nvd

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by disclosing the default free model (Workers AI), the requirement for a BYO key for Anthropic (with cost implications passed to user), and the returned data shape (per-model score, confidence, signals, raw_response + combined view).

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, efficiently covering purpose, usage, and output. 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?

For a tool with 4 parameters and no output schema, the description provides a comprehensive view: it explains the scoring range, model options, cost model, and example return fields. It does not cover error handling or rate limits, but these are less critical given the non-destructive and idempotent nature.

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

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

With 100% schema description coverage, the schema already explains each parameter adequately. The description adds context about the default model and the purpose of the `context` parameter (disambiguation), but does not significantly enhance parameter meaning beyond the schema.

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

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

The description clearly states the tool probes LLMs for what they know about a business/brand/product/topic and scores visibility (0-100) per model, using specific verbs and resources. It distinguishes from siblings like scan_competitor_ai_presence by focusing on quantitative scoring and multi-model probing, though explicit differentiation is lacking.

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

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

The description provides clear use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to use the Anthropic model (requires _apiKey). It lacks explicit when-not-to-use guidance or alternative tool references 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?

No annotations are provided, so the description carries the full burden. It discloses that Pipeworx 'picks the right tool, fills the arguments, and returns the result,' which adds useful behavioral context about automation. However, it doesn't cover potential limitations like rate limits, error handling, or data source reliability, leaving gaps for a tool with no annotation coverage.

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 and front-loaded, with the core purpose stated first, followed by behavioral details and concrete examples. Every sentence earns its place by clarifying functionality without redundancy, making it efficient and easy to parse.

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

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

Given the tool's complexity (natural language querying with backend automation), no annotations, and no output schema, the description does a good job explaining the core behavior and usage. It covers the input approach and process flow but lacks details on output format or error cases, which could be improved for full completeness.

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

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

Schema description coverage is 100%, with the single parameter 'question' well-documented in the schema. The description adds minimal value beyond the schema by emphasizing 'plain English' and 'natural language' in the examples, but doesn't provide additional syntax or format details. Baseline 3 is appropriate given the 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 the tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer from data source'), and distinguishes from siblings by emphasizing natural language querying without needing to browse tools or learn schemas. The examples further clarify the scope.

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

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

The description provides clear context on when to use this tool: for asking questions in plain English to get answers from data sources, without needing to browse other tools. It implicitly distinguishes from siblings like 'get_cve' or 'search_cves' by not requiring specific tool knowledge. However, it lacks explicit when-not-to-use guidance or named alternatives.

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

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

Annotations already declare readOnlyHint and idempotentHint; description adds specific behavior (extra LLM call, refusal reasons, extraction-only). 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 long but each sentence adds value, front-loaded with purpose. Minor redundancy but not wasteful.

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

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

Despite no output schema, description fully specifies return structure, refusal reasons, and cost tradeoff, making it complete for a complex tool.

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

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

Schema coverage is 100% with all aliases documented, so description adds little beyond what schema provides. Acceptable 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 is a 'hallucination-resistant answer mode for high-stakes reads' and contrasts with sibling ask_pipeworx, establishing distinct 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?

Explicitly advises use when answers will be quoted/cited/acted on and the agent must not invent facts, and recommends prefering ask_pipeworx for casual lookups.

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

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

The description goes far beyond the annotations (readOnlyHint, idempotentHint, etc.) by detailing safety mechanisms (low-confidence resolution short-circuit, closed market handling), resolver contract, parent event extractor, news field fallback, and response shapes. It adds significant context about blocking paths and edge cases.

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

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

The description is quite long but is well-structured with clear sections (e.g., CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES). Every sentence adds necessary information for correct usage. Some redundancy exists, but given the tool's complexity, the length is justified.

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

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

Given the tool's complexity (multiple classifiers, fan-out logic, response shapes) and the absence of an output schema, the description is remarkably complete. It covers resolution confidence, parent event extraction, news failure handling, safety for low-confidence matches, and liquidity warnings. 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?

