Civitai

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description adds critical behavioral details: it probes LLMs, returns per-model and combined results, default free model, and requires BYO API key for Anthropic (with direct payment). This provides full transparency on behavior and cost implications.

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

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

The description is three sentences long, front-loading the core action and output, then adding key details about model choice and use cases. Every sentence serves a purpose with no fluff.

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

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

Despite no output schema, the description explicitly states the return structure (per-model {score, confidence, signals, raw_response} + combined view). All parameters are covered, and the description addresses the typical use case comprehensively.

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

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

With 100% schema coverage, every parameter already has a description. The tool description adds further semantic value by explaining the purpose of each parameter in context (e.g., entity as brand/topic, models selection, _apiKey for Anthropic, context for disambiguation).

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

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

The description clearly states the tool probes LLMs for knowledge about an entity and scores visibility. It provides specific use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and distinguishes itself from siblings by its unique function of querying AI models for brand awareness.

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

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

The description gives explicit context for when to use (AI-marketing audits, brand checks) and mentions default vs. paid model options. However, it does not explicitly state when not to use or name alternative tools for related tasks, leaving some room for improvement.

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, and idempotentHint, so the description adds value by explaining the routing mechanism, the number of tools and sources, and the citation URI format. 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, dense paragraph but front-loads the key usage guidance ('PREFER OVER WEB SEARCH'). It is focused and concise, though could benefit from slightly more structured formatting (e.g., bullet points) for better scannability.

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 (many sources, no output schema), the description adequately covers purpose, usage, and output format (structured answer with citation URIs). It provides enough context for an AI agent to select and invoke the tool correctly.

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

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

Schema coverage is 100% with descriptions for all parameters, so baseline is 3. The description adds context by explaining that the 'question' parameter accepts natural language and multiple aliases, and provides concrete examples, exceeding the baseline.

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

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

Description clearly states that the tool routes questions to a large set of verified sources (3,744 tools, 884 sources) for authoritative structured data with citations. It lists specific domains like SEC filings, FDA data, economic stats, etc., distinguishing it from general web search.

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

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

Explicitly says 'PREFER OVER WEB SEARCH' and provides numerous examples of when to use ('what is', 'look up', 'find', etc.). It implies when not to use (when web search is adequate) but doesn't directly contrast with sibling tools like 'deep_research' or 'validate_claim'.

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

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

Description adds significant behavioral context beyond annotations: costs extra LLM call, returns refusal reasons, uses only tool result, routes like ask_pipeworx. 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?

Concise yet comprehensive: front-loaded with key purpose, then explains mechanism, return structure, and usage context. Every sentence adds value without redundancy.

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

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

Despite no output schema, description fully specifies return structure and all refusal reasons. Covers the tool's complex routing and extraction behavior, making it complete for agent usage.

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

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

Schema coverage is 100% with clear descriptions for all parameters. Description adds no additional meaning beyond schema; baseline of 3 is appropriate.

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

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

Description clearly states it is a hallucination-resistant answer mode for high-stakes reads, distinguishes from ask_pipeworx by specifying it extracts answers only from tool results and refuses if data doesn't support answer.

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

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

Explicitly says 'Use whenever an answer will be quoted, cited, or acted on' and 'prefer ask_pipeworx for casual lookups.' Also lists specific refusal reasons, guiding when not to use.

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

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

The description extensively details behaviors beyond annotations: fan-out to category-specific data packs, resolver contract with confidence levels and alternatives, parent event extractor, news fallback handling, safety short-circuiting for low-confidence and closed markets, spread warnings, and resolution-rule risk analysis. This far exceeds the read-only and idempotent hints.

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

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

The description is long but well-organized with clear sections and front-loaded purpose. Every sentence adds value, though the length could be trimmed. For a complex tool with many behaviors, the structure compensates.

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

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

Given no output schema, the description thoroughly explains return shapes, edge cases (low-confidence, closed markets, illiquidity, resolution rules), and response fields. It is complete enough for an agent to use the tool correctly without 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?

Although schema coverage is 100%, the description adds meaning beyond field descriptions: it explains how to format market input (slug, URL, question text), defines 'quick' vs 'thorough' depth with source counts, and elaborates on the include_raw default and response size implications.

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

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

The description clearly states 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call', providing a specific verb and resource. It also lists concrete use cases and distinguishes the tool from siblings through its unique fan-out behavior and evidence-packet output.

