Google_maps

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

The description aligns with annotations (read only, idempotent, non-destructive) and adds important context: default model is free, Anthropic requires a BYO key and billing, and output structure includes score, confidence, signals, and raw response. 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 concise, front-loaded with purpose, and efficiently covers models, output, and use cases in a single paragraph without extraneous 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's moderate complexity (4 params, no output schema), the description fully explains input, behavior, and return structure. Annotations cover safety, so no further details 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 valuable context beyond the schema: default model details, key requirement for Anthropic, and explanation of entity parameter scope. This enhances parameter understanding.

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

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

The description clearly states the tool probes LLMs for visibility of an entity, scoring per model. It distinguishes from sibling tools like scan_competitor_ai_presence by its specific output format and scoring mechanism.

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

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

The description lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to provide an API key. However, it does not explicitly mention when not to use this tool versus alternatives like scanning 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 that it returns structured answers with stable pipeworx:// citation URIs and routes to the right tool, which is valuable 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 front-loaded with the most important guidance ('PREFER OVER WEB SEARCH'), followed by use cases and examples. It is well-structured but slightly verbose; could be 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?

For a tool with no output schema and a single essential parameter, the description fully covers purpose, usage scenarios, and examples. No gaps remain for an agent to understand when and how to invoke 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 all parameters described as aliases for 'question'. The description repeats that but adds that it accepts multiple aliases. Given high schema coverage, baseline 3 is appropriate; no additional semantic value beyond schema.

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

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

The description clearly states the tool routes questions to a vast array of structured data sources (SEC, FDA, etc.) and distinguishes itself from web search with 'PREFER OVER WEB SEARCH'. It provides a specific verb ('asks') and resource ('Pipeworx'), making its 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 says when to use ('for questions about current or historical data...') and provides example queries. It recommends preferring this over web search. However, it does not explicitly exclude usage when sibling tools like 'deep_research' or 'bet_research' might be more appropriate, which would be helpful.

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

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

Annotations already declare readOnlyHint=true and no destructiveness. The description adds key behavioral traits: extraction step using only tool results, explicit refusal reasons (not_in_source, no_tool_match, etc.), and the extra LLM call cost. 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?

Front-loaded with purpose, then details, then usage guidance. Every sentence is informative and earns its place. The structure is logical and easy to parse for an AI agent.

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

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

Despite having no output schema, the description fully explains the return format (answer, evidence, confidence, source, fetched_at, refusal_reason) and possible refusal reasons. Given the tool's complexity, this is complete for an agent to invoke and handle results 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 the description adds minimal value beyond the schema. It notes that question aliases are accepted, but this is already in the schema. The baseline of 3 is appropriate as the schema handles parameter documentation.

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

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

The description clearly states the tool's purpose: a hallucination-resistant answer mode for high-stakes reads that extracts answers only from tool results. It distinguishes itself from the sibling ask_pipeworx by noting the extra LLM call cost and the specific use case for high-stakes queries.

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 guidance on when to use (answers quoted, cited, or acted on; must not invent facts) and when not to use (prefer ask_pipeworx for casual lookups). Also mentions the extra cost, helping the agent decide between siblings.

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

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

Annotations already indicate readOnlyHint, idempotentHint, and no destructiveness. The description goes far beyond by detailing fan-out behavior, response shapes, resolver contract with confidence levels, parent event extraction, news fallback mechanisms, safety short-circuits for low-confidence or closed markets, and resolution-rule risk. 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 quite long but front-loads the main purpose. While every sentence adds value, the verbosity and lack of bullet points or structured sections reduce conciseness. For a complex tool, it is acceptable but could be more terse.

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 with three parameters, no output schema, and multiple behavioral aspects (resolver, parent events, news, safety, resolution rules), the description is remarkably complete. It covers all edge cases and provides sufficient detail for an agent to use the tool correctly without needing external documentation.

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

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

Schema coverage is 100% with clear descriptions for all three parameters. The description adds value by providing examples for market input and explaining the depth parameter (quick vs thorough) and include_raw effect, which enhances understanding beyond schema alone.

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

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

The description clearly states it researches a Polymarket bet by pulling data in one call, distinguishing it from other polymarket tools like arbitrage and edges. The verb 'Research' and specific resource 'Polymarket bet' with examples of inputs and outputs make the purpose unmistakable.

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

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

The description explicitly states when to use the tool with phrases like 'Use for "should I bet on X"', providing clear context for invocation. However, it does not mention when not to use it or alternatives among sibling tools, though the context is sufficient for most agents.

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=true, and destructiveHint=false. The description adds context on handling off-calendar fiscal years, sorting by primary metric, and returning citation URIs. No contradictions.

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

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

The description is a single dense paragraph that front-loads common query patterns. It is concise but could benefit from bullet points for readability. Every sentence provides value.

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

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

Given the complexity of two entity types and different data sources, the description covers return format (paired data + citation URIs), sorting behavior, and performance benefits. Without an output schema, it adequately describes 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 coverage is 100%. The description elaborates on the enum values (company vs drug) explaining what data each pulls, and gives examples for values (tickers/CIKs for companies, drug names). It adds value beyond the schema.

