Transit Land

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds context: default model is free, _apiKey is passed through to Anthropic, and the return structure (per-model score, confidence, signals, raw_response + combined view). 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?

Two sentences, front-loaded with purpose and key details, no wasted words. Each sentence adds value.

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

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

With 4 parameters, no output schema, and no nested objects, the description explains what the tool does, when to use it, what parameters are for (including defaults and prerequisites), and what the return structure looks like (per-model + combined). Adequate for the tool's complexity.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value beyond schema by explaining the default model for 'workers-ai', the need for _apiKey for Anthropic, and the purpose of 'context' for disambiguation. This 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 verb 'probe' and resource 'LLMs for what they know about a business/brand/product/topic', and distinguishes from siblings like 'compare_entities' and 'scan_competitor_ai_presence' by focusing on AI visibility scoring.

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 (AI-marketing audits, pre-launch brand checks, competitive monitoring) and how to use different models with the _apiKey for Anthropic. It implicitly says when not to use (e.g., if not interested in visibility scores), but does not explicitly state exclusions.

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 openWorldHint. The description adds context about routing to multiple tools, returning structured answers with unique citation URIs, and covering a wide range of domains. No contradictions; adds 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?

Description is concise and front-loaded with the key message to prefer over web search. It includes a list of domains and examples, which adds clarity without being excessively verbose. Well-structured.

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

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

Given the complexity (many sources, no output schema), the description adequately explains the tool's purpose, usage, and output format (structured answer with citation URIs). It covers when to use and provides examples, making it sufficiently complete for an AI 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 all parameters described. The description provides helpful examples but does not add significant semantic details beyond the schema's property descriptions. 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 routes natural language queries to a vast set of structured data sources (3,436 tools, 780 sources) and returns answers with citations. The verb 'route' and scope are explicit, and it distinguishes itself from web search by being preferred for factual, structured data.

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

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

Explicitly recommends use over web search for specific domains (SEC filings, FDA data, etc.) and provides example queries. However, it does not explicitly state when not to use it (e.g., for subjective questions), leaving some ambiguity.

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

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

Annotations already provide safety hints (readOnlyHint, idempotentHint, destructiveHint). The description adds valuable behavioral context: refusal reasons, answer structure, and that it uses only tool result. 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 well-structured with key information front-loaded. It is somewhat lengthy but every sentence adds value; could be slightly more concise 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 the complexity of the tool (grounded answer, refusal handling, comparison to sibling), the description covers all necessary aspects: behavior, input, output structure, and use cases. No output schema, but return values are clearly described.

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 by explaining the aliases for the question parameter, but no additional semantics beyond what the schema already provides.

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

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

The description clearly states it is a 'hallucination-resistant answer mode for high-stakes reads' and explains its process: picks the right tool, fetches data, extracts answer from tool result only. It distinguishes itself from the sibling tool 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: 'Use whenever an answer will be quoted, cited, or acted on' and when not to use: 'prefer ask_pipeworx for casual lookups.' Also mentions cost trade-off.

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 goes far beyond by detailing resolve behaviors (low-confidence, closed markets, wide spreads), classification, fan-out logic, response shapes, resolver contract, and safety checks. No contradiction with annotations.

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

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

The description is front-loaded with purpose and structured with headers (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.). It is verbose but every section provides value for a complex tool. Could be slightly tighter without losing key 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 no output schema, the description comprehensively explains response shapes, special cases (low-confidence, closed markets, wide spreads), parent event extraction, and news fields. It covers all aspects an agent needs to correctly interpret results.

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 good descriptions for all three parameters. The description adds minimal extra parameter-specific meaning (e.g., examples of market_input and fan-out context indirectly). Baseline 3 is appropriate as schema already does the heavy lifting.

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

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

The description clearly states 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call,' with specific verbs and resources. It distinguishes from sibling tools like polymarket_arbitrage, polymarket_edges, and polymarket_kalshi_spread by focusing on general research.

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

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

The description explicitly says 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z"' and gives concrete examples. It does not directly name alternatives but implies differentiation from sibling tools. Could be improved with explicit when-not-to-use guidance.

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

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

Adds significant behavioral details beyond annotations: describes specific financial data pulled for companies, FAERS data for drugs, sorting by primary metric, handling of off-calendar fiscal years, and citation URIs. Annotations already indicate read-only and idempotent, which description aligns with.

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 natural language triggers and efficiently explains purpose, data sources, and constraints. Slightly verbose but each sentence adds meaningful 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?

