Macvendors

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

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) are all correctly reflected. The description adds value by explaining cost implications (free vs BYO key) and the return structure (per-model fields + combined view). No contradictions.

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

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

Two well-structured sentences. Front-loaded with the core action, followed by key details on defaults and output. No extraneous information.

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

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

Given 4 parameters, no output schema, and medium complexity, the description covers all necessary aspects: purpose, parameter usage, output format, and typical use cases. It is complete for an AI agent to select and use correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning beyond schema by clarifying the default model, the free tier, and the purpose of _apiKey. It provides context that schema cannot.

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

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

The description uses specific verbs (probe, score) and specifies the resource (LLMs) and outcome (visibility score 0-100). It clearly distinguishes from siblings like 'scan_competitor_ai_presence' by being more general.

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

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

Provides explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains the default vs. paid model option. However, it does not explicitly list when not to use or compare directly to 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, openWorldHint, idempotentHint, destructiveHint. The description adds context about returning structured answers with citation URIs and routing to multiple sources. No contradictions. It enhances 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 well-structured with bold headers, examples, and comparisons. While longer, every sentence adds value and is front-loaded with the key message. Slight verbosity prevents a 5.

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

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

Given the tool's complexity (routing to thousands of sources), the description covers scope, usage, alternatives, behavior, and output format. It is comprehensive and leaves no major gaps.

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

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

Schema coverage is 100%, with clear alias documentation. The description adds examples of valid questions and further clarifies the natural language input, providing value beyond the schema's basic 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 explicitly states the tool routes questions to a large set of tools/sources for authoritative structured data, and contrasts with sibling tools like ask_pipeworx_grounded and deep_research. The verb 'routes' and resource '4,770 tools' clearly define 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?

It provides explicit guidance: 'PREFER OVER WEB SEARCH', 'START HERE for most questions', and specifies when to use alternatives (e.g., grounded for hallucination-resistant, deep_research for broad questions). The description includes concrete examples of when to use, making it easy for the agent to decide.

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 valuable behavioral context: the refusal mechanism with explicit reasons, the fact that it costs an extra LLM call, and that it only uses the tool result. 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 somewhat long but front-loaded with purpose and key details. It includes structured refusal info and sibling comparison. Minor redundancy could be trimmed, 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?

No output schema, but the description lists the full return structure including success and refusal formats. Covers behavior, refusal reasons, cost trade-off, and comparison to sibling. 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 description coverage is 100%, with multiple aliases documented. The description does not add significant meaning beyond the schema; it mentions 'same routing as ask_pipeworx' but no specific param semantics. 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 is a 'Hallucination-resistant answer mode for high-stakes reads' and explains it routes to the right tool, fetches data, and extracts answers using only the tool result. It distinguishes from sibling ask_pipeworx by specifying extra cost and use case.

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

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

Explicitly says 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' and gives examples like financial, legal, medical. Also states 'prefer ask_pipeworx for casual lookups,' providing clear when-to-use and when-not-to-use guidance.

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

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

Annotations already indicate readOnly, idempotent, non-destructive. The description adds extensive behavioral detail: low-confidence short-circuiting, closed market handling, wide-spread warnings, resolution-rule risk parsing, and parent_event extractor. 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 very long and packed with information, but it is well-structured with labeled sections (RESPONSE SHAPES, RESOLVER CONTRACT, etc.) and front-loaded with the main purpose. However, it could be more concise without losing crucial 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?

Despite no output schema, the description thoroughly explains response shapes, resolver contract, parent_event extractor, news fields, safety mechanisms, and resolution-rule risk. It covers all essential aspects an agent needs to use the tool correctly and interpret results.

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

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

Schema coverage is 100%. The description adds value beyond schema by explaining the 'depth' parameter options (quick vs thorough), 'include_raw' with size implications, and 'market' with examples. This 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 explicitly states the tool researches a Polymarket bet by pulling Pipeworx data, with clear examples of inputs (slug, URL, question text) and outputs (evidence packet, market-vs-model comparison). It distinguishes from sibling tools like polymarket_arbitrage by focusing on per-bet evidence gathering.

