Rubygems

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds that default model is free and using Anthropic requires a BYO key with direct cost, which is useful behavioral info. No contradictions. Does not mention potential rate limits or data handling, but overall adds value.

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 but well-structured: first sentence core function, second default and API key details, third return format, fourth use cases. Every sentence is informative. 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?

No output schema, so description must explain return values. It lists per-model fields (score, confidence, signals, raw_response) and mentions a combined view. Could be more explicit about the combined view structure, but enough for an agent to understand what to expect. Overall complete given complexity and 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 coverage is 100%, but description adds significant meaning: specifies default model ('Workers AI Llama-3.3-70b'), explains that `_apiKey` is only needed for Anthropic and passes through directly, and clarifies that `context` helps disambiguate. This goes well beyond the schema alone.

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

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

Specific verb 'probe' and resource 'LLMs' clearly stated. Explicitly lists what is probed (business/brand/product/topic) and the output (score 0-100). Distinguishes from siblings by mentioning AI-marketing audits, pre-launch brand checks, competitive monitoring, which are not covered by other tools like ask_pipeworx or deep_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?

Clear usage context: AI-marketing audits, pre-launch brand checks, competitive monitoring. No explicit when-not-to-use or alternatives, but the description implies this is for checking AI visibility, which is distinct from other tools in the sibling list. A small gap, but context is adequate.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context: routes questions, fills arguments, returns citations, is one fast call, and works on every tier. Lacks details on error handling 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?

The description is somewhat lengthy but well-structured: front-loaded with key directive, followed by use cases, examples, and sibling comparisons. Every sentence adds value, though could be tightened slightly without losing substance.

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

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

Given the tool's complexity and lack of output schema, the description comprehensively covers purpose, usage, behavioral context, parameter aliasing, and examples. It fully equips the agent to select and invoke the tool 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 parameter descriptions. The description adds example questions and explains aliases, but does not provide deeper semantic guidance beyond what the schema already offers. 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 routes questions to a vast array of authoritative sources for structured data with citations, using specific verbs like 'prefer over web search' and listing concrete examples of use cases, distinguishing it from siblings.

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

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

The description explicitly states when to use this tool ('PREFER OVER WEB SEARCH', 'START HERE for most questions') and when to step up to alternatives like ask_pipeworx_grounded or deep_research, providing clear usage boundaries.

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?

Description adds substantial behavioral context beyond annotations: costs one extra LLM call, returns structured refusal reasons, and explains the extraction process. No contradiction with annotations (readOnlyHint, idempotentHint, etc.).

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 informative and front-loaded, but slightly verbose. Every sentence contributes value, though could be tightened slightly.

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

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

Despite lacking output schema, description fully explains return format, refusal reasons, and usage context. For a complex tool with 6 parameters, it is highly complete.

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

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

Schema coverage is 100% with descriptions for all parameters. Description only mentions aliases for the question parameter, adding minimal value beyond schema. Baseline 3 applies.

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

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

Description clearly states it is a 'hallucination-resistant answer mode for high-stakes reads' and specifies that it extracts answers using only tool result. It distinguishes from ask_pipeworx by noting the extra LLM call and use case.

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

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

Explicitly states when to use: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts'. Also advises preferring ask_pipeworx for casual lookups.

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 numerous behavioral traits beyond the annotations: it explains the fan-out mechanism with concrete examples, response shapes (market_match_confidence, evidence keys, news fallback fields), safety blocking for low confidence, wide spread handling, and resolution-rule risk. This far exceeds the annotation hints (readOnly, idempotent).

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 long but well-structured with section headers (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.). The core purpose is front-loaded. While every sentence adds value, the length might strain token limits; however, for a complex tool this level of detail is appropriate.

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 (multiple input types, classification, parallel fan-out, varied response fields, error handling, and safety checks), the description covers all critical aspects including edge cases (low confidence, closed markets, wide spreads, cancellation rules). Without an output schema, the description thoroughly documents the response structure.

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

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

The input schema already has clear descriptions for all 3 parameters (100% coverage). The tool description adds extra context, especially for the 'market' parameter (listing slug, URL, or text), and for 'include_raw' (explaining when to use true vs false). This adds value, though the schema alone is already strong.

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 explicitly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It clearly defines the inputs (slug, URL, question) and the output (evidence packet + comparison). While it doesn't directly contrast with sibling tools, the unique function is unmistakable.

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 for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also includes detailed warnings about low-confidence matches, closed markets, and resolution rules, helping the agent decide when to trust results and when to fall back.

