentences with no wasted words. Front-loads the core action and output, then adds optional details. Every sentence earns its place.

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

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

Given 4 parameters (1 required), no output schema, and good annotations, the description fully covers the tool's behavior, input semantics, and return format. It is complete and self-contained.

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

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

Schema coverage is 100%, yet the description adds meaning: it explains the default model ('workers-ai'), labels the _apiKey as 'BYO key—you pay Anthropic directly', and provides context examples ('Boston restaurant', 'B2B SaaS'). 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?

The description uses a specific verb ('probe') and resource ('LLMs for what they know about a business/brand/product/topic') and clearly states the output (visibility score 0-100). It mentions default model and optional Anthropic probing, which helps distinguish from sibling tools like 'scan_competitor_ai_presence'.

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 use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It provides clear instructions on default vs. paid Anthropic probing. However, it does not explicitly exclude alternative siblings or mention when not to use.

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

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

With no annotations provided, the description carries full burden and does well by explaining key behaviors: Pipeworx 'picks the right tool, fills the arguments, and returns the result.' It implies automation and abstraction but lacks details on rate limits, error handling, or data source limitations. No contradiction with annotations exists.

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 operational details and examples. Every sentence adds value: the first defines the tool, the second explains how it works, and the third provides concrete examples. No wasted words, efficiently 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 complexity (natural language processing to select and invoke tools) and lack of annotations or output schema, the description is mostly complete. It covers purpose, usage, and behavior well but could mention limitations or response formats. It adequately compensates for missing structured data.

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 single 'question' parameter. The description adds minimal value beyond the schema by emphasizing 'plain English' and 'natural language,' but doesn't provide additional syntax or format details. Baseline 3 is appropriate as 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: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('data source'), and distinguishes from siblings by emphasizing natural language input versus structured parameter-based tools like 'countries_by_currency' or 'search_countries'.

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

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

The description explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It contrasts with sibling tools that require specific parameters or structured queries, providing clear alternatives and exclusions for natural language versus structured interactions.

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

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

Discloses key behaviors: resolves market slug/URL/question, classifies bet type, fans out to relevant packs (e.g., crypto+fred+gdelt), returns evidence packet and market-vs-model comparison. Adds context beyond annotations (readOnly, openWorld, non-destructive).

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

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

Concise, front-loaded with purpose, no wasted sentences. Each sentence adds essential info: purpose, parameter details, fan-out logic, and when to use.

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 (multi-source fan-out, classification, evidence packet), description fully covers what the tool does and returns. No output schema, but description of return value suffices.

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 covers both parameters with descriptions. Description adds context: market accepts slug, URL, or question text; explains resolution. Depth enum explained with default. Schema coverage 100% but description adds 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?

Clearly states 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' Specific verb 'research' and resource 'Polymarket bet' with Pipeworx data. Implicitly distinguishes from siblings by being the core demo product for bet research.

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

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

Explicitly states use cases: 'should I bet on X?', 'what does the data say...', 'is there edge...'. Compares to alternative approach of discovering packs manually, claiming better conversion.

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 provides good behavioral context: data sources (SEC EDGAR, FDA), return type (paired data + URIs), and scope of comparison. Could add more about limits or errors but sufficient.

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 efficiently convey purpose, usage, data types, and benefits without excessive wording.

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?

While no output schema, description covers return format and sources. However, missing error handling or edge-case info slightly lowers 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 coverage is 100% and description adds value with examples and clarification of enum meanings and value formats, going beyond schema descriptions.

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

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

The description clearly states 'Compare 2–5 entities side by side in one call' with specific fields for company and drug types, and distinguishes from sibling tools like resolve_entity.

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

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

It specifies when to use (comparing entities) and highlights efficiency ('Replaces 8–15 sequential agent calls'), but does not explicitly mention when not to use or alternatives.

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

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

With no annotations provided, the description carries the full burden. It discloses the return format ('name, capital, and region'), which is helpful, but lacks details on error handling, rate limits, or authentication needs. No contradiction with annotations exists.

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

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

The description is appropriately sized with two sentences: one stating the purpose and one specifying the return format. It is front-loaded and wastes no words, making it highly 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 low complexity (1 parameter, no output schema, no annotations), the description is mostly complete. It covers purpose and return values, but could improve by addressing behavioral aspects like error cases or usage context.