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 ('Use for "should I bet on X"...') and describes the fan-out logic, but does not explicitly contrast with sibling tools or state when not to use this tool. The guidance is clear but lacks negatives.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds rich behavioral context: data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, sorting by primary metric, and output format with citation URIs. This fully discloses what happens 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 slightly lengthy but well-structured, starting with example triggers and clear usage instructions. Every sentence adds value, and the structure aids readability. A minor trim could improve conciseness, but it remains effective.

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

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

Given the tool's complexity (two entity types, multiple data sources) and no output schema, the description thoroughly explains what data is returned for each type, including sorting and citation URIs. It also quantifies the efficiency gain (replaces 8–15 lookups), making it complete for agent understanding.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds substantial meaning: for 'type' it explains the exact data pulled for each value, and for 'values' it clarifies the expected format (tickers/CIKs for companies, drug names). This goes well beyond the schema.

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

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

The description explicitly states it performs side-by-side comparisons of 2–5 companies or drugs in a single parallel call, with clear examples and synonyms like 'X vs Y' and 'head to head'. It distinguishes itself from sequential lookups, making the purpose unmistakable.

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

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

The description includes a strong directive: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also clarifies the context for each entity type ('company' vs 'drug'), effectively telling the agent when and how to use this tool over 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 operational details: it never invents (gaps[] for unanswered facets), returns structured findings packet with verbatim evidence, and requires account/paid plan. No contradiction with annotations.

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

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

The description is front-loaded with the key value proposition and then provides detailed elaboration. It is somewhat long but every sentence contributes meaningful context. Could be tightened slightly but remains well-structured.

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

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

Given the tool's complexity (multi-source, parallel decomposition, 3744 tools), the description covers the process, output format (findings packet with gaps), behavioral constraints (no fabrication), usage guidance, prerequisites, and time expectations. Lacks output schema but describes return structure 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% and both parameters have schema descriptions, so baseline is 3. The description adds value by explaining 'question' as natural language suitable for broad questions and 'depth' with explicit mappings (quick=3, standard=5, thorough=8) and cost implications, which goes beyond schema info.

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 a specific verb+resource ('Grounded multi-source research in ONE call') and details the decomposition and parallel routing to 3,744 tools across 884 sources, clearly distinguishing it from siblings like ask_pipeworx which is for single lookups.

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

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

Explicitly states 'Use for broad or multi-part questions' and contrasts with ask_pipeworx for single lookups. Also mentions prerequisites (Pipeworx account, paid plan for thorough depth) and time expectations (15-60s), providing clear when-to-use and when-not-to.

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 and idempotent. The description adds that it returns top-N tools with full schemas ready to call, no second lookup needed. 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 purpose: 'Find tools by describing the data or task.' Every sentence adds essential information without padding.

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

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

No output schema, but description says returns names, descriptions, and schemas with examples. Lacks detail on result count or pagination, but sufficient for a discovery tool given annotations.

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

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

Schema coverage is 100%, but the description adds value by explaining 'query' accepts natural language, lists aliases, and describes the limit parameter's effect (top-N). Only minor gap: no default value mentioned.

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 'Find tools by describing the data or task' and provides a list of domains (e.g., SEC filings, FDA drugs). It distinguishes from siblings by saying 'Call this FIRST when you have many tools available and want to see the option set.'

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 for' and recommends calling it first. This gives clear when-to-use and implies it replaces direct tool selection.

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, non-destructive, idempotent. Description adds context: USPTO PatentsView API sunset May 2025 with soft-fail, fallback from GDELT to GNews. This enriches understanding 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?

Description is detailed but well-organized: begins with example queries, then lists sources and return fields. Slightly lengthy but each sentence adds value. Efficient for a complex 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?

With no output schema, description fully details all returned data: cik, company_name, recent_filings up to 5 with URIs, fundamentals with specific fields, patents with sunset note, news fallback, LEI. Comprehensive and actionable.

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 both parameters (type, value) with examples. Description mostly repeats schema info but reinforces that names are unsupported and gives example tickers/CIKs. No significant new meaning beyond schema.

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

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

Description clearly states it provides a 'full cross-source profile of a US public company in ONE parallel call' and lists sources (SEC, XBRL, USPTO, news, GLEIF). Distinguishes from siblings like resolve_entity (names not supported) and compare_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 says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups for a holistic view.' Also states when not to use: 'names not supported – use resolve_entity first if only have a name.' Provides clear context and alternatives.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. Description says 'Delete' which aligns, but adds no extra behavioral detail (e.g., behavior on non-existent key). Adequate but minimal 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 clear sentences for purpose and usage, no wasted words. Key information is front-loaded and immediately understandable.