All three parameters have schema descriptions, and the description adds further meaning: 'depth' explains quick vs thorough, 'market' explains acceptable formats (slug, URL, question text), and 'include_raw' explains the trade-off in response size. Schema coverage is 100% and the description enhances understanding.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the verb (research), resource (Polymarket bet), and differentiates from sibling tools like polymarket_arbitrage by focusing on single-bet 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?

The description explicitly lists use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It provides extensive examples of fan-out for different bet types, giving clear context. However, it does not explicitly mention when not to use the tool or list specific 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?

With no annotations, the description carries the full burden. It mentions returns (paired data, resource URIs) but does not state whether the tool is read-only, what happens on invalid input, rate limits, or authentication needs. The behavioral context is adequate but not comprehensive.

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 three sentences, each adding distinct value: purpose, data details, and efficiency benefit. 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 the tool's simplicity (2 params, no nested objects, no output schema), the description covers what the tool does, its inputs, outputs (paired data + URIs), and constraints (2-5 entities). It is complete for an agent to use.

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

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

Schema coverage is 100%, baseline is 3. The description adds value by explaining the meaning of the 'type' enum values and the 'values' array with domain-specific examples (SEC EDGAR, FDA), beyond the schema's basic descriptions.

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

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

The description clearly states the tool compares 2-5 entities side by side in one call, with specific data fields for company and drug types. It distinguishes itself by noting it replaces 8-15 sequential agent calls, making its purpose unique among siblings.

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

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

The description explicitly states when to use the tool (comparing entities) and implies it replaces multiple calls, but does not explicitly state when not to use it or provide alternative tools for 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it performs a search based on natural language queries and returns relevant tools with names and descriptions. However, it lacks details on rate limits, error handling, or performance characteristics, which would be helpful for a search tool.

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 in the first sentence, followed by usage guidance. Both sentences earn their place by providing essential information without redundancy. It is appropriately sized and efficiently structured.

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

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

Given the tool's moderate complexity (search functionality with 2 parameters) and no output schema, the description is mostly complete. It explains the purpose, usage, and behavior well, but could benefit from mentioning the return format more explicitly (e.g., list of tools with metadata) since there's no output schema provided.

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 the schema already documents both parameters thoroughly. The description does not add any additional meaning or context beyond what the schema provides for the parameters. It mentions the query concept but doesn't elaborate on syntax or usage beyond the schema's examples.

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 with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), and explicitly distinguishes it from sibling tools by emphasizing its role for discovery among 500+ tools. It provides a clear action and target, making it easy to understand what the tool does.

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 ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task'), including a specific condition (500+ tools) and alternative context (vs. not using it). It clearly directs the agent on optimal usage scenarios.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, indicating a safe, read-only tool. The description adds significant behavioral context: it fans out across multiple APIs, soft-fails for patents (USPTO PatentsView API sunset), uses fallback (GDELT→GNews), and returns specific fields with URI links. This goes beyond annotations to disclose failure modes and data sources.

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

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

The description is relatively long but every sentence serves a purpose. It is front-loaded with example user queries, then states the primary action, usage preference, detailed output breakdown, and parameter constraints. No redundancy or filler—each part is essential for understanding the tool's operation and limitations.

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 (cross-source aggregation) and absence of an output schema, the description is remarkably complete. It lists all returned data: cik, company_name, recent_filings (with URIs), fundamentals, patents, news, LEI. It mentions sources (SEC, XBRL, USPTO, GDELT, GLEIF), fallbacks, and soft-failures. No critical information is missing for an AI agent to use this 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 coverage is 100%, but the description adds critical meaning beyond the schema: it specifies that 'value' must be a ticker or zero-padded CIK and explicitly states that names are not supported. It also gives examples ('AAPL', '0000320193'). This additional context clarifies the input format and constraints, aiding correct invocation.

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

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

The description clearly states the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call.' It specifies the verb ('profile'), resource ('US public company'), and scope ('cross-source'). It distinguishes from sibling tools like 'resolve_entity' and 'compare_entities' by noting when to prefer this tool over chaining single-source lookups.

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

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

Explicit usage guidance is provided: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also tells when NOT to use it: 'names not supported (use resolve_entity first if you only have a name).' This clearly delineates when to use this tool 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?

