Arcgis Dublinoh

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable context: default model is free, Anthropic requires BYO key (cost incurred), and the return structure (per-model score, confidence, signals, raw_response + combined view). No contradictions.

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

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

The description is concise (4 sentences) and front-loaded with the core purpose. Every sentence adds distinct value: purpose, default behavior, optional key, return format, use cases. No wasted words.

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

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

Given moderate complexity (4 params, 1 required), no output schema, and rich context signals, the description fully compensates by explaining the return structure, parameter roles, and use cases. The tool's behavior is thoroughly documented.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning beyond schema by specifying the default model (Workers AI Llama-3.3-70b free), the meaning of _apiKey (BYO key for Anthropic), and the return format. This extra context raises the score.

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

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

The description uses specific verbs ('Probe', 'score') and clearly identifies the resource ('LLMs', 'business/brand/product/topic'). It distinguishes the tool's purpose from siblings by focusing on LLM visibility scoring, which is unique among the sibling tools.

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

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

The description provides explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to use the _apiKey parameter. However, it does not explicitly state when not to use this tool or compare it to alternatives like 'scan_competitor_ai_presence'.

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=false. Description adds that it routes to 4,552 tools and returns structured answers with citation URIs, which is beyond annotations. No contradictions.

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

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

The description is lengthy but front-loaded with the key directive to prefer over web search. Every sentence adds value, though some repetition could be trimmed. Structurally sound with examples and 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?

Given the tool's complexity (4552 tools, many parameters), the description is comprehensive: explains purpose, usage, alternatives, and output format. No output schema is needed as return type is described. Complete for an AI agent to select and invoke correctly.

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

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

Schema coverage is 100% for the 6 parameters (all aliases for question). The description provides examples but does not add meaning beyond what the schema already conveys. Baseline of 3 is appropriate.

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

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

The description clearly states it answers factual questions from authoritative structured data with citations. It distinguishes itself from siblings like ask_pipeworx_grounded and deep_research by specifying when to use each.

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

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

Explicitly tells to prefer over web search, lists types of questions (factual, real-world), provides examples, and explains when to step up to other tools. Includes both 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?

The description adds behavioral details beyond annotations: only uses tool result, returns explicit refusal reasons with specific codes, and mentions cost (extra LLM call). Annotations (readOnlyHint, idempotentHint) are consistent; no contradiction.

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

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

The description is relatively long (3 sentences plus structured output format) but every sentence adds value: purpose, comparison, refusal handling, use cases, cost. Could be slightly tighter but remains efficient for the 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?

Despite no output schema, the description fully explains the return structure (answer, evidence, confidence, source, fetched_at, refusal_reason) and all error modes. For a retrieval tool with high-stakes use, this 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 all parameters described as aliases for 'question'. Description restates 'Your question in natural language' and lists aliases, adding marginal clarity. Baseline 3 is appropriate as schema already defines parameters fully.

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

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

The description clearly states it is a 'Hallucination-resistant answer mode for high-stakes reads' and distinguishes itself from sibling 'ask_pipeworx' by detailing the extraction-only approach and explicit refusal modes. The verb 'extracts' and resource 'answer from tool result' are 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?

Explicitly states when to use ('whenever an answer will be quoted, cited, or acted on') with examples (financial, legal, medical) and when not to ('prefer ask_pipeworx for casual lookups'). Provides clear alternatives and context.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description goes far beyond by detailing resolver contracts, parent event extraction, news field fallbacks, safety short-circuits, closed market handling, wide-spread liquidity warnings, and resolution-rule risk. This provides comprehensive behavioral transparency without contradicting annotations.

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

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

The description is very long and detailed, covering many edge cases and examples. While it is well-structured with clear sections (capitalized headings), it could be more concise. Some details, like the full classifier list and fan-out examples, are useful but contribute to verbosity. The front-loaded purpose helps, but overall it's overly expansive.

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

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

The tool has no output schema, so the description must compensate. It thoroughly explains return fields: result.market with liquidity metrics, result.analysis with model probability and edge warnings, result.market_match_confidence with resolution details, parent_event for partitioned bets, news fields with fallback information, and safety mechanisms. It also covers edge cases like low-confidence matches and closed markets, leaving no gaps.

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

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

Schema coverage is 100%, and the description adds context for all parameters: explains acceptable formats for 'market', default behavior for 'depth' (thorough), and the recommended default for 'include_raw' (false) to keep responses small. This 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 the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question text) and outputs (evidence packet + comparison). It also gives specific use cases ('should I bet on X', etc.), distinguishing it from siblings like polymarket_edges or polymarket_arbitrage.