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

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

The description clearly states it performs side-by-side comparison of 2-5 companies or drugs, with specific data sources for each type. It distinguishes from sibling tools like entity_profile (single entity) and deep_research (broader scope).

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 advises 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It provides example queries and specifies use cases. It does not explicitly list exclusions, but the guidance is strong.

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

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

Annotations already declare read-only, idempotent, non-destructive. The description adds significant context: explains decomposition and parallel routing, return format with explicit gaps, latency expectations (15-60s), and prerequisite (Pipeworx account, paid plan for 'thorough'). 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?

Front-loaded with core function in first sentence. Each subsequent sentence adds distinct value (parallel decomposition, evidence format, gaps, usage guidance, requirements, latency). No redundant or unnecessary content.

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 the return structure: structured findings packet with evidence, confidence, source, fetched_at, pipeworx:// citation, and explicit gaps[]. Also covers prerequisites and latency. Complete 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 coverage is 100% with clear descriptions for both parameters. The description does not add new information beyond what the schema already provides (e.g., question param description already says 'broad/multi-part is fine'). 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: 'Grounded multi-source research in ONE call' that decomposes questions, routes sub-questions in parallel, and returns a structured findings packet with citations. It distinguishes from sibling ask_pipeworx by noting it is for single lookups vs. broad research.

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

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

Explicitly states when to use: 'Use for broad or multi-part questions' and when not: 'use ask_pipeworx for single lookups'. Also provides depth options and account requirements, guiding appropriate usage.

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

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

Annotations declare readOnly, idempotent, non-destructive. The description adds that it returns top-N tools with full schemas, ready to call directly, and that it's a discovery layer. No contradictions, adds useful behavioral context.

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

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

Two sentences, front-loaded with core purpose, no wasted words. The list of domains is compact. Every sentence adds essential information.

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

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

Despite no output schema, the description explains return value (top-N tools with schemas). It covers usage, behavior, and parameters sufficiently for an agent. Minor omission: no mention of error handling for ambiguous queries, but overall complete enough.

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

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

Schema coverage is 100% with parameter descriptions. The description adds value by explaining aliases for 'query' (q, task, search, description) and providing examples, which are not present in the schema definition alone.

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

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

The description clearly states the tool finds tools by describing data or task, using the verb 'Find' and resource 'tools'. It distinguishes from all sibling tools, which are specific data retrieval or analysis tools, not a discovery tool.

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

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

Explicit usage guidance: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST... when you have many tools available'. This clearly differentiates from directly calling individual 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 indicate read-only, open-world, idempotent, non-destructive. The description adds further behavioral context: it fans out across multiple sources, mentions USPTO API sunset in May 2025 causing soft-fail, and describes fallback mechanisms (GDELT→GNews). 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 packed with information and front-loaded with examples. While it is relatively long, every sentence adds value. Minor redundancy could be trimmed, but overall it is structured well for an AI agent.

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

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

Despite lacking an output schema, the description explains the return structure (cik, company_name, recent_filings with URIs, fundamentals, patents, news, LEI), mentions limits (up to 5 filings), and covers failure behavior. This is complete for a complex 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 parameter descriptions. The description adds meaning by explaining that 'value' accepts ticker or zero-padded CIK but not names, and directs to resolve_entity for names. This goes 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 it provides a full cross-source profile of a US public company, listing specific sources (SEC, XBRL, USPTO, news, GLEIF) and outputs. It explicitly distinguishes from chaining single-pack lookups, making the purpose highly specific and differentiated from siblings.

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

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

The description explicitly says to prefer this tool over chaining single-pack lookups for holistic views, and advises using resolve_entity first if only a name is available. This provides clear when-to-use and when-not-to-use guidance with 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 destructive (true) and idempotent (true). Description adds context about why to delete (stale context, done task, clear sensitive data) and confirms it deletes by key. 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: first states the action, second gives usage guidance. No wasted words, efficient and front-loaded.

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

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

Simple tool with one parameter and full annotations. Description covers purpose, usage, and relationship to sibling tools. Return value not needed as 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 coverage is 100% with clear parameter description ('Memory key to delete'). Description mentions 'by key' but adds no additional meaning beyond the schema. Baseline 3 applies.

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

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

Description clearly states 'Delete a previously stored memory by key', using a specific verb ('delete') and resource ('memory by key'). It distinguishes itself from sibling tools like '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 states when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. Mentions pairing with 'remember' and 'recall', providing clear context and 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 indicate read-only, open-world, idempotent, non-destructive. The description adds context about fetching the page, extracting title/description/links, and emitting markdown format, which is consistent with annotations and provides behavioral detail beyond them.

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

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

The description is a single paragraph of about four sentences, front-loaded with the core purpose, and every sentence adds value. No redundant or irrelevant information.

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

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

For a tool with two parameters and no output schema, the description adequately covers the function, output format, and use cases. It could be more specific about URL requirements (e.g., root vs. any page), but the examples suffice.

