Imei

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

Annotations (readOnlyHint, idempotentHint, destructiveHint=false) are consistent. The description adds value by detailing return structure (per-model {score, confidence, signals, raw_response} + combined view) and the cost/authentication nuance for Anthropic. No annotation 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 with no filler. The first sentence defines purpose and output, the second adds usage nuance. All information is essential and well-structured.

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

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

For a tool with 4 parameters and no output schema, the description covers all necessary aspects: input semantics, defaults, return format, and use cases. No gaps identified.

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

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

Schema coverage is 100%, so baseline is 3. The description enhances each parameter: entity with concrete examples, models with default and supported values, _apiKey with purpose and note on direct payment, context with disambiguation example. This exceeds the baseline.

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

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

The description clearly states the tool's verb (probe) and resource (LLMs for entity visibility), with specificity on scoring (0-100) and use cases (AI-marketing audits, pre-launch brand checks). It distinguishes itself from sibling tools like ask_pipeworx and deep_research by focusing on visibility measurement rather than general Q&A.

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

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

The description provides explicit use cases and explains how to invoke additional models (with _apiKey for Anthropic). It does not explicitly state when not to use or compare to alternatives, but the context is clear enough for appropriate selection.

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

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

Annotations already declare read-only/open-world/idempotent behavior, so the bar is lower. The description adds substantial behavioral context: it routes to 5,501 tools across 1,441 sources, fills arguments, returns pipeworx:// citation URIs, works on every tier, and is one fast call. It also implies limitations by pointing to ask_pipeworx_grounded for verbatim evidence and confidence.

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 longer than ideal but front-loaded with the most important guidance ('PREFER OVER WEB SEARCH'). Every sentence provides useful context: use cases, sources, examples, alternatives, and breaking-news behavior. It is well-structured but could be tightened without losing meaning.

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

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

For a tool with no output schema and rich annotations, the description is comprehensive. It explains what the tool returns (structured answers with citations), when to use it, how to escalate to alternatives, and how it handles breaking news. It covers the key aspects an agent needs to select and invoke the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by clarifying the semantic scope of the question parameter: factual questions about real-world entities, events, numbers, with concrete examples like 'current US unemployment rate' and 'Apple's latest 10-K'. It doesn't detail syntax beyond what schema provides but enriches the expected input type.

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 routes factual questions to appropriate sources and returns structured answers with citations, using specific verbs like 'routes' and 'returns'. It distinguishes from siblings by naming ask_pipeworx_grounded and deep_research as alternatives, and clarifies it is the default entry point.

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 'PREFER OVER WEB SEARCH' and 'START HERE for most questions', giving clear when-to-use guidance. It also provides step-up alternatives: use ask_pipeworx_grounded for grounded answers and deep_research for broad/multi-part questions, covering exclusions and alternatives.

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

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

The description goes beyond the readOnly/idempotent annotations by disclosing the experimental nature: candidate routing improvements can be enabled live, and the last was retired on 2026-07-26. It also clarifies that this is a full working router, not a fallback, and that it currently matches ask_pipeworx exactly.

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 cover identity, current status, usage, and behavioral expectations without redundant detail. The key information is front-loaded: 'Beta version of ask_pipeworx' immediately establishes what this is.

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 annotations, full schema, and cross-reference to ask_pipeworx, the description provides all necessary context: what it does, its experimental mode, current state, and that it returns the same response shape. No output schema is needed because it matches the stable router.

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 already describes all six parameters with full descriptions, and the description adds that it uses the same arguments as ask_pipeworx. However, this is essentially a cross-reference; the schema provides the needed semantics, so the description adds little beyond the baseline.

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

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

The description clearly identifies this as a beta router identical to ask_pipeworx, with the same tool set and arguments. The phrase 'universal router' and reference to 5,501 tools specify the resource and scope, and the qualifier 'Beta version' distinguishes it from its stable sibling.

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

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

It explicitly states when to use it: 'Use it exactly like ask_pipeworx when you want the newest routing.' It also explains that results are compared against the stable router, giving the evaluative context, and notes the current no-candidate state, so an agent knows it currently behaves exactly like the stable version.