Comprehensively covers return format (paired data + citation URIs), sorting, and replaces 8-15 sequential lookups. No output schema exists, but description adequately explains what is returned. Complete for a read-only comparison tool with strong 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 value by specifying that tickers/CIKs are accepted for companies and providing example formats for both types. This clarifies expected input beyond schema constraints.

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 performs side-by-side comparison of 2-5 companies or drugs, specifying data sources for each type. It distinguishes from sibling tools by advocating preference over sequential single-entity 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?

Explicitly states when to use (for comparisons) and provides natural language triggers. Could improve by explicitly contrasting with entity_profile for single entity lookups, but the guidance is clear.

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

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

Annotations provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds substantial behavioral context: parallelism, verbatim evidence, citations, gaps array, no invention, estimated time (15-60s), and use of 3,725 tools. No contradiction with annotations.

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

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

The description is comprehensive but compact, front-loading the main value. Every sentence adds essential information, though it could be slightly more concise. However, the structure is logical 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?

Given the tool's complexity (multi-source parallel research), the description fully explains the return format (findings packet with evidence, confidence, source, citations, gaps). No output schema exists, so description carries the burden, and it succeeds. It also covers account requirements and timing.

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

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

Schema coverage is 100%, so baseline is 3. Description adds nuance: for depth, it explains the number of facets per level; for question, it clarifies that broad/multi-part questions are suitable and decomposition is intentional. This adds value beyond schema descriptions.

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

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

The description clearly states it performs 'Grounded multi-source research in ONE call', detailing decomposition, parallel routing, and structured output. It distinguishes from sibling tools like ask_pipeworx, making the purpose unambiguous.

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

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

Explicitly specifies when to use (broad/multi-part questions) and when not (single lookups, recommending ask_pipeworx). Also mentions prerequisites (Pipeworx account) and limitations (paid plan for 'thorough' depth).

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds value by specifying that stop_id must be a Transitland onestop_id. 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?

One sentence and an example; no wasted words. Front-loaded with purpose.

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

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

Adequate for a simple read operation with annotations and output schema. Could mention default behavior for service_date (default today) but schema covers it.

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

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

Schema coverage is 100% with descriptions for all 4 parameters. Description only adds an example for stop_id, which is already present in schema examples. Minimal extra 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?

Description clearly states 'Upcoming departures from a stop' with specific verb and resource. Specifies the required stop_id format (Transitland onestop_id) with an example. No sibling tool duplicates this 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?

Implies use when you have a stop_id and need departures, but lacks explicit when/not or alternative guidance. Sibling tools like search_stops exist but no direct alternative for departures.

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, idempotentHint, and destructiveHint. The description adds that the tool returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples)' and that each result is 'ready to call directly,' which is useful 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 a single paragraph with clear, front-loaded information. It is concise enough, though could be slightly tighter.

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 and moderate complexity, the description covers when to use, what it returns, and provides examples. It is complete for an agent to decide and invoke correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description mentions aliases and default/max limit but does not add significant new 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 starts with 'Find tools by describing the data or task,' clearly stating the tool's purpose as a discovery tool. It distinguishes itself from sibling tools by focusing on tool discovery rather than querying specific data sources.

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 advises to 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' Also lists numerous domains, giving clear context for when this tool is appropriate.

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, openWorld, idempotent, non-destructive. The description adds meaningful context: it notes the patents API is sunsetting and soft-fails, and mentions the news fallback mechanism. It does not describe error handling for missing tickers, but the overall transparency is strong.

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

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

The description is well-structured: it starts with user queries, states the core purpose, lists return fields and sources, and ends with input constraints. Every sentence adds value, and the information is front-loaded. Despite its length, it remains focused 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?

For a complex tool aggregating multiple sources, the description is comprehensive. It lists all major return components (CIK, filings, fundamentals, patents, news, LEI), mentions limitations (patents sunsetting, fallback logic), and specifies the number of filings (up to 5). No output schema exists, so the description carries full burden and does so excellently.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value beyond the schema by explaining that names are not supported and advising to use resolve_entity. It also provides examples of valid inputs (ticker or CIK). This extra guidance justifies a slightly higher score.

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

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

The description clearly states it provides a full cross-source profile of a US public company. It includes example user queries, lists sources (SEC, XBRL, USPTO, news, GLEIF), and distinguishes from chaining single-pack lookups. It effectively communicates the verb (profile) and resource (US public company).

