Anapioficeandfire

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable context: the scoring methodology per model, the cost implication for Anthropic (BYO key), and the return structure. 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 four sentences, front-loaded with the core purpose. Each sentence conveys essential information without redundancy or fluff. Structure is logical: purpose, default behavior, optional parameter, output summary.

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 4 parameters, no output schema, and simple structure, the description covers all necessary aspects: input explanation, default configuration, optional key handling, and return format (per-model fields + combined view). No gaps identified.

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

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

Schema coverage is 100%, but the description adds meaning beyond the schema. It clarifies the default model (Workers AI Llama-3.3-70b free), the requirement of _apiKey only for Anthropic, and the purpose of 'context' for disambiguation. This helps an agent understand parameter relationships.

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

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

The description clearly states the tool probes LLMs for knowledge about an entity and scores visibility (0-100). It specifies the verb 'probe', the resource 'LLMs', and the output 'score visibility'. This distinguishes it from siblings like 'ask_pipeworx' and 'scan_competitor_ai_presence' by focusing on brand visibility scoring across multiple 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 explicit use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It explains default model and how to add Anthropic with BYO key. While it doesn't explicitly mention when not to use or alternatives, the context is sufficient for appropriate 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 indicate readOnly, openWorld, idempotent, non-destructive. The description adds behavioral details: routes questions to 3,436 tools, fills arguments, returns structured answer with citation URIs. 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 appropriately sized, front-loaded with the key instruction, and each sentence adds meaningful information. Slightly verbose but justified given 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?

The description covers usage well with examples and citation behavior, but lacks details on error handling or return structure beyond 'structured answer with citation URIs'. Adequate but could be more complete given no output schema.

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

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

Schema coverage is 100% with 6 aliases for the same required parameter. The description provides extensive example questions and explains the natural language input, adding value beyond the schema's parameter descriptions.

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

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

The description clearly states the tool's purpose: to answer factual questions using authoritative structured data with citations. It distinguishes from siblings by explicitly preferring it over web search for specific domains and providing concrete examples.

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

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

The description explicitly states when to prefer this tool ('PREFER OVER WEB SEARCH for questions about...') and provides common query patterns ('what is', 'look up', etc.). It lacks explicit when-not-to-use conditions but gives clear context for appropriate use.

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

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

Annotations already mark it as safe and non-destructive; the description adds detailed refusal reasons (not_in_source, no_tool_match, etc.) and confirms it only uses tool result data, providing full transparency beyond annotations.

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

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

The description is a single paragraph but well-structured, front-loading purpose and behavior, then use cases and trade-offs. Every sentence adds value, though it could be slightly more concise.

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

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

Despite having no output schema, the description fully specifies the return shape (success and refusal modes). Combined with rich annotations and clear sibling differentiation, the tool is completely contextualized for agent usage.

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

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

Schema coverage is 100% with each parameter described as an alias for 'question'. The description reiterates 'your question in natural language' without adding new meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states it is a 'hallucination-resistant answer mode for high-stakes reads' and differentiates from the sibling 'ask_pipeworx' by specifying it extracts answers only from tool results, preventing invention of facts.

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

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

Explicitly tells when to use ('when answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'), along with a cost trade-off (extra LLM call).

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, idempotentHint, destructiveHint=false) are consistent with a research tool. The description adds extensive behavioral context: resolver contract, fan-out examples, response shapes, parent event extractor, news fallback, safety mechanisms (low-confidence short-circuit, closed market handling, spread warnings). 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: purpose first, then classifiers, fan-out examples, response shapes, resolver contract, parent event, news fields, safety. Every section adds value given the tool's complexity, though some redundancy exists (resolver contract mentioned twice).

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

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

No output schema, so the description must explain return fields thoroughly. It does: result.market fields, result.analysis with model probability and edge warnings, result.evidence keyed by source, resolver contract with match confidence and alternatives, parent event structure, news fallback fields, and blocking statuses. Very complete for the complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond the schema: it restates the enum meanings for depth but doesn't add new parameter semantics. The market parameter's acceptance of multiple formats is already 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 researches a Polymarket bet by pulling Pipeworx data. It specifies inputs (slug, URL, question text) and outputs (evidence packet + market-vs-model comparison). It distinguishes from siblings like polymarket_edges and polymarket_arbitrage by being the general research tool.