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 parameter, no output schema) and thorough annotations, the description covers purpose, usage, and relationships to siblings completely.

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 parameter description. The description mentions 'by key' but adds no additional meaning beyond the schema's 'Memory key to delete' and the example. Baseline 3 applies.

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

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

Clearly states 'Delete a previously stored memory by key.' Verb (delete), resource (memory), and scope (by key) are explicit. Distinguishes from siblings 'remember' and 'recall'.

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

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

Provides explicit when-to-use: 'Use when context is stale, the task is done, or you want to clear sensitive data...' Also suggests pairing with related tools, giving clear context and 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 value by detailing the process (fetches page, extracts title/description/key links, emits markdown) and output format (single text blob). 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 (three sentences), front-loaded with purpose, then process, then use cases. Every sentence adds value with no redundancy or wasted words.

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

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

Given the tool's simplicity (2 params, no output schema, rich annotations), the description is complete: it explains what it does, how it works, what output to expect, and when to use it. 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 description coverage is 100%, with url and max_links already described. The description adds no new information beyond the schema's parameter descriptions, meeting the baseline but not exceeding it.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the verb (generate), resource (llms.txt), and target audience (AI crawlers). It distinguishes from siblings like ai_visibility_check by focusing on file generation.

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

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

The description explicitly lists three concrete use cases (getting a client's site indexed, drafting own project, auditing competitor view), providing clear context for when to use. It lacks explicit 'when not to use' guidance but is still strong.

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

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive. The description adds that it is keyless (no API key needed) and defaults SFW, providing extra behavioral context 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?

Two sentences: first states purpose and output, second gives example and flags. No unnecessary words, front-loaded with core functionality.

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

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

No output schema, but the description lists all returned information (description, type, creator, tags, stats, versions). Covers keyless access and SFW default. With one simple parameter, this is fully 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?

Only one parameter 'id' with 100% schema coverage. The description adds example ids and reinforces the parameter's purpose, adding value beyond the schema description.

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

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

The description clearly states it gets full details for a single Civitai model by id, listing included fields (description, type, creator, tags, stats, versions). The verb 'Get' and resource 'model' are specific and distinct from siblings like 'search_models'.

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 a test example (id 4201) and notes Safe-For-Work default with NSFW flag surfaced. Does not explicitly contrast with alternatives like 'search_models', but context is clear enough for usage.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds behavioral details: default SFW filter, sorting by most-downloaded, no API key required ('Keyless'). These go beyond annotations and 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?

Three sentences with no redundancy: core action, usage examples, defaults/auth info. Front-loaded and 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?

Description covers core function, sorting, defaults, and auth. No output schema exists, but return format can be inferred; pagination not mentioned but limit parameter is in schema. Acceptable for a simple list 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 has 100% coverage with descriptions for all 4 parameters. The description adds context about output ordering but does not elaborate on parameter usage or provide additional hints 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 uses a specific verb 'Browse' and resource 'top community models of a given type on Civitai', includes sorting ('most-downloaded first'), and provides examples ('best SDXL checkpoints', 'top Flux LoRAs') that distinguish from search-oriented siblings.

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

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

The description gives clear usage context with examples ('Great for...'), implying when to use this tool. It does not explicitly mention alternative tools like search_models, but the context is sufficient for an agent to infer appropriate scenarios.

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

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

Annotations already provide readOnlyHint, idempotentHint, etc. Description adds return fields info. No contradiction. Does not elaborate beyond annotations, but sufficient.

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

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

Two concise sentences: first states purpose and return fields, second provides usage guidance. 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?

Simple read-only list tool with one optional parameter. Description covers return fields and usage guidance. No output schema needed. Complete for the tool's complexity.

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

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

Schema coverage is 100% for one parameter. Description implies default behavior (active only) but adds no extra meaning beyond schema. Baseline 3 is appropriate.

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

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

Description clearly states the verb 'list' and resource 'caller's active subscriptions'. Distinguishes from sibling tools like 'subscribe' 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?

Explicitly says 'Use this to review what you're monitoring before adding more or to find an id to cancel', guiding when to use and hinting at 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 are minimal (readOnlyHint=false, etc.), but the description adds value by stating the tool is free and doesn't count against quota, has a rate limit of 5 per identifier per day, and that feedback is reviewed daily and influences the roadmap. This provides behavioral context beyond annotations.