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 by detailing refusal behavior with specific refusal_reason values, the exact return shape (answer, evidence, confidence, etc.), and the mechanism of using only tool results. It also adds the cost implication. No contradiction with readOnlyHint=true or idempotentHint=true; in fact, the grounded extraction aligns with read-only semantics.

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

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

The description is concise yet information-dense, front-loaded with the core purpose, then explaining mechanics, return format, usage cases, and cost in a logical order. Every sentence adds value; no fluff or repetition. The length is justified by the complexity of the tool's fail-mode behavior.

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

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

Despite lacking an output schema, the description fully specifies the success return shape and all possible refusal reasons. It also covers routing behavior, source scope, and cost trade-offs. Given the tool's complexity, the description is remarkably complete and leaves no critical ambiguity for an agent deciding to invoke it.

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 already has 100% coverage with descriptions for all six parameters, all aliases for 'question.' The description does not add semantic detail about the parameter beyond what the schema provides; it only clarifies that the tool processes a natural language question, which is already in the schema. Baseling 3 is appropriate since the schema carries the burden.

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 explains the mechanism: same routing as ask_pipeworx, fetches data, then extracts answer using only tool result. This distinguishes it from sibling ask_pipeworx by emphasizing the grounded extraction, making the tool's unique role explicit.

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

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

Provides explicit usage guidance: 'Use whenever an answer will be quoted, cited, or acted on...' and contrasts with the alternative: 'prefer ask_pipeworx for casual lookups.' Also names the cost trade-off (one extra LLM call), giving clear when-to-use vs. when-not-to-use criteria.

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

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

The description goes far beyond the annotations (readOnlyHint, etc.) by detailing resolution logic, fan-out behavior, response shapes, safety mechanisms (low-confidence short-circuit, closed markets), and resolution-rule risk. It even warns about inspecting market_match fields before trusting analysis. This fully discloses all behavioral traits an agent needs.

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

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

The description is excessively long (over 400 words) and includes many detailed examples and edge cases. While it is well-structured with sections and bullet points, it is not concise. An AI agent would benefit from a shorter, more focused description that quickly conveys the essential behavior.

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 (resolution, classification, fan-out, response shapes, safety, resolution-rule risk), the description covers every aspect thoroughly. It explains the resolver contract, parent event extractor, news fields, and blocking conditions. There is no output schema, but the description compensates by detailing response shapes.

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

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

All three parameters (market, depth, include_raw) are fully described in the input schema with descriptions and examples. The tool description adds no additional semantic information about parameters beyond what the schema provides. With 100% schema coverage, a 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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the verb (research), resource (Polymarket bet), and scope (pulling data in one call). This distinguishes it from sibling tools like polymarket_arbitrage or polymarket_edges, which 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 provides explicit usage examples: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also details how the tool resolves markets and classifies bets, giving context on when to use it. However, it does not explicitly state when not to use it or mention alternatives from siblings, so it is not a perfect 5.

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, and idempotent behavior. The description adds valuable context: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorted results, citation URIs, and efficiency claims—all beyond annotations.

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

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

The description is front-loaded with example queries and is well-structured. It is longer than minimal but every sentence adds value. Minor redundancy exists (e.g., 'replaces 8-15 sequential lookups' restates efficiency), but overall it is concise and informative.

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

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

Given that there is no output schema, the description adequately describes return data: paired results with sorted primary metric and citation URIs. It covers the main expectations for a comparison tool. However, it could be more explicit about the structure of the returned data.

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 brief descriptions. The description enriches both parameters: explains type enum values with concrete data pulled (revenue, net income for company; adverse events for drug), clarifies values array constraints (2-5, tickers vs names), and adds nuance like fiscal-year handling for companies.

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

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

The description clearly states the tool performs side-by-side comparisons of 2-5 companies or drugs, with specific example queries. It distinguishes itself from single-entity tools like entity_profile and from sequential lookups, making its purpose unambiguous.

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

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

The description explicitly advises to prefer this tool over sequential single-pack lookups when comparing entities, and provides examples of when to use it. However, it does not include explicit exclusions or alternative tools for non-comparison tasks.