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

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

The description explicitly says to use this tool when the user asks for a holistic view and to prefer it over chaining single-pack lookups. It also provides input constraints: use ticker or CIK, not names, and directs to resolve_entity if only a name is available. This is excellent guidance.

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

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

Annotations already convey destructive and idempotent nature. Description adds context about when to delete (e.g., clear sensitive data) and pairing, but does not add 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?

Three sentences: action, usage guidance, sibling pairing. No wasted words, front-loaded with main action.

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

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

For a simple destructive tool with one parameter and no output schema, the description fully covers purpose, usage, and relationships. 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% with parameter 'key' described as 'Memory key to delete.' Description repeats 'by key' but 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 action ('Delete a previously stored memory by key') and distinguishes from siblings by mentioning 'Pair with remember and recall.'

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 lists when to use: 'when context is stale, the task is done, or you want to clear sensitive data.' Does not include when-not-to-use but provides adequate 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?

Discloses key behaviors: fetching the page, extracting title/description/key links, emitting standard markdown. Annotations already indicate read-only, idempotent, non-destructive; description adds context on the extraction process 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?

Front-loaded with action ('Generate a production-ready llms.txt'), concise at 3-4 sentences, no wasted words. Every sentence adds value including use cases.

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

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

Covers purpose, output format, use cases, and parameter hints. Missing details on error handling or URL validity, but sufficient given low complexity and annotations. No output schema but output is described as text blob.

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 already covers both parameters (url, max_links) with descriptions. The description adds a concrete example for url (e.g., specific landing page) and clarifies max_links default and max, providing marginal 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?

Clearly states it generates an llms.txt file for a URL, specifying the target audience (AI crawlers) and the output format. Distinguishes from sibling tools like ai_visibility_check by focusing on llms.txt generation.

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

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

Provides explicit use cases (client's site, own project, auditing competitor) but does not mention when not to use or alternatives among siblings. However, the context is clear enough for an agent to infer appropriate 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=true, destructiveHint=false, making the tool's safety clear. The description adds no additional behavioral details (e.g., data freshness, pagination). With annotations carrying the burden, a score of 3 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 a single sentence, front-loaded with the core purpose. It is efficient but could be slightly more informative without losing conciseness.

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

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

Given the tool's simplicity and existence of an output schema, the description covers the basic context (feeds across operators). However, it omits details about the limit parameter and does not clarify the scope of 'available.'

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 50% (spec documented, limit not). The description adds no parameter-specific information beyond the schema, failing to compensate for the missing limit documentation.

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

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

Description states 'Available GTFS feeds across operators,' clearly indicating a list operation over feeds. It distinguishes from sibling search tools by implying a broader listing, but does not explicitly differentiate from similar list 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?

No guidance on when to use this tool versus alternatives like search_agencies or departures_at_stop. The description provides no context for when listing all feeds is appropriate.

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, so the description does not need to repeat safety. It adds value by listing the returned fields (id, type, params, etc.), which annotations do not provide. 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?

Three sentences, front-loaded with the main action, followed by return fields and usage guidance. Every sentence earns its place 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?

The description covers purpose, return values, and usage context. With no output schema, it adequately describes the returned fields. The single parameter is well-documented in the schema, so the description is complete for this tool's complexity.

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

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

Schema coverage is 100% with the 'include_inactive' parameter already described in the schema. The description does not add additional meaning or context for this parameter beyond the schema, meeting the baseline.

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

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

The description clearly states 'List the caller's active subscriptions' with a specific verb and resource. It lists the return fields and distinguishes the tool's purpose from siblings like 'subscribe' and 'unsubscribe' by explaining when to use it.

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 to review what you're monitoring before adding more or to find an id to cancel,' providing clear context for when to invoke this tool. It does not state exclusions but implies it is the appropriate tool for reviewing subscriptions.

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 reveals rate limiting (5 per identifier per day), that it's free and doesn't count against quota, and that the team reads digests daily. This adds behavioral context beyond the annotations, which are all false. It does not describe the submission process or confirmation, but that is acceptable for a feedback tool.

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

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

The description is concise at ~100 words, front-loaded with purpose, and every sentence adds value. It is well-structured and avoids redundancy.

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

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

Given the tool's purpose (feedback submission) and the lack of an output schema, the description is complete. It covers what to include, constraints, and how feedback is used (affects roadmap). No additional information is needed.

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

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

Schema coverage is 100%, but the description adds value by explaining the enum values in context (e.g., 'data_gap = data Pipeworx does not currently expose'), providing guidance for the message field (specific, 1-2 sentences, 2000 chars), and describing the optional context object.