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

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

The description is concise, well-structured, and front-loaded with purpose. Every sentence adds value, covering usage, guidelines, behavior, and constraints without redundancy.

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

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

Given the tool's simplicity (3 parameters, no output schema), the description is complete. It explains all inputs, provides usage context, and describes the feedback lifecycle (review, roadmap impact). 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 baseline is 3. The description does not add significant new meaning beyond the schema; it reinforces the enum values and message instructions but does not provide additional parameter semantics.

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

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

The description clearly states the tool's purpose: to send feedback to the Pipeworx team about bugs, features, data gaps, or praise. It distinguishes from sibling tools by focusing on feedback, not queries or actions.

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

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

The description explicitly explains when to use each feedback type (bug, feature, data_gap, praise) and provides guidance on what to include and what to avoid (e.g., avoid pasting end-user prompts). It also mentions rate limits and quota, helping the agent decide appropriateness.

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 (read-only, idempotent), the description adds important behavioral details: data source (CF analytics-engine), no PII, caching window (5min-1h), and what is returned (top tools, packs, call volume). 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 brief yet comprehensive, with a clear lead sentence and bullet-pointed use cases. No unnecessary words; every sentence adds value.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description covers all necessary context: what is returned, data source, privacy, caching, and purpose. It is fully adequate.

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

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

The only parameter 'window' is well-described in schema (enum, description). The description further adds semantics: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This clarifies usage beyond the schema.

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

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

The description clearly states the tool returns trending tools and packs from Pipeworx with call volume over a window. It distinguishes from siblings like ask_pipeworx or discover_tools by focusing on aggregated trend data, not direct queries or listings.

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 lists three use cases: discovering hot data sources, confirming canonical tools, and aligning use cases. While it doesn't explicitly compare to alternatives, the use cases are clear and contextual.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds substantial behavioral context: the algorithmic approach, semantic anchor (Jaccard similarity >=0.30), partition filter, fill check against CLOB depth, and conditions when realizable edge is zero. 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-organized with clear sections but is relatively verbose. Some technical details could be condensed or moved to internal documentation. However, it front-loads the most critical requirement (one of event/topic required) and structures the rest logically.

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

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

Despite lacking an output schema, the description thoroughly explains the response structure for both modes, including fields like gap_pp, suggested_trade, reasoning, and partition_check details. It also covers edge cases like fill check and placeholder filtering. A quick example of the output would improve completeness.

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

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

Schema coverage is 100% with both parameters described. The description adds meaningful context beyond the schema by explaining when to use each parameter, providing example inputs, and noting that full Polymarket URLs are accepted for 'event'. This aids correct invocation.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket using monotonicity violations and partition-sum checks. It distinguishes two modes (event vs topic) and explains how each works, making it easy for an agent to select the correct mode.

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 that one of 'event' or 'topic' is required, and provides recommendations for each use case (event for specific market, topic for cross-event scanning). Also mentions when to use sibling tool 'polymarket_fill_risk' for custom sizing, giving clear guidance on 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?

Even with annotations (readOnly, openWorld, idempotent, non-destructive), the description adds rich behavioral context: explains caching policy (1h at KV level), details each model family (e.g., lognormal barrier, GDELT ratio), and describes the placeholder-slug filter and diagnostics. 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 and verbose, packed with technical details. While it is well-structured with some labeling (e.g., 'FIVE MODEL FAMILIES'), it could be more concise. Front-loads purpose but overall length may hinder quick comprehension.

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

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

Given the complexity (9 params, no output schema), the description is highly complete. It explains the output structure (by_segment, fed_candidates, diagnostics), the meaning of edge fields, and the model families. It ensures the agent knows what to expect.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant value beyond the schema by explaining the logic behind parameters (e.g., 'Edge is evaluated NET of slippage' for min_edge_pp, and the purpose of min_partition_leg_kelly). Some defaults are mentioned but not in schema.

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

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

The description clearly states the tool scans Polymarket markets and returns opportunities where Pipeworx data disagrees with market price. It specifies 'scan top Polymarket markets' and 'return opportunities', with a concrete use case 'what should I bet on today'. It distinguishes from siblings like polymarket_arbitrage by focusing on Pipeworx disagreement.

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 extensive context on when to use the tool, explaining the three segments, the filter knobs (min_liquidity, max_spread_pp), and the exclusion of Fed bets. It gives implicit guidance on filtering thin markets but does not explicitly name alternative tools for specific cases.

