Civitai

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

Annotations already indicate readOnly, idempotent, non-destructive. Description adds return shape (per-model score, confidence, signals, raw_response + combined view) and explains API key handling and payment model. No contradictions.

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

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

Single paragraph, front-loaded with purpose, then details, then use cases. Every sentence adds information, no fluff. Efficient and well-structured.

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

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

For a tool with 4 parameters and no output schema, the description outlines return structure and usage contexts. Explains model options and payment. Could detail scoring algorithm slightly, but sufficient for agent decision.

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

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

Schema coverage is 100% with good descriptions. Description adds value by clarifying default model for 'models' parameter and that '_apiKey' is passed straight through and only needed for Anthropic. Slight improvement over baseline 3.

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

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

Description uses specific verb 'probe' and resource 'LLMs', defines output 'score visibility (0-100) per model', and clearly distinguishes from sibling tools like 'ask_pipeworx' or 'entity_profile' by focusing on AI visibility across 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?

Explicitly states default model and how to use Anthropic with an API key, plus lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). Does not explicitly mention when not to use, but context is clear.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive behavior. The description adds that it routes to 5,305 tools, returns structured answers with citations, is fast, and works on all tiers. No contradictions.

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

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

The description is comprehensive but somewhat verbose. It is well-structured with a bold directive, explanation, examples, and escalation paths, though it could be more concise.

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

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

Given the tool's complexity (routing to thousands of sources), the description covers purpose, usage, examples, alternatives, and behavior. The lack of output schema is mitigated by mentioning structured answers with citations.

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

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

Schema coverage is 100% with all parameters described as aliases for 'question'. The description does not add additional meaning beyond what the schema already provides.

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

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

The description clearly states the tool answers factual questions using authoritative structured data, routing to many sources. It distinguishes itself from siblings like ask_pipeworx_grounded and deep_research with specific use cases.

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

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

Explicitly recommends preferring this over web search for factual questions, provides extensive examples of when to use, and clearly describes when to step up to alternatives like ask_pipeworx_grounded or deep_research.

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 mark it as read-only, non-destructive, and idempotent. The description adds that it is a full working router with experimental changes, and notes its fallback behavior (none, it's independent). This contextualizes the beta nature beyond annotations.

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

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

The description is front-loaded with the beta identity and current status. It uses three sentences effectively, though it could be slightly more terse without losing key details.

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

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

Given the tool's role as a beta variant, the description adequately covers its purpose, current identical state, and usage context. No output schema exists, but the description notes the response shape matches ask_pipeworx, which suffices.

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

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

All 6 parameters are fully documented in the input schema with descriptions. The description mentions 'same arguments' but adds no new semantic information beyond the 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?

The description clearly identifies it as a beta version of ask_pipeworx with identical capabilities but experimental routing. It distinguishes from the stable sibling by stating it's the experimental edge with candidate improvements.

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 using it exactly like ask_pipeworx when wanting the newest routing, and explains the comparison process and current state. This provides clear when-to-use guidance relative to alternatives.

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

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

Beyond annotations (readOnly, idempotent, openWorld), the description details the mechanism (routing, extraction-only), the output format (with evidence and refusal reasons), and the cost of an extra LLM call. No contradictions with annotations.

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

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

The description is well-structured, starting with the core benefit, then mechanism, return format, and usage guidelines. While somewhat lengthy (150 words), each sentence is informative and earns its place. Minor verbosity prevented a 5.

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

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

Given no output schema, the description comprehensively covers return values (answer, evidence, confidence, etc.) and failure reasons. It also explains the routing mechanism and cost trade-offs, making the tool's behavior fully predictable.

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 as 'Alias for question.' The description adds the context that the parameter is 'Your question in natural language' and lists multiple aliases, slightly enhancing understanding. Baseline is 3, but the extra clarification earns 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 identifies the tool as a hallucination-resistant answer mode for high-stakes reads, explicitly distinguishing it from its sibling ask_pipeworx by noting the extra LLM call cost and the extraction-only behavior. It uses specific verbs like 'extracts' and 'refuses' and clearly states the domain of use (financial, legal, medical).

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

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

Provides explicit guidance on when to use ('whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts') and when not to ('prefer ask_pipeworx for casual lookups'), including a cost trade-off. This is comprehensive and actionable.

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?

Extensively describes internal behavior: fan-out logic, response shapes, confidence scoring, parent event extraction, news fallback, safety short-circuits, and resolution-rule risk. Exceeds annotations by detailing many conditional behaviors.