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

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

The description clearly states the tool sends feedback about broken, missing, or needed things, and lists specific categories (bug, feature, data_gap, praise). It distinguishes itself from sibling tools like ask_pipeworx and discover_tools by focusing on feedback to the Pipeworx team.

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 conditions for use: when a tool returns wrong data (bug), when a desired tool is missing (feature/data_gap), or when something works well (praise). It also includes a 'don't' instruction against pasting end-user prompts, and notes rate limits and cost.

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 valuable behavioral context beyond annotations: indicates data source (CF analytics-engine), privacy (no PII), and caching behavior (5min-1h). It aligns with annotations (readOnly, idempotent) and provides extra details.

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

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

The description is concise with three sentences and bullet points. The purpose is front-loaded in the first sentence, and every sentence adds value without redundancy.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description fully covers what the tool returns (top tools, packs, volume) and how it works. It is complete for an agent to decide on invocation.

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

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

Schema coverage is 100% with parameter descriptions. The tool description adds meaning by explaining the effect of different window lengths (shorter for hot trends, longer for steady-state), going 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 trending tools and packs on Pipeworx, with specific resources (top tools, packs, call volume). It distinguishes from siblings like 'discover_tools' by emphasizing real-time agent usage.

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, confirming canonical choices, aligning use cases). It provides clear guidance on when to use the tool, though it doesn't explicitly state 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 indicate readOnly, openWorld, idempotent, non-destructive. Description adds rich behavior: explains monotonicity logic, partition checks, semantic anchor (Jaccard similarity), placeholder filtering, and response format. 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?

Front-loaded with requirement, well-structured with clear sections. Contains detailed technical specifics (thresholds, filters), which are helpful but slightly dense. Efficient overall.

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 and lack of output schema, description covers return format, thresholds, and filtering logic. Missing explicit error cases or empty result handling, but overall 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 covers both parameters with 100% description. Description adds extra context: explains modes, gives examples, accepts full URLs, and recommends single-event mode. This 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?

Description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes two modes (single-event and cross-event) with specific use cases, making purpose very clear and distinct 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?

Explicitly requires one of 'event' or 'topic', recommends which mode for which scenario, and explains when each is appropriate. Provides examples. Lacks explicit when-not-to-use or alternative tools, but context 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. The description adds valuable behavioral details including caching behavior ('Cached 1h at the KV level'), the five model families, and diagnostic output elements. 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 informative but verbose, spanning multiple paragraphs with detailed model explanations. While front-loaded with purpose, it could be more concise without losing essential guidance.

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, fed_candidates, _diagnostics) and includes diagnostic info for empty segments. It covers edge cases (e.g., Fed bets, caching) and is complete given the tool's complexity and 9 parameters.

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 parameters are fully documented in the schema. The description reinforces some parameter purposes (e.g., min_liquidity, max_spread_pp) but does not add substantial new semantic information beyond what the schema provides.

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

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

The description clearly states the tool's function: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It specifies the intended use case ('what should I bet on today') and distinguishes itself from siblings like polymarket_arbitrage and bet_research by focusing on Pipeworx-specific edge detection.

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

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

The description provides clear context for when to use the tool ('agents discover opportunities without paging hundreds of markets') and lists configurable knobs for filtering. However, it does not explicitly state when not to use it or compare to alternatives, leaving some ambiguity against sibling tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds rich behavioral details: reads from daily snapshots, returns tracked/expired/snapshot_dates, limits (60-day TTL), decay computation. 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 dense with information but not overly long. It front-loads the core question. Each sentence adds value, though structure could be slightly improved (e.g., bullet points for response format). Still 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?

No output schema, so description fully explains response structure (tracked, expired, snapshot_dates) with field meanings, limits, and parameter details. Comprehensive for a tool with two optional params and no output schema.

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

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

Schema covers both parameters with descriptions. The description adds context: days default 14 max 30, window default '1wk' and explains window as snapshot family. Since schema coverage is 100%, baseline 3, but description adds meaningful extra info, earning 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 states it provides edge persistence and decay telemetry from daily snapshots, answering a specific question about edge age and trend. This clearly distinguishes it from siblings like polymarket_edges (raw current snapshot) and polymarket_arbitrage (different concept).

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

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

The description implies usage when historical edge persistence is needed, contrasting fresh vs aged edges. It does not explicitly list when not to use or name alternatives, but the context of siblings makes it clear. Slight lack of explicit exclusion.