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 both parameters described. The description restates the URL parameter and default/max for max_links, 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 clearly states the tool generates an llms.txt file for a given URL, with explicit purpose for AI crawlers and output format. It distinguishes itself from siblings by focusing on a specific task not covered by other tools.

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

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

The description lists three specific use cases (client site indexing, own project drafting, competitor auditing). While it doesn't explicitly mention when not to use or alternatives, the use cases are clear and relevant.

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 and destructiveHint false. The description adds that it returns only the caller's active subscriptions by default and lists the specific fields returned, supplementing the annotations with useful scope and output 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 (two sentences) and front-loaded with the core action and output, followed by usage guidance. 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?

The description covers purpose, usage, and returned fields well. However, it lacks details on pagination or limits for large result sets, which could be important for a listing 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?

With 100% schema description coverage, the baseline is 3. The description does not add extra meaning beyond the schema for the include_inactive parameter, so it meets the baseline without improvement.

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 lists the caller's active subscriptions and specifies the returned fields. It distinguishes itself from siblings subscribe and unsubscribe by focusing on listing existing subscriptions.

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

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

The description explicitly advises using this tool to review monitoring before adding more subscriptions or to find an id to cancel. This provides clear context, though it does not explicitly mention when not to use it.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds specific context: requires a Google Maps API key and details on travel modes. While not adding entirely new behavioral insights, it provides necessary operational constraints beyond annotations.

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

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

The description is concise and well-structured, front-loading the core purpose. Every sentence provides useful information without redundancy, achieving high information density.

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

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

Given the presence of an output schema, the description adequately covers input semantics, usage context, and key constraints (API key, mode options). It is complete for an agent to understand and invoke the tool correctly.

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

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

Schema coverage is 100% with all parameters described. The description adds value by explaining the API key requirement and providing examples of origin/destination formats (address or lat,lng). It also lists mode options, enhancing understanding beyond the schema.

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

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

The description clearly states 'Google Maps directions from A to B' with multiple modes and examples. It effectively distinguishes this tool from sibling mapping tools like maps_distance_matrix and maps_elevation by specifying its purpose as providing turn-by-turn directions.

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 advises to prefer this tool over alternatives like Mapbox or OpenRouteService specifically for public-transit routing, citing Google's superior transit data. This provides clear guidance on when to use this tool.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds behavioral context: it returns a matrix of distances and durations, supports multiple modes. No contradictions.

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

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

The description is a single sentence but packs substantial information: purpose, examples, modes, and use cases. Could be more structured (e.g., bullet points) but is not overly long.

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

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

Given the complexity of a distance matrix tool, the description adequately explains inputs, modes, and use cases. Output schema exists (not shown), so return values are covered. 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 description coverage is 100% (all 4 parameters documented). The description adds value by clarifying pipe-separated format for origins/destinations and listing modes, beyond the schema definitions.

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

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

The description clearly states the tool computes a travel time matrix between N origins and M destinations, with specific verb 'compute distance and duration matrix'. It distinguishes from sibling tools like maps_directions by explicitly mentioning matrix and multi-point use cases.

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

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

The description provides explicit use cases ('delivery routing, multi-stop optimization, transit-heavy planning') and lists available modes. However, it lacks explicit when-not-to-use guidance or direct comparison to alternatives like maps_directions.

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

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds context about using the Google Maps Elevation API and batch query format via pipe-separated locations. 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: first defines purpose, second explains input format. Front-loaded and concise with no superfluous text.

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

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

Given the presence of an output schema and comprehensive annotations, the description covers the essential purpose and input format. No additional details are needed for a straightforward 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%, so baseline is 3. The description confirms the pipe-separated format for batch queries, but this is already documented in the schema's description and examples. Minimal added 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 tool returns elevation in meters for lat/lng coordinates via Google Maps Elevation API. It distinguishes from sibling tools like maps_geocode or maps_directions by specifying the exact resource and action.

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 elevation queries but does not explicitly state when to use this tool vs alternatives, nor does it provide exclusions or prerequisites. The context of sibling tools helps infer purpose, but guidance is implicit.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: 'requires Google Maps API key' and 'Premium-quality geocoding (highest accuracy for US addresses).' This goes beyond what annotations provide, even though it does not mention rate limits or failure modes.

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

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

The description is extremely concise: two sentences pack the core purpose, quality claim, and usage guidance. There is zero fluff, and the most important information is front-loaded.

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

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

Given the existence of an output schema, the description does not need to detail return values. It covers purpose, usage context, key requirement (API key), and quality attributes. The tool is simple and the description is sufficient for an AI agent to select and invoke it correctly.

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

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

Schema coverage is 100% with both parameters described. The description mentions the API key requirement and address conversion but does not add meaning beyond the schema; it merely restates concepts. Baseline 3 is appropriate since the 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 the tool's purpose: 'Geocode [address] via Google Maps' and 'convert an address to lat/lng + formatted address via Google Maps Platform.' It uses a specific verb (geocode) and resource (address), and by mentioning premium quality for US addresses it distinguishes itself from sibling map services like maps_directions or maps_place_search.