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 'currency' parameter. The description does not add meaning beyond what the schema provides, such as examples or edge cases, meeting the baseline for high coverage.

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

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

The description clearly states the tool's purpose with a specific verb ('Find') and resource ('all countries that use a given currency'), and distinguishes it from siblings by focusing on currency-based lookup rather than language, region, code, or general search.

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 currency-based country lookup is needed, but does not explicitly state when to use this tool versus alternatives like 'countries_by_language' or 'search_countries'. No exclusions or prerequisites are mentioned.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates the tool returns specific fields, implying a read-only operation, but does not mention potential limitations like partial matches, case sensitivity, or error handling. It adds some context (return fields) but lacks details on performance, rate limits, or data freshness.

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 and follows with return details. Every word earns its place, with no redundancy or unnecessary elaboration, 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 tool's low complexity (one parameter, no output schema, no annotations), the description is adequate but has gaps. It explains what the tool does and what it returns, but lacks usage guidelines and behavioral details like error cases or data scope. It meets minimum viability but could be more complete for optimal agent use.

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

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

The input schema has 100% description coverage, so the parameter 'language' is well-documented in the schema. The description adds no additional parameter details beyond implying the tool uses this input, but with only one parameter and high schema coverage, the baseline is 3. The description's clarity on output compensates slightly, raising it to 4.

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

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

The description clearly states the specific action ('Find all countries where a given language is spoken') and the resource ('countries'), distinguishing it from siblings like countries_by_currency or countries_by_region. It also specifies the exact return fields (name, capital, region, population), making the purpose unambiguous.

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 such as search_countries or get_country_by_code. It mentions the parameter 'language' but does not specify use cases, exclusions, or comparisons to sibling tools, leaving the agent to infer usage from the tool name 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It states it's a list operation but doesn't mention whether it's read-only, if there are rate limits, authentication needs, pagination behavior, or error handling. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves beyond basic functionality.

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 communicates the tool's purpose, scope, and output. Every word earns its place with no redundancy or unnecessary information, making it appropriately sized and front-loaded.

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

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

Given the simple single parameter with full schema coverage and no output schema, the description adequately covers the basic functionality. However, it lacks details about behavioral aspects (rate limits, errors, etc.) and doesn't explain the return format beyond listing fields, leaving some gaps in completeness for practical 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%, with the region parameter fully documented in the schema (including allowed values). The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline score 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 specific action ('List all countries'), target resource ('in a geographic region'), and output fields ('with name, capital, population, and flag'). It distinguishes from siblings like 'countries_by_currency' or 'search_countries' by specifying region-based filtering rather than currency, language, code, or general search.

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 countries by region, but provides no explicit guidance on when to use this tool versus alternatives like 'countries_by_currency' or 'search_countries'. It mentions the region parameter but doesn't clarify scenarios where region-based listing is preferred over other filtering methods.

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 key behavioral traits: it's a search operation that returns the most relevant tools, and it should be called first in specific scenarios. However, it doesn't mention rate limits, authentication needs, or error conditions, leaving some behavioral aspects unspecified.

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

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

The description is appropriately sized with two sentences that each serve distinct purposes: the first explains what the tool does, the second provides usage guidance. There is no wasted language, and the most critical information (purpose and when to use) 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 (search functionality with 2 parameters) and no annotations or output schema, the description does well by explaining purpose and usage guidelines. However, it lacks details about return format (though it mentions tools with names and descriptions) and doesn't address potential limitations or error cases, leaving some 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 adds minimal value beyond the schema by mentioning 'search by describing what you need' which aligns with the query parameter, but doesn't provide additional semantic context about how parameters interact or affect results.

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', 'returns') and resource ('Pipeworx tool catalog'), distinguishing it from sibling tools which focus on country data rather than tool discovery. It explicitly mentions searching by describing needs and returning relevant tools with names and descriptions.

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.' This gives clear context about usage scenarios and distinguishes it from alternatives (sibling tools handle country data, not tool discovery).

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 bears full burden. It discloses return format (pipeworx:// citation URIs), supported inputs (ticker or CIK), limitation on names (requires resolve_entity), and performance note on federal contracts. Could mention error handling or rate limits, but for a read-oriented tool this is largely 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?

Compact paragraph with no wasted words. Front-loaded with core purpose. Each sentence adds value: what, what data, return format, efficiency gain, alternative. Ideal length and structure.