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, idempotentHint, and destructiveHint. The description adds valuable behavioral details: handles off-calendar fiscal years, returns sorted results by primary metric, and provides citation URIs. 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 moderately lengthy but every sentence adds value. It is front-loaded with natural language examples. A small reduction in verbosity could improve readability, but it remains concise enough.

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 covers what is returned (paired data + citation URIs), data sources, edge cases (off-calendar years), and sorting behavior. It fully addresses the complexity of the tool's two modes.

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 enriches both parameters: for 'type' it explains the real-world data sources (SEC, FAERS) and for 'values' it clarifies format (tickers/CIKs for company, names for drug) and constraints (2-5 items).

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 starts with example queries and explicitly states 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call,' clearly defining the verb and resource. It distinguishes from sibling tools by urging preference over sequential lookups.

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 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing clear when-to-use guidance. It also breaks down what data each entity type pulls, giving context for appropriate 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds critical behavioral context: account required, paid tier for 'thorough', 15-60s latency, returns gaps[] when unanswered, and that it decomposes questions into parallel facets. 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 comprehensive but long; however, it is front-loaded with the most critical information (account requirement and alternatives) and organized in a logical flow. Each sentence contributes value, but some trimming could improve conciseness.

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

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

With only 2 parameters and no output schema, the description fully covers input semantics, behavior, constraints (account, paid plan), performance expectations, and return structure (findings packet with gaps[]). No gaps remain for the intended use case.

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%, providing clear descriptions for both parameters. The description adds value by explaining that 'depth' defaults to standard and that 'question' can be broad/multi-part, though this is somewhat redundant with 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 uses a specific verb ('researches') and resource ('Pipeworx's 1259 structured data sources'), clearly distinguishing it from siblings like ask_pipeworx by specifying it is 'not open-web search' and returns a structured findings packet.

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

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

Explicitly states when to use (broad/multi-part questions over structured data), when not to use (single lookup, breaking news), and provides specific alternatives: ask_pipeworx for single lookups and current news.

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 safe read-only (readOnlyHint, idempotentHint, destructiveHint false). Description adds valuable context: returns schemas with curated examples, results ready to call directly, and it's 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?

Front-loaded with purpose, then lists domains, then output details. Some redundancy but overall efficient 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 6 parameters with full schema coverage and no output schema, description effectively explains behavior, when to use, and output format. No gaps for the use case.

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. Description mentions 'query' input via natural language but adds no new parameter semantics beyond what's in schema.

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

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

Clearly states it 'finds tools by describing the data or task' and lists many domains (SEC filings, financials, etc.). Distinguishes from sibling tools by being a discovery/search tool, not a domain-specific one.

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

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

Explicitly says 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' Provides clear context for when to use, though no explicit when-not.

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

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

Annotations already declare readonly, open world, idempotent, and non-destructive. The description adds valuable details: parallel fan-out across multiple sources, specific return fields, USPTO sunset soft-fail, and news fallback chain. 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 somewhat lengthy but every sentence adds value. It starts with example queries for rapid understanding. A minor reduction in detail about fallback chains could improve conciseness, but it's 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?

Despite no output schema, the description comprehensively lists return fields (cik, company_name, recent_filings with URIs, fundamentals sorted, patents, news, LEI) and notes edge cases (patent sunset soft-fail). No missing critical context for an agent.

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

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

Schema coverage is 100% with descriptions for both parameters. The description goes further by explaining the format requirements (zero-padded CIK, no names) and hinting at future enum expansion. This adds 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 explicitly states that the tool provides a full cross-source profile of a US public company in a single parallel call. It lists specific data sources and return fields, distinguishing it from chaining single-pack lookups and from sibling tools like compare_entities or 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?

The description gives clear when-to-use guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also specifies accepted inputs (ticker or CIK), warns against names, and advises using resolve_entity if 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?

Annotations already provide idempotentHint=true and destructiveHint=true. Description confirms destructive nature and adds context about clearing sensitive data, which is valuable. 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?

Two sentences, front-loaded with the main action, no wasted words. Efficient and clear.

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, destructive tool with high schema coverage and annotations, the description covers when to use, what it does, and relationship to siblings. No output schema needed.

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

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

Schema coverage is 100% and the description does not add extra meaning beyond the schema's parameter description ('Memory key to delete'). Baseline 3 applies.

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

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

The description clearly states the action ('Delete') and the resource ('a previously stored memory by key'). It distinguishes from siblings 'remember' and 'recall' by explicitly naming them as paired 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?