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

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

The description explicitly says 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z"', providing clear context. It does not explicitly state when not to use or list alternatives, but the usage guidance is strong and specific.

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: for type='company' it pulls latest 10-K financials from SEC EDGAR/XBRL with proper fiscal year handling, for type='drug' it pulls FAERS counts, FDA approvals, and trial counts. It also notes results are sorted by primary metric and returns 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 relatively long but front-loaded with example queries that quickly convey purpose. Every sentence adds value, though some repetition (e.g., explaining both entity types) could be slightly reduced. Overall, it is well-structured and efficient.

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

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

Given the tool has only 2 parameters, no output schema, and comprehensive annotations, the description is complete. It explains what data is returned for each type, sorting behavior, and citation format. No gaps for agent to infer.

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

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

With 100% schema coverage, the baseline is 3. The description adds meaning by explaining the practical semantics of 'type' (company vs drug) and 'values' (tickers/CIKs for companies, drug names). It also clarifies min/max items and provides examples, which is helpful but not essential since schema already defines these.

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: side-by-side comparison of 2-5 companies or drugs. It uses specific verbs like 'compare' and provides example queries. It distinguishes from siblings by noting it replaces sequential single-pack 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?

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing clear guidance on when to use this tool. It covers both entity types and implies when not to use (for single entity lookups).

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds critical context: account requirements, expected latency (15-60s), the fact that it decomposes questions and uses 4,770 tools in parallel, returns a findings packet with verbatim evidence, confidence, source, and explicit gaps[], and never invents data. This far exceeds the annotation baseline.

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 most critical information (account requirement, alternative) and then flows logically into functionality, process, output, and use cases. Every sentence adds essential insight; there is no wasted text. Despite length, it is efficiently organized.

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

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

Given the tool's complexity (parallel research over many sources), the description covers all necessary context: prerequisites, alternatives, behavior, output format (findings packet with citations and gaps), limitations (breaking news not suitable), and expected duration. Without an output schema, description compensates well.

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% (both parameters described). The description adds context: explains the depth enum (quick=3, standard=5, thorough=8) and emphasizes that broad/multi-part questions are acceptable. While schema already provides enums, the description clarifies the default and practical meaning, which is helpful.

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

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

The description clearly identifies the tool as 'grounded multi-source research across Pipeworx's 1241 STRUCTURED data sources'. It uses specific verbs and resources, and differentiates from siblings like ask_pipeworx and bet_research by emphasizing its multi-faceted, parallel-research nature. The examples reinforce the intended use case.

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

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

Explicit guidance is given: 'Best for broad/multi-part questions over structured data', and alternatives are suggested: 'For a single lookup use ask_pipeworx' and 'For BREAKING or colloquial CURRENT-NEWS ... prefer ask_pipeworx'. Account requirements and fallback are also stated upfront.

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

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

Annotations already mark it as read-only and idempotent. The description adds useful behavioral context: returns top-N tools with names, descriptions, and full input schemas, ready to call directly without a second lookup.

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 that is front-loaded with purpose and usage. It is thorough but slightly long; could be shortened without losing clarity.

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

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

Given no output schema, the description fully explains what is returned (names, descriptions, schemas) and the behavior (ready to call directly). This is complete for the tool's purpose.

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 each parameter. The description mentions aliases but does not add significant meaning beyond the schema. Baseline score of 3 applies.

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

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

The description clearly states it finds tools by describing the data or task, and lists many specific domains (SEC filings, FDA drugs, etc.). It distinguishes from sibling tools by being a discovery tool, not a specific function.

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

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

Explicitly says 'Call this FIRST when you have many tools available and want to see the option set' and 'Use when you need to browse, search, look up, or discover what tools exist'. Provides clear context for when 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, and non-destructive nature. The description adds significant behavioral context: it fans out across multiple sources, notes the USPTO PatentsView API sunset in May 2025 with soft-fail, describes fallback (GDELT→GNews), and details return fields. No contradiction with annotations.