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

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

The description explicitly says 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' This provides clear guidance. However, it does not explicitly state when not to use the tool or mention alternative tools, which would have elevated the score.

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 (readOnly, idempotent, etc.), the description details data sources for company (SEC EDGAR/XBRL fields) and drug (FAERS, FDA, trials), sorting by primary metric, and off-calendar fiscal year handling.

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?

Dense information with every sentence contributing, but slightly lengthy. Starts with examples and clear purpose, then details. Good balance of completeness and conciseness.

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

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

Despite no output schema, description explains return format (paired data + citation URIs) and sorting. Adequate for agent to select and invoke correctly, with minor lack of output structure details.

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

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

Schema coverage is 100%, but description adds meaning: specifies values as tickers/CIKs for company and drug names, and clarifies data pulled per type. Adds value beyond schema.

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

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

The description clearly states the tool performs side-by-side comparisons of 2-5 entities (companies or drugs) in a single call, with example queries like 'compare X and Y'. It distinguishes from sibling tools like 'entity_profile' which handles single entities.

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

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

Explicitly advises 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear when-to-use guidance and noting it replaces 8-15 sequential 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, openWorldHint, idempotentHint, and destructiveHint are all set appropriately. The description adds critical behavioral details: account required, thorough depth needs paid plan, returns gaps[] for unanswered facets, does not invent, does not do open-web search, and typical duration of 15-60s. 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 lengthy but well-structured, front-loading critical account information and then explaining purpose, behavior, and alternatives. Every sentence adds value, though some minor trimming could improve conciseness without losing clarity.

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

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

Given no output schema, the description thoroughly explains what the agent can expect: a findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, and gaps array. It also covers duration and the decomposition process, leaving little ambiguity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the depth enum values (quick=3 facets, standard=5, thorough=8) and clarifying that the question can be natural language and multi-part. This goes 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 identifies the tool as performing grounded multi-source research across structured data sources, decomposing questions into facets, and returning a findings packet. It distinguishes from siblings like ask_pipeworx by specifying it is for broad/multi-part questions, not single lookups or current news.

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

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

Explicitly states when to use (broad/multi-part questions over structured data) and when to prefer alternatives (single lookup: ask_pipeworx; breaking news: ask_pipeworx). Also mentions account requirement and paid plan for 'thorough' depth, providing clear usage boundaries.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that it returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples)' and that results are 'ready to call directly, no second schema lookup needed.' This adds useful context beyond annotations, though it doesn't mention potential limitations.

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 but slightly lengthy due to the enumerated list. It is front-loaded with purpose and examples. Could be streamlined, but every sentence adds value.

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

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

Given the tool has 6 parameters with full schema coverage and no output schema, the description covers purpose, usage, output structure (top-N tools with schemas and examples), and when to call first. It is complete for an agent to effectively use the tool.

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

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

Schema coverage is 100% with descriptions. The description reinforces the main parameter 'query' with natural language examples and lists aliases (task, q, description, search). It adds meaning by explaining the query format ('accepts task, q, description, search as aliases') and showing example queries.

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

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

Description clearly states the tool finds tools by describing data or task. It lists many example domains (SEC filings, FDA drugs, etc.) and explicitly differentiates from siblings by stating 'Call this FIRST when you have many tools available and want to see the option set.' This distinguishes it from individual data 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 says 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools...' It also implies not for direct answer retrieval by saying 'not just one answer.' This gives 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?

The description details the tool's behavior beyond annotations: it fans out across multiple sources, returns specific fields (CIK, filings with URIs, fundamentals, patents with soft-fail, news fallback). 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 relatively long but well-structured and front-loaded with examples. Every sentence adds value, though it could be slightly more concise without losing information.

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

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

Despite no output schema, the description comprehensively explains return values (CIK, filings, fundamentals, patents, news, LEI) and limitations. It 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?

Input schema already describes parameters well with 100% coverage. The description adds nuance: value must be ticker or zero-padded CIK, and reinforces that names are not supported. This adds useful context.

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

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

The description clearly states the tool provides a full cross-source profile of a US public company in one parallel call. It gives specific example queries and distinguishes itself from chaining individual lookups and from sibling tools like resolve_entity.

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

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

The description explicitly says when to use (holistic view, 'tell me about X') and when not to (names not supported, use resolve_entity first). It also notes the patents sunset limitation, providing clear 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 declare destructiveHint=true and idempotentHint=true. Description adds no further behavioral traits beyond 'delete', which is already evident. It doesn't discuss idempotency, side effects, or permissions, so adds limited value beyond annotations.