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 complex tool combining multiple data sources, the description adequately covers scope, inputs, output format, and exclusions. Missing explicit mention of error cases or size limits, but given no output schema, the return format hint suffices. A minor gap but overall complete enough.

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 descriptions, but the description adds critical context: explains type only 'company' today, specifies value must be ticker or CIK (not names), and advises using resolve_entity if only name is available. This significantly enhances parameter understanding.

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

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

The description clearly states 'Full profile of an entity across every relevant Pipeworx pack in one call.' It lists specific data types (SEC filings, financials, patents, news, LEI) and differentiates from sibling tools like resolve_entity and compare_entities, and mentions 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?

Explicit guidance on when to use (for comprehensive entity profile, replaces 10-15 calls) and when not (for federal contracts, use usa_recipient_profile directly). Provides clear alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool deletes a memory, implying a destructive mutation, but doesn't clarify if deletion is permanent, reversible, requires specific permissions, or has side effects. 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 that directly states the tool's action without unnecessary words. It is front-loaded and wastes no space, 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 tool's destructive nature (deletion), lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral risks, return values, or error conditions, which are critical for safe and effective tool invocation in this context.

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

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

The schema description coverage is 100%, with the single parameter 'key' fully documented in the schema as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format or examples, but the schema provides sufficient baseline information, warranting a score of 3.

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 a specific verb ('Delete') and resource ('stored memory by key'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'recall' (which likely retrieves memories) or 'remember' (which likely stores them), missing an opportunity for full sibling distinction.

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 how it relates to sibling tools like 'recall' or 'remember', leaving the agent to infer usage context independently.

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 the tool as read-only, idempotent, and non-destructive. The description adds value by detailing the internal process ('Fetches the page, extracts title/description/key links') and output format, providing complete transparency without contradiction.

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

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

Three concise sentences: purpose, process, and use cases. All information is front-loaded, no filler, and every sentence adds value. Excellent structure for quick agent comprehension.

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 two-parameter tool with no output schema, the description covers all necessary aspects: what it does, how it works, output format, and use cases. Annotations further confirm safety, making the definition 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 clear descriptions for both parameters (url and max_links). The description adds context about the output but does not significantly enhance parameter understanding beyond the schema, so baseline score of 3 is appropriate.

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

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

The description clearly states that the tool generates an llms.txt file for a URL for AI crawlers, with a specific verb ('Generate'), resource ('llms.txt file'), and scope ('for any URL'). It also lists concrete use cases, distinguishing it from unrelated sibling tools.

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

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

The description provides explicit use cases ('getting a client's site indexed by AI, drafting llms.txt for your own project, auditing a competitor'). It lacks explicit when-not-to-use instructions, but the contexts are clear and no alternative tools exist on the server for this task.

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 describes the lookup behavior but lacks details on error handling (e.g., invalid codes), rate limits, authentication needs, or what 'full country information' includes. This is a significant gap for a tool with no 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 is front-loaded with the core purpose and includes necessary examples, 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 low complexity (1 parameter, no nested objects) and high schema coverage, the description is adequate but incomplete. It lacks output details (no output schema) and behavioral context, which is needed for full understanding, especially with no annotations.

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 parameter. The description adds minimal value by reiterating the code format (ISO 3166-1 alpha-2/alpha-3) and providing examples ('US', 'USA'), but no additional semantics 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 a specific verb ('Get') and resource ('full country information'), and it distinguishes from siblings by specifying the lookup method (by ISO code) rather than by currency, language, region, or search.

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

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

The description implies usage context by specifying the input format (ISO codes), but it does not explicitly state when to use this tool versus alternatives like 'search_countries' or other sibling tools. No exclusions or prerequisites are mentioned.

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

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

No annotations are provided, so the description carries the full burden. It discloses the rate limit (5 per identifier per day) and content rules, but does not mention whether feedback is anonymous, if acknowledgments are sent, or how feedback is processed. For a simple feedback tool this is adequate but not exhaustive.

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: purpose, usage content rule, and rate limit. No unnecessary words, front-loaded with the core function. Every sentence earns its place.

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

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

For a low-complexity tool with 3 parameters, no output schema, and clear schema descriptions, the description covers all essential aspects: what to send, how to format it, and constraints. It is fully sufficient for an 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%, reducing burden. The description adds value by explaining each enum variant in 'type', detailing the 'context' object fields, and specifying message constraints (plain text, 1-2 sentences, 2000 chars max). This goes beyond the schema's minimal descriptions.