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

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

The description is front-loaded with a set of example queries, making the tool's purpose immediately clear. While it is relatively long, every sentence adds necessary detail about sources, return fields, and constraints. Slightly wordy but well-structured, earning a 4.

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 the tool (multi-source, no output schema), the description is comprehensive: it lists all return components, mentions soft-failures and fallbacks, and covers required input format. It could mention behavior when data is missing (e.g., ticker not found), but overall it provides sufficient context for an agent to use the tool correctly.

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

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

Schema coverage is 100% with descriptions, but the description reinforces and expands on parameter usage (e.g., ticker or zero-padded CIK, names not supported). It adds examples and clarifies constraints beyond the schema, which is valuable for 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 provides a full cross-source profile of a US public company in one parallel call, with specific examples like 'Tell me about X' and 'give me the rundown on $TICKER'. It lists the data sources and return fields, making the purpose unambiguous and distinguishing it from siblings like resolve_entity.

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

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

The description explicitly says to ALWAYS PREFER this over chaining single-pack lookups for holistic views, and notes that names are not supported—use resolve_entity first. This provides clear when-to-use and when-not-to-use guidance, with direct reference to an alternative sibling tool.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. The description adds context about clearing sensitive data and being paired with other tools, but does not elaborate on irreversibility or effects beyond deletion.

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 the core action, zero 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 delete tool with no output schema, the description covers purpose, usage, and pairing with siblings. It lacks explanation of return values, but that is typical for destructive operations.

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' for the key parameter. The tool description does not add further parameter details beyond what the schema provides.

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

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

The description clearly states 'Delete a previously stored memory by key,' specifying the verb (delete) and resource (memory by key). It distinguishes from siblings remember (store) and recall (retrieve) by focusing on deletion.

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

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

The description explicitly states when to use the tool: when context is stale, task is done, or to clear sensitive data. It also pairs with remember and recall, indicating alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds behavioral context: fetches the page, extracts title/description/key links, and emits markdown, without contradicting annotations.

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

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

The description is reasonably concise with clear front-loading of purpose. However, the list of AI crawlers and the phrase 'so AI crawlers... can index the site cleanly' adds slight verbosity without core value.

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

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

For a low-complexity tool with 2 parameters and no output schema, the description fully covers what the tool does, how it works (fetch, extract, emit), and its output format (markdown text blob). No gaps.

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

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

Schema coverage is 100% with clear descriptions for both parameters (url and max_links). The description adds no additional 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 the tool generates a production-ready llms.txt file for any URL, specifying the verb 'generate', resource 'llms.txt file', and purpose for AI crawlers. It distinguishes itself from siblings by listing specific use cases like getting a client's site indexed or auditing a competitor.

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 provides usage scenarios: getting a client's site indexed, drafting for own project, or auditing competitor. It does not mention when not to use or alternatives, but the context is clear and practical.

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, so description adds value by listing return fields. No contradictions.

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

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

Two sentences, front-loaded with action and result fields, 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?

Despite no output schema, description lists all return fields. Parameter is simple and optional, so tool is fully described.

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 the single optional parameter fully (100% coverage). Description does not add extra meaning beyond the schema, meeting baseline.

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

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

Description clearly states the tool lists the caller's active subscriptions and specifies the fields returned. It distinguishes from sibling tools like subscribe and unsubscribe.

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

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

Explicitly advises when to use: to review subscriptions before adding more or to find an id to cancel. Provides clear context with 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 indicate readOnly, openWorld, idempotent, non-destructive. Description adds valuable context: keyless, rate-limited, and source API. No contradictions.

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

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

Two sentences, front-loaded with core purpose, followed by usage details. 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?

No output schema, and description does not specify return format (e.g., JSON with manufacturer name). Purpose is clear, but response shape is missing for a lookup tool.

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

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

Parameter 'mac' is fully described in schema with format examples; description only reaffirms 'first 6 hex digits'. Schema coverage is 100%, so description adds minimal extra meaning.

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

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

Clearly states the tool performs a MAC address to hardware manufacturer lookup using macvendors.com. Distinguished from siblings by its specific function; no ambiguity.