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

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

Beyond annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses response structure (tracked, expired, snapshot_dates), data limitations (60-day TTL, snapshot gaps), and that decay is based on daily closes, not intraday. No contradiction with annotations.

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

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

The description is well-structured with clear sections: purpose, response details, limitations. Each sentence adds value without redundancy. Appropriate length for the tool's complexity.

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

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

Given the complexity (time-series analysis, trend classification, expiration tracking) and lack of output schema, the description thoroughly explains all return fields and edge cases, making it self-contained for an AI agent.

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

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

Schema coverage is 100%, so baseline is 3. The description repeats parameter defaults and adds context like 'lookback' and 'snapshot family', but does not significantly enhance 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's purpose: tracking edge persistence and decay over time from daily snapshots. It distinguishes itself from sibling 'polymarket_edges' by focusing on historical trend rather than current state, using the example of a fresh vs. old wide edge.

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

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

The description explains when to use the tool ('answers how long has this edge existed and is it shrinking?') and implies alternatives (for current edges, use polymarket_edges). However, it does not explicitly state when not to use or provide direct alternative tool names.

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

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

Description adds significant behavioral context beyond annotations (readOnlyHint, idempotentHint). It details return fields (top_of_book, vwap_fill_price, slippage_pp, etc.) and explains that partial basket fills strand traders unhedged. No contradiction with annotations.

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

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

The description is well-structured with sections for single-market and basket modes, front-loading the core purpose. While somewhat verbose, every sentence serves a purpose—usage guidance, parameter clarification, or behavioral warnings. Minimal 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 complexity (two modes, multiple return fields, no output schema), the description is comprehensive. It covers return values for both modes, explains edge cases (thin books, partial fills), and provides thresholds. The warning about dominant loss mode in arb bots is especially valuable.

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

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

Schema description coverage is 100%, but the description enriches meaning by clarifying mode differences (single-market vs basket), default behaviors for `side` in basket mode, and the interpretation of `size_usd` as settlement notional in basket mode. This adds value beyond the schema alone.

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

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

The description clearly states the tool computes 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes, which differentiates it from sibling tools like polymarket_arbitrage. The verb+resource is specific and actionable.

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

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

Explicit guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains why (theoretical overround on thin books) and warns about partial fills converting arbitrage to unhedged directional positions.

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

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

Annotations confirm read-only, idempotent, non-destructive behavior. The description adds rich behavioral details: the spread's 2-25pp range, conditions for meaningful deltas, compatibility_warning scenarios, temporal alignment, and skipped cross counters. No contradiction with annotations.

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

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

The description is relatively long but well-structured with clear sections (modes, response, safety fields). Every sentence serves a purpose, though some redundancy exists (e.g., repeated mention of compatibility_warning). Fine for a complex 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?

No output schema exists, so the description compensates by explaining response fields (leg-by-leg prices, top_spreads_pp, safety fields). It covers key aspects but lacks an explicit structure example. Overall sufficient for agent to understand output.

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 parameters are well-documented. The description adds value by listing valid topic values, explaining override behavior, and hinting at poor topic reliability, which helps the agent understand parameter implications beyond the schema.

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

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

The description explicitly states the tool computes cross-venue spreads between Kalshi and Polymarket, with a clear verb (compute spread) and resource (two platforms). It distinguishes from siblings like polymarket_arbitrage and polymarket_edges by focusing on cross-venue comparison.

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

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

The description explains two modes (topic shortcuts vs explicit tickers) and warns that most pre-mapped topics currently yield compatibility_warnings. It provides context on when spreads are tradeable, though it does not explicitly list alternative tools or when not to use.

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

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

Annotations indicate read-only and idempotent. The description adds contextual behavior: listing all keys if key omitted, scoping to identifier, and pairing with remember/forget. It does not contradict annotations and adds value beyond them.

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

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

The description is concisely packed into two sentences. It front-loads the core purpose and covers use case, scoping, and sibling relationships without unnecessary words.

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

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

Given the tool's simplicity (1 optional parameter, no output schema) and rich annotations, the description covers behavior, use case, scoping, and sibling links. It does not describe return format, but this is acceptable as output schema is absent.

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

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

The schema description coverage is 100% with 1 parameter. The description adds meaning by clarifying that omitting the key lists all saved keys, which is not evident from the schema's description alone.