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?

Very detailed and well-structured, but overly long. Front-loaded with purpose, but later sections are dense with technical edge cases. Could be more concise for agent parsing without losing critical information.

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

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

Comprehensively covers input, output shapes, error states, safety checks, and edge cases (e.g., closed markets, wide spreads, cancellation rules). No output schema exists, so description adequately compensates.

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

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

Despite 100% schema coverage, the description adds value by explaining how parameters work (e.g., market accepts slug, URL, or text; depth quick vs thorough; include_raw summarization). Gives examples and clarifies effects beyond schema definitions.

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

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

The description clearly states it researches Polymarket bets by pulling Pipeworx data, with specific usage examples like 'should I bet on X'. It distinguishes from sibling tools by focusing on bet research and data fan-out.

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 (bet research questions) and provides detailed instructions on inspecting confidence matches and handling closed markets. Includes examples of fan-out for different bet types and warnings about edge 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 readOnly, openWorld, idempotent, non-destructive. Description adds: data sources (SEC EDGAR/XBRL for company, FAERS for drug), fiscal-year handling, result sorting by primary metric, and citation URIs. No contradiction.

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

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

Description is somewhat lengthy but each clause adds value. Front-loaded with usage triggers and purpose. Could be slightly tighter, but overall 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 no output schema and moderate complexity, description covers purpose, parameters, data sources, sorting, and efficiency gains. Lacks error handling details but sufficient for selection and invocation.

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

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

Schema coverage is 100%, so baseline is 3. Description adds meaning: explains 'type' values and what each fetches, gives examples for 'values', and clarifies sorting behavior. Provides both general and specific parameter info 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 verb 'compare' and resource 'entities', lists trigger phrases, specifies 'side-by-side comparison of 2–5 companies or drugs' and contrasts with sequential single-pack lookups, distinguishing it from siblings 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?

Explicit guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and 'Replaces 8–15 sequential lookups'. Provides example queries and when to use, though no explicit '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?

Beyond annotations (readOnlyHint, openWorldHint, etc.), describes decomposition, parallel execution, output structure (evidence, gaps, contradictions, citations), hop field, semantic excerpting, and latency. Also discloses paid plan for 'thorough' depth.

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

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

Dense with value but lengthy. Front-loaded with crucial info (account, alternative). Could be slightly more structured, but every sentence is purposeful.

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

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

Given no output schema and many siblings, description fully explains output (findings packet with evidence, gaps, contradictions, citations) and covers edge cases (gap recovery, semantic excerpting, latency). Extremely thorough.

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?

Both parameters fully explained in schema (100% coverage). Description adds meaning: details depth values (quick=3, standard=5 with gap recovery, thorough=8 with contradictions) and clarifies that question is natural language and broad/multi-part is fine.

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

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

Clear verb 'research' and specific resource 'Pipeworx's 1391 STRUCTURED data sources'. Distinguishes from siblings like ask_pipeworx by emphasizing multi-source parallel decomposition.

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: best for broad/multi-part structured questions, not for single lookups or breaking news (prefer ask_pipeworx). Also notes account requirements and alternative tool for non-signed-in 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?

Annotations indicate safe read-only, idempotent behavior. Description adds that results include full schemas with examples and are ready to call directly, no contradiction.

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

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

Concise, front-loaded with key purpose and usage; every sentence adds value with no redundancy or fluff.

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

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

No output schema but description explains return value (top-N tools with schemas and examples). Sufficient for a discovery tool given complexity.

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

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

100% schema coverage with all parameters described. Description adds context with examples of natural language queries and lists aliases, enhancing understanding beyond schema.

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

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

Clearly states the tool finds tools by describing data or task, lists example domains, and distinguishes from siblings by advising to call first 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 states when to use (browsing, searching, discovering tools) and when to call first, providing clear context for use vs alternatives.

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

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

Annotations already indicate idempotent, read-only, non-destructive. Description adds valuable context: parallel call fans out across multiple sources, returns specific fields, notes patents may soft-fail, and limits filings to 5. 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?

Long but efficiently dense; front-loaded with example queries and clear action. Every sentence contributes meaning, though slight reduction in length would improve conciseness.

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

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

No output schema exists, so description fully specifies return values (cik, company_name, filings with URIs, fundamentals, patents, news, LEI), caveats (patents soft-fail), and input constraints. Complete enough for a complex, multi-source 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 baseline 3. Description adds crucial context: value supports ticker or CIK but not names, and type currently only 'company'. Enhances schema info beyond the baseline.