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 instructions on how to pass the MAC address (full or first 6 hex digits) and mentions rate limit. No explicit when-not or alternatives, but siblings are not related.

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 (5 per identifier per day), that it's free and doesn't count against tool-call quota, and that feedback is reviewed daily. No contradiction with annotations; adds context beyond them.

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

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

Concise and well-structured: first sentence defines purpose, followed by usage scenarios and constraints. Every sentence adds value.

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

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

Given the three parameters (type, context, message) and lack of output schema, the description covers usage, constraints, and what to include. Complete for this simple 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 coverage is 100% with parameter descriptions in the schema. The tool description does not add new semantic details beyond the schema, so baseline of 3 is appropriate.

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

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

The description clearly states the tool's purpose: sending feedback about bugs, missing features, or praise. It distinguishes itself from sibling tools by focusing on feedback, not queries or actions.

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

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

Explicitly specifies when to use each feedback type (bug, feature, data_gap, praise) and provides guidance on what to include (describe in terms of Pipeworx tools/packs, not user prompts). Also mentions rate limits and quota exemption.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds valuable context: data source (CF analytics-engine), no PII, output structure, and caching (5min-1h). This goes beyond annotations.

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

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

The description is concise (5-6 sentences) and well-structured: what it does, what it returns, use cases, data source, caching. Every sentence adds value without repetition.

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

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

Given no output schema, the description clearly states what is returned (top tools, top packs, total call volume) and provides data provenance and caching details. The annotations cover safety, so completeness is high.

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 one optional parameter. The description adds meaning by explaining the default and the trade-off between short and long windows ('shorter windows surface what's hot right now; longer windows show steady-state demand').

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

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

The description starts with 'What other AI agents are calling on Pipeworx right now' and clearly states it returns top tools, top packs, and call volume. This is specific and distinct from sibling tools like discover_tools or ask_pipeworx.

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

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

Three explicit use cases are listed, covering discovery, confirmation, and alignment. The description also explains the window parameter's effect. However, it does not explicitly state when not to use this tool or name alternatives for different needs.

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

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

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false) are consistent with the description, which adds rich behavioral detail: scanning top ~200 markets, Jaccard similarity threshold (0.30), placeholder filtering, fill check against depth. 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 thorough but somewhat verbose (multiple paragraphs). However, it is well-organized with clear sections for no-args, event mode, topic mode, semantic anchor, partition filter, response, and fill check. Minor redundancy could be trimmed, but structure aids readability.

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

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

Given the complexity (two modes, multiple checks) and no output schema, the description covers response structure (opportunities[] and partition_check fields) and fill check output. Missing explicit error handling or edge cases, but sufficient for an AI agent to understand the tool's behavior.

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

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

Schema coverage is 100% with descriptions, but the tool's description adds significant value: explains event slug format with examples, topic as seed question, and the behavioral differences between the two parameters. This goes 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 identifies arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes from siblings by detailing two modes (event and topic) and references sibling tools like polymarket_fill_risk. The verb 'find' and resource 'Polymarket arbitrage opportunities' are specific.

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

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

Explicit guidance is given: call with no args for trending scan, pass 'event' for per-event checks (recommended for specific markets), or 'topic' for cross-event scanning. It explains when to use each mode and warns against trading when realizable edge is ≤0, directing to polymarket_fill_risk for custom sizing.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive. Description adds extensive behavioral details: caching at KV level, output segments, edge calculation, Kelly fractions, market warnings, diagnostics. No contradiction with annotations.

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

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

The description is long but well-structured with sections for model families and response top-level. Every sentence carries useful information, though some redundancy could be trimmed for 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 9 parameters, 100% schema coverage, no output schema, and high complexity, the description explains edge metrics, diagnostics, caching, and knob usage thoroughly. Lacks only explicit return format but compensated by detailed response structure.

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

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

Schema covers 100% of parameters with descriptions. Description adds context and use cases for parameters like min_partition_leg_kelly, tradeable-edge filters, 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 scans Polymarket markets for opportunities where Pipeworx data disagrees with market price, with specific model families and output structure. It distinguishes from siblings like polymarket_arbitrage by focusing on disagreement-based edges.