Provides explicit conditions for use: 'when context is stale, the task is done, or you want to clear sensitive data'. Mentions pairing with 'remember' and 'recall' as 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 read-only, idempotent, non-destructive behavior. The description adds context about fetching external pages and outputting markdown, which goes beyond annotations. 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 two sentences plus a bulleted list of use cases. It is front-loaded with the action and output, and every part adds value. Could be slightly tighter but is appropriately sized.

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 2 parameters, no output schema, and rich annotations, the description covers purpose, output format, and usage scenarios. It is complete enough to guide invocation without 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 baseline is 3. The description does not add new parameter details beyond the schema; it only reinforces the context of the action. No extra semantics provided.

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 generates a llms.txt file for AI crawlers, with specific actions like fetching and extracting. It is not a tautology and distinguishes from siblings by its unique focus on llms.txt generation, though it does not explicitly compare to siblings 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 provides explicit use cases (getting client's site indexed, drafting for own project, auditing competitor) but does not include exclusions or when not to use this tool versus alternatives. The context is clear enough for typical scenarios.

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, idempotentHint, destructiveHint, openWorldHint. Description adds that it returns dependencies with version requirement strings, which is useful but not extensive behavioral context beyond what annotations imply.

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

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

Two sentences, front-loaded with purpose, every word adds value. 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?

For a simple query tool with 2 parameters, full schema coverage, and rich annotations, the description is complete: explains purpose, parameters, output, and optional behavior. No gaps.

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

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

Schema coverage is 100%, baseline 3. Description adds usage semantics for 'version' parameter with 'Omit version to get the latest,' and clarifies output format ('version requirement string'), surpassing baseline.

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

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

Description specifies verb 'Get', resource 'dependencies for a Ruby gem', and includes scope (runtime and development) and behavior (returns version requirement strings, omit version for latest). Clearly distinguishes from siblings like get_gem and get_reverse_dependencies.

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

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

Provides explicit usage guidance: 'Omit version to get the latest.' Does not explicitly mention when not to use or alternatives, but context is clear for a simple query tool.

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, idempotentHint, openWorldHint, and destructiveHint. The description adds behavioral context by specifying it returns 'latest version, authors, license, descriptions, download counts, and project/source URLs', implying up-to-date information. No contradictions, but it does not elaborate on network behavior or data freshness beyond 'latest version'.

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

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

Two sentences, front-loaded with purpose, includes key return fields and usage context. No wasted words.

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

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

Given the tool's simplicity (one parameter, output schema present, rich annotations), the description is complete enough. It covers purpose, return data, and usage guidance. Could mention case sensitivity or gem name format, but not necessary.

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 description and examples for the 'name' parameter. The description adds little beyond the schema, saying 'by name' and providing example queries, but the schema already covers parameter semantics adequately.

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

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

The description clearly states the verb 'Get full metadata' and the resource 'published Ruby gem by name', and lists the specific fields returned. It distinguishes from sibling tools by explicitly mentioning use cases like 'before calling get_versions/get_dependencies'.

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 for "what is gem X?", "tell me about Ruby gem Y", or before calling get_versions/get_dependencies.' This clearly indicates when to use the tool and when to use 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 readOnly and idempotent; description adds the 50-name cap, which is a behavioral trait not covered by 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?

Three sentences, front-loaded with action, then use cases, then limitation. Every sentence adds value with no redundancy.

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

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

With a single parameter fully described in schema and an output schema present, the description covers purpose, use cases, and a behavioral limit—sufficient for effective tool usage.

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 already describes the parameter; description adds no further details beyond repeating the example from schema, so baseline 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?

Description uses specific verb 'List' and resource 'gems that depend on this gem', clearly differentiating from sibling 'get_dependencies' which does the opposite.

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

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

Provides explicit use cases ('ecosystem impact', 'risk surface') and a constraint ('API caps at 50 names per request'), 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?

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds that it returns version history with specific fields and explains the limit parameter's default and ordering, adding value 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 concise sentences: first defines the tool's function, second provides usage examples. Efficient and front-loaded with key information.

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

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

Given the presence of an output schema, the description adequately covers the tool's purpose and return fields. The limit parameter behavior is explained. Could be more detailed on the output structure, but output schema handles that.

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

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

Schema coverage is 100% with descriptions for both parameters (name and limit). The description restates the limit behavior ('default 25, max 200, latest first') already present in the schema, so it adds no new semantic meaning.

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

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

The description clearly states the verb 'Get' and the resource 'full version history' for a Ruby gem. It differentiates from siblings like 'get_gem' by specifying it returns every version with details. The sample queries reinforce 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?