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

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

The description specifies the exact action: produce a cross-source profile of a US public company. It distinguishes from siblings by recommending this over chaining single-source lookups and referencing resolve_entity for name resolution.

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

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

Explicitly states when to use (holistic view) and when not (if only a name, use resolve_entity first). Specifies valid inputs (ticker or CIK) and excludes names, providing clear decision criteria.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true. The description adds no new behavioral context beyond stating it deletes a memory, which is consistent but not additive.

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 short sentences, front-loaded with the verb, no unnecessary words. Every sentence is essential.

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 delete tool with one parameter and annotations covering safety, the description is complete: it specifies purpose, usage conditions, and relation to siblings.

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

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

Schema coverage is 100% with clear description for the 'key' parameter. The tool description adds no additional meaning beyond what the schema already provides.

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

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

The description uses a specific verb (Delete) and resource (previously stored memory), and explicitly pairs with remember and recall to differentiate from siblings.

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

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

Explicitly states when to use: when context is stale, task done, or clearing sensitive data. Also mentions pairing with remember and recall, providing 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?

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds that it fetches an external page and extracts content, which is consistent and provides additional context. No contradictions.

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

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

The description is concise, front-loaded with the main action, and every sentence adds value. No wasted words.

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

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

The description fully explains input (URL, optional max_links) and output (llms.txt markdown blob). For a simple tool with no output schema, this is complete and sufficient.

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

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

Both parameters have complete descriptions in the input schema (100% coverage). The description does not add new information beyond what the schema provides, meeting the baseline.

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

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

The description clearly states it generates an llms.txt file for a URL, specifying the target users (AI crawlers) and the exact process (fetches, extracts, emits). It distinguishes itself from siblings by its unique 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 use cases (client site indexing, personal projects, competitor auditing), which guides when to use the tool. It lacks explicit when-not-to-use or alternatives, but the context is clear enough.

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

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

The description adds value beyond annotations by noting it 'Defaults Safe-For-Work; nsfw flag is surfaced' and 'Keyless', providing important behavioral context. It also clarifies the returned data structure, which is not covered by annotations.

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

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

The description is clear and front-loaded with key information. It is concise but includes useful example and notes, slightly longer than minimal but still efficient.

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

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

Given the tool's simplicity (single parameter, no output schema) and strong annotations, the description is complete. It covers what the tool returns, safety defaults, and access requirements ('Keyless').

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

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

Schema coverage is 100% for the single parameter 'id'. The description adds an example and context but does not significantly add meaning beyond the schema's description.

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

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

The description clearly states 'Get full details for a single Civitai model by id', specifying the exact resource and action. It lists the returned information (description, type, creator, tags, stats, versions) and distinguishes from sibling 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?

The description provides context that this tool retrieves a single model by ID and gives an example ID. However, it does not explicitly state when to use this tool versus alternatives like search_models or when not to use it.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, making safety clear. The description adds context about default mature content exclusion, keyless access, and sorting order, which are not in 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?

Three sentences, front-loaded with purpose, followed by examples and defaults. No fluff. Every sentence contributes meaning.

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

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

The tool has no output schema, and the description does not specify return format. However, given the simple browse nature and rich annotations, it mostly covers what is needed. Missing output description is a minor gap.

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

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

Schema description coverage is 100%, so parameters are well-documented. The description adds value through examples but does not provide additional meaning beyond the 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?

The description clearly states the verb 'browse' and resource 'top community models of a given type on Civitai', specifying the sort order (most-downloaded first) and optional base model filter. This distinguishes it from sibling tools like 'search_models' which likely perform searches.

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 examples ('best SDXL checkpoints', 'top Flux LoRAs') implying typical use cases. It also notes defaults (Safe-For-Work, keyless). However, it does not explicitly state when to avoid using this tool or mention alternatives, such as 'search_models'.

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

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

Annotations already declare readOnlyHint and idempotentHint. Description adds details on returned fields (id, type, params, etc.) and defaults to active subscriptions. 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 concise sentences that convey purpose and usage. No unnecessary words, though could be slightly more 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?

For a simple list tool with no output schema, the description covers return fields and primary use case, sufficient for selection.

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

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

Schema coverage is 100%, with a well-described optional boolean parameter. Description does not add additional meaning beyond acknowledging active vs inactive.

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 lists active subscriptions for the caller, with a specific verb and resource. It distinguishes from siblings like subscribe and unsubscribe by focusing on listing.