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, idempotentHint, etc. The description adds behavioral context beyond annotations: return structure (verdicts like clean/degraded/cannot_fill), forced directional risk warnings, and the impact of partial fills. It does not contradict 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 relatively long but front-loaded with purpose. Every sentence provides necessary context for this complex tool, including return details and warnings. It could be slightly more concise, but given the two-mode complexity, it is well-structured and informative.

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

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

No output schema exists, so the description must cover return values. It does so thoroughly: for single-market it lists top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict; for basket it lists theoretical_sum, realizable_sum, capture_ratio, profit_usd, per-leg fill, thin_legs, max_clean_notional_usd, forced_directional_risk. Edge cases (thin books, unhedged directional risk) are addressed, making it 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%, so baseline is 3. The description adds meaning beyond schema: explains the dual interpretation of size_usd (spend vs target proceeds for single-market; settlement notional for basket), side defaults for baskets (auto based on partition sum), and clarifies the difference between market and event parameters. This enrichment 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 function as an edge check against live order-book depth, specifies two modes (single-market and basket), and explicitly distinguishes its use from sibling tools polymarket_arbitrage and polymarket_edges. This provides a specific verb and resource with clear differentiation.

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?

Usage guidelines are explicit: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It explains why (theoretical overround not capturable, partial fills cause unhedged risk). However, it does not explicitly state when not to use it or alternative tools for other scenarios, missing some exclusion guidance.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds significant behavioral context: compatibility_warning conditions (no matched pairs with skipped_cross_type>0 or both venues >5 legs), temporal_alignment field, and skipped cross-type/subtype counters. No contradiction with annotations.

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

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

The description is detailed but well-structured with clear sections for modes, response, and safety fields. It is front-loaded with purpose. Some details (e.g., skipped_cross_type/subtype counters) could be trimmed, but overall it earns its 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 no output schema, the description fully explains return fields (leg-by-leg prices, top_spreads_pp, compatibility_warning, temporal_alignment) and handles edge cases. It is complete for a complex cross-venue spread 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 each parameter. The description adds value by explaining the topic shortcuts list and that explicit tickers override the topic-mapped side. It provides examples and clarifies usage 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 it computes 'Cross-venue spread between Kalshi and Polymarket for the same resolving question' and distinguishes two modes (topic shortcuts and explicit tickers). It differentiates itself from siblings like polymarket_arbitrage by focusing on cross-venue 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 clearly explains when to use (finding arbitrage signals between venues) and when not (if compatibility_warning indicates non-equivalent bet shapes or temporal misalignment). It warns that 'pre-mapped ≠ tradeable' and notes that real spreads are rare, providing explicit guidance.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds important scoping detail: values are tied to the agent's identifier (anonymous IP, BYO key hash, or account ID). 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?

Three sentences, front-loaded with main purpose. Every sentence adds value: purpose, usage scenario, scoping, and pairing with related tools. No wasted words.

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

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

For a simple retrieval tool with annotations and schema covering all parameters, the description is complete. It explains pairing with remember/forget, scoping, and listing behavior. No output schema is needed for such a straightforward operation.

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

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

Schema has 100% coverage with description for key parameter. Description adds the behavior of omitting the key (list all keys), which aligns with the schema's optional requirement and provides concrete use case context.

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

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

Description clearly states the tool retrieves a value saved via remember or lists all keys when omitted. It specifies the resource (saved values) and verb (retrieve/list), and distinguishes from sibling tools 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?

Describes when to use: to look up context the agent stored earlier without re-deriving. Mentions pairing with remember and forget. While it doesn't explicitly list alternatives, the context is clear enough for the agent to decide.

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

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

The description states that setting mark_read:true flags events as read, implying state mutation. However, annotations declare readOnlyHint=true, contradicting this behavior. This is a serious inconsistency that undermines transparency.

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

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

The description is concise, with three front-loaded sentences covering purpose, return fields, filtering options, and an alternative access method. 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?

The description covers essential behavioral details (mark_read, polling, alternative endpoint) and return contents, which compensates for the lack of output schema. However, the contradiction slightly reduces completeness.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context (e.g., ISO timestamp for 'since', default limit) but does not significantly enhance understanding beyond the schema descriptions.

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

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

The description clearly states the tool retrieves recent alerts from a subscription feed, with specific fields. It distinguishes from sibling tools like list_feeds by focusing on fired events, but does not explicitly differentiate from all 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 provides guidance on filtering by type and since, using mark_read, and mentions polling suitability. It also gives an alternative HTTP endpoint for scripts/dashboards, offering context on when to use this tool vs. other access methods.