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

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

The description clearly states what the tool does: retrieve a saved value or list all keys. It gives concrete examples (user's target ticker, address, research notes) and distinguishes from siblings (remember, forget). The verb 'retrieve' and resource 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?

The description explains when to use the tool: to look up context stored earlier, avoiding re-derivation. It mentions scoping to an identifier and pairing with remember/forget. While not explicit about when not to use, the 'without re-deriving' phrase implies an alternative approach.

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 no destructiveness. The description adds behavioral context such as mark_read consuming events, polling suitability, and feed persistence. 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 with five sentences, front-loaded with the core purpose. Every sentence adds value (filtering, fields, mark_read, polling, alternative endpoint). No redundancy.

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

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

The description covers key aspects: what is returned (fields), filtering options, mark_read behavior, and alternative access. While it does not mention the unread_only parameter explicitly, the schema covers it, and the mark_read explanation implicitly relates. No output schema exists, but the description compensates by listing key return fields.

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 each parameter described. The description adds value beyond schema by giving an example filter (sec_8k), clarifying that since expects an ISO timestamp, and explaining the behavior of mark_read. This extra context aids correct invocation.

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

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

The description clearly states it pulls fired events from a subscription feed and returns the most recent alerts with specific fields (source, citation_uri, raw event). It distinguishes itself from sibling tools like list_subscriptions by focusing on recent alerts and offering filtering and mark_read 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?

The description provides guidance on when to use this tool (pulling recent alerts from subscription feed) and mentions an alternative REST endpoint for scripts/dashboards. It does not explicitly contrast with all siblings, but the context is sufficient for most use cases.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive. The description adds substantial behavioral detail: fanned-out sources (SEC, GDELT/GNews, USPTO), fallback logic (rate-limited or 5xx), USPTO soft-fail due to API sunset, and the structured return format. 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 front-loaded with purpose and uses clear examples. It is slightly verbose with multiple sentences and clauses, but each part adds necessary context. Efficient for the amount of information conveyed.

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

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

Despite lacking an output schema, the description fully specifies the return structure (changes[] grouped by source, total_changes count, citation URIs). It covers all three data sources and their edge cases. For a tool with three parameters and complex behavior, 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%, so baseline is 3. The description adds value by explaining the 'since' parameter format with examples (ISO dates and relative shorthands like '7d', '30d', '3m', '1y') and providing a typical monitoring suggestion ('30d' or '1m'). It also clarifies the 'value' parameter can be ticker or zero-padded 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's purpose as a change feed for a company in a time window, with explicit example queries like "What's new with X". It distinguishes from the sibling sibling tool entity_profile by contrasting dynamic vs static profiles.

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

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

The description gives explicit guidance on when to use this tool versus entity_profile. It also describes fallback behavior (GDELT to GNews) and soft-fail for USPTO. However, it does not exhaustively list when not to use it beyond the single alternative.

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 context beyond annotations: scoping by identifier, persistence details (authenticated users get persistent memory, anonymous 24h). Annotations already indicate idempotent and non-destructive, and description aligns 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 front-loaded with the core purpose, followed by usage guidance and behavioral details. Every sentence adds value; no redundancy or filler.

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

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

For a simple 2-parameter tool with no output schema, the description fully covers purpose, usage, and behavior. No gaps remain for an agent to understand correct invocation.

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

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

Schema coverage is 100% with descriptions for both key and value. The tool description provides usage examples (like 'subject_property', 'target_ticker') adding context, but does not substantially expand on the schema's parameter meaning, 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 the tool's action ('Save data the agent will need to reuse later') and resource ('key-value pair'). It distinguishes from sibling tools by mentioning recall and forget, establishing a clear purpose.

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

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

The description provides explicit guidance on when to use ('when you discover something worth carrying forward') and when not to (implicitly, by pairing with recall/forget). It also notes session persistence differences between authenticated and anonymous users.

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

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

The description adds value beyond annotations by revealing that each call 'cascades through several lookup endpoints internally' and that using it 'replaces 2-3 manual lookups'. Combined with annotations (readOnlyHint, idempotentHint, etc.), the agent has a solid understanding of the tool's internal behavior and safety profile.

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

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

The description is a single, well-structured paragraph that front-loads example queries and key usage guidance. While comprehensive, it could be slightly more concise by removing the introductory 'What's the ticker for…' litany, but every sentence contributes value.

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

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

Given no output schema, the description thoroughly explains return values for both entity types (e.g., ticker, CIK, citation URI for company; RxCUI, ingredient for drug). It also covers internal behavior (cascading lookups) and usage context, making it fully self-contained for a 2-parameter tool.