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

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

The description gives explicit guidance: 'Use when Mapbox/MapTiler/OpenStreetMap miss the address.' This provides a clear alternative scenario. It could be improved by stating when not to use it (e.g., for reverse geocoding), but the sibling tools list includes maps_reverse_geocode, so the 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 and idempotentHint, so safety is covered. The description adds the specific data fields returned (address, phone, hours, etc.), which gives behavioral context about what the agent will receive. It does not mention potential issues like rate limits or review pagination, but that is not critical here.

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 example queries, no wasted words. Everything earns its place.

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

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

Given the tool has a simple two-parameter schema, full output schema, and rich annotations, the description covers purpose, prerequisite, and typical use case. No gaps for a lookup 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% and parameters are described. However, the description adds important context: the place_id must come from maps_place_search, which is not in the schema. This helps the agent understand the parameter's provenance.

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

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

The description starts with example queries that clearly indicate the tool retrieves detailed business info. It verb+resource ('full details for a Google Place') and distinguishes from sibling `maps_place_search` by stating it requires a place ID from that search.

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

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

Explicitly states 'Requires a place ID from maps_place_search. Use after search to drill into one specific business.' This tells the agent when to use it and what prerequisite to satisfy, with clear instructions.

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, etc. Description adds useful behavioral context: what it returns (names, addresses, ratings, distances), quality (Premium-quality), and prerequisite (API key). 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 typical query patterns, followed by purpose, return info, and usage guidance. Every sentence adds value. No fluff.

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

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

Covers tool purpose, return values, usage context, and key requirements. Could mention result limits or pagination, but with output schema available, the description is largely 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 covers 100% of parameters with descriptions. Description adds example usage patterns (e.g., location in query vs parameter) but does not provide deeper semantics beyond what schema already offers. Baseline 3 is appropriate.

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

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

Clear verb+resource: 'find nearby businesses and points of interest via Google Maps Places'. Provides specific examples (restaurants, hotels, coffee shops) and distinguishes from sibling tools by explicitly recommending over Yelp/OSM and implying it's the search variant among maps_* 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?

Explicitly states when to use 'over Yelp/OSM when you want Google's POI coverage and ratings' and requires a Google Maps API key. Could be more explicit about when not to use (e.g., for detailed place info use maps_place_details), but the context is clear enough.

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, etc. The description adds the API key requirement and accuracy context, which is useful but not critical. 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 concise (two sentences) but the first sentence uses informal formatting (quotes, slash) that may be slightly unclear. Still, it is front-loaded with the core purpose.

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

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

Given the presence of output schema and full parameter schema, the description is complete enough. It adds usage guidance and API key requirement, leaving no major 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% and descriptions in schema are sufficient. The description does not add new meaning beyond what the schema provides for parameters.

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

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

The description clearly states the tool does reverse geocoding ('coordinates → address') via Google Maps, using a specific verb and resource. It distinguishes itself from sibling tools like maps_geocode by noting it is for reverse geocoding and mentions US accuracy.

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 clear use case: 'Use when you need Google's street-address parsing (most accurate for US).' This gives context but does not explicitly exclude other cases 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?

The description fully discloses behavioral traits beyond annotations: it's a feedback submission (non-destructive, not read-only), rate-limited to 5 per identifier per day, and free (doesn't count against tool-call quota). 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 concise (about 120 words) and front-loaded with the core purpose. Every sentence adds distinct value: purpose, usage, formatting advice, team response, and limits. No redundant or irrelevant content.

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 feedback submission tool with 3 parameters (2 required, 1 with nested object) and no output schema, the description covers all necessary details: feedback types, message content rules, optional context fields, rate limits, and quota impact. It leaves no practical 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%, so baseline is 3. The description adds value by reinforcing the message parameter guidance ('describe in terms of Pipeworx tools/packs, don't paste prompt') and explaining the enum values' meanings in context, raising the score above baseline.

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

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

The description explicitly states the tool's purpose: to report issues (broken, missing), request features, or give praise. It uses a specific verb ('Tell') and resource ('Pipeworx team'), clearly distinguishing it from sibling tools which are primarily research-oriented.

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 detailed usage scenarios: bug, feature/data_gap, praise, and explicitly advises against pasting end-user prompts. It also includes rate limits and quota information, offering clear when-to-use and 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?

Annotations already indicate read-only, idempotent, non-destructive. Description adds meaningful context: data source (CF analytics-engine), no PII, caching window (5min-1h). 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 packing purpose, use cases, and technical details without fluff. Front-loaded with key function, then reasoning, then data source.

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

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

For a simple tool with one optional param and no output schema, the description covers all needed aspects: what, why, data origin, privacy, caching. 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 provides enum and description for 'window'. Description adds semantic nuance ('shorter windows surface hot now; longer show steady-state demand'), going 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 the tool returns top tools, top packs, and total call volume over specified windows. Use cases are listed, and the verb 'returns' is specific. Distinct from siblings like discover_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?