No annotations are provided, so the description carries the full burden. 'Delete' implies a destructive mutation, but it doesn't disclose whether this is permanent, requires specific permissions, has side effects (e.g., affecting other tools), or what happens on success/failure. For a destructive tool with zero annotation coverage, this is a significant gap.

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, efficient sentence with zero waste. It's front-loaded with the core action ('Delete') and resource, making it easy to parse quickly.

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 destructive nature, lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permanence, error handling, or return values, which are critical for safe usage. The high schema coverage doesn't compensate for these gaps in a mutation 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%, with the parameter 'key' documented as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format, examples, or constraints. With high schema coverage, the 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 ('Delete') and the resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'recall' or 'remember', but the verb 'Delete' strongly implies a destructive operation versus retrieval or storage.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing memory key), exclusions, or relationships to sibling tools like 'recall' (likely for retrieval) or 'remember' (likely for storage).

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating no side effects. The description adds value by explaining the process: fetches the page, extracts title/description/key links, and emits standard markdown. 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 two paragraphs: introduction of core functionality and a bullet-style usage list. It is concise and front-loaded, though the 'Useful for' section could be considered extraneous but aids usage guidelines.

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 low complexity, rich annotations, and complete schema, the description adequately covers what the tool does and returns. It does not explain error handling or edge cases, but for a read-only, idempotent 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% for both parameters (url and max_links). The description adds minimal meaning beyond the schema, simply mentioning 'any URL' for url and not elaborating on max_links. Baseline 3 is appropriate.

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

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

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

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 'Useful for' section listing concrete scenarios (getting a client's site indexed, drafting for own project, auditing competitor). It provides clear context but does not explicitly state when not to use the tool, such as for checking existing AI visibility (handled by ai_visibility_check sibling).

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?

No annotations are provided, so the description carries the full burden. It discloses the return content ('full details including description, severity, and affected products'), which is valuable behavioral information. However, it lacks details on error handling, rate limits, authentication needs, or data freshness.

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, efficient sentence that front-loads the purpose and includes essential details without waste. Every part earns its place by clarifying the action, input, and output.

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

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

Given the tool's simplicity (one parameter, no annotations, no output schema), the description is reasonably complete. It covers purpose, input example, and return content. However, without an output schema, it could benefit from more detail on the structure of 'full details' to aid the 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 description coverage is 100%, with the parameter 'cve_id' well-documented in the schema. The description adds minimal value by reinforcing the example format, but does not provide additional semantics beyond what the schema already specifies.

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 specific action ('Fetch a specific CVE') and resource ('by its ID'), with an explicit example ('CVE-2021-44228'). It distinguishes from sibling tools by specifying retrieval of a single CVE rather than recent or search operations.

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

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

The description implies usage context by specifying 'a specific CVE by its ID,' suggesting this tool is for known CVE lookups. However, it does not explicitly state when to use this versus the 'recent_cves' or 'search_cves' alternatives, nor does it provide exclusions or prerequisites.

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, destructiveHint. Description adds that it lists subscriptions and returns specific fields, but no new behavioral context beyond annotations.

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

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

Two concise sentences, front-loading the purpose. Every sentence adds value with 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?

Describes returned fields and usage context. For a simple list tool with comprehensive annotations, this is adequate. Missing information about pagination is not critical.

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

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

Single parameter 'include_inactive' is fully described in the schema (100% coverage). Description does not redundantly explain it, so baseline score applies.

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

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

Clearly states the tool lists the caller's active subscriptions and specifies the returned fields (id, type, etc.). Distinguishes from sibling tools by mentioning use case for finding an id to cancel.

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

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

Provides explicit when-to-use guidance: review current subscriptions before adding more, or find an id to cancel. Does not state when not to use or mention alternatives, but the context is clear.

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

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

Discloses rate limit and free usage, and advises on content, but does not describe what happens after sending (e.g., confirmation, response). No annotations exist, so description carries full burden.

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 with clear front-loading of purpose, efficient use of words, 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?

Adequate for a simple feedback tool, but missing description of post-submission behavior (e.g., no confirmation mentioned). Output schema absent, so description should cover 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?