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 extensive behavioral detail beyond annotations: account/paid tier requirements, latency expectations, the gaps[] mechanism (never invented), contradictions[] for standard/thorough, semantic excerpting, citation URI fetchability, and the hop field. This gives the agent a strong mental model of execution.

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 every sentence earns its place. It is logically organized: prerequisites, core function, usage guidance, depth semantics, return format, and performance. No wasted words; the density is justified by the tool's complexity.

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

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

With no output schema, the description fully describes the return packet (evidence, confidence, source, fetched_at, citations, gaps[], contradictions[]), latency, account requirements, and edge cases like breaking news. Complete enough for an agent to invoke 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?

The description enriches both parameters. It clarifies that 'question' can be broad/multi-part, and it explains 'depth' with specific facet counts and hop behavior (quick=3, standard=5 with gap recovery, thorough=8 with iterative hop). This goes well beyond the schema's brief descriptions.

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

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

The description clearly states the tool's function: grounded multi-source research across 1441 structured data sources, decomposing questions into facets and routing to 5501 tools in parallel. It distinguishes itself from open-web search and from siblings like 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?

Explicitly states when to use (broad/multi-part questions over structured data) and when not (single lookup, breaking/current news), naming ask_pipeworx as the alternative in each case. Also notes account requirements and when to use ask_pipeworx if not signed in.

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 readOnlyHint, idempotentHint, and destructiveHint. The description adds that it returns top-N most relevant tools with names, descriptions, and full input schemas ready to call directly, 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 clear and front-loaded with the core purpose. However, it includes a long list of example domains that could be more concise. Overall, every sentence earns its place.

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

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

Given no output schema, the description explains the return format (tools with names, descriptions, schemas) and usage context. It could mention pagination or error handling, but for a discovery tool it is sufficiently complete.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description provides example queries (e.g., 'look up FDA drug approvals') but does not add significant new meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists many example domains, and its role of discovering other tools distinguishes it from the sibling tools which are specific utilities.

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 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear context for when to use it, though it does not explicitly mention 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?

Beyond annotations (readOnly, idempotent), the description details return fields (filings, fundamentals, patents, news, LEI), notes the patents API sunset/soft-fail behavior, and clarifies input formats. 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 examples and preferences, then systematically lists return fields. It is verbose but each sentence adds unique information; could be slightly tighter 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?

With no output schema, the description compensates by exhaustively listing returned data (CIK, filings with URIs, fundamentals sorted, patents status, news, LEI). It also covers limitations (names not supported, patents soft-fail) and usage context.

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

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

Schema covers both parameters at 100% coverage, but the description reinforces meaning: type is currently fixed to 'company', value must be ticker or zero-padded CIK, and explicitly excludes names. Adds value by clarifying 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 returns a 'full cross-source profile of a US public company' with specific verbs like 'fans out across' and lists concrete examples. It distinguishes from siblings like resolve_entity by noting that name lookups require a different tool.

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

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

Explicitly says 'ALWAYS PREFER over chaining single-pack...lookups when the user asks for a holistic view' and advises using resolve_entity if the user only has a name, providing clear contextual guidance and alternatives.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. Description adds context about clearing sensitive data but does not go beyond what annotations imply.

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 two sentences with clear front-loading of the primary action. No unnecessary words.

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

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

Given low complexity, description covers purpose, usage, and pairing. Missing mention of idempotency or return value, but annotations cover the former.

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

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

Schema already covers the only parameter 'key' with a description. Description merely restates 'by key', adding no 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?

Description clearly states the verb 'Delete' and resource 'memory by key'. It also distinguishes from siblings by mentioning pairing with 'remember' and 'recall'.

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

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

Explicitly states when to use (context stale, task done, clear sensitive data) and suggests pairing with alternatives (remember, recall). Lacks explicit 'when not to use' but is still very clear.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that it fetches a page, extracts title/description/key links, and outputs standard markdown. 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?

The description is concise, front-loaded with the main purpose, followed by process details and use cases. Every sentence adds value with no redundant content.