Explicitly provides three use cases (discovering hot data, confirming canonical tool, aligning use case). Could mention when not to use or alternative tools, but the guidance is clear enough.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds extensive behavioral context: monotonicity checks, partition-sum checks, Jaccard similarity threshold (≥0.30), partition filter for placeholder slugs, fill check via arbitrage.fill_check against live CLOB depth, and conditions for null arb signals. 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 long but well-structured with clear sections (mode explanations, semantic anchor, partition filter, fill check). It uses bold and line breaks for readability. However, it includes some implementation details (e.g., Jaccard similarity, placeholder fraction) that could be shortened without losing essential usage 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?

The description covers all aspects needed for correct invocation: required input, modes, constraints (Jaccard threshold, placeholder filter), response structure (opportunities, partition_check), integration with fill check (polymarket_fill_risk). No output schema exists, but the response structure is described in sufficient detail for an agent to understand what to expect.

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

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

Schema coverage is 100% with both parameters described. The description adds significant meaning beyond schema: explains that 'event' triggers single-event mode (checks order and partition), 'topic' triggers cross-event mode (searches related events), and provides concrete examples of slug formats and seed questions. It also explains internal logic like walking child markets and checking partition sum.

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 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It specifies the resource (Polymarket) and verb (Find). It distinguishes from sibling tools like polymarket_edges and polymarket_fill_risk by focusing on arbitrage detection with specific methods.

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

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

The description explicitly states that both 'event' and 'topic' parameters are optional but at least one is required, and calling with no arguments fails. It provides recommended use cases for each mode (event for specific markets, topic for cross-event scanning) and explains what cross-event mode catches that single-event misses.

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 read-only, idempotent, non-destructive. Description adds extensive behavioral details: caching at KV level, response structure with diagnostics, edge calculation with slippage, Kelly sizing caps, and warnings about unreliable Fed data. 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?

Description is verbose but well-structured with clear sections for model families and response segments. It conveys necessary detail but could be more concise; sentences are dense with technical 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?

Output schema missing, but description thoroughly explains response structure (by_segment, diagnostics), field meanings (edge_pp_net, kelly_fraction), and edge cases (why segments empty, Fed exclusion). Fully covers expected output for complex 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 detailed parameter descriptions. The tool description lists parameters under 'TRADEABLE-EDGE KNOBS' but adds minimal new meaning beyond the schema, only contextualizing them. 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 tool scans Polymarket markets for pricing disagreements with Pipeworx data, designed for daily opportunity discovery. It distinguishes from siblings by mentioning it avoids paging hundreds of markets and details three response segments, making purpose very specific.

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

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

Provides clear usage context (daily discovery, cached hourly, Fed bets excluded). However, it doesn't explicitly compare to direct siblings like polymarket_arbitrage or polymarket_edge_tracker, missing a clear when-to-use vs alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety. The description adds behavioral context: response structure, snapshot gaps meaning, history depth limits (60-day TTL), and that decay is from daily closes not intraday. 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 lengthy but well-structured into ARGS, RESPONSE, and LIMITS sections. Every sentence adds value, though it could be slightly more concise. It is front-loaded with the core 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?

Despite no output schema, the description comprehensively details the response fields (tracked[], expired[], snapshot_dates[]) and their subfields. It also covers limits, assumptions, and behavior gaps, making the tool fully understandable for correct invocation.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value beyond the schema by specifying default values and clarifying the window options ('24hr | 1wk | 1mo'). It also notes a slight discrepancy (max vs clamp) but overall enriches parameter understanding.

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

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

The description uses specific verbs ('track', 'answers') and identifies the resource ('polymarket_edges snapshots'). It distinguishes from siblings by focusing on persistence and decay versus current edge data, with an illustrative example ('fresh wide edge vs 3-week-old wide edge').

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 implies when to use this tool (to understand edge longevity and decay trends) and implicitly contrasts with sibling tools like polymarket_edges. However, it lacks explicit 'when not to use' or direct comparisons to alternatives.

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

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

Annotations declare `readOnlyHint`, `idempotentHint`, and `destructiveHint=false`. The description adds significant behavioral context: it walks the order book ladder, returns detailed fill metrics, and warns about thin legs and forced directional risk. 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 well-structured with clear sections (REQUIRES, SINGLE-MARKET, BASKET, USE THIS) and front-loads the core purpose. It is relatively long but each sentence adds necessary detail for the two distinct modes. Minor redundancy could be trimmed, but overall efficient.

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

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

Given the absence of an output schema, the description fully explains return values for both modes, including top_of_book, vwap_fill_price, slippage_pp, shares_filled, and verdict for single-market; and theoretical_sum, realizable_sum, capture_ratio, profit_usd, thin_legs, forced_directional_risk for basket. It also covers edge cases and risks, making it complete 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 coverage is 100% (all four parameters described). The description adds value beyond the schema by explaining default values, mode-specific interpretations (e.g., `size_usd` as settlement notional in basket mode), and auto-detection for side in basket mode. While the schema already covers basics, the description enhances understanding.