Input schema has 100% description coverage, so parameters are well-documented. The description reinforces usage but adds minimal new semantic value beyond schema.

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

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

Clearly states the tool sends feedback to the Pipeworx team, listing specific use cases (bug reports, feature requests, etc.), and is distinct from sibling tools that query data or manage memory.

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 (bug reports, feature requests, etc.) and provides a negative guideline (avoid including end-user prompt verbatim) but does not explicitly contrast with alternative tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: data source (CF analytics-engine), privacy (no PII), returned fields (pack, tool, count), and caching policy (5min-1h). No contradictions.

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

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

The description is concise at three sentences, front-loading the main purpose. It avoids wasted words, though the use cases could be slightly compressed. Structurally 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?

While there is no output schema, the description sufficiently indicates the return format (top tools, top packs, total call volume). Combined with annotations, it provides adequate context for an agent to understand the tool's capabilities.

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 'window', with enum values and a description. The description adds meaning by explaining that shorter windows show hot trends and longer windows show steady-state demand, which aids selection.

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

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

The description clearly states the tool returns trending tools, packs, and call volume from Pipeworx agent usage. It specifies the resource (Pipeworx usage data) and the action (returns trending info). It distinguishes itself from siblings like discover_tools by focusing on aggregate agent behavior.

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

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

The description lists three explicit use cases (discovering hot data sources, confirming canonical tools, aligning use cases). It does not explicitly state when not to use, but the context makes it clear it's for trending insights, not specific queries.

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 safety profile is covered. The description adds substantial behavioral context: it explains the algorithmic checks (monotonicity, partition sum, Jaccard similarity), filters (placeholders), and response structure. However, it omits details like error handling, rate limits, or performance characteristics, so it's not a perfect 5.

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

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

The description is fairly long but well-organized with sections (SEMANTIC ANCHOR, PARTITION FILTER). It front-loads the critical requirement (requires one of event or topic). While a bit verbose, the detail is necessary for correct usage. It earns a 4 for good structure and efficiency, lacking slightly in brevity.

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 describes the response fields (opportunities[], partition_check) and algorithmic details. It covers the two parameters thoroughly. However, it lacks information on error scenarios, limits, or prerequisites (e.g., network access). Given the tool's moderate complexity, the description is largely complete but could be improved.

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 for both parameters. The description adds value by providing examples of valid inputs ('fed-decision-may-2026') and explaining how each parameter affects tool behavior (single-event vs cross-event mode). This extra context justifies a score above the baseline of 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?

The description clearly states that the tool finds arbitrage opportunities on Polymarket using specific methods (monotonicity violations and partition-sum checks). It distinguishes two distinct modes of operation (event and topic) and provides concrete examples, making the purpose unambiguous and differentiating it from sibling tools like 'polymarket_edges' which likely focus on different aspects.

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', warns that calling with no args fails, and provides detailed guidance on when to use each mode. It recommends event for a specific market and topic for cross-event scanning, and even explains what cross-event catches that single-event misses (e.g., end-date patterns). This is excellent usage guidance.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds substantial behavioral context: it explains the three model families, edge calculation, Kelly fractions, slippage assumptions, caching (1h at KV level), and diagnostic output. 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 long but well-structured: it starts with purpose, then details the three segments, followed by knobs and response structure. While every sentence adds information, some technical depth (e.g., specific alpha values for sports) could be condensed. Overall, it is appropriately front-loaded and organized.

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

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

Despite no output schema, the description thoroughly explains the response structure (by_segment, fed_candidates, _diagnostics), how knobs affect results, and caching. Given the tool's complexity (9 parameters, three segments, no output schema), the description provides complete guidance for an AI agent to understand and use the tool correctly.

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

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

All 9 parameters have descriptions in the input schema (100% coverage). The description adds extra context for several parameters, such as explaining 'min_partition_leg_kelly' in detail and how 'slippage_pp' interacts with edge calculation. This goes beyond the schema, adding meaningful value.

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

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

The description explicitly states 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price', providing a specific verb+resource. It distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_kalshi_spread by focusing on mispricing within Polymarket using Pipeworx data.