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

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

Two sentences: first states purpose, second provides usage guidelines. No filler, every sentence earns its place. Highly efficient for a one-parameter tool.

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

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

Given the tool's simplicity (single required param, no output schema), the description covers purpose, usage, and sibling context. It lacks mention of return behavior or error cases, but these are minimal for a delete action.

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

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

Schema coverage is 100% with a clear schema description for 'key'. The main description only reiterates 'by key' without adding semantic detail. Baseline 3 applies as schema fully documents the parameter.

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

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

Description clearly states 'Delete a previously stored memory by key' with a specific verb and resource. It explicitly distinguishes from siblings by mentioning 'Pair with remember and recall', making the tool's unique role obvious.

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 when-to-use scenarios: context stale, task done, or clearing sensitive data. Also recommends pairing with remember and recall, giving practical guidance on alternatives and complementary 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?

Describes the process: fetches the page, extracts title/description/key links, and emits markdown. This adds behavioral context beyond the annotations (readOnlyHint, idempotentHint) which already indicate safety. 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?

Three well-structured sentences with no wasted words. First sentence front-loads the purpose, second explains the process, third lists use cases. Excellent conciseness.

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

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

For a simple read-only tool with 2 parameters and no output schema, the description covers purpose, process, and output format ('single text blob'). It adequately prepares an agent to 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 descriptions for both parameters. The description does not add significant new meaning beyond the schema; it mentions extraction of links but doesn't elaborate on max_links parameter. 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?

Clearly states the tool generates a production-ready llms.txt file for any URL, specifying the verb 'generate', resource 'llms.txt', and purpose for AI crawlers. The description distinguishes from sibling tools like ai_visibility_check by focusing on file generation rather than checking presence.

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

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

Provides explicit use cases: getting a client's site indexed, drafting for own project, or auditing competitor view. While it doesn't explicitly exclude scenarios or name alternatives, the context is clear and helpful for decision-making.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. Description adds specific outputs (fields, geometry type, count, capabilities), enhancing transparency beyond annotations.

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

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

Single sentence, concise, front-loaded with the core action and outputs. No wasted words.

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

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

With one parameter and no output schema, the description fully covers what the tool returns and how it's used, leaving no gaps.

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

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

Schema coverage is 100%, so the description's addition of an example URL provides marginal value. 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?

Clearly states verb 'Get', resource 'ArcGIS Feature/Map Service layer's schema', and lists specific outputs (fields, geometry type, count, capabilities). Distinguishes itself from sibling 'query_layer' which focuses on data 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?

Implicitly defines usage by stating the tool retrieves schema by URL. While it doesn't explicitly exclude alternatives, the purpose is clear enough for an agent to decide when 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by specifying the returned fields and that it only lists the caller's active subscriptions, but the behavioral safety is well-covered by annotations.

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

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

Two concise sentences: first states purpose and return fields, second gives usage guidance. No 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?

For a simple list tool with one optional parameter, the description covers purpose, return fields, and usage context. Siblings provide clear alternatives. No output schema needed since return fields are explicitly listed.

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% (include_inactive well-described). Description implies the parameter's effect by mentioning 'active subscriptions' and hinting that inactive are excluded by default, but does not explicitly reference include_inactive. 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 'List the caller's active subscriptions' and enumerates return fields (id, type, params, created_at, last_fired_at, fire_count). It distinguishes from siblings like subscribe/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?

Explicitly advises to use 'before adding more or to find an id to cancel', providing clear context for when to invoke this tool versus others.

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

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

The description adds significant behavioral context beyond the annotations, including that it is free, does not count against tool-call quota, is rate-limited to 5 per identifier per day, and that the team reads digests daily with roadmap impact. 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 a single, well-structured paragraph of 4-5 sentences. It is front-loaded with purpose, then covers usage and constraints, with no extraneous information. Every sentence earns its place.

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

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

Given the tool's simplicity (feedback submission) and the richness of the schema (which explains nested objects), the description covers all necessary aspects: what, when, how, and behavioral constraints. No output schema is needed for a confirmation response.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by providing meta-guidance on how to write the message (e.g., don't paste user prompts, describe in terms of tools/packs), which enhances understanding beyond the schema's field 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 identifies the tool's purpose: sending feedback to the Pipeworx team about bugs, missing features, or praise. It specifies the verb 'tell' and the resource 'Pipeworx team', and distinguishes itself from sibling tools by its unique feedback function.