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

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

Given no output schema, the description explains the output format (single text blob) and the process. It covers the tool's functionality adequately, though error handling is not mentioned but annotations cover safety.

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

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

Schema coverage is 100% with both parameters fully described. The description does not add new semantic details beyond what the schema provides, 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, using specific verbs and resources. It distinguishes itself from sibling tools like scan_competitor_ai_presence by focusing on llms.txt generation.

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

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

The description includes explicit use cases (e.g., indexing client sites, drafting for own project, auditing competitors). It lacks explicit when-not-to-use or alternative suggestions, but the context is clear enough.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, fully covering safety. The description adds the precise computation behavior (Luhn check digit) and input constraints (first 14 digits). No contradictions. With high annotation coverage, the description adds useful but not critical context.

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

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

A single sentence contains all essential information: action, input scope, algorithm, and use case. No filler or redundancy. Excellent 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?

For a simple, single-parameter tool with no output schema, the description is fully complete. It explains what the tool does, when to use it, and what input is expected. The output (a single check digit) is implicit and does not require further explanation.

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

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

Schema coverage is 100% with a clear description of the 'digits' parameter ('The first 14 digits (no check digit).'). The tool description reinforces this but does not add new semantic info beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the action ('Compute'), the algorithm ('Luhn check digit'), the input scope ('first 14 digits of an IMEI'), and the use case ('complete or repair an IMEI'). It effectively distinguishes from sibling validate_imei.

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 the use case ('use to complete or repair an IMEI'). While it does not explicitly contrast with validate_imei or other alternatives, the context of sibling tools makes the differentiation clear. A slightly more explicit alternative mention would elevate to a 5.

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=true, idempotentHint=true, destructiveHint=false. The description adds value by detailing the return fields (id, type, params, created_at, last_fired_at, fire_count) and the default scope (active subscriptions). It does not contradict annotations.

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

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

The description is two sentences long, front-loaded with the purpose, and every sentence adds value. 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?

The tool has low complexity with 1 optional parameter. The description covers purpose, return fields, and usage guidance. It lacks mention of pagination or ordering, but for a standard list tool this is acceptable. Output schema is absent but return fields are 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?

The input schema has 1 parameter (include_inactive) with a full description. Schema coverage is 100%. The tool description does not add additional meaning beyond the schema, so 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 'List the caller's active subscriptions.' It uses a specific verb and resource, and distinguishes from sibling tools like subscribe and unsubscribe by being a read-only list operation.

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

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

The description explicitly advises when to use this tool: 'to review what you're monitoring before adding more or to find an id to cancel.' This provides clear guidance and implies alternatives (subscribe/unsubscribe).

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 annotations all false, the description carries the full behavioral burden and delivers thoroughly: it explains the claim_token workflow for tracking feedback, discloses the 5-per-identifier-per-day rate limit, states it's free/no quota, and warns against pasting end-user prompts. No annotation contradiction exists.

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 every sentence earns its place: purpose, when-to-use, exclusions, workflow, rate limit, and cost. It is front-loaded with the core purpose sentence and follows a logical sequence without redundancy.

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

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

Given no output schema, the description explains the return behavior (claim_token, later status retrieval) adequately. It also covers rate limits, scope boundaries, and reporting etiquette, making it complete for a feedback tool of this complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaningful behavior beyond the schema by clarifying that filing without an account returns a claim_token and that later calls should pass it back as pipeworx_feedback({claim_token:...}). It also instructs users to describe issues in terms of Pipeworx tools/packs, guiding the message and context parameters, though not drastically.

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

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

The description opens with a specific verb and resource ('Tell the Pipeworx team something is broken, missing, or needs to exist'), clearly distinguishing it from sibling tools. It further enumerates concrete use cases (bug, feature/data_gap, praise), making the purpose unmistakable.

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

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

Explicit when-to-use guidance is provided for each feedback type, and equally explicit when-not-to-use guidance ('ONLY for tools served by this Pipeworx connection', 'file it with that server instead' for other MCP servers). This is exemplary alternatives 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds non-redundant context: self-aggregating from CF analytics-engine, no PII, caching behavior (5min-1h), and output structure (pack, tool, count). Adds value 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?