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

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

The description clearly indicates when to use the tool ('what should I bet on today') and provides context for the three model families and tradeable-edge knobs. However, it does not explicitly state when not to use it or compare it to alternatives, missing a small opportunity for clearer guidance.

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

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

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive. The description adds crucial context: spread direction (Kalshi minus Polymarket), compatibility warnings (bet shape mismatches, no overlap), temporal alignment checks, and counters for skipped comparisons. This fully informs the agent about behavior 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 well-structured with clear sections, bullet points, and bold formatting. However, it is somewhat verbose, repeating explanations of compatibility scenarios. Every sentence adds value, but slight trimming could improve conciseness without losing clarity.

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

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

Despite lacking an output schema, the description thoroughly explains the response format (leg-by-leg prices, spreads in pp) and safety fields (compatibility_warning, temporal_alignment, counters). It covers edge cases, limitations, and the behavior for both modes, making the agent well-informed for correct invocation.

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

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

Schema coverage is 100%, with descriptions already present. The description enhances understanding by explaining the topic list and how explicit parameters override topic mappings. It also provides examples in the schema. A slight deduction for not adding novel constraints on parameter usage beyond mode selection.

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

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

The description clearly states it computes cross-venue spreads between Kalshi and Polymarket for the same resolving question, distinguishing it from siblings like polymarket_arbitrage. It details two modes (topic shortcuts and explicit pairings), making the purpose specific and actionable.

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

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

The description explicitly outlines when to use each mode (topic for pre-mapped macros, explicit for custom pairings) and warns that most topics return compatibility warnings, not tradeable spreads. It also explains safety fields that indicate when spreads are invalid due to bet shape mismatches or temporal gaps.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the dual functionality (retrieve by key vs list all) and persistence across sessions, which are valuable behavioral traits. However, it doesn't address important aspects like error handling (what happens if key doesn't exist), performance characteristics, or format of returned memories. The description adds meaningful context but leaves gaps in behavioral understanding.

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 perfectly concise with two sentences that each earn their place. The first sentence states the core functionality with conditional behavior, and the second sentence provides important context about session persistence. There's zero wasted language, and the information is front-loaded with the most critical usage information first.

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

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

Given the tool's moderate complexity (dual functionality, session persistence) with no annotations and no output schema, the description does well but has gaps. It covers the core operations and temporal scope effectively. However, without an output schema, the description doesn't explain what format memories are returned in or what the list operation returns. For a memory retrieval tool, understanding the return format is important but not 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 description coverage is 100%, so the schema already documents the single parameter. The description adds valuable semantic context by explaining the conditional behavior: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' This clarifies that omitting the parameter triggers a different operation (listing) rather than being an error. Since there's only one parameter, the baseline would be 4, and the description enhances understanding of the parameter's role.

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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations. The description explicitly mentions retrieving context saved earlier in current or previous sessions.

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: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It also specifies the alternative behavior based on parameter presence. The context about retrieving from current or previous sessions gives clear temporal boundaries for usage.

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

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

The description adds behavioral context beyond annotations, including return field details (source, citation_uri, raw payload) and the mark_read flag behavior. 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 (4 sentences) and well-structured: main purpose first, then key fields, filtering, and alternative access. 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 the tool has 5 optional params, no output schema, but rich annotations, the description fully covers return values, filtering, mark_read semantics, and alternative access. Complete for agent usage.

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

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

Schema coverage is 100%, so baseline 3. Description adds value with examples (e.g., 'sec_8k' for type) and clarifies mark_read behavior, 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?

The description clearly states the tool pulls fired events from subscription feed and returns most recent alerts with specific fields. It distinguishes from sibling tools like 'recall' or 'search_within' by focusing on subscription alerts.

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

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

The description provides clear context on when to use the tool versus alternatives (scripts/dashboards) and explains filtering options. It lacks explicit when-not-to-use guidance but is sufficient.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. Description adds useful context: fans out to multiple sources, parallel call, fallback logic, and USPTO sunset issue. Could mention rate limits or error handling more explicitly, but already 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?