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

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

The description explicitly lists use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and explains when the tool returns blocking statuses (low_confidence_match, market_closed_or_inactive, illiquid_wide_spread). It advises inspecting market_match_confidence before using analysis. However, it does not explicitly compare to siblings or say when not to use.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, fully covering safety traits. The description adds no behavioral context beyond the basic purpose, missing opportunities to clarify traits like openWorldHint (e.g., data source) or response behavior.

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

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

The description is extremely concise at three words, containing no fluff. However, it is overly brief, risking ambiguity. It earns a high score for efficiency but loses a point for potential under-specification.

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

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

For a simple retrieval tool with one required parameter and an output schema, the description is minimally adequate. It conveys the core function, but lacks details about the return structure or constraints. Given the low complexity and rich annotations, it is acceptable but not exemplary.

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 0%, putting the burden on the description to explain the 'id' parameter. The description only mentions 'by id' without specifying type, range, format, or examples, providing minimal added meaning beyond the parameter name.

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 'Single book by id' clearly indicates the tool retrieves a single book resource by its identifier. It differentiates from 'books' (plural list) and other sibling tools like 'character' and 'characters', providing a specific verb+resource structure, though the verb is implied rather than explicit.

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

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

No guidance is provided on when to use this tool versus alternatives such as 'books' for listing all books, or other sibling tools. The description lacks context on prerequisites, filters, or exclusions, leaving the agent to infer usage from the name alone.

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, but the description adds no behavioral context about pagination, filtering behavior, or result limits. It does not contradict annotations.

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

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

Extremely brief (3 words) but lacks structure and detail. Under-specification rather than efficient 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?

With 5 parameters and an output schema, the description is incomplete. It omits pagination, date filtering, and other context needed for effective use.

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

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

Schema description coverage is 0%, and the description does not explain any parameter semantics (e.g., name filters by name, page/pageSize for pagination, date range). Schema examples partially compensate but are not part of the 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 states the action (search/list) and resource (books), but is vague and does not differentiate from the sibling 'book' tool, which likely handles individual book details.

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

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

No guidance on when to use this tool versus alternatives like 'book' or other search tools. No when-not-to-use scenarios are provided.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. Description adds no additional behavioral context beyond what annotations provide. Not contradictory.

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?

Extremely concise single phrase. Every word earns its place. 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?

Tool is simple with one required param, no nested objects, and rich annotations including output schema. Description is sufficient for basic retrieval, though could mention typical output or error conditions.

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 0% and description only says 'by id' without explaining what the ID represents (e.g., database identifier, external ID). Fails to add meaningful semantics for the single parameter.

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

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

Description 'Single character by id.' clearly states the verb (retrieve) and resource (character) with identifier. Distinguishes from sibling 'characters' (plural) which likely lists multiple.

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?

Implied usage when you have a character ID, but no explicit guidance on when to use this versus 'characters' or other entity tools. No exclusion criteria or alternatives mentioned.

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 not destructive. The description adds no behavioral context beyond this, such as what data is returned, pagination, or limitations.

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

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

The description is extremely concise but at the cost of completeness. It is front-loaded but fails to provide necessary details about the tool's behavior, parameters, or usage.

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

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

Given the tool has 8 parameters, zero schema descriptions, and a sibling tool ('character'), the description is severely incomplete. It does not explain what characters are, how pagination works, or how it differs from related tools.

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

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

Input schema has 8 parameters with 0% description coverage. The description does not explain any parameter semantics, leaving the agent to infer meaning from parameter names (e.g., 'name', 'gender', 'culture'). This is insufficient for accurate tool invocation.

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

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

The description clearly states the action (search/list) and resource (characters), but does not distinguish from the sibling tool 'character' (singular) which likely retrieves a single character. This is clear but lacks sibling differentiation.

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

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

No guidance is provided on when to use this tool versus alternatives like 'character', 'houses', or other search tools. The description does not specify context, prerequisites, or 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, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral details: parallel call efficiency, handling of off-calendar fiscal years, sorting by primary metric, and citation URIs. No contradictions.

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

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

The description is comprehensive but slightly verbose (4-5 sentences). It is front-loaded with trigger phrases and purpose. Every sentence adds value, but some details could be more tightly integrated.