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 scenarios: bug, feature/data_gap, praise. It gives concrete examples (tool returns wrong data, tool missing, etc.) and includes important constraints like not pasting end-user prompts, using tool/pack terminology, and mentions rate limits and quota exemptions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds value by explaining the data source (CF analytics-engine), absence of PII, and caching behavior (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?

The description is front-loaded with the main purpose, uses clear language, and every sentence serves a purpose. It is concise without sacrificing necessary detail.

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

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

Given no output schema, the description adequately explains return values (top tools, packs, volume). It also covers caching, data source, and privacy, making it complete for a read-only, idempotent tool with one optional parameter.

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

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

Schema coverage is 100%. The description adds semantic meaning to the `window` parameter by explaining that shorter windows surface 'hot' trends while longer windows show steady-state demand, going beyond the schema's enum listing.

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: returning top tools, top packs, and call volume over recent windows. It uses specific verbs ('returns') and resources ('top tools, top packs, total call volume') and distinguishes itself from sibling tools like `ask_pipeworx` or `discover_tools` by focusing on aggregated trending data.

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

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

Three explicit use cases are provided: discovering hot data sources, confirming canonical tools, and checking alignment. The description implies appropriate contexts but does not explicitly mention when to avoid using it or name alternatives among siblings.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive behavior. The description adds extensive context: internal logic (Jaccard similarity, partition filter, fill check), scanning scope (top 200 markets), and output structure. 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?

Front-loaded with purpose and modes, but description is lengthy and dense. While comprehensive, it could be more succinct by separating output details or using subsections. No wasted sentences, but brevity could improve.

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

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

Given the tool's complexity and absence of output schema, the description comprehensively covers all aspects: input parameters, internal logic, output format, and edge cases (fill check, placeholder filtering). No gaps remain for effective 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 description coverage is 100%, and the description significantly expands meaning with examples and explanation of modes for each parameter. It clarifies when to use event vs topic and provides valid slug/question formats.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket using monotonicity violations and partition-sum checks. It distinguishes three modes (trending_scan, event, topic) and differentiates from sibling tools by specifying unique functionality.

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 instructions for when to use each mode, including no args for trending_scan, event for specific markets, and topic for cross-event scans. Provides examples of valid parameters and warns when not to trade (realizable_edge_pp <= 0). References an alternative tool (polymarket_fill_risk) for custom sizing.

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, and nondestructive behavior. The description adds valuable behavioral context: caching at KV level for 1h, response structure with diagnostics, and detailed model families. This extends beyond the annotations without contradiction.

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

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

The description is verbose but well-structured with clear sections for model families, tradeable-edge knobs, and response top-level. Every sentence adds detail, though it could be slightly more concise for an 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?

Given the 9 parameters (none required), 100% schema coverage, and lack of output schema, the description provides comprehensive context, including diagnostics for empty segments and response structure. It sufficiently explains the tool's behavior.

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

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

Parameter schema coverage is 100%. The description adds meaningful explanations beyond schema, such as 'Edge is evaluated NET of slippage' for min_edge_pp and actionable advice for min_liquidity: 'Set to 5000 to drop thin-book opportunities'.

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: scanning Polymarket markets for opportunities where Pipeworx data disagrees with market price. It specifies the use case 'what should I bet on today' and distinguishes from siblings like polymarket_arbitrage and polymarket_edge_tracker.

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

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

Provides detailed guidance on when to use: 'agents discover opportunities without paging hundreds of markets'. It explains tradeable-edge knobs and category filters, and explicitly notes that Fed bets are excluded from ranking. However, it does not explicitly state when not to use or mention alternatives.

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

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

Annotations mark the tool as readOnlyHint, openWorldHint, idempotentHint, and destructiveHint: false. The description adds significant behavioral context beyond annotations: it explains that data comes from daily snapshots, decay numbers are computed from daily closes, and history is bounded by 60-day TTL. It also clarifies that snapshots are written on cache-miss, so gaps occur. 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 relatively long but front-loaded with the core purpose. It uses structured formatting with parameter explanation and output sections. While every sentence adds value given the complexity, some redundancy exists (e.g., repeating default values also in schema). Overall, it is well-organized and informative without being overly verbose.

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

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

Without an output schema, the description carries the full burden of explaining return values. It thoroughly describes the three output arrays (tracked, expired, snapshot_dates) with field meanings and computation details. It also covers limitations and caveats (TTL, snapshot granularity). For a tool with 2 optional parameters and complex time-series data, the description is exceptionally complete.