Well-structured and concise. Front-loaded with common query phrases, then breaks down behavior, parameters, output, and alternative tool. Every sentence adds value. No redundancy.

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

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

Given no output schema, description adequately explains output (structured changes grouped by source + total_changes + citation URIs). Covers all parameters, sources, and limitations. Also mentions fallback and alternative tool for completeness.

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

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

Schema coverage is 100%, but description adds significant value: explains 'type' limitation, provides examples for 'since' (ISO date and relative shorthand with common values), and describes 'value' as ticker or CIK. Also notes typical monitoring usage for 'since'.

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: 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It provides specific verbs ('What's new with X', 'latest on Y') and distinguishes from sibling 'entity_profile' by explicitly mentioning when to use that instead.

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 for monitoring changes over time; use 'entity_profile' for static profiles. Also explains fallback behavior (GDELT→GNews) and source limitations (USPTO soft-fail).

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the date format requirement but does not cover other behavioral traits such as rate limits, authentication needs, error handling, or what the return format looks like (e.g., list of CVEs with details). This leaves significant gaps for a tool that fetches data.

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 in the first sentence, followed by a specific format requirement. It is appropriately sized with zero waste, making it efficient and easy to parse.

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

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

Given the complexity of fetching CVEs (a data retrieval operation with potential for large results), no annotations, and no output schema, the description is incomplete. It lacks information on return values (e.g., structure of CVE data), pagination, error cases, or performance considerations, which are important for effective tool 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?

The schema description coverage is 100%, so the schema already documents all parameters (start, end, limit) with descriptions and defaults. The description adds value by reinforcing the ISO 8601 format requirement with an example, but does not provide additional meaning beyond what the schema offers, meeting the baseline of 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?

The description clearly states the verb 'Fetch' and the resource 'CVEs published within a date range', making the purpose specific and understandable. However, it does not explicitly differentiate this tool from its siblings 'get_cve' and 'search_cves', which would be needed for a score of 5.

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 no guidance on when to use this tool versus its siblings 'get_cve' and 'search_cves'. It only specifies a date format requirement, which is more about parameter semantics than usage context. No explicit alternatives or exclusions are 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the persistence differences between authenticated users ('persistent memory') and anonymous sessions ('last 24 hours'), and the cross-tool context functionality. However, it doesn't mention potential limitations like storage capacity, rate limits, or error conditions.

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 perfectly sized and front-loaded with the core purpose in the first sentence. Every sentence earns its place: the first states what it does, the second provides usage context, and the third adds important behavioral context about persistence. There's zero wasted 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 2-parameter tool with no annotations and no output schema, the description provides good coverage of the tool's behavior and usage. It explains what the tool does, when to use it, and important persistence characteristics. The main gap is the lack of information about return values or error conditions, but given the tool's relative simplicity, the description is reasonably 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?

With 100% schema description coverage, the input schema already documents both parameters thoroughly. The description doesn't add significant meaning beyond what the schema provides - it mentions 'key-value pair' but doesn't elaborate on parameter usage, constraints, or best practices beyond what's in the schema descriptions.

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

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

The description clearly states the tool's purpose with specific verb ('store') and resource ('key-value pair in your session memory'), and distinguishes it from siblings by specifying it's for saving data across tool calls. It provides concrete examples of what to store ('intermediate findings, user preferences, or context'), 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 provides clear context on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but doesn't explicitly mention when not to use it or name specific alternatives. It implies usage scenarios but lacks explicit exclusions or comparisons with sibling tools like 'forget' or '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?

With no annotations, the description discloses return values (ticker, CIK, name, URIs) and that it's a single call. It does not mention side effects or auth needs, but as a read-like operation, the transparency is adequate.

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. The first sentence states the purpose, the second provides details and example. Every sentence adds value and is 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?

Without an output schema, the description comprehensively explains return values and the tool's value proposition (replacing 2-3 calls). It covers version, type limitation, and accepted input formats.

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

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

Schema coverage is 100% and descriptions are clear. The description adds examples and the v1 limitation, enhancing understanding 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?