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

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

The description clearly states the tool's purpose as a 'realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes. It explicitly differentiates from sibling tools like `polymarket_arbitrage` and `polymarket_edges` by positioning it as a prerequisite risk check.

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

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

The description provides explicit usage instructions: REQUIRES one of `market` or `event`, and recommends use before acting on `polymarket_arbitrage` or `polymarket_edges` signals above ~$500. It also warns against using it without proper context, explaining risks like partial basket fills converting arb into directional positions.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds deep behavioral context: compatibility_warning conditions, temporal alignment, skipped cross types, and the rarity of real spreads. This far exceeds the baseline.

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

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

The description is relatively long but every sentence serves a purpose. It's front-loaded with the core concept and then details. Could be slightly more structured, but appropriate for the tool's complexity.

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, but the description explicitly explains the response structure (leg-by-leg prices, top_spreads_pp, safety fields). It covers compatibility warnings, temporal alignment, and skipped cross types. Everything an agent needs to interpret results is included.

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

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

Schema coverage is 100%, but the description adds significant value: it explains the two modes, how overriding works, and provides examples. This helps the agent understand parameter interplay beyond type/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 computes cross-venue spreads between Kalshi and Polymarket. It specifies the verb ('Cross-venue spread'), resource (two prediction markets), and distinguishes from sibling tools, none of which are similar.

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 two usage modes (topic shortcuts vs explicit tickers) and warns that most pre-mapped topics are not currently tradeable. It doesn't explicitly state when not to use the tool, but the guidance is clear enough for an agent to decide.

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

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

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that the tool is scoped to an identifier (anonymous IP, BYO key hash, or account ID) and retrieves previously saved values, providing useful context beyond the annotations.

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

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

The description is three sentences, front-loaded with the core function, followed by examples and scoping. Every sentence earns its place with zero waste.

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

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

Given one optional parameter, no output schema, and rich annotations, the description is complete. It covers purpose, usage, scoping, and relationships with siblings, leaving no obvious 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% and the schema description already explains the key parameter well. The description adds examples of what keys might represent (target ticker, address, research notes) but does not significantly expand on the schema's explanation. Baseline 3 is appropriate.

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

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

The description clearly states the tool retrieves a value saved via remember or lists all keys if the key argument is omitted, with concrete examples of use cases. It effectively distinguishes itself from sibling tools like remember and forget.

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

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

The description explicitly states when to use the tool ('to look up context the agent stored earlier') and how to use it (omit key to list all). It mentions pairing with remember and forget, but does not explicitly state when not to use it, which is implied but not fully explicit.

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 mark_read:true flags events as read, implying a write side-effect. However, annotations declare readOnlyHint=true, creating a contradiction. The description also adds useful context about the feed being persisted and alternative access, but the contradiction is severe.

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

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

The description is four sentences, each serving a distinct purpose: purpose, return content, filtering/mark_read, and alternative usage. It is front-loaded with the action and resource, with no redundant or 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 five parameters, no output schema, and rich annotations, the description covers key aspects: return fields, filtering, mark_read behavior, and alternative endpoint. It could mention pagination or the effect of mark_read when false, but overall it provides sufficient context for effective use.

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

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

All parameters are described in the schema (100% coverage). The description adds value by giving an example filter type ('sec_8k'), specifying ISO timestamp format for since, and explaining the effect of mark_read. This enhances understanding beyond the schema.

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

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

Description clearly states 'Pull fired events from your subscription feed' and specifies the type of data returned (source, citation_uri, payload). It distinguishes from sibling tools like list_subscriptions by focusing on reading alert events rather than managing subscriptions.

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

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

Description provides usage context: filtering by type and since, polling works fine, and an alternative endpoint for scripts. However, it does not explicitly state when not to use this tool or compare it directly to siblings, which would elevate clarity.

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, destructiveHint false. The description adds context: fans out to multiple sources, fallback logic, USPTO soft-fail, returns structured changes with URIs. It does not contradict annotations. Missing details on error handling or rate limits beyond GNews fallback.

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

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

The description is a single well-structured paragraph with no wasted words. Each sentence adds necessary detail: purpose, examples, sources, fallback, parameter formats, return structure, and alternative tool. It front-loads the core purpose with query examples.

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

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

Despite no output schema, the description explains the return structure (changes[] grouped by source, total_changes count, citation URIs). It covers key usage scenarios, parameter details, source behavior, and distinguishes from a sibling tool. Three required parameters are fully described. No gaps identified.

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

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

Schema coverage is 100% (all parameters described). The description adds value by explaining 'since' accepts ISO date or relative shorthand with examples (e.g., '30d'), suggests typical monitoring usage, and clarifies 'value' accepts ticker or CIK. This goes beyond schema descriptions.

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

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

The description clearly specifies the tool returns a change feed for a company from SEC EDGAR, GDELT/GNews, and USPTO within a time window. It distinguishes from the sibling tool 'entity_profile' by contrasting its purpose (dynamic feed vs. static profile).