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

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

Explicitly built for 'what should I bet on today' and notes agents can discover opportunities without paging hundreds of markets. Mentions tradeable-edge knobs and caching. Does not explicitly state when not to use compared to siblings, but context is clear.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint as true, and destructiveHint false. The description adds significant behavioral context: history bounded by 60-day TTL, decay from daily closes (not intraday), snapshot gaps explained, and response 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?

The description is fairly lengthy but well-structured, starting with the core purpose, then detailing response fields, and ending with limitations. Some redundancy could be trimmed, but the complexity justifies the length.

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 absence of an output schema, the description thoroughly covers return values (tracked, expired, snapshot_dates), behavioral details (decay computation, first_seen, trend), and limitations (TTL, snapshot gaps). It provides complete context for agent 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 both `days` and `window` described. The description adds minor value (e.g., default and clamp for days, snapshot family phrasing) but does not substantially enhance the schema's explanations.

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

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

The description clearly states the tool provides 'edge persistence and decay telemetry' from daily snapshots, answering how long an edge has existed and if it's shrinking. It distinguishes itself from the sibling `polymarket_edges` by focusing on historical trends rather than current edges.

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

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

The description gives clear context on when to use this tool (to differentiate fresh edges from old ones) but does not explicitly mention when not to use it or name alternatives like `polymarket_edges`. It implies usage for historical analysis but lacks explicit exclusion.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint; the description adds extensive behavioral context including ladder walking, return fields (top-of-book, VWAP, slippage, verdict), basket per-leg details, thin_legs, forced_directional_risk, and explicit warning about partial fills converting arb to directional risk.

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

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

The description is comprehensive but somewhat lengthy; however, it is well-structured with clear sections for single-market and basket modes, uses all-caps keywords for emphasis, and each sentence adds value. Minor redundancy with schema descriptions.

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 fully explains all return values and behaviors for both modes, including edge cases and warnings. It covers everything an agent needs to understand tool capabilities and risks.

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 beyond the schema: explains size_usd differences between single-market and basket, side default logic for basket, and clarifies that market and event accept slugs or full URLs.

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

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

The description clearly states the tool's purpose as a realizable-vs-theoretical edge check against live order-book depth, and explicitly distinguishes it from sibling tools like polymarket_arbitrage and polymarket_edges by specifying when to use it before 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 provides explicit when-to-use guidelines (before acting on arb signals or trades above $500) and when-not-to-use warnings (theoretical overround on thin books not capturable, partial fills causing unhedged positions). It also notes the requirement of one of market or event parameters.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds significant behavioral context: two modes, response structure (leg-by-leg prices, spread array), safety fields (compatibility_warning, temporal_alignment, skipped counters), and caveats about meaningless spreads. 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 front-loaded with purpose and modes, then details response and safety fields. Each sentence adds value, though it is somewhat lengthy. Could be slightly more structured but remains efficient.

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

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

Despite no output schema, the description thoroughly explains the response format (leg-by-leg prices, spread array, safety fields like compatibility_warning and temporal_alignment). Covers complexity of cross-venue matching and caveats. Complete for the tool's 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%, but description adds meaning by explaining the two complementary modes (topic vs explicit), providing example values for topic, and clarifying that explicit parameters override the topic-mapped side. This goes beyond schema descriptions.

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

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

Clearly states it computes cross-venue spread between Kalshi and Polymarket for the same resolving question. Distinguishes from siblings like polymarket_arbitrage by explicitly mentioning both venues and the concept of spread. Verb and resource are specific.

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

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

Provides explicit guidance on when to use, including warnings that most pre-mapped topics return compatibility warnings and are not tradeable. Describes two modes (topic shortcuts vs explicit overrides). Does not directly name alternatives but contextually unique 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 declare readOnlyHint, idempotentHint, destructiveHint; description adds scoping detail (anonymous IP, BYO key hash, or account ID). No contradictions.

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

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

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

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?