Provides concrete use cases ('what versions of X exist?', 'when did Y release version Z?'). While it doesn't explicitly state when not to use, the examples guide appropriate usage and implicitly distinguish from other gem 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?

Annotations already indicate read-only, idempotent, non-destructive behavior. Description adds value by listing return fields and confirming active subscriptions. 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?

Two concise sentences: first lists return fields, second provides usage guidance. No redundancy or unnecessary 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 simple parameter and the annotations, the description sufficiently explains return values and usage context. Lacks mention of response format, but the listed fields are adequate.

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

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

Schema coverage is 100% with a well-described optional boolean parameter. Description does not add additional parameter semantics beyond what the schema provides; baseline score 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?

Clearly states the tool lists the caller's active subscriptions and specifies the return fields. Distinguishes from sibling tools like subscribe and unsubscribe by focusing on listing active ones.

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

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

Provides explicit use cases: review before adding more subscriptions or find an id to cancel. Does not mention alternatives but context with siblings 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?

All annotations are false, but the description adds significant behavioral details: rate limit (5 per identifier per day), free usage, no quota consumption, team review process, and roadmap impact. 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?

Description is concise yet comprehensive. Each sentence serves a purpose: purpose, usage scenarios, constraints, and behavioral notes. Front-loaded with primary action. 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?

Despite no output schema, the description covers all necessary aspects: use cases, what to include/exclude, rate limits, quota effects, and team processing. Complete for a feedback 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?

Input schema has 100% coverage with detailed descriptions for type enum and context object. The description adds extra semantic value by advising to describe issues in terms of Pipeworx tools/packs and not to paste end-user prompts, enhancing the message parameter.

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: sending feedback to the Pipeworx team about bugs, missing features, data gaps, or praise. It distinguishes itself from sibling tools (e.g., ask_pipeworx, discover_tools) by focusing solely on feedback rather than queries or actions.

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

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

Explicitly specifies when to use the tool: for bugs, feature requests, data gaps, or praise. Also provides negative guidance ('don't paste the end-user's prompt') and clarifies context requirements. This fully differentiates from 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?

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description adds behavioral details: data source (CF analytics-engine), no PII, caching duration (5min-1h), and output structure (pack, tool, count). This fully informs the agent of the tool's behavior.

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

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

The description is well-structured with a clear first sentence, bullet-pointed use cases, and additional context. It is concise enough for a simple tool, though slightly verbose; every sentence adds value.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description is complete: it explains input, output, caching, and privacy. Minor omissions like exact output format are acceptable.

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 enriches the parameter 'window' by explaining the trade-off between shorter windows (hot right now) and longer windows (steady-state demand). Schema coverage is 100%, so the description adds value beyond the schema's enum values.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a recent window. It specifies the verb 'returns' and the resource 'trending data', distinguishing it from sibling tools like 'discover_tools' by focusing on real-time popularity signals.

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 canonical tools, aligning use case) and implies when to use it (for trending/popularity). It does not explicitly mention alternatives but provides enough context for an AI agent to decide.

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

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

Annotations provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds substantial context: semantic anchors (Jaccard threshold), partition filter (placeholder removal), fill check against CLOB depth, and detailed output fields. 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 lengthy but well-structured with front-loaded purpose and clear sections (SEMANTIC ANCHOR, PARTITION FILTER). Every sentence adds value; the density is justified by the tool's complexity. Could be slightly tighter but remains 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 no output schema, the description thoroughly covers return values (opportunities array, partition_check object, fill_check details). It explains algorithmic decisions, mode selection, and failure conditions. Complete for an arbitrage detection 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% (both parameters documented). The description greatly enriches the schema: provides examples for event slug, explains cross-event mode for topic, and details the Jaccard similarity and partition filter logic.

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 explicitly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes three modes (no args, event, topic) and contrasts with sibling tools like polymarket_edges and polymarket_fill_risk.

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

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

The description provides clear when-to-use guidance for each mode: no args for trending scan, event for single-event analysis, topic for cross-event scanning. It explains why topic mode is beneficial ('catches patterns that single-event misses') and references another tool (polymarket_fill_risk) for custom sizing, but lacks explicit when-not-to-use statements.

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 read-only, idempotent, open-world, and non-destructive. Description adds detailed behavioral context: caching at KV level keyed on all knobs, edge calculation details (edge_pp_net after slippage, Kelly fractions, 24h-move warning), and diagnostics. 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?