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

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

Description explicitly states 'Send feedback to the Pipeworx team' and enumerates specific use cases (bug reports, feature requests, missing data, praise). Clear verb and resource with no ambiguity, and it naturally distinguishes from sibling tools (no other feedback tool exists).

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 tells when to use the tool (e.g., bug reports, features) and provides an explicit exclusion: 'do not include the end-user's prompt verbatim.' Rate limit is also stated. Although no direct comparison to alternatives is given, the tool is unique among siblings so guidance is sufficient.

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

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

Discloses caching behavior (5min-1h), data source (CF analytics engine), no PII, and output format beyond annotations. Annotations indicate read-only, idempotent, non-destructive; description adds valuable operational context.

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

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

Approximately 100 words, front-loaded with purpose, uses bullet-like lists for use cases. 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 simple one-parameter input and no output schema, description covers what the tool returns, caching, data derivation, and use cases. Fully complete for its 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% with one parameter. Description adds semantic guidance: shorter windows for hot trends, longer for steady-state. Enhances the enum description 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?

States clearly it returns top tools, packs, and call volume over a recent window. Distinguishes from sibling tools like 'discover_tools' by focusing on AI agent usage trends.

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

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

Lists three explicit use cases (discovering hot data sources, confirming canonical choice, alignment check) but does not explicitly advise when not to use or name 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 declare readOnlyHint=true and destructiveHint=false; the description aligns by explaining the tool searches, groups, and checks monotonicity without mutation. It adds value by describing cross-event detection and return format, but annotations already cover the safety profile.

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 yet comprehensive, using about 6 sentences. It is front-loaded with the purpose and clearly structures the two modes with examples. No superfluous information.

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

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

Despite no output schema, the description mentions returns include ranked opportunities with trade direction and reasoning. Given the tool's two-mode complexity and its role as an arbitrage detector, the description fully covers what the tool does, when to use each mode, and what to expect.

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 coverage, both parameters are documented in the schema. The description adds valuable context beyond the schema: explains the two modes, provides example inputs ('when-will-bitcoin-hit-150k', 'Strait of Hormuz traffic returns to normal'), and describes the tool's behavior with each mode.

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 across related markets. It specifies two distinct modes (event and topic), which differentiates it from siblings like polymarket_edges.

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

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

The description explicitly explains when to use each mode: event for a single event slug, topic for cross-event searches. It provides a concrete example of cross-event catching cases missed by single-event mode. However, it could be more explicit about when not to use or alternatives.

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

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

Annotations already indicate readOnlyHint and non-destructive. The description adds significant behavioral context: details the algorithm (scans top markets, groups by asset, fetches price history once, computes model probability, ranks by |edge|), model sources (FRED + live coinpaprika), and output format (top N ranked by edge magnitude with trade direction). No contradictions.

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

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

The description is a single, well-structured paragraph of 5 sentences, each adding value. It front-loads the core purpose, then provides processing details, output, and intended use. No redundant or unnecessary information.

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

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

Despite having no output schema, the description adequately explains the return value (top N ranked by edge magnitude with suggested trade direction). It covers inputs, algorithm, and intended use. For a read-only discovery tool with 3 optional parameters, this 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 clear parameter descriptions. The tool description adds narrative context (e.g., 'Returns top N ranked by edge magnitude' for limit, 'volume window' for window, 'minimum edge' for min_edge_pp) but does not significantly enhance meaning 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 tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price, with specific verb and resource. It distinguishes itself from siblings like 'polymarket_arbitrage' and 'bet_research' by focusing on edge discovery for crypto-price bets.

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 it's built for the 'what should I bet on today' question, implying use for opportunity discovery. It notes it covers crypto-price bets (V1), so it's not for other bet types. While it doesn't explicitly name alternatives, the intended use case is clear.

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

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

Annotations already indicate read-only, open world, idempotent, not destructive. The description adds context about typical price disparities (2-25pp) and the arb signal, enhancing transparency 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 concise, uses a clear two-paragraph structure, and includes examples for modes. 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 no output schema, the description adequately specifies return types (prices, spread). It covers essential context without gaps, though more precise data formats could be included.

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?

All three parameters are described in the schema (100% coverage). The description adds meaning by explaining the topic pre-mapping and the override behavior, exceeding the baseline expectation.