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

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

Schema description coverage is 100% for both parameters. The description adds some context (defaults, constraints like clamp 2-30, and the notion of 'snapshot family'), but the schema already provides adequate descriptions. Baseline 3 is appropriate as description adds marginal value beyond schema.

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

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

The description clearly states the tool's purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It distinguishes from siblings by specifying it answers temporal questions about edge duration and decay, contrasting with the likely snapshot tool (polymarket_edges). The verb 'track' and specific output fields (tracked, expired, snapshot_dates) make the purpose unambiguous.

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

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

The description provides explicit use-case context ('a fresh wide edge and a 3-week-old wide edge are different trades') and explains what the tool answers. It implies when to use (historical analysis) and mentions limitations (60-day TTL, snapshot start). However, it does not explicitly state alternatives or when NOT to use, though the context and sibling list suggest alternatives exist (e.g., polymarket_edges for current data).

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, destructiveHint=false. The description adds significant behavioral context: it walks the order book ladder, returns verdicts (clean|degraded|cannot_fill), and warns about forced directional risk. No contradiction with annotations; the description enhances transparency beyond what annotations provide.

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

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

The description is relatively long but efficiently packed with information. It uses bold for key terms and front-loads the core purpose. The structure separates single-market and basket modes clearly. A bit verbose but justified given the 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?

Given the tool's complexity (4 params, no output schema), the description is thorough. It explains return values for single-market (top_of_book, vwap_fill_price, slippage_pp, etc.), the verdict, and for basket mode additional fields (theoretical_sum, realizable_sum, capture_ratio, etc.). Without an output schema, this description is essential and covers all necessary details.

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?

Despite 100% schema coverage, the description adds crucial meaning: it explains the difference between single-market and basket modes, how size_usd is interpreted differently (max spend vs target proceeds for single-market; settlement notional for basket), and the default behavior for side in basket mode (auto from partition sum). This goes well beyond the schema descriptions.

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

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

The description clearly states the tool performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes with explicit details on inputs and outputs. It also differentiates from sibling tools like polymarket_arbitrage and polymarket_edges by specifying when to use this tool (before acting on those signals).

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

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

The description explicitly says 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains when not to use it by warning that theoretical overround on thin books is not capturable and that partial basket fills convert an arb into an unhedged directional position, which is the dominant loss mode.

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 the annotations (readOnly, idempotent, non-destructive), the description details safety fields (compatibility_warning, temporal_alignment, skipped_cross_type), explains when spreads are meaningless (temporal gap, non-equivalent bet shapes), and reveals counters for dropped comparisons. 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 well-structured with front-loaded purpose, clear sections for modes, response, and safety fields. Though verbose for a simple tool, the complexity of cross-venue matching justifies the length. Every sentence adds value, but some minor redundancy exists (e.g., repeating 'pre-mapped ≠ tradeable').

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

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

Without an output schema, the description thoroughly explains the response structure (leg-by-leg prices, top_spreads_pp, compatibility_warning, temporal_alignment, skipped counters). It covers all essential details an agent needs to interpret results correctly, given 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?

While schema coverage is 100% and describes parameters, the description adds significant value: it lists exact topic macros, explains how explicit tickers override topic-mapped sides, and provides examples. This gives the agent deeper understanding of parameter interactions beyond raw 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 computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It explains two modes (topic shortcuts and explicit event identifiers), distinguishing it from sibling tools like polymarket_arbitrage which likely focus on intra-venue arbitrage. The verb 'compute spread' is specific to the resource.

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

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

The description provides clear usage context: when to use topic mode vs explicit mode, and warns that most pre-mapped topics currently return compatibility warnings, implying not to assume tradeability. However, it does not explicitly compare to sibling tools or state when not to use this tool in favor of 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, idempotentHint, and destructiveHint, so the description adds marginal value by noting it returns attribute rows and geometry. No contradictions. The description lacks details on authorization requirements or rate limits, which would be helpful given no auth annotation.

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

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

The description is two sentences: the first introduces the tool and its parameters, the second provides a concise example. Every sentence adds value with no redundancy or fluff.

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

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

With no output schema, the description covers the essential behavioral details (returns rows and geometry), parameter usage, and a sample query. It could be slightly more explicit about the return format or pagination, but overall it is sufficiently complete for an AI agent to use the tool correctly.