Description is verbose (approx. 300 words) and packed with technical detail. It is front-loaded with purpose but becomes heavy on model family exposition. Could be more concise for quick agent parsing.

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 thoroughly explains the response structure: by_segment with three segments, fed_candidates/fed_note, and diagnostics. It covers edge fields, filter knobs, and special cases like Fed bets and placeholder filters, making the return format clear.

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 parameter descriptions. Description briefly references knobs like min_liquidity and max_spread_pp but does not add significant meaning beyond what the schema 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?

Description clearly states the tool scans Polymarket markets for edge opportunities where Pipeworx data disagrees with market price. It positions the tool as a 'what should I bet on today' resource. However, it does not distinguish from siblings like polymarket_arbitrage or polymarket_edge_tracker.

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 usage context (discover opportunities without paging) and explains parameter knobs for filtering (min_liquidity, max_spread_pp, etc.). However, it does not explicitly state when to prefer this tool over siblings or when not to use it.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds significant behavioral context beyond annotations: limits (60-day TTL, snapshot enabling), computation details (decay from daily closes, not intraday), and output structure explanation. 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 relatively long but every sentence adds value, from purpose to output fields to limits. It is front-loaded with the core question and well-structured with sections (RESPONSE, LIMITS). Slightly verbose but efficiently packed.

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 comprehensively explains the return structure (tracked[], expired[], snapshot_dates[]) and their fields. It also covers edge cases like gaps in snapshots and median lifespan. For two parameters, this is thorough and leaves no critical ambiguity.

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

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

Schema description coverage is 100%, but the description adds value by explaining defaults and ranges (days: default 14, max 30; window: default '1wk') and tying parameters to the output structure (snapshot family). It also describes the full response schema, which is absent from the input schema, further enhancing 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 the tool's purpose: 'Edge persistence and decay telemetry' answering 'how long has this edge existed and is it shrinking?'. It uses a specific verb ('telemeter') and resource ('edge persistence'), effectively distinguishing it from sibling tools like polymarket_edges or polymarket_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?

The description gives implied usage context by contrasting fresh vs. old edges, suggesting when the tool is valuable. However, it lacks explicit guidance on when to use this tool instead of alternatives, and does not mention when not to use it or prerequisites.

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=true, idempotentHint=true, and destructiveHint=false, so safety is clear. The description adds behavioral context: it walks the ladder, returns a verdict (clean|degraded|cannot_fill), and explains partial basket fill risks. However, since annotations cover the safety profile, a 4 is appropriate.

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 long but well-structured with clear headings (REQUIRES, SINGLE-MARKET, BASKET, USE THIS). It front-loads the core purpose. For a complex dual-mode tool, the length is justified and every sentence adds value. No redundancy.

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

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

Given no output schema, the description thoroughly explains return fields (top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict, theoretical_sum, realizable_sum, capture_ratio, profit_usd, per-leg fill detail, thin_legs, max_clean_notional_usd, forced_directional_risk). It covers both modes completely and provides sufficient context for an agent to understand the tool's behavior and outputs.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning beyond the schema: it explains side defaults (auto for basket), how size_usd is interpreted differently in single-market vs basket, and the meaning of market/event parameters. This extra context justifies a 4.

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

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

The description clearly states the tool's purpose as a 'realizable-vs-theoretical edge check against live CLOB order-book depth'. It distinguishes between two modes (single-market and basket) and explicitly differentiates this tool from siblings like polymarket_arbitrage by positioning it as a pre-trade validation step.

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 guidelines: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It also explains when to use each mode (requires market or event) and warns about when not to use it (thin books, partial fills).

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 far beyond annotations by detailing safety fields (compatibility_warning, temporal_alignment) and scenarios where spreads are meaningful or not. Explains skipped_cross_type counters and the logic behind matched_pairs checks, providing comprehensive behavioral context.

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

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

The description is thorough but relatively concise given the complexity. It front-loads the main purpose, then organizes details into modes, response, and safety fields. Could be slightly tighter but 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?

Despite lacking an output schema, the description comprehensively explains the response structure (leg-by-leg prices, matched spread array, safety fields). It covers edge cases and provides enough context for correct usage.

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, the parameter descriptions are already clear. The tool description adds value by explaining the two modes (topic vs explicit) and how parameters interact, enriching the agent's understanding beyond the schema.

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

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

The description clearly states it calculates the cross-venue spread between Kalshi and Polymarket for the same resolving question, distinguishing it from intra-venue arbitrage tools. It specifies two modes and provides concrete context about the spread range and signal interpretation.