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

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

Provides explicit use cases: reviewing subscriptions before adding more or finding an id to cancel. Does not mention when not to use or alternatives, but context is clear.

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

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

Description discloses rate limits ('5 per identifier per day'), free usage, and no tool-call quota impact, beyond what annotations provide. Annotations only state non-read-only and non-destructive, so the description adds valuable behavioral context.

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

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

Description is somewhat lengthy but well-structured with purpose first, then usage, then behavioral notes. Each sentence adds value. Could be slightly more concise but no dead weight.

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

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

Given no output schema, the description adequately covers purpose, usage, constraints, and parameter usage. It does not describe submission confirmation, but for a feedback tool this is acceptable.

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

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

Schema coverage is 100%, but description adds meaningful guidance like 'Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt.' This clarifies how to use the message field beyond the schema's plain text definition.

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

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

Description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It specifies the verb (tell/feedback) and the resource (Pipeworx team about tools), distinguishing it from sibling tools that focus on queries or research.

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

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

Description explicitly lists when to use: for bugs, feature/data_gap, praise, etc. It also advises against pasting the end-user's prompt, providing clear guidance on appropriate use versus alternatives.

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

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

Beyond annotations (readOnly, idempotent), description adds caching behavior, data source (CF analytics), privacy assurances (no PII), and response structure. Fully discloses safety and performance characteristics.

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 tight paragraphs with front-loaded main result, clear use cases, and efficient technical details. 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?

Even without output schema, description fully covers return values, data source, caching, and privacy. Satisfies all informational needs for a read-only 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 and enum descriptions. Description adds value by explaining trade-offs between short and long windows, enhancing understanding beyond the schema.

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

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

Clearly states it returns trending tools, packs, and call volume over recent windows. Distinguishes from sibling tools like discover_tools and ask_pipeworx by focusing on what's currently popular.

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

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

Explicitly provides three concrete use cases (discovering hot data sources, confirming canonical choices, checking alignment). Lacks 'when not to use' but the use cases are specific enough to guide 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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. Description adds rich behavioral details: scanning behavior, semantic anchor (Jaccard similarity), partition filter, and fill check callouts. No contradictions.

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

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

Description is very detailed and informative, but somewhat verbose. Could be structured more concisely with bullet points or shorter paragraphs. However, it is front-loaded with main purpose.

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

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

Given tool complexity (arbitrage with modes, checks, filters), description is thorough: explains filling check, partition filter, Jaccard similarity, and references sibling tool for custom sizing. No output schema, so description adequately covers return value structure.

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

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

Schema coverage is 100%, but description adds significant meaning beyond schema: examples for event slugs, explanation of trending_scan with no args, and semantic details for topic mode. Provides actionable context for agent.

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

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

Description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks, and distinguishes two modes (event and topic) with specific use cases. It differentiates from siblings like polymarket_edges and polymarket_edge_tracker.

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

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

Explicit guidance on when to use each parameter: no args for trending_scan, event for specific market, topic for cross-event scan. Recommends event mode for specific markets. Lacks explicit when-not-to-use, but context is clear.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and no destructiveHint. The description adds extensive behavioral context: caching behavior ('Cached 1h at KV level'), model family details, edge calculation (edge_pp_net, kelly_fraction), and diagnostic fields. No contradiction with annotations.

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

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

The description is long but well-structured with capitalized section headers and clear separation of concerns. It is front-loaded with purpose and key model families. However, some repetition and detailed model explanations could be trimmed without losing core value.

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

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

Given the tool's complexity (9 parameters, no output schema, complex logic), the description covers all necessary aspects: response structure (by_segment segments), knobs, diagnostics, caching, and even market-specific notes (fed_candidates). It fully enables an agent to invoke and interpret results.

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

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

Schema covers 100% of parameters, but the description adds significant semantic value beyond the schema: it explains the role of each parameter (e.g., min_edge_pp is 'NET of slippage', slippage_pp default and context, min_partition_leg_kelly vs min_kelly distinction) and provides actionable usage advice.

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-resource combination ('Scan top Polymarket markets and return opportunities') and explicitly distinguishes its purpose from sibling tools by stating it is built for 'what should I bet on today' discovery, avoiding paging through hundreds of markets.

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 (discovery of opportunities) and provides guidance on tradeable-edge knobs (min_liquidity, max_spread_pp, min_partition_leg_kelly) and diagnostics. However, it does not explicitly state when NOT to use it or name alternative tools like polymarket_arbitrage 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?