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

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

With 100% schema coverage, the description still enriches both parameters: for 'type' it explains each enum value's meaning and associated output fields, and for 'value' it provides specific accepted formats (e.g., ticker, CIK, name for company; brand/generic for drug) with examples like 'AAPL' and 'ozempic'. This fully compensates for the schema's minimal descriptions.

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

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

The description clearly states the purpose of resolving user-spoken names to canonical identifiers with specific verb 'resolve' and resource 'name to ID'. It uses concrete examples like 'ticker for...' and explicitly lists supported entity types (company, drug) with detailed output expectations, effectively distinguishing from sibling tools like entity_profile.

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

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

The description provides explicit guidance with 'Use FIRST whenever you have a name but need an ID', establishing a clear when-to-use condition. It does not explicitly state when not to use or name alternatives, but the examples and scope naturally imply its primary role in ID resolution.

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

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

Annotations already declare read-only, idempotent, non-destructive. The description adds that it probes each entity with ai_visibility_check and returns ranked results with score, confidence, signal density. Does not contradict annotations and provides sufficient behavioral context beyond them.

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

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

Two sentences, front-loaded with purpose, no fluff. Every word 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?

No output schema, but description adequately describes return format (ranked list with score, confidence, signal density). Could mention entity count constraints, but schema already covers that. Overall, sufficient for a read-only comparison tool with good annotations.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by clarifying that the first entity is the subject and that 'context' disambiguates common names, which 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 it compares AI visibility across multiple entities side-by-side, ranks them, and surfaces most/least recognized. It explicitly differentiates from the sibling tool ai_visibility_check by focusing on multi-entity 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?

Provides explicit context for competitive AI-marketing audits with an example question. Implies when not to use (single entity) by referencing ai_visibility_check as the probing mechanism. Mentions the first entity is the subject, setting expectations for usage.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description discloses key behaviors: fans out across deps.dev and bundlephobia, partial failure handling ('bundlephobia's first measurement... can take 5-30s; sources_failed will list it if it times out'), and return structure (summary block, per-advisory detail, links, alternative versions).

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 rich but efficient, front-loading the core purpose. It is slightly verbose but every sentence adds value. Could be slightly more concise, but overall well-structured.

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

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

Given no output schema, the description thoroughly explains return values (summary block fields, advisory details, links, alternative versions) and partial failure behavior. It also specifies ecosystem limitations. Highly complete for an AI agent.

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

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

Schema has 2 parameters with 100% description coverage. The description adds no new semantic value beyond the schema: it mentions 'defaults to latest published version' which is already in the schema's version description. Minimal added meaning.

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

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

The description clearly states it is a composite check for evaluating whether to add an npm package, covering license, advisories, version history, and bundle size. It distinguishes from sibling tools like bet_research or compare_entities by focusing specifically on npm package evaluation.

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

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

Explicit usage guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' Also notes ecosystem limitations and alternatives: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.'

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

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

Annotations already cover readOnly, idempotent, non-destructive. The description adds key behavioral traits: defaults to Safe-For-Work (mature excluded) and is keyless. It also lists returned fields (download stats, base model, creator, trigger words), though pagination/rate limits are absent.

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, information-packed sentence with no filler. It front-loads the main action and includes essential details (SFW, keyless) without excess.

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

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

Given no output schema, the description explains return values (stats, base model, etc.) and default behavior. It omits result ordering/pagination but parameters cover that. Sufficiently complete for a search tool.

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

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

Schema coverage is 100% with detailed descriptions for all 5 parameters. The description only mentions query and type generically, adding no new meaning beyond the schema. 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 Civitai's catalog of AI image-generation models by text query and/or type, listing model types and return fields. It distinguishes from sibling tools like get_model (retrieve specific model) and list_by_type (list without text query) by highlighting the combined query/type search.

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

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

The description implies usage for finding models via text/type but does not explicitly state when to use alternatives (e.g., get_model for known IDs). No when-not-to-use guidance is given, leaving it inferred.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds technical details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, and offset verification. Fully transparent.

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?

Five focused sentences, each adding value: purpose, usage guidance, pairing, technical details, and limits. No wasted words.

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

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

For a tool with three parameters and no output schema, the description covers functionality, use cases, limitations, and technical behavior completely. No gaps.