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

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

Annotations already declare it as read-only, idempotent, and non-destructive. The description adds valuable behavioral details: fan-out to multiple sources, fallback logic, and soft-fail behavior for USPTO 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 front-loaded with common queries, then explains fan-out and parameters, and ends with an alternative. It is well-structured but slightly verbose; could be more concise while retaining all 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?

Although there is no output schema, the description explains the return format (grouped by source, total count, citation URIs) and covers fan-out behavior and parameter details. It adequately addresses the tool's complexity.

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

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

With 100% schema description coverage, baseline is 3. The description adds examples for 'since' (ISO and relative) and 'value' (ticker or CIK), and notes that 'type' only supports 'company', providing clarity 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 provides a 'change feed for a company in the last N days/weeks/months' and explicitly contrasts with the sibling entity_profile tool, making the purpose unambiguous.

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

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

The description includes explicit guidance: 'Use entity_profile instead when you want the static profile... regardless of window.' It also gives example queries that illustrate typical use cases.

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

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

Annotations provide idempotent=true, readOnlyHint=false, destructiveHint=false. Description adds key behavior: key-value scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous. 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?

Single paragraph, first sentence states purpose, every sentence adds value. No wasted words: covers when, how, retention, and siblings.

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 2 simple params, no output schema, and good annotations, the description fully explains scope, persistence, and usage pattern. 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 covers 100% of parameters with descriptions. Description adds examples (e.g., 'subject_property', 'target_ticker') but no further semantic constraints. 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 starts with 'Save data the agent will need to reuse later'—a specific verb and resource. It explicitly distinguishes from siblings ('recall', 'forget'), making purpose and context clear.

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?

Describes when to use ('discover something worth carrying forward') and pairs with recall/forget. Lacks explicit when-not-to-use but is sufficient for the agent.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavioral context: internal cascading through multiple lookup endpoints and efficiency improvement ('replaces 2-3 manual lookups'). Also details return formats for each entity type, including citation URIs.

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

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

Description is moderately sized but well-structured, starting with usage examples, then clear instruction, then supported types. Every sentence adds value, though a slight tightening could improve conciseness without losing 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 tool has only 2 parameters and no output schema, the description provides comprehensive context: return values, citation URIs, internal cascading. It covers all needed information for an agent to select and 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 covers both parameters at 100% but description adds substantial meaning: for 'type' it explains output details per entity; for 'value' it gives input examples (ticker, CIK, name, brand/generic drug). This goes far beyond the schema's minimal descriptions.

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

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

The description starts with concrete examples of natural language queries, clearly defines the tool as resolving names to canonical identifiers, and specifies supported types (company, drug) with detailed return information. It uniquely positions the tool among siblings by focusing on identifier resolution.

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

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

Explicitly states 'Use FIRST whenever you have a name but need an ID', which is a clear usage guideline. It implies when not to use (if ID already known) but does not exhaustively list alternatives. Sibling tools do not directly compete, so no explicit exclusions needed.

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

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

Annotations already indicate readOnly, idempotent, and non-destructive. The description adds behavioral context: it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized, plus return fields (score, confidence, signal density). No contradiction with annotations.

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

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

The description is three sentences with a parenthetical example. Every sentence is substantive: first states core function, second explains process, third gives use case. No redundant 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 no output schema, the description adequately explains the return type (ranked list with score, confidence, signal density). Parameters are well-documented. The tool's purpose and internal behavior are fully covered.

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

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

Schema coverage is 100%, but the description adds meaning beyond schema: clarifies that the first entity is treated as the 'subject' for narrative, and for the 'models' parameter, it explains defaults and the requirement for anthropic key. These are not in the schema descriptions.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, using 'Compare', 'Probes', and 'ranks'. It distinguishes itself from the sibling 'ai_visibility_check' (single entity) by emphasizing multi-entity comparison and ranking.

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 a concrete use case ('competitive AI-marketing audits') and an illustrative example. It implies when to use this tool over a single-entity check, but does not explicitly state when not to use it or mention 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 readOnly and idempotent. Description adds critical behavioral context: fans out to two services, partial failures degrade gracefully, timing for bundlephobia (5-30s), and lists sources_failed. No contradiction.

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

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

The description is fairly long but well-structured: starts with purpose, then usage, then behavior, then output. Every sentence adds value, though a bit verbose; could be slightly trimmed without losing meaning.

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?

Without an output schema, the description fully details the return format: summary block fields, per-advisory detail, links, alternative versions. It also covers partial failures and timing, making it complete for an agent to understand expectations.