Description is four sentences with a bullet list. Every sentence is meaningful, no waste, front-loaded with the core behavior.

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 read-only tool with one optional parameter and no output schema, the description fully covers purpose, usage, behavior, and parameter semantics.

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 an enum for window. Description adds guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This adds 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 'Returns the top tools, top packs, and total call volume' with specific verb and resource. It distinguishes from sibling tools like 'discover_tools' by focusing on trending usage rather than available tools.

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

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

The description provides three specific use cases (discovering hot data sources, confirming canonical tool, seeing alignment) but does not explicitly mention when not to use it or compare 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?

Discloses behavioral traits beyond annotations, including Jaccard similarity threshold, partition filter for placeholders, fill check with realizable edge, and conditions for not trading. No contradiction with annotations (readOnlyHint, etc.).

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

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

Description is lengthy but well-structured: uses clear paragraphs for each mode and key concepts (semantic anchor, partition filter, response, fill check). Some redundancy could be trimmed, but overall organization aids comprehension.

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

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

Despite no output schema, describes response fields (opportunities[], partition_check details, fill check results). Covers all modes, edge cases (low similarity, placeholders), and refers to related tool. Complete for an agent to use correctly.

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

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

Schema coverage is 100% but description adds substantial meaning: explains expected input formats (slugs/URLs for event, seed question for topic), describes what each mode does, and clarifies behavior like cross-event scanning and similarity checks.

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 explicitly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It clearly distinguishes three modes (no args, event, topic) and differentiates from sibling tools like polymarket_edges and polymarket_fill_risk by specifying unique functionality and use cases.

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

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

Provides explicit guidance on when to use each mode: 'Call with NO args for a trending_scan', 'event (recommended for a specific market)', 'topic (for cross-event scanning)'. Also advises against trading when fill check fails and directs 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 (readOnlyHint, idempotentHint) are consistent. The description adds extensive behavioral details: model families, caching, diagnostics, Fed exclusion reasons, and edge computations, all 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 informative and front-loaded with purpose, but is verbose. Every sentence adds value, but structure could be more organized (e.g., sections).

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, no output schema, and complex response structure, the description thoroughly explains segments, edge metrics, diagnostics, and caching, leaving 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%, providing baseline 3. The description adds context (e.g., 'tradeable-edge filter' for max_spread_pp) and explains how parameters interact, adding marginal 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 clearly states it scans Polymarket markets for opportunities where Pipeworx data disagrees with market price, distinguishing it from siblings like polymarket_arbitrage and polymarket_edge_tracker through specific model families.

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 clear context for use ('what should I bet on today') and describes knobs for filtering, but lacks explicit when-not-to-use guidance or direct comparison 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 indicate read-only and idempotent; the description adds critical behavioral details: snapshots on cache-miss, gaps meaning no scan, decay computation from daily closes, and lifespan median as competition clock. No contradiction.

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

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

Description is detailed but efficiently structured: purpose first, then response fields, then limits. Each sentence adds value; no redundancy. Slightly long but justified by complexity.

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

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

Despite no output schema, the description fully explains all three response arrays (tracked, expired, snapshot_dates) with edge cases and limits. Complete for a read-only analytics 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 covers both parameters fully; description reinforces defaults (14 days, '1wk') and adds context by tying them to the response structure (e.g., window affects snapshot family). Adds meaningful context beyond schema.

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

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

The description clearly states it provides edge persistence and decay telemetry from daily snapshots, and contrasts with the sibling 'polymarket_edges' by focusing on historical tracking. The verb 'answers' and specific output fields make the purpose obvious.

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

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

The description explains what the tool returns and highlights limits (e.g., 60-day TTL, snapshot gaps). It implies use when needing edge persistence history but lacks explicit when-not or alternative tool comparisons.

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 behavioral context: walks the ladder, returns fill details and verdicts (clean|degraded|cannot_fill), and warns about forced directional risk. No contradictions. Since annotations do heavy lifting, description supplements well.