Covers retrieval and listing behaviors, scoping, and pairing with remember/forget. No output schema, but return values are implied. Could mention result format but not essential.

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 'key' has clear schema description and additional context in description ('omit to list all keys'), fully covering behavior.

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

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

Description clearly states verb ('Retrieve' or 'list') and resource ('value previously saved' or 'all saved keys'), and distinguishes from sibling tools 'remember' and 'forget'.

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

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

Provides concrete examples of when to use (user's target ticker, address, prior research notes) and mentions scoping and pairing with remember/forget. Lacks explicit when-not-to-use but context is clear.

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

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

Beyond annotations that declare the tool as read-only and idempotent, the description adds concrete behavior: it returns source, citation_uri, and raw payload, and explains the optional mark_read side effect. This is transparent without contradicting annotations.

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

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

The description is three sentences long, front-loaded with purpose, and each sentence provides essential information without redundancy or fluff. It efficiently covers return content, filtering, side effects, and 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?

For a tool with 5 parameters and no output schema, the description covers return fields, filtering, and mark_read behavior. It mentions polling and an alternative endpoint. It does not explain unread_only behavior, but the schema handles that. Overall, it is well-rounded.

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 value by providing an example for type ('sec_8k') and explaining how mark_read affects subsequent calls, improving practical 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 returns the most recent alerts from the subscription feed, with specific verbs ('pull' and 'returns'). It distinguishes itself from sibling tools like list_subscriptions by focusing on fired events, though it does not explicitly differentiate from all 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 provides clear context on when to use the tool, including filtering options (type, since) and the mark_read flag. It also mentions polling suitability and an alternative endpoint, but lacks explicit when-not-to-use guidance.

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

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

Annotations already mark it as readOnly, idempotent, and non-destructive. The description adds valuable behavioral context: fan-out behavior to multiple sources, GDELT→GNews fallback, USPTO soft-fail, and return format. No contradictions with annotations.

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

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

The description is tightly written with every sentence providing value. It front-loads example queries and uses concise language, making it easy to parse. No filler or redundancy.

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

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

Given the tool's complexity (fan-out to multiple sources, fallback logic, soft-fail) and the absence of an output schema, the description covers return structure (changes grouped by source, total_changes, citation URIs). It is complete for an agent to 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?

Input schema has 100% coverage. Description adds extra meaning: explains the type enum (only 'company'), gives examples for since (ISO date vs relative shorthand like '7d', '30d'), and clarifies value can be ticker or CIK.

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

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

The description states the tool's purpose clearly: a change feed for a company's recent events using multiple sources. It provides example queries that match user intents like 'What's new with X' and explicitly distinguishes itself from sibling tool entity_profile.

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

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

The description includes explicit guidance on when to use this tool versus entity_profile: 'Use entity_profile instead when you want the static profile...'. It also provides example query patterns and suggests typical monitoring windows like '30d'.

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

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

The description adds significant behavioral context beyond annotations: scoping by identifier, persistence for authenticated users vs. 24-hour retention for anonymous sessions. 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?

Four concise sentences with a clear front-loaded purpose, no wasted words, and logical flow.

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

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

Covers purpose, storage mechanism, scoping, persistence rules, and related tools. Adequate for a simple key-value store with 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 description coverage is 100% and already provides examples and explanations for both parameters. The main description does not add meaningful new information 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 states a specific verb ('Save') and resource ('data the agent will need to reuse later'), clearly distinguishing it from sibling tools like 'forget' 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?

It provides explicit guidance on when to use the tool ('when you discover something worth carrying forward') and mentions pairing with recall and forget, but does not explicitly state when not to use it.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, providing a safety profile. The description adds valuable context: 'Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups,' and it details the exact return fields for each type (e.g., ticker, CIK, citation URI for companies). No contradictions.

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

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

The description is lengthy but well-structured: starts with user-facing examples, then a clear one-sentence purpose, then usage guidance, then detailed type breakdowns. Every sentence adds value, though minor trimming 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 simplicity (2 params, no output schema), the description fully compensates by explaining all return fields and behavior for each type. It leaves no ambiguity about what the tool does or returns.