The description adds substantial behavioral context beyond the annotations (readOnlyHint, openWorldHint, etc.). It details data source (daily snapshots), computed metrics (trend, decay), and limitations (cache-miss dependency, TTL). No contradiction with annotations.

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

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

The description is dense but well-structured, front-loading the core purpose and then detailing response fields and limits. Every sentence adds value, though it is relatively long. Could be slightly more concise but remains efficient.

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

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

With no output schema, the description fully explains the response structure (tracked[], expired[], snapshot_dates[]) with clear field meanings. It also covers query constraints, data limitations, and edge cases (gaps, missing days), providing a complete picture.

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

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

Schema coverage is 100%, and the description summarizes parameters (days, window) but does not add significant new semantics beyond what the schema already provides. The description gives default values and constraints, but this is already in 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 provides edge persistence and decay telemetry from daily snapshots, answering 'how long has this edge existed and is it shrinking?' It distinguishes between fresh and old edges, differentiating from sibling tools that likely provide current edges or arbitrage.

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

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

The description explains when to use the tool (to understand edge persistence and decay) and provides context contrasting fresh vs. old edges. It includes limitations (60-day TTL, start date) but does not explicitly mention when not to use it or list alternative tools for other 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=true, idempotentHint=true, and destructiveHint=false. The description adds rich behavioral context: walks the ladder, returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict, and for baskets details like thin_legs and forced_directional_risk. 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 informative but somewhat long. It is front-loaded with the core purpose and each sentence adds value. Some minor redundancy (e.g., repeating 'default') could be trimmed, but overall it is well-structured and efficient.

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

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

Despite no output schema, the description details all expected return fields (top_of_book, vwap_fill_price, slippage, verdict, etc.) and covers edge cases (thin_legs, forced_directional_risk). It fully explains the tool's behavior for both modes, making it complete for an agent to invoke correctly.

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

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

Schema covers 100% of parameters, but description adds significant meaning: explains single-market vs basket modes, default values for side and size_usd, interpretation of size_usd (max spend vs target proceeds vs settlement notional), and auto side logic. 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 clearly states the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes, using specific verbs and resource references. The purpose is distinct from sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

Explicitly states when to use: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' Also provides context on when not to use (below ~$500) and warns about partial basket fills leading to unhedged positions. No ambiguity.

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

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. Description adds detailed behavioral traits: compatibility_warning conditions (matched_pairs:0 with skipped_cross_type>0 or both venues>5 legs), temporal_alignment implications, skipped_cross_type/subtype counters. No contradictions with annotations.

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

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

Description is thorough but somewhat verbose (~200 words). However, it is well-structured with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS) and front-loaded purpose. Every sentence provides useful information, but could be tightened without losing clarity.

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

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

Despite no output schema, the description explains response structure (leg-by-leg prices, top_spreads_pp), safety fields (compatibility_warning, temporal_alignment), and drop counters. Covers the tool's full complexity, including edge cases like temporal mismatch and shape incompatibility.

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

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

Schema coverage is 100% (all 3 parameters described). Description adds meaning beyond the schema: lists all 10 topic shortcuts, explains override semantics for explicit tickers, and contextualizes usage (e.g., topic auto-fetches matching events).

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 opens with 'Cross-venue spread between Kalshi and Polymarket for the same resolving question,' clearly stating the verb (compute spread), resource (cross-venue pairs), and scope (same question). Distinguishes from siblings like polymarket_arbitrage which focus on single-venue arb.

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

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

Explicitly describes two modes (topic shortcuts vs. explicit tickers) and when each applies. Includes warning that most pre-mapped topics return compatibility warnings, setting realistic expectations. Lacks explicit comparison to alternatives like polymarket_arbitrage, but usage is clear from context.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by explaining scoping to identifier and the listing behavior when key is omitted, but does not disclose the exact return format or error handling.

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

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

The description is concise with four sentences, each adding distinct value. It front-loads the main action and avoids redundancy.

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

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

For a simple tool with one optional parameter and comprehensive annotations, the description covers purpose, usage, parameter behavior, and pairing with siblings. No important gaps are present.

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

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

Schema coverage is 100%, and the description adds semantic meaning beyond the schema: it explains that omitting the key results in listing all saved keys, which is not fully captured in the schema description.

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

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

The description clearly states the tool retrieves a value saved via 'remember' or lists all saved keys when no key is provided. It uses specific verbs and resources, effectively distinguishing from the sibling tools 'remember' and 'forget'.