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 explains the return format (paired data + citation URIs) and sorting behavior, compensating for the lack of an output schema. It covers data sources and handling of fiscal years but omits error handling or edge cases like unknown tickers.

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

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

Schema coverage is 100% with both parameters described. The description adds meaning beyond the schema by explaining the enum values (company vs drug) with specific data sources, and providing examples and bound constraints for the values array.

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

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

The description clearly states the tool does side-by-side comparison of 2–5 companies or drugs in one parallel call, with specific data metrics for each type. It distinguishes from sequential single-entity lookups (e.g., entity_profile) by explicitly preferring this tool for comparisons.

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 to prefer this tool over sequential lookups when comparing entities, and gives example query phrases. However, it does not explicitly state when not to use it (e.g., if only one entity is needed).

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds that the tool returns top-N relevant tools with full input schemas and curated examples, ready to call directly. This explains the return format and behavior 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 three sentences, front-loaded with the core action, and each sentence adds essential information. No wasted words, and the structure is logical: purpose, then usage context, then output 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 purpose and lack of output schema, the description completely explains what the tool does, when to use it, and what it returns (top-N tools with schemas). The input schema is well-covered, and no additional context is needed.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds value by explaining that the query parameter accepts natural language descriptions and provides example queries, as well as listing the domains it can search.

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 ('Find tools') and resource ('by describing the data or task'), clearly distinguishing it from sibling tools that are more specific. It states the tool's purpose as a discovery mechanism for available tools.

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

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

Explicitly says to 'Call this FIRST when you have many tools available and want to see the option set', providing clear when-to-use guidance. It also implies when not to use (when you already know the tool) by positioning itself as a discovery 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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds significant behavioral context: fans out across multiple sources, notes patents API sunset (May 2025) with soft-fail behavior, news fallback (GDELT→GNews), and specific return fields (URIs, sorted fundamentals). 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?

Description is a single paragraph that front-loads usage examples and key behavior. It is moderately concise but contains some repetition ('names not supported' mentioned twice). Structure is logical but could be slightly tighter.

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

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

Given two required parameters and no output schema, the description adequately details return fields (CIK, filings with URIs, fundamentals, patents, news, LEI) and fallback behavior. Missing details on error handling beyond soft-fail, but overall sufficient for a profile 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 descriptive enums and examples. Description adds value by providing concrete examples ('AAPL', '0000320193'), reiterating that names are not supported, and clarifying that only 'company' is currently supported. This goes beyond the schema but is not extensive.

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

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

Description clearly states it provides a 'full cross-source profile of a US public company in ONE parallel call' with specific example queries like 'Tell me about X' and 'give me the rundown on NVDA'. It distinguishes from siblings by explicitly preferring this over chaining individual lookups, and lists the returned data (CIK, filings, fundamentals, patents, news, LEI).

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

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

Explicitly advises 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view'. Also states when not to use: 'names not supported (use resolve_entity first if you only have a name)', providing a direct alternative tool name.

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

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

Annotations already provide destructiveHint=true. Description adds 'Delete' which aligns, but does not add deeper behavioral context like error handling or confirmation.

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

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

Two sentences, no fluff, front-loaded with action. 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?

Adequate for a simple one-parameter tool with annotations. Lacks details on behavior when key doesn't exist, but overall complete given no output schema.

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

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

Schema coverage is 100% with description 'Memory key to delete'. Description does not add extra meaning beyond schema.

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

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

Clearly states 'Delete a previously stored memory by key', specifying the action and resource. Distinguishes from siblings like remember and recall.

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

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

Explicitly states use cases: 'Use when context is stale, the task is done, or you want to clear sensitive data'. Also pairs with related tools remember and recall.

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

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

Annotations already indicate readOnly, idempotent, non-destructive behavior. Description adds process details: fetches page, extracts title/description/links, emits standard markdown. 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 with a bulleted list of use cases. Every sentence adds value, front-loaded with purpose. No wasted words.

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

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

Given simple tool with two parameters and no output schema, description fully explains input, output format, and use cases. No gaps.

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

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

Schema has 100% coverage on both parameters. Description mentions 'key links' which aligns with max_links parameter, and adds context that output is a text blob for site-root. Adds useful nuance beyond schema.