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

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

Schema covers 100% of parameters with descriptions. The description adds minor but useful context, like flagging on truncation and clarifying the text limit. While schema already handles most, the extra detail justifies a 4.

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

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

The description clearly states the tool performs semantic search inside a fetched record, using examples like SEC 10-K or article. It distinguishes itself from sibling tools by mentioning it pairs with ask_pipeworx_grounded and saves context.

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

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

Explicitly says when to use: 'when the record is too big to cram into the prompt'. Provides context on what it accomplishes (saves context, returns relevant passages) and mentions pairing with a sibling tool.

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

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

Annotations indicate non-readonly, idempotent, nondestructive. The description adds key behavioral details: account requirement, delivery channel mechanics (always-on feed, email, SMS with verification and rate limits, webhook signing and auto-disable), and return of subscription ID.

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: purpose first, then requirements, supported types, and delivery channels. It is slightly lengthy but every sentence adds value. Front-loading of key info is good.

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

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

Given no output schema, the description covers return value ('new subscription id') and important edge cases (e.g., webhook secret delivered once, auto-disable). While very detailed, it omits explicit mention of return type format (e.g., string ID), but this is minor.

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

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

With 100% schema coverage, the description still adds substantial meaning: it explains each type's parameter structure (e.g., sec_8k items, polymarket_edge topic), delivery object constraints (SMS phone verification, webhook signing), and validation rules.

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

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

The description clearly states the tool creates a proactive monitoring subscription to a live-data event stream, lists supported types (sec_8k, polymarket_edge, etc.), and returns a subscription ID. It distinguishes from sibling tools like list_subscriptions and unsubscribe by focusing on creation.

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

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

The description explains when to use (creating a subscription) and provides prerequisites (requires Pipeworx OAuth account) and constraints (anonymous/BYO cannot persist). It implicitly contrasts with read-only tools but does not explicitly state when not to use.

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

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

Annotations already declare readOnlyHint, openWorldHint, etc. Description adds that it returns dynamic content from the live catalog and provides structured examples, which is useful behavioral context beyond the annotations.

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

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

Description is somewhat long but well-structured, front-loading the key scenario and listing examples. Each sentence adds value; no redundancy.

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

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

For a simple one-parameter tool with no output schema, the description thoroughly explains what is returned (category-bucketed questions with tool shapes). It also contextualizes the tool within the ecosystem.

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 `topic` is fully described with expected values (finance, pharma, etc.) and usage guidance. Schema coverage is 100%, but description adds meaningful context enabling correct invocation.

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

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

Description clearly states the tool is the onboarding entry point, lists the types of questions it returns (company financials, drugs, etc.), and mentions it provides tool+argument shapes. It distinguishes itself from siblings by focusing on helping agents discover what to ask.

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 this tool first when the agent doesn't know what Pipeworx can do, and describes when to use the topic parameter. Lacks explicit 'when not to use' but the context is clear given sibling tools.

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

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

Beyond annotations, the description adds critical behavioral context: ownership enforcement, deactivation (not deletion), and impact on historical events via 'recent_alerts'. 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?

Two sentences that front-load the primary action, then succinctly cover constraints and side effects. No filler.

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 covers ownership, destructiveness, and data retention. Complete and self-contained.

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

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

The single parameter 'id' is well-described in the schema as a UUID returned by subscribe. The description adds minimal extra meaning, but schema coverage is 100%, 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 the action ('Cancel a subscription by id') and the resource (subscription). It distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' by specifying cancellation.

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 explicitly mentions ownership enforcement ('you can only cancel your own subscriptions'), providing clear context for use. It also explains that the row is deactivated, not deleted, offering insight into when this tool is appropriate versus other operations.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds details on returned verdict types, structured form, citation, and efficiency (replaces multiple calls). No contradiction with annotations.

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

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

Begins with example invocations, states use case, scope, and output. While slightly long, every sentence adds value. Front-loaded with key info.

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

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

For a tool with 1 parameter and no output schema, the description covers input examples, domain scope, authoritative sources, and output fields. Adequately complete for its complexity.

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

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

Single parameter 'claim' with schema description. Schema coverage is 100%, so baseline 3. Description adds examples and domain context but no additional syntax or format details 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 it performs natural-language claim verification against authoritative sources, with specific example queries. It distinguishes itself from sibling tools by focusing on fact-checking financial claims for public US companies.

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 whenever the agent needs to check whether something a user said is factually correct.' Provides scope and what it replaces, but lacks explicit exclusion criteria for out-of-scope claims.

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