Description clearly states the tool resolves an entity to canonical IDs, gives a concrete example with company type, and specifies the accepted inputs and outputs. It distinguishes from sibling tools which are about queries and memory.

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 indicates when to use (single call to replace multiple lookups) and provides version and type context. It does not explicitly state when not to use or name alternatives, but the use case is well-defined.

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. The description adds that it internally calls 'ai_visibility_check' for each entity and returns a ranked list with scores, confidence, and 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?

Four sentences with front-loaded purpose, then details, then example. Every sentence adds value without redundancy. Highly 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 specifies the return format: ranked list with score, confidence, signal density per entity. It also explains the internal probe to 'ai_visibility_check' and the narrative role of the first entity.

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

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

With 100% schema description coverage, the baseline is 3. The description adds context: first entity treated as 'subject', 'context' disambiguates names, and 'models'/'_apiKey' are optional. This goes slightly 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 compares AI visibility across multiple entities side-by-side, with specific verbs 'compare' and 'probe'. It distinguishes from siblings like 'ai_visibility_check' and 'compare_entities' by focusing on AI presence and ranking.

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 gives a use case ('competitive AI-marketing audits') and an example question, implying when to use. It does not explicitly state when not to use, but the context is clear enough for typical scenarios.

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

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds valuable context: partial failure behavior, bundlephobia measurement delay (5-30s), and sources_failed listing. 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?

Well-structured single paragraph, front-loaded with core purpose. Every sentence adds value, though slightly long; could be more structured with bullet points but still 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?

For a tool with 2 params and no output schema, the description covers purpose, use cases, ecosystem scope, error behavior, and return structure (summary block details). Complete enough for an agent to decide and 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?

100% schema coverage, but description adds beyond: explains scoped packages (@types/node) accepted and version defaults to latest. This enriches the agent's understanding of parameter constraints.

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

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

The description clearly states the tool's purpose: a composite check for adding an npm package, covering deps.dev and bundlephobia sources. It distinguishes from siblings by being a one-call composite, unlike simpler tools for specific checks.

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 use cases: when an agent asks about safety, popularity, size, or cost. Also clarifies scope (NPM only v1) and directs other ecosystems to deps.dev:version, with graceful degradation handling.

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?

No annotations are provided, so the description carries full burden. It mentions the return format (CVE ID, description, severity, CVSS score) but lacks critical behavioral details such as pagination, rate limits, authentication needs, error handling, or data freshness. For a search tool with zero annotation coverage, this is insufficient.

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 concise sentences with zero waste: the first states the purpose, and the second specifies the return format. It is front-loaded and appropriately sized, earning its place efficiently.

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

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

Given the tool's moderate complexity (search with two parameters) and no annotations or output schema, the description is minimally adequate. It covers purpose and return format but lacks behavioral context and usage guidelines, making it incomplete for optimal agent operation.

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 the schema fully documents both parameters (query and limit). The description adds no parameter-specific information beyond implying keyword search, which is already covered in the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'Search CVE vulnerabilities by keyword' specifies the verb (search) and resource (CVE vulnerabilities). It distinguishes from 'get_cve' (likely fetch single CVE) and 'recent_cves' (likely fetch recent CVEs without search), but doesn't explicitly contrast them, keeping it at 4 rather than 5.

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 no guidance on when to use this tool versus alternatives like 'get_cve' or 'recent_cves'. It mentions what the tool does but offers no context on appropriate use cases, exclusions, or comparisons with siblings, leaving the agent to infer usage.

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

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

Beyond the annotations (readOnly, idempotent), the description discloses the embedding model (BGE-base-en), chunking strategy (500-char overlapping windows), character cap (200K with truncation and flagging), and output features (passages with offsets). This goes well 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 four sentences, each adding distinct value: purpose, use case, pairing suggestion, and technical details. No wasted words, and the most critical information is front-loaded.

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

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

Despite having no output schema, the description covers input behavior, truncation, result properties (offsets, scores), and integration with a sibling tool. It is sufficiently complete for an agent to understand when and how to use the tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds examples for the query parameter and specifies default value for limit (5), improving clarity without being redundant.