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

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

With 100% schema coverage, baseline is 3. The description adds meaning by explaining what each parameter accepts (e.g., for company 'accepts ticker, CIK, or company name — auto-disambiguated') and provides examples for both types, going beyond the schema's minimal descriptions.

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

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

The description states a specific verb and resource: 'resolve a user-spoken NAME to the canonical/official identifier.' It provides concrete examples ('What's the ticker for…', 'find the CIK for…') and distinguishes the tool from siblings by emphasizing it translates names to IDs required by other 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?

Explicit usage guidance is provided: 'Use FIRST whenever you have a name but need an ID.' Supported types are listed with details. However, the description does not explicitly mention when not to use this tool or compare it to sibling tools like 'lookup' or 'entity_profile' for cases where IDs are already known.

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 the tool as readOnly, openWorld, idempotent, and non-destructive. The description adds operational context: it calls ai_visibility_check for each entity, ranks results, and returns specific fields (score, confidence, signal density). This supplements the annotations without contradiction.

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

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

Two sentences: first explains the core action, second provides context and return format. Every sentence is informative, 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 clearly states the return value (ranked list with score, confidence, signal density per entity). It also notes the probing mechanism. For a tool with 4 parameters and no output schema, this is fully adequate.

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

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

Schema coverage is 100%, meaning all parameters have descriptions in the schema. The description adds only a minor detail about the 'entities' parameter (first entry treated as subject). This is baseline value, not a significant addition.

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

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

The description clearly states it compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and returns a ranked list. This distinguishes it from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison).

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

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

The description provides a use case ('competitive AI-marketing audits') and an example question. It implicitly differentiates from ai_visibility_check by emphasizing multi-entity comparison. However, it does not explicitly state when not to use or list 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 indicate read-only, open-world, idempotent, non-destructive. Description adds significant context: fans out across multiple sources, returns summary and per-advisory details, and handles partial failures with 5-30s delays. No contradiction.

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

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

Single paragraph with dense, useful information. Every sentence serves a purpose: purpose, use case, limitations, behavior. No redundancy or fluff.

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

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

Given the tool's complexity (composite check) and lack of output schema, the description fully explains what is returned and how it behaves. Covers partial failures, timing, and fallback options. Complete for agent 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 clear descriptions for both parameters. Description adds value by explaining the return structure (summary block, advisories, links, alternatives), which goes beyond schema. Baseline 3 plus extra context justifies 4.

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

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

The description clearly states the tool is a composite check for npm packages, covering license, advisories, bundle size, and more. It uses specific verbs like 'scan' and 'check' and distinguishes from siblings by explicitly limiting to NPM ecosystem.

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

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

Provides explicit use cases: when an agent asks about safety, popularity, or size of a package. Also states limitations: 'NPM ecosystem only in v1' and directs to other tools for other ecosystems. Mentions graceful degradation on timeouts.

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 valuable technical details: embedding model (BGE-base-en), windowing (500-char overlapping), character cap (200K with truncation flag), and output includes character offsets for verification.

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 well-structured: starts with core purpose, then usage context, then technical details. It is slightly verbose but every sentence adds value. Would benefit from slight tightening.

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 return format (passages with offsets and scores), limitations (200K char cap), and integration with sibling tool. It provides sufficient information for an agent to use the tool correctly.

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

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

Schema coverage is 100%, but description adds extra meaning: examples for query (e.g., 'supply-chain risk'), explains the cap on text, and clarifies that limit is max passages. This enhances 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 clearly states it performs semantic search inside a fetched record, specifying inputs (text, query) and outputs (top-N passages with offsets and scores). It distinguishes from siblings by explicitly naming ask_pipeworx_grounded and describing the workflow.

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 advises use when the record is too large for the prompt and mentions pairing with ask_pipeworx_grounded. It does not explicitly state when not to use, but the context is clear and helpful.

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 (OAuth requirement, delivery channel specifics, auto-disable for webhooks) beyond annotations. However, it contradicts the idempotentHint=true annotation, as creating a subscription would typically not be idempotent (new id each call). This reduces trust.