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

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

Explicitly describes when to use which mode (topic vs explicit), and warns that pre-mapped topics often yield compatibility warnings, indicating limited tradeability. Provides guidance on interpreting response fields like compatibility_warning and temporal_alignment to avoid misusing results.

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, idempotentHint=true, and destructiveHint=false. The description adds value by mentioning scoping to the user's identifier and suggesting pairing with remember and forget, which provides behavioral context 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 (two sentences, well under 50 words) and front-loaded with the core function. Every sentence adds useful information without redundancy.

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

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

Given the tool is simple (one optional parameter, no output schema, no nested objects), the description covers all necessary aspects: what it does, when to use, scoping, and pairing with siblings. There are no gaps.

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

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

The input schema has 100% coverage with a clear description of the 'key' parameter. The description reiterates the same behavior (omit to list all keys) without adding new syntactic or formatting details, so it meets the baseline but does not exceed it.

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

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

The description clearly states the tool retrieves a value saved via remember or lists all keys if no key argument is provided. It uses specific verbs and distinguishes behavior based on the parameter, differentiating it from sibling tools like remember and forget.

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

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

The description explains when to use the tool (to look up previously stored context) and provides examples (user target ticker, address, research notes). It also notes scoping by identifier. It doesn't explicitly state when not to use, but the sibling pairings (remember, forget) are clear from context.

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

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

Annotations already cover safety (readOnly, idempotent, non-destructive). The description adds useful behavioral details like the effect of mark_read on future calls and the nature of returned payloads, without contradicting annotations.

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

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

The description is concise (4 sentences) with a clear front-loaded purpose. Every sentence adds value: purpose, returned fields, filtering, mark_read behavior, and alternative access.

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 5 optional parameters and no output schema, the description covers core functionality, filtering, and side effects. It could detail the output structure more, but the mention of specific fields is sufficient for most use cases.

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 parameter descriptions. The description enriches with examples (e.g., "sec_8k"), clarifies that 'since' expects an ISO timestamp, and explains the mark_read side effect, adding value beyond the schema.

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

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

The description clearly states the tool pulls fired events from subscription feed, specifies the returned fields (source, citation_uri, raw payload), and differentiates from sibling tools by noting the alternative REST endpoint for scripts/dashboards.

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 polling and provides an alternative access method, but does not explicitly contrast with sibling tools like list_subscriptions or recent_changes, leaving some ambiguity about when to use 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?

Annotations already cover safety (readOnlyHint, idempotentHint). Description adds rich behavioral context: fans out to multiple sources, fallback behavior, USPTO soft-fail, return format with grouped changes and citations. 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 coherent paragraph, front-loaded with examples. Every sentence adds value, though slightly long. Could benefit from bullet points for readability, but clear and efficient.

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

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

Given complexity (multiple sources, fallback, output format) and no output schema, the description is complete. Explains return structure: changes[] grouped by source, total_changes, pipeworx URIs. No gaps.

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

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

Schema coverage is 100%, but description adds meaningful context: 'since' accepts ISO or relative with examples, suggests '30d' for typical monitoring; clarifies 'type' only supports 'company'; explains 'value' accepts ticker or CIK. Adds value beyond schema.

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

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

The description clearly states the tool provides a change feed for a company combining SEC filings, news, and patents, with explicit query examples. It distinguishes from sibling tool 'entity_profile' which returns a static 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?

Provides explicit query examples and when to use alternatives. Tells when to use 'entity_profile' instead. Explains fallback logic for news sources (GDELT preferred, GNews as fallback).

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 idempotentHint=true and destructiveHint=false; description adds valuable details like scoping, persistence duration for authenticated vs anonymous, and data privacy.

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 redundant sentences, and well-structured for quick understanding.

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?

Complete for a simple key-value store; no output schema needed. Covers scope, persistence, and integration with related tools adequately.

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

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

Schema provides 100% coverage with descriptions for key and value; description adds practical examples for key naming and clarifies value is free text, enhancing schema's informativeness.

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 'Save data the agent will need to reuse later' and provides concrete examples, distinguishing it from siblings like recall and forget.

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

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

Explicitly says when to use ('when you discover something worth carrying forward') and mentions companion tools (recall, forget) for retrieval and deletion.

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, idempotentHint, and destructiveHint. The description adds useful behavioral context: 'Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.' This provides insight into internal processing without contradicting annotations.

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

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

The description is well-structured with examples at the start. It is slightly long but every sentence adds value. Could be marginally more concise, but front-loading the purpose and usage makes it effective.