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

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

Description clearly states it generates an llms.txt file for AI crawler indexing, specifying action, output, and purpose. Distinct from siblings which focus on other tasks like AI visibility checks or betting.

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

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

Explicitly lists three use cases (client indexing, personal projects, competitor auditing), providing clear context. Lacks explicit when-not-to-use or alternatives, but the use cases are sufficient.

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

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

Annotations already provide readOnlyHint, destructiveHint, and idempotentHint. The description adds no extra behavioral context beyond 'by id'. With annotations, minimal additional disclosure is acceptable, but no mention of error cases or response structure.

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

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

Extremely concise: 4 words, one sentence, no unnecessary information. Every word earns its place.

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

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

For a simple retrieval tool with one parameter, annotations, and an output schema, the description provides sufficient context. It communicates the core action without needing elaboration.

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 0%, but the single parameter 'id' is intuitive from its name and required status. The description says 'by id' reinforcing its role, but does not add format or constraints beyond the schema example.

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 "Single house by id." clearly states the tool retrieves a specific house entity using an identifier. It distinguishes from sibling tool 'houses' which likely lists multiple houses, providing specific verb+resource+scope.

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

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

No guidance on when to use this tool versus alternatives like 'houses' or other entity tools. Does not mention prerequisites or context for 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?

The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, fully covering safety and behavioral traits. The description adds no extra behavioral context, but given the rich annotations, this is minimally acceptable.

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 too brief (two words) and under-specified. While concise, it sacrifices necessary detail. Every sentence should earn its place, but here there is only one short phrase that does not adequately describe the tool's purpose or usage.

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

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

Given the tool has 9 parameters (all optional), an output schema, and no nested objects, the description is insufficient. It fails to set context for what 'houses' are, what filtering options exist, or what the response contains. The output schema partially compensates, but the description should provide a high-level overview.

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 0%, meaning none of the 9 parameters have descriptions. The tool description 'Search/list houses' provides no parameter semantics whatsoever. The agent has no guidance on how to use parameters like name, region, or boolean filters.

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 is for searching/listings houses, which is a clear verb+resource combination. However, it does not differentiate from the sibling 'house' tool, which likely retrieves a single house. The context suggests these are fictional houses, but the description lacks explicit context.

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

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

No guidance is provided on when to use this tool versus alternatives like 'house' or other search tools. There are no prerequisites, exclusions, or situational cues. The agent must infer usage from the name alone.

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

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

Annotations already provide readOnlyHint, idempotentHint, etc. Description adds context by listing returned fields and mentioning default filtering for active subscriptions, exceeding what annotations alone convey.

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 with front-loaded purpose. Every sentence adds value: purpose + return fields, then usage guidance. No redundancy or filler.

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

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

Given low complexity, schema coverage, and annotations, the description is largely complete. It lists return fields and usage context. Could explicitly state it's scoped to the caller, but 'caller's' already implies that.

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

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

Schema coverage is 100% with a clear description for the single parameter. The description adds minimal extra value beyond stating the default behavior (active only), which is already implied.

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

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

The description clearly states 'List the caller's active subscriptions' with a specific verb and resource. It distinguishes from sibling tools 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 guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This implies when to use vs. subscribe/unsubscribe, though no explicit alternatives are named.

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

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

Annotations are neutral (no hints set). The description adds valuable context: the tool is free, doesn't count against tool-call quota, is rate-limited, and signals affect the roadmap. 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 slightly long but well-structured: it starts with the core action, then usage conditions, then constraints. Every sentence adds value, though a very minor trim 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?

Given the tool's complexity (3 parameters, 2 required, nested object, no output schema), the description covers all necessary aspects: feedback types, message content guidance, optional context, rate limiting, and quota. It is complete for a feedback 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 description coverage is 100%, but the description adds meaning beyond the schema by explaining each parameter's purpose with examples (e.g., type enum meanings, context fields as optional structured info, message specifics). This helps the agent understand how to fill them correctly.

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 is for giving feedback to the Pipeworx team about bugs, features, data gaps, or praise. It uses specific verbs ('tell', 'use when') and distinguishes itself from sibling tools which are data-fetching or analysis tools.

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

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

The description explicitly lists when to use the tool for different feedback types (bug, feature/data_gap, praise) and includes constraints like rate limiting and quota exemption. However, it does not explicitly state when not to use it, but the positive guidance is strong.