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

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

The description provides explicit usage examples ('What's new with X', 'latest on Y', etc.) and explicitly says when to use 'entity_profile' instead. It also explains fallback behavior (GDELT preferred, GNews on rate limit/5xx) and the USPTO soft-fail.

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 persistence details: authenticated users get persistent memory, anonymous sessions retain for 24 hours. Beyond annotations which provide idempotentHint=true. 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, front-loaded with primary purpose. Every sentence adds value. No unnecessary words.

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

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

For a simple 2-parameter tool with full schema coverage and no output schema, the description covers purpose, usage, persistence, and cross-reference to sibling tools. Complete.

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

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

Schema coverage is 100% with descriptions for both key and value. Description adds example naming conventions ('subject_property') but does not significantly enhance understanding 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 states specific verb 'Save data' and resource 'memory key-value pair'. Distinguishes from sibling tools like 'recall' and 'forget'.

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

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

Clearly indicates when to use: when discovering something worth carrying forward. Mentions pairing with recall and forget. No explicit when-not, but context is clear.

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

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

Annotations already indicate read-only, idempotent, and open-world hints. The description adds behavioral details: internal cascading through multiple endpoints, specific return types for company and drug (including CIK, RxCUI, citation URIs), and auto-disambiguation for companies. This enriches understanding 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 moderately concise, efficiently packed with examples and details. It front-loads with query examples and uses bullet-like structure for types. While every sentence earns its place, it could be slightly more streamlined without losing value.

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

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

Given no output schema, the description fully explains return values for both entity types, including citation URIs. It also mentions internal cascading, which helps set expectations. For a lookup tool with two parameters and rich outputs, the description is complete.

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

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

Schema has 100% description coverage with clear parameter descriptions. The tool description adds extra context: for 'value' it explains accepted inputs per type (ticker, CIK, name for company; brand/generic for drug) and what each returns, which goes beyond the schema's brief descriptions.

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

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

The description clearly states the tool's purpose: resolving names to canonical identifiers. It provides specific query examples and distinguishes from siblings by focusing on identifier lookup, which is unique among the listed tools.

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

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

The description explicitly says 'Use FIRST whenever you have a name but need an ID.' This provides clear guidance on when to use the tool. While it doesn't explicitly list alternatives, the context implies this is the primary tool for ID resolution, and it replaces multiple manual lookups.

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

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

Annotations already indicate readOnlyHint, idempotentHint, etc. The description adds that it returns a ranked list with score, confidence, and signal density, and explains it probes each entity with ai_visibility_check. This goes beyond what annotations provide, offering useful process and output 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?

Two sentences, no wasted words. The first sentence front-loads the action and result, the second provides use case and example. Perfectly sized for quick comprehension.

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

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

Given the complexity (4 params, composite tool, no output schema), the description explains the return format (ranked list with fields) and the probing mechanism. It could further clarify 'AI visibility' but references the underlying tool, making it sufficient.

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

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

Schema coverage is 100% with descriptions for all 4 parameters. The description adds extra semantics by stating the first entity is treated as the 'subject' for narrative, which aids in understanding the ordering and output narrative.

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

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

The description clearly states it compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison) by specifying the AI visibility focus.

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 notes 'useful for competitive AI-marketing audits' and gives an example question, providing clear context. Does not explicitly state when not to use it or alternatives, but the context is strong 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 provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: it fans out across multiple services, partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed lists timeouts. This exceeds what annotations offer.

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 every sentence adds value. It is front-loaded with the core composite purpose and flows logically. Could be slightly more concise, but structure is good.

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 only 2 parameters and no output schema, the description thoroughly covers what the tool returns (summary block, per-advisory detail, links, alternative versions) and how it handles edge cases (timeouts, partial failures). It is fully 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 coverage is 100%, with descriptions for both parameters. The description adds extra context beyond the schema: scoped packages (e.g., '@types/node') are accepted, and version defaults to latest when omitted. This adds meaningful semantics.

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

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

The description clearly states it is a composite check for whether to add an npm package, covering license, advisories, version history, and bundle size. It distinguishes itself from sibling tools (none of which are similar) and provides a specific verb-resource combination.

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

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

Explicitly says when to use it: when an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. Also notes ecosystem limitations (NPM only in v1) and alternative paths for other ecosystems, providing clear context on usage scope.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds valuable technical details: BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, a 200K char cap with truncation flag, and character offsets. This goes beyond annotations without contradiction.

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

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

Three well-structured sentences with no wasted words. The key purpose is front-loaded, followed by usage guidance and technical details. Every sentence serves a distinct purpose, making it easy to parse.

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

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

Given the tool has no output schema, the description adequately covers return values (top-N passages with character offsets and similarity scores) and edge cases (truncation flag for long inputs). With only 3 parameters and good schema coverage, the description is complete.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds context beyond the schema: it clarifies that 'query' is a natural-language query (with examples), 'text' has a max length hint, and 'limit' has a default. It also hints at the return format (passages with offsets and scores).