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

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

All parameters are described in the schema (100% coverage), and the description adds context by categorizing them as SQL-like and providing a sample usage. This goes beyond what the schema alone offers, making the tool more intuitive for an AI agent.

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 queries an ArcGIS Feature Service/Map Service layer using SQL-like parameters, and it explicitly ties the URL to the sibling tool search_datasets, distinguishing it from metadata or discovery tools.

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

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

The description provides a concrete usage tip (where="1=1" + out_fields="*") and implies its role for data retrieval versus siblings like layer_info or search_datasets. However, it does not explicitly state when not to use this tool or list alternatives.

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

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

Annotations already declare readOnlyHint and idempotentHint. Description adds value by explaining scoping (anonymous IP, BYO key hash, account ID) and listing behavior. No contradictions.

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

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

Three sentences, front-loaded with primary action, no wasted words. Efficient and clear.

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

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

For a single optional parameter tool with full schema and annotations, description covers purpose, usage context, side effects, and scoping. No output schema needed.

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

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

Schema coverage is 100% and already describes the key parameter and listing behavior. Description echoes this but adds no new parameter-specific details beyond pairing hints.

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

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

The description clearly states the verb 'retrieve' and resource 'value previously saved via remember', with explicit listing behavior when key omitted. Differentiates from sibling tools remember and forget.

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

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

Description gives concrete examples of when to use (look up context like ticker, address, notes) and mentions scoping and pairing with remember/forget. No explicit when-not-to-use, 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 declare readOnlyHint and destructiveHint, which the description complements by explaining mark_read mutation (flagging events read). Adds behavioral detail about polling suitability and alternative endpoint.

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

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

Concise, front-loaded with main action, each sentence adds value (purpose, fields, filters, side effect, alternative access). 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?

Given 5 parameters, no output schema, and rich annotations, the description covers all essential aspects: purpose, parameters, side effects, and alternative usage. Complete for an agent to use correctly.

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

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

Schema coverage is 100%, and the description adds concrete examples (e.g., 'sec_8k' for type, ISO timestamp for since) and explains the effect of mark_read on subsequent calls, enriching the schema descriptions.

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

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

The description clearly states the tool pulls fired events from a subscription feed, specifies the return fields (source, citation_uri, raw payload), and differentiates from siblings like list_subscriptions by focusing on event retrieval.

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 filtering options (type, since) and mark_read behavior, and mentions an alternative direct HTTP GET endpoint for scripts and dashboards. Lacks explicit when-not-to-use but gives good context.

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

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

Annotations mark the tool as read-only, idempotent, etc. The description adds context about fanning out to multiple sources, fallback logic, and USPTO soft-fail, which is beyond the annotations and consistent with 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 dense paragraph but every sentence adds value. It could be slightly more structured (e.g., bullet points), but it's concise and front-loaded with usage 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?

Given the tool's complexity (multiple sources, fallback, parameter format), the description covers everything: sources, fallback, parameter details, return structure ('changes[]' with 'total_changes' and URIs), and distinction from sibling tool. No output schema exists, but return structure is mentioned.

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

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

Schema coverage is 100%, but the description adds value by explaining the 'since' parameter accepts ISO dates or relative shorthand with examples ('7d', '30d'), and the 'value' can be ticker or CIK.

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

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

The description clearly states the tool provides a change feed for a company in a given time window, enumerates the sources (SEC, GDELT/GNews, USPTO), and distinguishes itself from the sibling 'entity_profile' 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?

It gives explicit query examples, explains when to use (e.g., 'What's new with X'), provides fallback behavior (GDELT→GNews), and advises to use 'entity_profile' for static profiles.

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

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

Discloses scoping by identifier, persistence differences between authenticated and anonymous sessions, and pairing with recall/forget. Annotations already indicate non-read-only, idempotent, non-destructive; description adds 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?

Three sentences, front-loaded with purpose, no wasted words. 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?

The tool is simple with 2 parameters and no output schema. The description covers purpose, usage context, behavioral traits, and persistence settings, leaving no important gaps for a storage 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 provides example key formats and indicates value is text, but doesn't add significant meaning beyond what the schema already provides.

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

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

The description clearly states the verb 'Save' and resource 'data to reuse later', and distinguishes from sibling tools like 'recall' and 'forget' by specifying the action of storing key-value pairs for cross-session reuse.

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

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

Explicitly describes when to use: when discovering something worth carrying forward, with concrete examples. Mentions pairing with recall and forget, but does not explicitly state when not to use it.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable context: internal cascading through several lookups, that it replaces 2-3 manual steps, and the exact output fields (ticker, CIK, citation URI, RxCUI, ingredient, brand). 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 moderately long but front-loaded with example queries. Every sentence adds necessary detail (supported types, return format, usage hint). Could be slightly trimmed, but remains efficient and well-organized.