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

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

Annotations already indicate readOnly, idempotent, not destructive. The description adds valuable context: derived from CF analytics-engine, no PII, caching behavior (5min-1h), and self-aggregating signal. 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 three concise sentences plus a bulleted list of use cases. Front-loaded with main purpose, then usage guidance, then technical details. Every sentence adds value, no fluff.

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

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

Despite no output schema, the description explains return values (top tools, packs, call volume), source (no PII), and caching. Covers all essential aspects for an agent to decide and invoke correctly.

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

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

Schema covers 100% of parameters with a description explaining the enum values and trade-off (short windows = hot, long = steady-state). The overall description reinforces this. Adds value beyond schema by explaining the default and usage context.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a recent window. It distinguishes itself from siblings like ask_pipeworx and discover_tools by focusing on trending data and providing 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?

The description explicitly lists three use cases (discovering hot data sources, confirming canonical tools, aligning with agent needs). While not explicitly stating when not to use, the use cases provide clear context. Lacks direct comparison to siblings but still guides usage well.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds significant behavioral context: the two operational modes, the monotonicity and partition-sum checks, the semantic anchor (Jaccard similarity ≥0.30), the partition filter (placeholder removal), and the response structure. It also notes conditions like skipped_low_similarity and placeholder fraction limits. No contradictions with annotations.

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

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

The description is front-loaded with a strong requirement warning. It is logically structured: requirement, mode explanations, semantic anchor, partition filter, then response. While somewhat lengthy due to the tool's complexity, every sentence serves a purpose and there is minimal redundancy.

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

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

Given the moderate complexity (two modes, specific checks), excellent schema coverage, and no output schema, the description is complete. It explains the return structure (opportunities[] with fields, partition_check in event mode). It also covers failure conditions (call with no args fails, low similarity, placeholder filter). This is sufficient for an agent to understand and use the tool effectively.

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

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

Schema description coverage is 100%, with both parameters having clear descriptions. The tool description adds value by explaining the required condition ('REQUIRES one of event or topic'), elaborates on each mode's behavior, and provides examples. However, it does not introduce new parameter constraints not 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 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It specifies the verb 'find', resource 'arbitrage opportunities on Polymarket', and methodology. While sibling tools exist (e.g., polymarket_edges, polymarket_kalshi_spread), this tool's unique focus on arbitrage makes it distinct, and the description effectively communicates its 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 explicitly requires one of 'event' or 'topic' and warns that calling with no args fails. It explains when to use each mode: event for a specific market, topic for cross-event scanning. It also provides practical examples and notes that cross-event mode catches patterns single-event misses. However, it does not explicitly compare against sibling tools or state when not to use this tool.

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

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

The description richly expands on annotations by detailing the three model families, edge calculations (e.g., lognormal barrier, GDELT ratio, partition_overround), filtering logic (e.g., placeholder-slug filter, min_partition_leg_kelly), caching behavior ('Cached 1h at the KV level'), and diagnostics. No contradiction with annotations (readOnlyHint, idempotentHint, etc.).

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 excessively long and verbose, diving into technical details of model families and edge formulas. While front-loaded with purpose, many sentences are dense and could be summarized for an AI agent. Not every sentence earns its place; conciseness is sacrificed for completeness.

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

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

Given no output schema, the description thoroughly explains the return structure (by_segment, fed_candidates, _diagnostics) and provides context for why segments might be empty. It covers caching, filter skips, and edge components, making it fully self-contained for an AI agent.

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

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

Schema coverage is 100% for all 9 parameters, and the description adds value by grouping parameters as 'TRADEABLE-EDGE KNOBS' and explaining how they interact (e.g., min_liquidity/max_spread_pp drop unrealizable opportunities). It does not simply repeat schema descriptions.

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

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

The description clearly states the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It specifies the resource (Polymarket markets) and the action (returning opportunities from Pipeworx data disagreements), and distinguishes it from typical market scanning by focusing on edge detection without 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 provides context with 'Built for "what should I bet on today"' and details the output structure and filtering knobs, but does not explicitly exclude when not to use this tool versus alternatives like polymarket_arbitrage. The guidance is clear enough for an AI agent to understand typical use cases.