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 spread' and explains the two modes. However, it does not explicitly differentiate from sibling tools like 'polymarket_arbitrage', which may overlap in 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 outlines two usage modes (topic vs explicit) with examples, implying when to use each. It does not mention when not to use the tool or provide alternatives, leaving some 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?

No annotations are provided, so the description carries the full burden. It discloses that memories can be retrieved from 'earlier in the session or in previous sessions,' implying persistence across sessions, which is useful behavioral context. However, it doesn't cover error handling (e.g., what happens if the key doesn't exist), performance aspects, or format of returned data, leaving gaps for a tool with no 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 front-loaded with the core functionality in the first sentence, followed by usage guidance. Both sentences earn their place by providing essential information without redundancy. It's appropriately sized for a simple tool with one optional parameter.

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 (1 optional parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, and parameter semantics effectively. However, it lacks details on return values (e.g., format of retrieved memories or list output), which is a minor gap since there's no output schema to compensate.

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, with the parameter 'key' documented as 'Memory key to retrieve (omit to list all keys).' The description adds semantic context by explaining that omitting the key lists 'all stored memories,' reinforcing the schema's guidance. Since schema coverage is high, the baseline is 3, but the description provides additional clarity on the omit behavior, warranting a higher score.

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: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It specifies the verb ('retrieve'/'list') and resource ('memory'), distinguishing it from sibling tools like 'remember' (store) and 'forget' (delete). However, it doesn't explicitly differentiate from 'discover_tools' or other siblings beyond the memory context.

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

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

The description provides explicit usage guidance: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It also specifies when to omit the key ('omit key to list all keys'), offering clear context for when to use each mode. This directly addresses alternatives by tying usage to saved memories.

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

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

No annotations provided, but the description fully discloses the fan-out behavior to SEC, GDELT, USPTO and the return format (structured changes, total_changes, URIs). Clearly indicates read-only nature.

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 covering purpose, behavior, parameters, and return type. 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 data sources) and no output schema, the description covers all necessary context: what it does, how it fans out, accepted parameter formats, and return structure.

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

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

Schema coverage is 100%, and the description adds value by explaining 'since' format (ISO date and relative like '7d', '30d', '1y') and that 'value' accepts ticker or CIK. This goes beyond the schema enum and descriptions.

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

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

The description clearly states the tool retrieves changes for an entity since a given time, with specific details for type='company' (SEC, GDELT, USPTO). It distinguishes from siblings like 'ask_pipeworx' and 'entity_profile' by focusing on recent changes.

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

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

The description provides explicit use cases ('brief me on what happened with X' or change-monitoring) but does not explicitly exclude alternative 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 effectively describes key behavioral traits: it's a storage operation (implied mutation), specifies persistence differences between authenticated users (persistent) and anonymous sessions (24-hour lifespan), and clarifies the cross-tool context utility. It doesn't mention rate limits or error conditions, but covers the essential behavior well.

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 front-loaded: the first sentence states the core purpose, and the second sentence adds crucial usage context and behavioral details. Every sentence earns its place 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?

For a 2-parameter tool with no annotations and no output schema, the description provides excellent context about what the tool does, when to use it, and key behavioral aspects (persistence differences). It doesn't describe the return value or error cases, but given the tool's relative simplicity and the clarity provided, it's nearly complete.

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

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

Schema description coverage is 100%, so the schema already fully documents both parameters (key and value). The description doesn't add any parameter-specific information beyond what's in the schema descriptions. This meets the baseline expectation when schema coverage is complete.

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 a specific verb ('Store') and resource ('key-value pair in your session memory'), and distinguishes it from sibling tools like 'forget' (which presumably removes) and 'recall' (which presumably retrieves). It explicitly mentions what gets stored and where.

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.' It also distinguishes usage contexts by mentioning authenticated vs. anonymous sessions, helping the agent choose appropriately based on session type.

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

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

No annotations are provided, so the description must convey behavioral traits. It discloses the version (v1), input variations, and output components. It implies a read-like operation with no destructive actions. However, it does not mention potential failure modes or authorization requirements, which are not critical for this simple lookup 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 concise: two sentences with no extraneous text. The first sentence states the core function, and the second provides details on version, inputs, outputs, and benefit. 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 the simplicity of the tool (2 parameters, no output schema), the description covers the essential aspects: purpose, input formats, output returns, and efficiency gain. It is complete enough for an agent to select and invoke correctly, though it could mention error behavior or case sensitivity briefly.