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 values for both types, the internal cascading, and the tool's purpose as a replacement for manual lookups. It does not cover error handling or edge cases, but for a simple resolution tool this is 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 type and value described clearly. The tool description adds extra meaning: e.g., 'auto-disambiguated' for company, specific examples for value (ticker, CIK, name), and what each type returns. This enriches the schema-defined 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 opens with example queries (ticker, CIK, RxCUI) and states the tool resolves a name to a canonical identifier. It lists two specific entity types and what each returns, clearly distinguishing this from sibling tools that are about research, alerts, or specific domains.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID.' It also details each supported type and accepted inputs. However, it does not mention when to avoid using the tool or explicitly differentiate from tools like entity_profile or compare_entities that also work with IDs.

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 behavioral details: probes each entity internally with ai_visibility_check, ranks results, and returns a ranked list with score, confidence, signal density. No contradictions.

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

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

The description is two sentences long, front-loaded with the main purpose. Every sentence adds value with no fluff.

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

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

No output schema exists, but the description specifies the return format: ranked list with score, confidence, signal density per entity. The tool is moderately complex, and the description covers its input, behavior, and output sufficiently.

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 meaningful context: the first entity in 'entities' is treated as the 'subject' for narrative, 'models' defaults to 'workers-ai' free tier, and 'context' disambiguates common names. This goes beyond the schema descriptions.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and surfaces rankings. It distinguishes from sibling ai_visibility_check by focusing on comparison.

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

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

The description provides clear context ('useful for competitive AI-marketing audits') and an example question. It implies usage context but does not explicitly state when not to use or list alternatives.

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

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

The description adds significant context beyond annotations: it discloses partial failure graceful degradation, bundlephobia first measurement timeout (5-30s), and that sources_failed will list timed-out sources. These details are not captured in annotations and inform the agent of potential delays and partial results.

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 but front-loads the core purpose and then provides necessary details. While dense, every sentence serves a purpose. Splitting could improve readability but it remains concise and informative.

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

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

Given no output schema and moderate complexity, the description covers input parameters, output fields, per-advisory detail, links, alternative versions, and limitations. It mentions partial failures and timeout behavior. Minor omission: no mention of error handling for invalid package names, but overall complete for an agent to use.

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

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

Schema coverage is 100%, so baseline is 3. The description adds extra value by providing examples for package (scoped packages like '@types/node') and specifying default behavior for version ('Defaults to the latest published version when omitted'). This enhances understanding beyond schema descriptions.

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

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

The description clearly states the tool performs a composite check on npm packages across deps.dev and bundlephobia, answering 'should I add this package'. It specifies the exact resource (npm package) and action (check), and distinguishes from sibling tools by being npm-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 explicit usage triggers: 'Use whenever an agent asks is X safe / popular / small' and also specifies limitations with alternatives: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' This gives clear when-to-use and when-not-to-use 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 declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context by specifying the output fields and the intended downstream workflow, which is valuable 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 two sentences, front-loading the core action and return fields, then providing a clear next-step instruction. No unnecessary words or repetition.

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

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

Given 3 parameters (all documented in schema), no output schema, and rich annotations, the description fully explains what the tool does, what it returns, and how to use the result. No gaps remain.

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

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

Schema coverage is 100% and the parameter descriptions in the schema are identical to the description (e.g., examples for query). The description does not add meaning beyond what the schema already provides, so a 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 it searches City of Dublin GIS datasets by keyword and lists the returned fields (name, summary, record_count, owner/org, Feature Service url). It also explicitly connects to sibling tools query_layer and layer_info, distinguishing its role as a search/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?

The description provides clear usage context: search by keyword, then pass the url to query_layer or layer_info for further data. It does not explicitly state when not to use it, but its purpose is well-defined and complementary to siblings.

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

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

Annotations already declare read-only and idempotent. The description adds valuable behavioral details: embedding model (BGE-base-en), chunking (500-char overlapping windows), character limit (200K with truncation flag), and output structure (offsets, similarity scores).

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

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

Description is front-loaded with the core purpose and all sentences are informative. No redundancy, efficient use of words to convey mechanism, limits, and pairing with another tool.