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 with clear sections (REQUIRES, SINGLE-MARKET, BASKET) and front-loaded key purpose. Each sentence adds value, though slightly lengthy; could be tightened without losing clarity.

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

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

No output schema, yet description comprehensively lists return fields (top_of_book, vwap_fill_price, slippage_pp, etc.) and verdicts. Covers thin legs, directional risk, and profit calculations for both modes. 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%, but description adds substantial meaning: explains mutual exclusivity of market vs event, default side behavior in basket mode, size interpretation for buys vs sells and basket notional. Adds format details (slug/URL) and default values, going well beyond schema.

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

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

Description clearly states it's a 'Realizable-vs-theoretical edge check' against order-book depth, distinguishing single-market and basket modes. It explicitly differentiates from sibling tools like polymarket_arbitrage and polymarket_edges by stating 'USE THIS before acting on' those signals.

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

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

Explicit usage guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Also explains risks of partial fills and theoretical overround, providing clear when-to-use and when-not-to-use context.

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

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

Annotations indicate read-only, idempotent, and non-destructive behavior. The description extensively adds context: two modes, response structure, safety fields (compatibility_warning, temporal alignment, skipped counters), and a warning that pre-mapped topics are often not tradeable. No contradiction with annotations.

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

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

The description is dense but well-organized with uppercase labels and bullet-like structure. Every sentence adds value, though it could be slightly trimmed 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 having no output schema, the description thoroughly explains the response structure (leg-by-leg prices, top spreads, safety fields). It covers edge cases (compatibility, temporal alignment) and warns about common pitfalls. Highly complete for the tool's complexity.

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

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

Schema coverage is 100% (baseline 3). The description adds meaning by explaining each parameter: topic with list of 10 shortcuts, and explicit tickers with override logic. This goes beyond the schema's type 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: computing cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes itself from other polymarket-related siblings by focusing on cross-venue comparison.

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

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

Provides clear guidance on two modes (topic shortcuts and explicit tickers) and explains when compatibility warnings fire. Implicitly tells when not to use (when bet shapes are non-equivalent) but does not explicitly list alternatives among siblings.

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

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

Annotations already declare readOnly and idempotent. Description adds scoping to identifier and behavior of omitting key (list all). 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 well-structured sentences. Front-loads core action and purpose. 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., string value or list). Could be more complete about 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 coverage is 100% with description per parameter. Description adds examples but no new semantic detail 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 retrieves a value by key or lists all keys if omitted, with concrete examples (ticker, address, research notes). It 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?

Explicitly says when to use: to look up previously stored context without re-deriving. Mentions pairing with remember/forget. Could be more explicit about 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?

Description adds value beyond annotations by explaining the mark_read parameter which modifies read state. Despite readOnlyHint annotation claiming no side effects, the description honestly discloses this behavioral trait, making it transparent.

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

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

Four sentences with no wasted words. Each sentence adds essential information: core purpose, return contents, filtering options, mark_read behavior, 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?

Despite no output schema, description enumerates return fields (source, citation_uri, raw payload). Parameters are well-documented, alternative access is provided, and annotations cover safety. Complete for a read-focused paginated list tool.

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

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

Schema coverage is 100%, so baseline is 3. Description adds context: example filter type 'sec_8k', ISO timestamp for since, limit range 1-200, and behavior of mark_read, going 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?

Description clearly states 'Pull fired events from your subscription feed,' specifying the verb and resource. It distinguishes from sibling tools like list_subscriptions (which lists subscriptions, not events) by focusing on retrieving alert events from the feed.

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?

Explanation of filtering by type and/or since provides usage context. Mention of polling and alternative direct URL access gives guidance on when to use this tool versus scripts/dashboards, but doesn't explicitly exclude 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?

Although annotations declare readOnly, idempotent, etc., description adds crucial behavior: parallel call, fan-out to three sources, error handling (GNews fallback, patent soft-fail), and return format (grouped changes, 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?

Description is long but justified by complexity. Front-loaded with query examples, but some details (e.g., patent API sunset) could be compressed. No wasted sentences.