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

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

The description adds substantial behavioral context beyond annotations: safety fields (compatibility_warning, temporal_alignment), explanation of skipped cross types, and conditions when spreads are meaningless. 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 thorough and well-structured with clear sections and bolded keywords. It is slightly verbose but each sentence adds value. Could be tightened in some areas.

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

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

Given the complexity of cross-venue spreads and no output schema, the description is complete: it explains response format (legs, spread, safety fields) and covers edge cases (temporal misalignment, non-equivalent bet shapes).

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining that explicit parameters override topic-mapped sides and providing example values for topic. It clarifies the interaction between modes.

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: computing cross-venue spreads between Kalshi and Polymarket for the same resolving question. It distinguishes two modes (topic shortcuts and explicit parameters) and explains the core value (detecting price differences).

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 (when bet shapes are equivalent) and warns about compatibility issues, temporal alignment, and that most pre-mapped topics are not tradeable. However, it does not explicitly contrast with siblings like polymarket_arbitrage.

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

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

Annotations provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds behavioral context about scoping and pairing with remember/forget, which goes beyond annotations. No contradictions.

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

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

Three sentences, front-loaded with purpose, all information is relevant and non-redundant. No unnecessary words.

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

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

Given the simplicity of the tool (1 optional parameter, no output schema), the description fully covers purpose, usage, scoping, and relationships with sibling tools. 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% with a description for the 'key' parameter. Description adds meaning by explaining that omitting the key lists all keys and provides an example, 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?

Description uses specific verbs ('retrieve', 'list') and resources ('value previously saved via remember', 'all saved keys'), and clearly distinguishes from sibling tools like 'remember' and 'forget'.

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

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

Description states when to use (look up context stored earlier) and scope (anonymous IP, BYO key hash, account ID). It implicitly pairs with remember/forget but does not explicitly state when not to use or compare with other sibling tools.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. Description adds value by detailing mark_read behavior (flagging events read so next call shows newer ones) and noting the same feed is available via HTTP. No contradictions.

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

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

Four sentences, front-loaded with main purpose, no redundancy. Each sentence adds necessary context (return fields, filters, side effect, alternative access).

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 fields (source, citation_uri, raw payload). Also mentions alternative HTTP endpoint. Lacks details on pagination or error handling, but sufficient for typical polling use case.

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 contextual value for 'mark_read' by explaining its impact on subsequent calls. Other parameters (type, since) are mentioned but not deeply expanded, which is acceptable given schema descriptions.

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

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

Description clearly states 'Pull fired events from your subscription feed' and details what is returned (source, citation_uri, raw payload). It specifies filtering options and a side effect (mark_read), distinguishing it from other tools like 'recent_changes'.

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

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

No explicit when-to-use or when-not-to-use compared to sibling tools. Mentions an HTTP endpoint as an alternative for scripts/dashboards, but does not guide agent on tool selection among siblings.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds valuable behavioral context: fans out to multiple APIs (SEC, GDELT/GNews, USPTO), explains fallback logic (GDELT preferred, GNews on rate-limit/5xx), mentions USPTO soft-fail, and describes return structure (changes grouped by source, total_changes count, citation URIs). This enriches transparency beyond annotations.

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

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

The description is somewhat long but each sentence adds value. It is front-loaded with example queries. No redundant information. Could be slightly more concise but overall efficient.

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

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

Despite no output schema, the description explicitly states the return format: 'structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs'. It also covers fallbacks and soft-fail scenarios. For a complex tool with multiple data sources, this is complete and self-contained.

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

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

Input schema covers all 3 parameters with descriptions (100% coverage). The description adds value by providing usage examples for 'since' (relative shorthand recommended) and 'value' (ticker or CIK). 'type' is limited but clarified. This is above baseline but not exceptional.

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

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

The description clearly states the tool provides a change feed for a company over a time window, using multiple examples like 'what's new with X'. It distinguishes from sibling tool 'entity_profile' by contrasting dynamic changes vs static profile.

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

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

The description explicitly advises to use 'entity_profile' when wanting a static profile. It also provides guidance on 'since' parameter format and typical usage ('30d' or '1m'). However, it does not explicitly state when not to use this tool beyond the static profile scenario.

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 idempotentHint=true and destructiveHint=false. The description adds that data is scoped by identifier, with persistence differences for authenticated vs anonymous users, which is useful behavioral context beyond the annotations.