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

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

Given the complexity of two entity types with different outputs and no output schema, the description fully explains what is returned for each type, including citation URIs. It provides a complete picture for the agent to understand the tool's behavior.

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

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

Schema description coverage is 100%, but the description adds significant meaning beyond the schema. It explains what each type returns (e.g., for company: ticker, CIK, company name, citation URI; for drug: RxCUI, ingredient, brand) and acceptable input formats (e.g., ticker, CIK, or name for company).

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 a user-spoken name to a canonical/official identifier, with specific verb 'resolve' and resource 'entity'. It provides clear examples of typical queries, distinguishing it from sibling tools which focus on research, alerts, or other functions.

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

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

The description explicitly says 'Use FIRST whenever you have a name but need an ID', giving clear when-to-use guidance. It details supported types and what each returns, though it does not explicitly list when not to use it or name 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?

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds that it probes each entity with ai_visibility_check and returns a ranked list with score, confidence, and signal density, providing useful behavioral context beyond the annotations.

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

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

The description is three sentences long, each earning its place: first states core function, second explains internal mechanism, third provides use case and output structure. No redundant text.

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

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

Despite no output schema, the description fully covers the tool's purpose, internal operation, output format (ranked list with metrics), parameter semantics, and typical use case. It is complete for the given complexity and peer tools.

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?

While schema coverage is 100%, the description adds significant semantic value: it clarifies that the first entity is the 'subject' and the rest are competitors, and explains the models options (workers-ai default, anthropic with API key).

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

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

The description clearly states the tool compares AI visibility across multiple entities, ranks them, and surfaces the most/least recognized. It distinguishes from sibling ai_visibility_check by emphasizing 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 explicitly suggests use for competitive AI-marketing audits, providing a concrete example. It does not list when not to use, but the sibling array includes ai_visibility_check, implying that single-entity checks should use that tool.

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 and idempotentHint. Description adds valuable behavioral details: partial failures degrade gracefully, bundlephobia first measurement may take 5-30s, and sources_failed lists timeouts. 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?

Though lengthy, the description is well-structured and every sentence adds value. It could be slightly more terse, but the information density justifies the length.

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

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

Despite lacking an output schema, the description fully explains return fields, partial failure behavior, and limitations. No significant gaps remain.

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 fully. Description adds subtle but useful extras: scoped packages are accepted for 'package' and version defaults to latest for 'version'.

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 is a composite check for npm packages, aggregating data from deps.dev and bundlephobia. It distinguishes itself from siblings like get_dependencies and get_versions by offering a one-call overview.

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 tells when to use: for questions about safety, popularity, size, or cost of adding a package. Also provides guidance on ecosystem scope and alternatives for non-NPM packages.

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, idempotentHint, and non-destructive. The description adds context about sorting by relevance and returned fields (name, version, downloads, info text), which is useful 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 sentences, front-loaded with action, every sentence adds value. No unnecessary 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 output schema exists and annotations cover safety, the description sufficiently explains the search behavior and result structure. Minor gap: no mention of sorting direction.

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 parameters. The description adds that search is by keyword in name/description, but does not elaborate on page/limit usage beyond schema.

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

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

The description clearly states the tool searches RubyGems by keyword, which differentiates it from specific gem lookup tools like get_gem. However, it does not explicitly distinguish from sibling tools, so it gets a 4.

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?

No guidance on when to use this tool vs alternatives. The description only states what it does, not the context or prerequisites. Implied usage is minimal.

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

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

Annotations already declare readOnly, idempotent, openWorld. Description adds critical behavioral details: uses BGE-base-en embeddings + cosine over 500-char overlapping windows, cap 200K chars (longer inputs truncated and flagged), returns passages with character offsets and similarity scores. 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?

Description is a single paragraph with no wasted words. It front-loads the core action and then adds details. Could be slightly tighter, but it's very efficient for the amount of information conveyed.

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 thoroughly explains what the tool returns: top-N passages with character offsets and similarity scores. It covers the embedding model, window size, character cap, and how it integrates with grounded answering. Complete for a semantic search tool.

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

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

Schema coverage is 100% with descriptions for all three parameters. Description adds value by explaining the purpose of each parameter: text is the document to search, query is natural-language (with examples), limit controls max passages (1-20, default 5). Also clarifies that text has a max length.

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 title 'Search Within a Source' and description clearly identify the action: semantic search inside a previously fetched record. The verb 'search within' and resource 'a source' are specific. It distinguishes from siblings like search_gems by focusing on searching inside a specific text, not general search. Examples (SEC 10-K, article) reinforce the 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?