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

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

Explicitly states when to use: 'look up context the agent stored earlier... without re-deriving it from scratch.' It implies alternatives by mentioning pairing with 'remember' and 'forget', and highlights scoping to an identifier.

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 important behavioral context (mark_read side effect, polling suitability). However, annotations declare idempotentHint=true, but describing mark_read as causing state changes that affect subsequent calls contradicts idempotence. This annotation contradiction undermines transparency.

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

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

The description is three sentences, front-loading the purpose, then detailing fields, filtering, side effect, and alternative access. No redundant information.

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

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

Given 5 parameters, no output schema, and no nested objects, the description covers purpose, return fields, filtering, side effect, polling guidance, and alternative access. It is sufficiently complete for an agent to use correctly.

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

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

Schema coverage is 100% (baseline 3). The description adds value by providing examples (e.g., 'sec_8k' for type, ISO timestamp for since) and explaining the mark_read behavior 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 uses a specific verb ('Pull') and resource ('fired events from your subscription feed'). It clearly states what the tool returns and distinguishes it from siblings by focusing on subscription feed alerts with specific fields (source, citation_uri, payload).

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 filtering by type and since, and recommends polling. It also provides an alternative HTTP endpoint for scripts and dashboards. However, it does not explicitly state when not to use this tool or compare it with siblings like list_subscriptions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description builds on that with concrete behavioral details: fans out to multiple sources, fallback logic, soft-fail for USPTO, and return structure. However, it does not mention potential rate limits or error handling beyond the GDELT→GNews fallback, preventing a perfect score.

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 and front-loaded with the core purpose and example queries. It efficiently conveys multiple data sources, fallback behavior, parameter details, and return format. Each sentence adds value, though the length is slightly higher than minimal, earning a 4 rather than a 5.

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

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

Despite lacking an output schema, the description explains the return format (structured changes[] grouped by source, total_changes count, and citation URIs). It covers multiple sources and their fallback behavior, making it fairly complete for a complex tool. However, it doesn't specify failure modes or timeout behavior, preventing a perfect score.

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

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

Schema coverage is 100%, and the description adds significant value beyond the schema by explaining the `since` parameter's accepted formats (ISO date and relative shorthand with examples like "7d", "30d", "1y"), clarifying that `value` can be a ticker or CIK, and confirming `type` only supports "company". This fully compensates for any omitted parameter details.

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 within a specified time window, with explicit example queries ("What's new with X", "latest on Y"). It also distinguishes from the sibling tool entity_profile by specifying when to use that alternative.

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 this tool versus entity_profile ("Use entity_profile instead when you want the static profile..."). It also explains the fallback behavior between GDELT and GNews and the window parameter usage, giving clear context for appropriate invocation.

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

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

Discloses key-value storage, identifier scoping, and persistence differences between authenticated (permanent) and anonymous (24-hour) sessions. Complements annotations without contradiction.

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

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

Four sentences, each informative and well-sequenced. No redundancy, front-loaded with purpose.

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

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

Complete coverage for a simple key-value tool given annotations and no output schema. All essential aspects addressed.

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

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

Schema already describes key and value well. Description adds practical examples and clarifies value accepts any text, but marginal value beyond schema.

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

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

Description clearly states the tool saves data for reuse across sessions, with specific examples like 'resolved ticker' and 'user preference'. Distinguishes from sibling tools recall and forget.

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

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

Explicitly says when to use ('discover something worth carrying forward') and provides scoping and retention details. Also directs to pair with recall and forget.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds that each call cascades through several lookup endpoints internally, which is valuable behavioral insight beyond what annotations provide.

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

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

The description is moderate length but front-loaded with examples, structured clearly by supported types. Every sentence contributes value, though slight tightening could improve conciseness.

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

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

Despite no output schema, the description fully explains return values for both entity types, including citation URIs. With only two parameters and thorough explanation, the description is contextually complete for a lookup tool.

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

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

Schema coverage is 100%, but the description adds substantial meaning: for company type it explains acceptable inputs (ticker, CIK, name) and output format (ticker + CIK + name + citation URI); for drug type it explains brand/generic input and output (RxCUI + brand + ingredient + citation). 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 opens with clear examples ('What's the ticker for...') and explicitly states the tool resolves a name to a canonical ID. It distinguishes supported types (company, drug) and what each returns, separating it from siblings that likely require already-resolved IDs.

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 advises 'Use FIRST whenever you have a name but need an ID' and notes that using the tool replaces 2-3 manual lookups. While it does not explicitly mention when not to use it or list alternatives, the context is clear for when this tool should be invoked.