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

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

The description is concise at about 3 sentences, front-loaded with purpose, and every sentence earns its place with no fluff.

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

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

Given the simplicity of the tool (key-value store), the description is complete. It mentions scoping, persistence, and pairing with recall/forget. No output schema but write tools often omit return details.

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

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

Schema provides 100% coverage with descriptions for key and value. The description adds naming conventions and examples for keys, adding meaning beyond the schema.

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

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

The description clearly states 'Save data the agent will need to reuse later' and provides specific examples like resolved ticker, target address, user preference. It distinguishes from siblings recall and forget by explicitly pairing with them.

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

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

The description says 'Use when you discover something worth carrying forward' and mentions pairing with recall and forget. It gives clear context but does not explicitly state when not to use or alternatives beyond the trio.

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

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

The description discloses that the tool 'cascades through several lookup endpoints internally,' adding behavioral context beyond annotations. However, it does not mention failure modes, rate limits, or return format edge cases, which would be helpful for full transparency.

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

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

The description is concise yet information-dense, starting with real-world query examples and then clearly listing supported types with their specific outputs. Every sentence serves a purpose without redundancy.

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

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

Given the lack of an output schema, the description thoroughly explains return values for both entity types, including citation URIs. It covers all needed context: when to use, what input is accepted, what output to expect, and how it simplifies multiple lookups.

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 significant meaning by detailing what each type returns (e.g., company returns ticker, CIK, company_name, citation URI) and providing examples of acceptable values. This goes well beyond the schema's brief descriptions.

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

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

The description clearly states the tool resolves user-spoken names to canonical/official identifiers, with specific examples like ticker, CIK, and RxCUI. It distinguishes itself from siblings by emphasizing 'use FIRST whenever you have a name but need an ID,' making its purpose unambiguous.

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

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

The description explicitly advises 'Use FIRST whenever you have a name but need an ID,' providing strong usage context. It also explains that each call replaces 2-3 manual lookups, implicitly guiding when to prefer this over multiple other tools.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds value by explaining the internal process (calls ai_visibility_check for each entity, ranks results) and the return format (score, confidence, signal density). No contradictions.

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

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

Three short sentences: first states purpose and process, second provides usage example, third specifies return content. No fluff, front-loaded, every sentence earns its place.

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

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

Given no output schema, the description adequately explains what is returned (ranked list with score, confidence, signal density per entity). It also covers all parameters implicitly through the process description. Sufficient for an agent to understand the tool's full behavior.

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

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

Schema coverage is 100% with descriptions. The description adds extra meaning: first entity is treated as 'subject' for narrative, and notes that omitting models defaults to just workers-ai. These nuances aid correct parameter usage.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, uses specific actions (probes with ai_visibility_check, ranks, surfaces most/least recognized), and distinguishes from sibling tool ai_visibility_check which is for single entities.

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

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

The description provides a concrete use case (competitive AI-marketing audits) and implicitly contrasts with single-entity check via mention of ai_visibility_check. However, it does not explicitly state when not to use or list alternatives.

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

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

Annotations already set readOnly, idempotent, non-destructive. The description adds crucial behavioral details: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. It also outlines the return structure (summary block, advisories, links, alternatives).

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

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

The description is relatively long but front-loaded with the core purpose. Every sentence adds unique information. Could be slightly trimmed but remains 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?

Given the composite nature (two external APIs), lack of output schema, and potential for partial failures, the description is thorough. It covers inputs, outputs, failure handling, and performance nuance. An agent can fully understand what to expect.

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

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

Schema coverage is 100% with both parameters documented. The description adds minor value: clarifies that package accepts scoped packages and version defaults to latest. This is helpful but not substantial 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 opens with a clear purpose: a composite check for adding an npm package. It names the exact resources (deps.dev and bundlephobia) and specifies the scope (NPM only), distinguishing it from any sibling tools.

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

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

Explicitly states when to use: when an agent asks about safety, popularity, size, or cost of adding a package. Also provides exclusion guidance: for non-NPM ecosystems, use deps.dev:version directly. 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?

Discloses embedding model (BGE-base-en), chunking (500-char overlapping windows), character limits (200K chars with truncation flag), and return fields (offsets, similarity scores). Annotations already declare idempotent and read-only hints, and description adds contextual behavior.