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 sections for requirements, types, and delivery. It is front-loaded with purpose. Could be slightly more concise, but every sentence adds value.

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

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

The description covers all essential aspects: required account, type-specific usage, delivery channels with constraints, and the return value. No output schema exists, but the return of subscription id is noted. Sufficient for a complex tool with nested parameters.

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%, yet the description adds extensive meaning: explains type-specific parameters (e.g., items codes, topic, series_id) and delivery channel requirements (SMS cap, webhook signing). This goes far 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 specifies the action 'Create' and the resource 'proactive monitoring subscription to a live-data event stream', and distinguishes from sibling tools like list_subscriptions and unsubscribe by focusing on creation and persistence.

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 clear context for when to use (creation) and prerequisites (Pipeworx OAuth account). Does not explicitly list exclusions or alternatives, but the context is sufficient for an agent to decide.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds context about returning live catalog examples with tool+argument shapes, which goes beyond the annotations. No contradictions.

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

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

The description is thorough but slightly verbose. It front-loads alternatives and then provides detailed behavior, which is justified for an onboarding tool. Could be trimmed but 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?

Given the complexity of the tool (discovery, categorization, examples) and absence of output schema, the description covers all essential aspects: purpose, usage modes, return content, and relationship to meta-tools. Fully adequate 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?

The only parameter 'topic' is fully explained in the description: it lists example values (finance, pharma, etc.) and clarifies behavior when omitted. The schema coverage is 100%, but the description adds meaningful usage context 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 it is the onboarding entry point, returning category-bucketed example questions. It explicitly distinguishes from sibling tools like ask_pipeworx and discover_tools by specifying its role as a first step for new users.

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

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

It provides explicit guidance: 'Use this FIRST when you do not yet know what Pipeworx can do for you' and explains when to omit or pass the topic parameter. No ambiguity about when to use it versus 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?

The description discloses that the row is deactivated (not deleted) and that historical events remain available via recent_alerts. This adds behavioral context beyond annotations (which already indicate non-destructive, idempotent, write operation). No contradiction with annotations.

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

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

Two sentences, front-loaded with the core action. Every sentence contributes essential information (action, ownership, side effect). 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?

For a simple tool with one parameter and no output schema, the description covers purpose, ownership enforcement, and the key behavioral nuance (deactivation vs deletion). It also references a sibling tool (recent_alerts) for context. Complete and sufficient.

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

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

The input schema covers the single parameter 'id' with a description 'Subscription id (uuid) returned by subscribe.' The description adds only 'by id,' which doesn't enhance meaning beyond the schema. Since schema coverage is 100%, baseline 3 applies.

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

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

The description clearly states 'Cancel a subscription by id' with a specific verb and resource. It also adds ownership enforcement and deactivation behavior, distinguishing it from siblings like subscribe or 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?

Explicitly states ownership enforcement ('you can only cancel your own subscriptions'), providing a key usage guideline. It mentions that the row is deactivated, not deleted, and references recent_alerts for historical events, giving partial context for when not to use alternatives. However, it doesn't explicitly list alternative tools or scenarios to avoid.

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) already indicate safe, read-only behavior. The description adds details on return values (verdict, structured form, citation, percent delta) and notes it replaces multiple sequential calls, providing useful context beyond annotations.

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

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

The description is somewhat lengthy but well-structured: it leads with trigger phrases, states the purpose, then specifies scope and benefits. Every sentence adds value, though some minor trimming 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 single parameter and no output schema, the description adequately covers supported claim types, output format, and behavior details (like replacement of sequential calls). It provides sufficient context for an agent to decide when to use and 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?

With 100% schema coverage, the baseline is 3. The description enriches the lone 'claim' parameter by giving examples and explaining expected input format (natural-language factual claim), which adds value 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: natural-language claim verification against authoritative sources, with specific examples of usage. It distinguishes from sibling tools by explicitly calling out the claim verification function and supported domain (company-financial claims).

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and specifies the supported claim scope (company-financial claims for public US companies). It could be improved by explicitly mentioning alternatives among siblings for non-financial claims.

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