Explicitly states when to use: 'when the record is too big to cram into the prompt'. Mentions alternatives: 'Pairs with ask_pipeworx_grounded'. Provides constraints: 200K char cap with truncation. No 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?

The description discloses behavioral traits beyond annotations: it returns a new subscription id, requires specific authentication, explains delivery channel constraints (SMS cap, webhook signing, auto-disable after failures), and lists supported event types. Annotations indicate idempotent and non-destructive, which aligns with creating a subscription.

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 dense with useful information and is front-loaded with the main purpose. It could be slightly more concise, but every sentence adds valuable context. The structure is logical: purpose, types, delivery 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 3 parameters (2 required) and no output schema, the description is remarkably complete. It covers return value, prerequisites, type-specific parameters, delivery channels with constraints, and error conditions like auto-disable. The agent has sufficient information to use the tool 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%, and the description adds significant value by providing detailed examples and constraints for each parameter. For instance, it explains the 'params' structure for each subscription type and delivery channel options with practical examples (phone verification, email pattern, webhook signature).

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: 'Create a proactive monitoring subscription to a live-data event stream.' It specifies the action (create), resource (subscription), and scope (live-data event stream), distinguishing it from sibling tools like list_subscriptions and unsubscribe.

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

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

The description provides clear when-to-use guidance, including prerequisites (Pipeworx OAuth account) and constraints (anonymous + BYO cannot persist subscriptions). It lists supported subscription types and delivery channels, but does not explicitly state when not to use this tool 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 already declare readOnlyHint=true, destructiveHint=false, etc. The description adds context that it returns category-bucketed example questions with exact tool+argument shape from the live catalog, which goes beyond annotations. However, it could mention that it is a fast, read-only operation.

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

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

The description is a single paragraph with essential information, but starts with a series of alternative phrasings that are somewhat verbose for an AI agent. Still, it is structured effectively and front-loads the key purpose.

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

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

Given no output schema, the description explains the return format (category-bucketed example questions with tool+argument shape), when to use it, and how to control via topic. This is fully complete for a simple read-only tool with one optional parameter.

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

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

Schema coverage is 100% with a clear description of the topic parameter listing focus areas. The description reiterates this and adds context that omitting gives a cross-category spread. The parameter meaning is well-covered by schema, so description adds moderate extra value.

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

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

The description clearly states the tool returns example questions categorized by domain, using specific verbs like 'returns' and specifying the resource. It distinguishes from siblings like ask_pipeworx and discover_tools by positioning itself as the onboarding entry point.

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

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

The description explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. It also provides guidance on calling with no arguments for full spread vs. with a topic to focus, and hints at alternatives like ask_pipeworx.

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 value beyond annotations by detailing ownership enforcement and the deactivation (not deletion) behavior. Annotations indicate idempotentHint true and destructiveHint false, which aligns with the description, and no contradictions are present.

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 two sentences that front-load the action and constraint, then add nuance. Every word contributes 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?

The tool is simple (one param, no output schema), and the description fully covers its purpose, usage constraints, and behavioral details. It is complete for the given 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 description coverage is 100%, with the single parameter 'id' well-described in the schema itself. The description does not add new semantic meaning beyond what the schema provides, so a score of 3 is appropriate.

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

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

The description clearly states the action 'Cancel a subscription by id' and specifies the resource (subscription). It also adds ownership enforcement and behavioral nuance (deactivate not delete), making it distinct from siblings like 'subscribe' or 'list_subscriptions'.

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 ownership enforcement ('you can only cancel your own subscriptions') and explains the deactivation behavior. While it does not name alternative tools, the context is clear for when this tool should be used versus 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?

Annotations provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds details on return values (verdict, structured form, citation, percent delta) and notes it replaces multiple calls. 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?

Description is front-loaded with usage examples and signals. Each sentence adds value, though slightly long. Could be trimmed slightly without losing clarity.

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 of natural-language claim verification with structured output, description is comprehensive: covers input, domain, return types, and efficiency gains. No output schema but description provides sufficient detail.

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 with 100% schema coverage. Description adds value beyond schema by providing natural-language examples and explaining how the claim is processed, including domain specifics.

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 verifies natural-language claims against authoritative sources, specifically company-financial claims via SEC EDGAR + XBRL. It differentiates from siblings by noting it replaces 4-6 sequential calls.

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

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

Explicitly says when to use: 'whenever the agent needs to check whether something a user said is factually correct.' Also specifies domain (company-financial claims for public US companies). Doesn't explicitly say when not to use, but domain restriction is clear enough.

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