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 that the tool performs semantic search inside a fetched record using a natural-language query. It distinguishes itself from sibling tools by specifying it is for searching within already-fetched text, not for external searches or other operations.

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

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

Explicit usage guidance is provided: use when a record is too large to fit in the prompt, and pair with ask_pipeworx_grounded for grounded answers. It also implies not to use when the full text can be included, making the guideline complete.

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 behavioral traits beyond annotations: requires OAuth account (persistence), returns new subscription id, SMS delivery cap (10/day), webhook secret one-time return. Annotations already indicate idempotentHint=true, so description not needing to repeat is fine.

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

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

Front-loaded with purpose, well-structured with types and delivery sections, but slightly verbose with parameter examples; still efficient overall.

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 for a complex tool: covers all subscription types, their parameters, delivery options, constraints, and return value (subscription id). No output schema exists, but return is implied.

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?

Adds significant meaning beyond schema: explains each subscription type with concrete examples (e.g., sec_8k item codes, polymarket_edge topics), delivery channel details (e.g., webhook HMAC signing, auto-disable after failures), and constraints (verified phone, SMS cap).

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 creates a proactive monitoring subscription, distinguishes from siblings like list_subscriptions and unsubscribe with specific verb 'Create' and resource 'subscription'.

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

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

Provides explicit context for when to use: creating subscriptions for live data streams. Mentions prerequisites (Pipeworx OAuth account) and type-specific examples, but does not explicitly exclude alternatives or state when not to use.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds behavioral context: returns category-bucketed example questions with exact tool+argument shape, drawn from live catalog. No contradiction with annotations.

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

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

The description is front-loaded with the key onboarding query and clearly structured. However, it is slightly wordy with some redundancy (e.g., repeated 'What can I ask Pipeworx?'). Still, 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?

Despite lacking an output schema, the description fully explains what the tool returns: category-bucketed example questions with tool+argument shape. It covers the single parameter and usage context. It is complete for an onboarding 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% for the single parameter 'topic'. The description adds meaning by providing examples ('finance', 'pharma', 'betting') and explaining that omitting it gives a full cross-category spread. This goes beyond the schema's description.

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

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

The description clearly states the tool's purpose as an onboarding entry point for new users to discover what Pipeworx can do. It uses specific verbs like 'returns category-bucketed example questions' and distinguishes from sibling tools by noting it should be used first when you don't know what Pipeworx offers.

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

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

The description explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains the optional topic parameter for focus. No explicit when-not-to-use, but context is clear.

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

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

Adds value beyond annotations by stating ownership enforcement and soft-delete behavior (deactivation, not deletion), with link to historical availability.

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

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

Two concise sentences. First sentence delivers action and constraint. No wasted words.

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

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

For a simple tool with one parameter and no output schema, the description fully explains behavior, constraints, and downstream effects.

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 already describes the parameter. The description adds no further detail 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?

Clear verb ('cancel') and resource ('subscription by id'). Distinguishes from related siblings like 'subscribe' and 'list_subscriptions'.

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

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

The context is clear: use to cancel a subscription by ID. Ownership constraint is stated, but no explicit comparison to alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false, and the description adds valuable context: returns a verdict with structured data, citations, and percent delta. No contradiction with annotations.

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

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

The description is well-organized with trigger phrases, usage context, domain limitations, and return value summary. While slightly verbose, every sentence contributes meaning and no information is redundant.

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

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

Given the single parameter and no output schema, the description fully explains what the tool does, its domain, and the return format (verdict, structured form, citation, delta). An agent can confidently select and invoke this 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 a detailed description of the 'claim' parameter. The tool description adds context about the claim format (e.g., 'Apple's FY2024 revenue was $400 billion') and the return structure, exceeding 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 specifies the tool's function: natural-language claim verification with trigger phrases ('fact check', 'verify', etc.). It distinguishes itself from siblings by focusing on factual verification of claims, particularly company-financial data, and mentions it replaces multiple sequential calls.

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

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

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct.' Also notes the version 1 limitation to company-financial claims, implying when not to use (e.g., non-financial claims). Could be improved by explicitly mentioning alternative tools for other domains, but it's clear enough.

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