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

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

Two sentences front-loaded with purpose and usage, followed by concise technical details. 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?

Explains mechanics (embeddings, chunking, truncation, offsets) and pairs with ask_pipeworx_grounded. Despite missing output schema, description sufficiently covers return format. Annotations provide additional safety context.

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

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

Schema covers all 3 parameters with descriptions. Description adds no further parameter-specific meaning beyond schema; baseline 3 due to high schema coverage.

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, and distinguishes from siblings by noting it is for when the record is too large, pairing with 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?

Explicitly states when to use ('Use when the record is too big to cram into the prompt') and mentions a complementary tool, but does not explicitly state when not to use.

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

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

Beyond annotations (readOnlyHint=false, idempotentHint=true), the description adds behavioral details: requires OAuth, returns subscription ID, delivery channel limits, and a warning about patent_grant matching behavior. It does not explicitly confirm idempotency but adds significant 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?

The description is front-loaded with core purpose and prerequisites, followed by detailed type and delivery information. While long, it is well-structured and each sentence adds value. Could be slightly more concise but remains clear.

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

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

Given no output schema, the description explains return value, covers all types, delivery channels, and prerequisites. It lacks explicit mention of idempotency behavior when creating an existing subscription, but overall is quite complete.

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

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

The description enriches the schema with concrete examples for each type's params, explains delivery constraints (SMS cap, verification), and provides warnings for patent_grant. This adds substantial meaning beyond the schema's parameter descriptions.

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

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

The description clearly states the tool creates a proactive monitoring subscription to a live-data event stream and returns a subscription ID. It distinguishes from siblings like 'unsubscribe' and 'list_subscriptions' by specifying the creation action and providing detailed type examples.

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 prerequisites (OAuth account), supported subscription types with example parameters, and delivery channel constraints (SMS cap, phone verification). While it doesn't explicitly list when not to use, the context of sibling tools like 'recent_alerts' for retrieval implies appropriate usage.

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

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

The description adds behavioral context beyond annotations: it explains that the row is deactivated (not deleted) and historical events remain available via 'recent_alerts'. This complements the annotations (destructiveHint=false) and provides useful transparency. 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 concise with two sentences, front-loading key information (action, ownership, effect). Every sentence adds value without redundancy.

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

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

For a simple one-parameter tool with no output schema, the description covers the essential aspects: action, ownership constraint, and behavioral impact. It references the related sibling 'recent_alerts' for context, making it fairly complete.

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

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

Schema coverage is 100% and the schema describes the 'id' parameter as 'Subscription id (uuid) returned by subscribe.' The description mentions 'by id' but adds no additional semantics beyond what the schema provides. Baseline score of 3 is appropriate.

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

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

The description clearly states the action 'Cancel a subscription by id' and the resource (subscription via id). However, it does not explicitly differentiate from sibling tools like 'subscribe' or 'list_subscriptions', which are closely related.

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

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

The description includes a key usage condition: 'Ownership is enforced — you can only cancel your own subscriptions.' However, it lacks guidance on when not to use this tool or mention of alternatives like 'recent_alerts' for viewing historical data.

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

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

Annotations already indicate safe, read-only behavior. The description adds significant behavioral detail: the verdict types returned, structured format, actual value with pipeworx:// citation, percent delta, and an efficiency claim (replaces 4–6 sequential calls). No contradictions.

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

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

The description is moderately long but well-structured, front-loading common usage patterns and domain. Every sentence adds value; some minor redundancy in listing patterns could be trimmed.

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 simple input (single claim) and no output schema, the description adequately covers what the tool does, what it returns (verdict types, citation, delta), and its efficiency. Minor gap: does not explain the return format in detail, but the listed elements suffice.

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

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

The single 'claim' parameter has 100% schema coverage with a clear natural-language example. The description provides usage pattern examples but does not add meaning beyond what the schema already conveys. 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 uses multiple operational verbs ('verify', 'fact check', 'confirm or refute') and clearly identifies the specific domain (company-financial claims via SEC EDGAR + XBRL). It distinguishes itself from sibling tools, none of which perform claim verification, making the purpose unambiguous.

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

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

The description explicitly states when to use the tool ('whenever the agent needs to check whether something a user said is factually correct') and scopes the domain. It does not explicitly list when not to use, but the domain restriction is clear enough.

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