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

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

Schema description coverage is 100%, so baseline is 3. Description adds examples like '18.3.1' and confirms scoped packages accepted, but doesn't add significant new semantics beyond the schema's parameter descriptions.

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

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

The description clearly states the verb ('composite check') and resource ('npm package'), and distinguishes from siblings by specifying it covers npm only in v1. It explains exactly what the tool does: a combined check across deps.dev and bundlephobia for safety, size, and alternatives.

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: 'when an agent asks is X safe / popular / small or what does adding lodash cost me'. Also states NPM ecosystem only in v1 and mentions fallback for other ecosystems, providing clear 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds only a definition ('Operators are the entities running services.'), which provides context but does not significantly enhance behavioral understanding beyond the annotations. No mention of rate limits, required permissions, or return behavior.

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

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

The description is a single, front-loaded sentence that conveys the essential purpose. It has no wasted words and is immediately understandable. This is an exemplary model of conciseness.

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

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

Given the high schema coverage, rich annotations, and presence of an output schema, the description is sufficient for basic understanding. It lacks details about result filtering options or expected output, but these are covered by the schema and output schema. Slightly more context about the returned entities could improve completeness, but it is not critically deficient.

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 all parameters have adequate descriptions in the schema itself. The tool description does not add any additional semantic clarification beyond what is already in the schema. Hence, baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Search transit agencies/operators by name or geo.' It distinguishes from sibling tools like search_routes and search_stops by specifying the target resource (agencies/operators). The verb 'Search' and resource 'agencies' are specific and unambiguous.

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

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

The description implies when to use (when searching for agencies), but does not provide explicit guidance on when to use this tool versus alternatives like search_routes or search_stops. No mention of when not to use or prerequisites. The guidance is minimal and relies on user inference.

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 the route_type mapping, which is helpful, but does not disclose additional behavioral traits like pagination, filtering behavior, or result structure.

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

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

The description is extremely concise with two sentences. The first sentence states the purpose, and the second adds valuable mapping information. No wasted words.

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

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

Given the complexity of 8 optional parameters, the description covers key search dimensions but lacks detail on combining parameters, the role of location parameters (lat/lon/radius), or output schema. Adequate but not comprehensive.

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

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

With only 25% schema description coverage, the description partially compensates by listing searchable fields (name, type, agency, location) and providing route_type values. However, it does not explain all parameters or clarify ambiguous ones like 'agency' vs. 'agency_id'.

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 routes by multiple criteria (name, type, agency, location), which is a specific verb+resource combination. It distinguishes from sibling tools like search_agencies and search_stops by focusing on routes.

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 finding routes by attributes, but does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention when not to use it or any 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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds the search behavior and near-point functionality but doesn't disclose additional traits beyond annotations. It does not contradict 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 extremely concise, with two sentences that front-load the purpose and key usage pattern. Every word earns its place with no redundancy.

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

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

Given the presence of an output schema (not shown) and comprehensive annotations, the description adequately covers the tool's purpose and primary use cases. The parameter documentation is partially assisted, and the tool's simplicity makes the description sufficient.

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

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

Schema coverage is 43%, with only stop_id, radius_m, and served_by_route_type having descriptions. The description explains the role of lat, lon, and radius_m for proximity searches, adding value over the schema. However, no additional meaning is provided for name, limit, or stop_id 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 specifies the tool searches for stops/stations and distinguishes two modes: by name or by location (lat+lon+radius_m). It differentiates from sibling tools like search_agencies and search_routes.

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

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

The description explicitly tells when to use lat+lon+radius_m for proximity searches, implying the name parameter for text-based searches. It provides clear context but doesn't mention 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 readOnly, idempotent, non-destructive. Description adds significant behavioral details: uses BGE-base-en embeddings with cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, and output includes character offsets and similarity scores. No contradictions.

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

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

Four sentences, each earning its place: purpose, usage, pairing, technical details. No redundant phrases, well-organized from high-level to specifics.

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

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

Given no output schema, description explains return format (passages with offsets and scores). Covers embedding method, truncation, and pairing with sibling tool. No obvious gaps for a search tool of this complexity.

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

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

Schema coverage is 100% (all parameters described). Description adds value with examples for query, clarifies default and range for limit, and reiterates text length cap. Goes beyond baseline of 3.

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

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

The description clearly states the tool performs semantic search inside a fetched record, with specific verb 'search inside' and resource 'fetched record'. It distinguishes from siblings by contrasting with ask_pipeworx_grounded and explaining when to use it (record too big for prompt).