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 (semantic search, chunking, embeddings) and lack of output schema, the description sufficiently covers algorithm, return type, pairing recommendation, and truncation behavior. Leaves 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%, so baseline is 3. The description adds examples for each parameter (e.g., 'text' as SEC 10-K body, 'query' as natural language questions, 'limit' with default and range), 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 the tool performs semantic search inside a fetched record, returning passages with offsets and scores. It distinguishes from siblings like ask_pipeworx_grounded and contrasts with putting the full record in the prompt.

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

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

Explicitly says when to use: when the record is too large for the prompt. Provides concrete examples (SEC 10-K, article) and explicitly pairs with ask_pipeworx_grounded, guiding the agent on workflow.

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 goes well beyond annotations: it details authentication requirements, delivery channel constraints (phone verification, SMS cap, webhook auto-disable), and the always-on feed. 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 dense but well-organized with clear sections for requirements, types, and delivery. Each sentence adds value, though it could be slightly more streamlined.

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 multiple types and delivery channels, and the absence of an output schema, the description is highly complete: it covers all parameters, return value, authentication, limits, and webhook details.

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

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

While schema coverage is 100% and baseline is 3, the description adds significant value with type-specific examples (e.g., items codes, topic) and detailed delivery constraints, surpassing 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 verb 'create', the resource 'proactive monitoring subscription', and the outcome 'returns the new subscription id'. It distinguishes the tool from siblings like list_subscriptions and unsubscribe.

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

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

The description specifies when to use the tool (to set up monitoring), mentions requirements (Pipeworx OAuth account), and lists supported types. However, it does not explicitly state when not to use it or provide 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 already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds that the tool returns example questions with tool+argument shapes and is the onboarding entry point, complementing annotations without contradiction.

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

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

The description is well-structured with front-loaded alias phrases, followed by purpose, return value, and usage instructions. While slightly long, every sentence adds value and there is no redundancy.

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

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

Given the tool's role as an onboarding entry point with many siblings, the description covers purpose, usage, parameters, and return value comprehensively. Even without an output schema, the return is described clearly as category-bucketed example questions with tool/argument shapes.

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 description. The tool's description adds meaning by explaining the optional topic parameter's purpose to focus on specific categories, listing possible values, and indicating that omitting it gives a full spread.

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 identifies the tool as the onboarding entry point for new agents, specifying it returns category-bucketed example questions and links to meta-tools. It distinguishes from siblings like ask_pipeworx and discover_tools by being the first use when the agent doesn't know capabilities.

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

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

Explicitly states 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools', providing clear context for when to invoke. Does not explicitly list when not to use, but the guidance is sufficient.

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

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

The description explains that the row is deactivated (not deleted) and that historical events remain available via recent_alerts, going beyond the annotations (destructiveHint: false, idempotentHint: true) to clarify side effects.

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 efficiently convey the core action, ownership rule, and behavioral nuance without any 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 tool with one parameter and no output schema, the description fully covers purpose, usage constraints, and behavioral impact.

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 'id' already described in the schema. The description adds no further semantics beyond 'by id', so baseline 3 is appropriate.

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

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

The description clearly states 'Cancel a subscription by id.' with specific verb and resource, and 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?

The description explicitly mentions ownership enforcement ('you can only cancel your own subscriptions'), providing context for when to use. It does not explicitly state when not to use or name alternatives, but the context is clear.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, establishing a safe, read-only profile. The description adds valuable behavioral details: it returns a verdict, structured form, actual value with citation, and percent delta, and explains it replaces 4–6 sequential calls. This context about internal behavior and output richness goes beyond annotations.

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

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

The description is a single, well-structured paragraph that front-loads the core purpose and supported query patterns. Every sentence adds value: domain, sources, output, and efficiency benefit. It is slightly long but not wasteful.

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 single parameter and no output schema, the description does a good job explaining the tool's domain, return values, and rationale. It could mention error handling or limitations for unsupported claim types, but overall it provides sufficient context for an AI agent to use the tool correctly.

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

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

The input schema has 100% description coverage for the single 'claim' parameter, providing clear examples. The tool description repeats the natural-language nature but does not add new semantic information beyond the schema. With full schema coverage, baseline of 3 is appropriate.

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

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

The description clearly specifies the tool's purpose: verifying natural-language factual claims against authoritative sources, specifically company-financial claims using SEC EDGAR + XBRL. It provides example query patterns and distinguishes itself by replacing multiple sequential calls, setting it apart from sibling research 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 states 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear context. It also notes the domain limitation to company-financial claims, implying when not to use (e.g., non-financial claims). However, it does not explicitly list alternative tools or provide exclusions.

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