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, but the description adds value by providing concrete examples (AAPL, 0000320193, Apple) for the 'value' parameter and clarifying that 'type' is currently limited to 'company'. This goes beyond the schema's enum description.

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

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

The description clearly states the tool resolves an entity to canonical IDs, specifies the supported entity type 'company' for v1, and lists accepted inputs (ticker, CIK, name) and outputs (ticker, CIK, name, URIs). This distinguishes it from sibling tools which are unrelated to entity resolution.

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 notes it 'replaces 2–3 lookup calls,' implying efficiency. It provides clear context for when to use (obtaining canonical IDs for a company) but does not explicitly mention when not to use it or compare to alternatives. However, siblings do not include alternative entity resolution tools, so the guidance is adequate.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds valuable context: it explains the internal call to ai_visibility_check, the ranking process, and the output structure (score, confidence, signal density). This goes beyond annotations without contradiction.

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

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

The description is three sentences: purpose, process, use-case example. It is front-loaded and every sentence adds value with no fluff.

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

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

Despite no output schema, the description explains output structure (ranked list with score, confidence, signal density) and internal behavior (probes each entity with ai_visibility_check). It also clarifies entity ordering. Given annotations and schema richness, it is complete.

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

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

Schema coverage is 100%, but the description adds key meaning: entities[0] is the 'subject' for narrative, the rest are competitors; models default to 'workers-ai' unless 'anthropic' with _apiKey. This clarifies schema semantics and the relationship between 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: 'Compare AI visibility across multiple entities side-by-side.' It specifies the verb (compare, probes), resource (AI visibility across entities), and distinguishes from siblings like ai_visibility_check by indicating it compares multiple entities using that single-entity 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 provides context for use ('useful for competitive AI-marketing audits') and an example query. It implies differentiation from ai_visibility_check for single-entity probes, but does not explicitly state when not to use it 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds value by specifying the return fields (common name, official name, capital, etc.) and flag emoji, which helps understand output format. However, it doesn't mention behavioral traits like rate limits, error handling, or whether the search is case-sensitive, leaving gaps in transparency.

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

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

The description is appropriately sized with two sentences: one stating the purpose and parameter, and another detailing the return values. It's front-loaded with the core functionality, and every sentence adds value without waste. However, it could be slightly more structured by separating usage guidance from output details.

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 (1 parameter, no annotations, no output schema), the description is somewhat complete but has gaps. It covers the purpose and output fields, which is helpful, but lacks details on behavioral aspects like performance or limitations. Without annotations or output schema, more context on usage and results would improve completeness.

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

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

Schema description coverage is 100%, so the schema already documents the 'query' parameter with its type and description. The description adds minimal semantics beyond the schema by implying the search is by name, but it doesn't provide additional details like search algorithm or match specificity beyond what's in the schema. Baseline 3 is appropriate as 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: 'Search for countries by name' specifies the verb (search) and resource (countries). It distinguishes from siblings by focusing on name search rather than currency, language, region, or code-based lookup. However, it doesn't explicitly mention how it differs from siblings beyond the search parameter.

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

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

The description implies usage context through 'Search for countries by name,' suggesting this tool is for name-based queries. However, it doesn't provide explicit guidance on when to use this vs. alternatives like 'countries_by_currency' or 'get_country_by_code,' nor does it mention any exclusions or prerequisites for usage.

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

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

With no annotations, the description covers return values (verdict, structured form, actual value with citation, percent delta) and explains it replaces multiple agent calls. It lacks details on errors, rate limits, or prerequisites, but is adequate for a read-like 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 concise sentences: purpose, domain/data sources, and return value plus value proposition. No wasted words, all information is relevant 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 no output schema, the description lists all return fields (verdict, structure, value, citation, delta) and explains the tool's role. For a single-parameter tool, this 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?

The single 'claim' parameter has 100% schema coverage, and the description adds meaning with examples and expected format (natural-language factual claims), exceeding the schema's generic description.

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

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

The description clearly states it validates natural-language claims using authoritative sources, specifies the domain (company-financial claims) and data sources (SEC EDGAR + XBRL), and distinguishes itself by replacing multiple sequential agent 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?

It specifies when to use it (for company-financial claims) but does not explicitly mention when not to use it or alternatives among sibling tools. However, the description implies its specialized scope.

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