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

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

Description explicitly states when to use ('record is too big to cram into the prompt') and pairs with ask_pipeworx_grounded. It implies not to use when record fits in prompt, but does not provide explicit when-not statements or alternative tools beyond the paired one.

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: requires OAuth account, type-specific parameters, delivery channels, and limits (10/day SMS cap). However, it does not address idempotency despite idempotentHint=true.

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

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

The description is fairly long but front-loaded with the main purpose and packed with necessary details. Each sentence adds value, but it could be slightly more concise.

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 (3 params, nested objects, many sibling tools), the description is remarkably complete. It covers return value, authentication, type options, delivery channels, and constraints, all without an output schema.

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

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

Schema coverage is 100%, and the description adds significant meaning: examples for each type, parameter formats, and delivery options. It goes beyond the schema to explain usage and constraints.

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 creates a proactive monitoring subscription to a live-data event stream, with specific verb 'Create' and resource 'subscription'. It distinguishes from siblings 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 includes when to use (e.g., subscribing to specific event types), requirements (Pipeworx OAuth account), and constraints (anonymous/BYO cannot persist). It lacks explicit comparison to sibling tools but provides sufficient 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 indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds value by explaining what the tool returns (category-bucketed example questions with tool+argument shape) and how to call it (with or without topic). It does not contradict annotations. The behavioral traits are transparent 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 front-loaded with the purpose and includes all necessary information in a structured way. It is slightly verbose (includes full list of categories) but every sentence adds value. Could be shortened by removing the parenthetical list of categories, but it's still 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?

The description fully explains what the tool does (returns example questions), the return structure (category-bucketed with tool+argument shape), and behavior with different inputs (no topic vs specific topic). Since there is no output schema, the description compensates adequately. It also mentions the categories covered, making it complete for an onboarding 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 one parameter 'topic' described as 'Optional focus area...' The description adds meaning: 'pass `topic` (e.g. "finance", "pharma", "betting") to focus. Omit for a cross-category spread.' This provides context on how to use the parameter and example values, enriching beyond the schema's description.

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

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

The description clearly states the tool's purpose: 'What can I ask Pipeworx?' and 'the onboarding entry point for an agent that just connected and wants to know what is worth asking.' It specifies that it returns category-bucketed example questions with tool+argument shape. This distinguishes it from siblings like 'discover_tools' (which likely discovers tools themselves) and 'ask_pipeworx' (a meta-tool for asking questions).

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

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

The description provides explicit usage guidance: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).' It also contrasts with alternatives by mentioning those meta-tools. The user knows exactly when to invoke this tool (onboarding) vs. other 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?

Discloses deactivation (not deletion) and ownership check, adding behavioral context beyond annotations. No contradiction with annotations: destructiveHint=false aligns with 'deactivated'. Good transparency.

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

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

Two sentences, no filler, front-loaded with the core action. Every sentence serves 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?

With one parameter, no output schema, and good annotations, the description is complete. It explains ownership, deactivation, and links to recent_alerts for context.

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

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

Schema coverage is 100% and the description adds minimal value beyond 'by id'. It confirms the id is from subscribe, but doesn't add new semantic meaning. 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 'Cancel a subscription by id' with the verb 'cancel' and resource 'subscription'. It distinguishes from siblings like 'subscribe' and 'recent_alerts' by noting ownership enforcement and deactivation vs deletion.

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 'Ownership is enforced — you can only cancel your own subscriptions', providing a clear usage constraint. It also hints at an alternative (recent_alerts) for historical data, but could be more explicit about when not to use.

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

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

Annotations already declare readOnly/idempotent/destructive. Description adds behavioral details: replaces multi-step calls, returns specific verdict types, actual value with citation, and percent delta. 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?

Single paragraph packed with information, front-loaded with trigger phrases. Could be more scannable but no waste; 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?

Despite no output schema, description thoroughly explains return values (verdict types, citation, delta) and the replacement of sequential calls. Complete for this tool's complexity.

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

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

Schema description coverage is 100% (1 param with full description). Description adds example claims and emphasizes natural-language format, providing marginal 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?

Description clearly states the tool's purpose: natural-language claim verification against authoritative sources, with specific trigger phrases and domain scope (company-financial claims). It distinguishes itself by being the only sibling tool for fact-checking.

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 'Use whenever the agent needs to check factual correctness' and specifies supported domain. Lacks explicit when-not-to-use or alternatives among siblings, but the clear scope compensates.

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