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 a safe, idempotent, read-only profile. The description adds detailed output expectations (ranked list with score, confidence, signal density) and special handling of the first entity as 'subject', which goes beyond annotations.

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

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

Four sentences, well-structured: main purpose first, then process, then use case. No wasted 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?

With 4 parameters fully described, no output schema but clear return structure given, annotations covering safety, and siblings providing alternatives, the description is complete for an agent to select and invoke correctly.

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

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

Schema coverage is 100%, but the description adds meaningful context for each parameter: entities explains 'first is subject', models and _apiKey clarify conditions, context describes disambiguation role. This adds value beyond the schema.

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

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

Description clearly states it compares AI visibility across multiple entities, using ai_visibility_check under the hood, and returns a ranked list. It includes an example use case and distinguishes from sibling tools like ai_visibility_check.

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

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

Explicitly states the tool is for competitive AI-marketing audits, implying when to use. The sibling tool ai_visibility_check is mentioned as the building block, making the alternative clear.

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

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

Annotations already declare readOnly, idempotent, non-destructive. The description adds significant behavioral context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, sources_failed will list timeouts, and the return structure is detailed (summary block, per-advisory detail, links, alternative versions). 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 long but well-structured and front-loaded with the purpose. Every sentence contributes information about behavior, scope, or usage. While some minor trimming could be possible, the length is justified by 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?

Despite no output schema, the description thoroughly explains what the tool returns (summary fields, per-advisory detail, links, alternative versions) and handles edge cases like partial failures and timing. This completeness is appropriate for a composite tool with rich 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 baseline is 3. The description adds value beyond schema: for 'package' it mentions scoped packages are accepted, for 'version' it clarifies default behavior (latest when omitted) and provides an example. This is useful extra context.

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

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

The description starts with a clear verb+resource: composite check for adding an npm package. It explicitly states it fans out across deps.dev and bundlephobia, and names specific aspects (license, advisories, version history, bundle size, etc.). It distinguishes from siblings by being the only tool focused on npm dependency 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?

Explicitly states when to use: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also notes the scope (NPM only in v1) and points to alternatives for other ecosystems: '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 declare readOnlyHint, openWorldHint, idempotentHint. Description adds 'Keyless' (no auth needed) and 'Defaults to Safe-For-Work', which are behavioral traits not covered by 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 concise sentences that front-load the core purpose. No redundant wording; every phrase serves a purpose.

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

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

Even without an output schema, the description lists return fields (download stats, base model, creator, trigger words) and covers model types, search modes, and maturity filtering. Fully adequate 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%, so baseline 3. Description reinforces 'Keyless' and provides usage examples that clarify parameter usage, adding value beyond the schema.

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

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

The description uses specific verbs and nouns: 'Search Civitai's catalog of community AI image-generation models' and lists model types. It clearly distinguishes from siblings like 'get_model' (single retrieval) and 'list_by_type' (no 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?

It states default Safe-For-Work and that it's keyless, implying open usage. However, it does not explicitly contrast with siblings like 'get_model' or 'list_by_type', so the guidance is slightly implicit.

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

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

Discloses embedding model (BGE-base-en), chunking strategy (500-char overlapping windows), character limit (200K chars) with truncation flag, and output details (passages with offsets and similarity scores). Annotations already indicate idempotence and read-only, and description adds rich behavioral context.

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

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

Description is concise and front-loaded with the core purpose. Every sentence adds value without redundancy, covering usage, pairing, and technical details in a logical flow.

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

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

Given no output schema, the description adequately explains what the tool returns (top-N passages with offsets and scores). It also covers edge cases (truncation) and provides enough technical detail for an agent to use the tool correctly.

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

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

Schema already covers all parameters with descriptions (100% coverage), so baseline is 3. The description adds value by explaining the purpose of each parameter in context, providing examples for query, and confirming the default for limit.

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

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

The description clearly states 'Semantic search INSIDE a fetched record' with specific verb and resource. It distinguishes from sibling tool ask_pipeworx_grounded by explaining the pairing and difference in grounding strategy.

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 use when record is too large for the prompt, explaining context savings and quoting verification. Also mentions pairing with ask_pipeworx_grounded for grounded responses.

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

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

Annotations indicate readOnlyHint=false, destructiveHint=false, idempotentHint=true. The description adds important behavioral context: OAuth requirement, delivery caps (10/day SMS), phone verification needed. However, idempotentHint=true may conflict with 'create' semantics, though description does not address idempotency.