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 'Semantic search INSIDE a fetched record' and gives concrete examples (SEC 10-K body, article, tool result). It distinguishes itself from the sibling tool 'ask_pipeworx_grounded' by explaining the pairing and use case. The verb 'search within' plus resource 'record' is 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?

Explicitly states when to use: 'Use when the record is too big to cram into the prompt'. It also explains the benefit (saves context, returns only relevant passages) and pairs with a sibling tool 'ask_pipeworx_grounded' for grounded responses. No alternative usage is mentioned, but the context is clear.

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

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

Annotations indicate readOnlyHint=false, idempotentHint=true, destructiveHint=false. The description adds valuable context: requires Pipeworx OAuth account, delivery restrictions (SMS cap, webhook auto-disable), and the always-on feed. No contradiction with annotations. Could mention idempotency behavior explicitly.

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 front-loaded purpose and logical sections for types and delivery. While comprehensive, it is slightly verbose; some details (e.g., webhook signing) could be streamlined without losing value.

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

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

Given the tool's complexity (3 parameters, nested objects, no output schema), the description covers auth, type-specific parameters, delivery channels, and limitations. It mentions return of subscription ID but lacks details on response structure or idempotency, which are minor gaps.

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

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

Schema coverage is 100% with detailed descriptions. The description significantly enriches each parameter with practical examples (e.g., items codes for sec_8k, topic for polymarket_edge, delivery limitations). This goes well beyond the schema, providing actionable guidance for correct invocation.

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 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' which is specific about the action and resource. However, it does not explicitly distinguish from sibling tools like 'list_subscriptions' or 'unsubscribe', limiting clarity in context of 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?

The description provides implicit usage guidance through examples of types and delivery channels, but lacks explicit when-to-use vs. when-not-to-use instructions compared to siblings. For example, it doesn't say 'Use this for persistent monitoring; use recent_alerts for one-time polling.'

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 behavioral context: it is the onboarding entry point, returns example questions from the live catalog with exact tool+argument shape, and can be scoped by topic. 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 thorough but slightly long due to listing alternative phrasings and examples. However, it is well-structured with a clear purpose statement, parameter guidance, and a 'use first' directive. Every sentence adds value.

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

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

Given that an output schema is absent, the description adequately describes the return value (category-bucketed example questions). Annotations are rich, so the description does not need to repeat safety aspects. The tool's role as an onboarding entry point is 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% and the description enriches the single optional parameter 'topic' by listing example focus areas (finance, pharma, etc.) and explaining behavior when omitted. This adds significant value beyond the schema 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: answering common onboarding questions like 'What can I ask Pipeworx?' by returning category-bucketed example questions. It distinguishes itself from sibling tools by being the entry point for new users, with explicit use cases.

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 instructs to use this tool FIRST when unfamiliar with Pipeworx, and advises on how to call with no arguments or a topic. It mentions learning how to call meta-tools, though it does not explicitly list when not to use it or compare to 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?

Beyond annotations, the description reveals that the row is deactivated (not deleted) and historical events remain available via recent_alerts. This adds significant value 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?

Two sentences, each earning its place with essential information. Front-loaded with the core purpose, then behavioral 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?

For a single-parameter tool with no output schema and helpful annotations, the description covers purpose, ownership, behavioral outcome, and side effects. It is complete.

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

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

The input schema covers 100% of the parameter with a description. The tool description does not add new information about the parameter beyond what the schema already states.

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 'Cancel a subscription by id,' which is a specific verb and resource. It clearly distinguishes from sibling tools like 'subscribe' and 'list_subscriptions'.

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

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

It states ownership enforcement ('you can only cancel your own subscriptions'), providing a clear condition for use. However, it does not explicitly mention when not to use this tool or suggest 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, openWorld, idempotent, non-destructive. The description adds useful behavioral details: returns a verdict with 5 categories, extracted structured form, actual value with citation, percent delta; also notes the tool replaces 4-6 sequential calls. 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 concise yet comprehensive, front-loaded with typical user phrasing, then clear purpose, usage context, domain specifics, and output details. 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?

No output schema exists, so the description properly explains return values (verdict types, structured form, citation, delta). It mentions domain limitation (v1 supports only company-financial claims). Could be slightly more exhaustive about verdict types but sufficient for decision-making.

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

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

Only one parameter 'claim' with schema coverage 100% (description already in schema). The tool description adds meaning by providing example formats and clarifying the domain, which helps the agent formulate correct input beyond the schema's generic description.

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

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

The description clearly states the tool's purpose: natural-language claim verification against authoritative sources, specifically company-financial claims via SEC EDGAR + XBRL. It starts with typical user intents ('Is it true that...') making the purpose immediately recognizable.

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

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

The description says 'Use whenever the agent needs to check whether something a user said is factually correct,' and specifies the domain (company-financial claims for public US companies). It does not explicitly list when not to use or compare to siblings, but the domain limitation provides implicit guidance.

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