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. While lengthy, every sentence adds necessary detail about types, delivery, and constraints. Could be slightly more concise but well-organized.

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

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

Given no output schema and 3 parameters with nested objects, the description is remarkably complete. It covers all supported subscription types, delivery options, prerequisites, and even webhook HMAC signing details. No gaps remain.

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

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

Schema coverage is 100%, so baseline 3. The description adds substantial value beyond schema: detailed examples for each type's params, explanation of delivery channels including webhook signing. This exceeds what the schema provides.

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

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

The description clearly states the tool creates a proactive monitoring subscription and returns a subscription ID. It distinguishes from sibling tools like list_subscriptions and unsubscribe by focusing on creation. Supported types and delivery channels are enumerated.

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

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

The description specifies prerequisites (Pipeworx OAuth account), supported types, and delivery options. It implies when to use this tool (proactive monitoring), but does not explicitly state when not to use it or offer alternatives like recent_alerts.

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

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

The description adds significant behavioral context beyond annotations: it returns category-bucketed examples with exact tool+argument shapes, drawn from a live catalog. Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, and the description is fully consistent while enriching the understanding of what the tool produces.

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

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

The description is a single paragraph but is well front-loaded with common queries. It includes a list of categories and usage guidance. While dense, every sentence serves a purpose. Could be slightly more structured with bullet points, but it remains efficient and informative.

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

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

Given no output schema and the tool's role as an onboarding assistant, the description thoroughly explains what is returned (category-bucketed example questions with tool+argument shapes) and when to use it. It also references related meta-tools, making it complete for an agent to understand its use.

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

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

Schema coverage is 100%. The description adds meaning beyond the schema by explaining the effect of omitting topic (full spread) and providing example focus values (finance, pharma, etc.). It also clarifies the optional nature and the outcome of using no arguments.

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 this is an onboarding entry point that returns category-bucketed example questions. It distinguishes from siblings like ask_pipeworx by specifying when to use it first. The verb+resource is specific: 'suggest' (or provide) 'questions' about what Pipeworx can do.

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 FIRST when you do not yet know what Pipeworx can do for you' and explains how to focus with topic parameter. It references meta-tools as alternatives for specific queries, providing clear context for when to use this tool vs others.

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

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

The description adds significant context beyond annotations: it explains ownership, that the record is deactivated (not deleted), and that historical events remain accessible. This aligns with annotations (destructiveHint: false) and adds value.

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

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

The description is two sentences long, front-loaded with the core action, and every word adds value. No extraneous text.

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

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

For a single-parameter, no-output-schema tool, the description fully covers purpose, constraints, and consequences. It is complete given the tool's simplicity.

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

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

Schema coverage is 100%, and the description does not add extra meaning beyond what the schema provides for the 'id' parameter. It mentions the id is returned by 'subscribe', but that is already implied in 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 uses a specific verb 'Cancel' and resource 'subscription by id', clearly distinguishing it from siblings like 'subscribe' and 'list_subscriptions'. It also clarifies the behavioral nuance (deactivation vs deletion).

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

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

The description explicitly states ownership enforcement ('you can only cancel your own subscriptions'), providing a clear condition for use. However, it does not mention when not to use or suggest alternatives like 'unsubscribe_all' or using another 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 (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false) already indicate safe, read-only behavior. The description adds valuable context: it explains the internal routing logic (SEC vs. grounded pipeline), the verification process with verbatim evidence, and the return verdict along with citations. This goes beyond the annotations without contradicting 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 moderately long but efficient; each sentence contributes necessary information. It front-loads with examples and a clear purpose statement, then details the two processing pathways. The structure is logical and easy to parse, though it could be slightly more compact without losing meaning.

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

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

Despite the absence of an output schema, the description fully explains the return fields (verdict, actual value, citation, reasoning). It also covers the two distinct processing paths and the types of claims handled, making the tool's behavior and output completely understandable 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 the baseline is 3. The description adds extra meaning by explaining the tolerance_pct default and its override purpose (e.g., hallucination detection with 1-2%), which provides guidance beyond the schema's description. It also clarifies that the claim is a natural-language factual statement, reinforcing 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: natural-language claim verification against authoritative sources. It provides specific example queries and explains the two processing paths (SEC EDGAR for financial claims, grounded pipeline for others), distinguishing it from sibling tools like deep_research or ask_pipeworx_grounded.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It details the supported claim types and processing flows, but does not explicitly state when not to use the tool or list alternatives. However, the usage context is clear and well-defined.

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