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 output schema, description adequately explains return: structured changes grouped by source, total_changes count, pipeworx:// citation URIs. Covers all necessary aspects for correct 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 has 100% coverage, but description enriches each parameter: 'since' accepts ISO dates and relative shorthand with examples; 'value' can be ticker or CIK; 'type' only 'company' today. Provides guidance like 'Use "30d" for typical monitoring'.

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 (change feed), concrete resource (company), and actionable query examples: "What's new with X". Explicitly distinguishes from sibling 'entity_profile' by stating use case difference.

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 patterns and when-not-to (static profile via entity_profile). Details fallback behavior (GDELT→GNews), soft-fail for patents, and typical 'since' values ('30d', '1m').

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 details beyond annotations: scoping by identifier, persistence differences between authenticated users (persistent) and anonymous (24 hours). No contradictions with annotations (idempotentHint=true, etc.).

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

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

The description is concise (4 sentences) and well-structured: starts with purpose, then usage guidelines, then behavioral details, all without redundancy.

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

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

Given the tool's simplicity (2 string params, no output schema), the description covers purpose, usage, parameter semantics, and behavioral aspects comprehensively.

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

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

Schema coverage is 100% with descriptions, but the description adds value by providing concrete usage examples for key (e.g., 'subject_property') and value ('findings, addresses, notes'), enhancing 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 verb 'save' and the resource 'data' (key-value pair). It defines the tool's purpose as storing information for reuse, distinguishing it from siblings like recall and forget.

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

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

The description explicitly says 'Use when you discover something worth carrying forward' and mentions pairing with recall and forget. This provides clear context and alternatives.

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

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

Annotations already declare readOnly=true, idempotent=true, and non-destructive. The description adds valuable context: it cascades through several lookup endpoints internally, returns citation URIs (pipeworx://edgar/company/{cik}, pipeworx://rxnorm/concept/{rxcui}), and handles auto-disambiguation for company inputs. These behavioral details go beyond the safety hints.

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

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

The description is well-structured: it opens with realistic user queries, then states purpose, then details supported types and return values. Every sentence adds value—no filler. Length is appropriate for the tool's complexity.

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

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

There is no output schema, so the description aptly covers return formats, citation URIs, and input flexibility. The tool's internal cascading behavior is disclosed, making the description complete for a 2-parameter lookup tool. The agent has all needed info to select and invoke it 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?

The schema already provides 100% parameter coverage with descriptions and examples, so the baseline is 3. The tool description adds extra meaning by specifying exact output fields for each type (e.g., 'ticker + 10-digit CIK + company_name', 'RxCUI + ingredient + brand') and noting auto-disambiguation, which helps the agent understand the value beyond schema 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 uses a specific verb ('resolve') and resource ('user-spoken NAME to canonical/official identifier'), and clearly distinguishes from sibling tools like entity_profile (which likely provides profile data). It lists supported types (company/drug) and specific return values (ticker, CIK, RxCUI), making the tool's scope 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 gives explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains it replaces 2-3 manual lookups and lists input formats. However, it does not explicitly state when NOT to use it or name alternative tools, so it falls short of a 5.

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 value by explaining the tool makes multiple probes (behavioral context) and returns a ranked list with score, confidence, and signal density, going 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 three concise sentences, front-loaded with purpose, mechanism, and use case. Every sentence adds value with no fluff, making it 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?

With no output schema, the description specifies the output format (ranked list with score, confidence, signal density per entity). It explains input parameters, the underlying ai_visibility_check, and provides a usage example. For a tool with 4 params and no nested objects, this is fully complete.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds nuance: explains the first entity is treated as the 'subject' for narrative, and clarifies model usage (omit for just workers-ai). This additional context raises the score.

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, using ai_visibility_check, and ranks results. It provides a concrete example question and distinguishes from sibling tools by focusing on batch comparison for competitive audits.

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 a clear use case ('competitive AI-marketing audits') and an illustrative question, but does not explicitly state when not to use this tool or compare it to alternatives like compare_entities or individual ai_visibility_check calls.

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 rich behavioral context beyond annotations: partial failures, bundlephobia first-measure delay of 5-30 seconds, sources_failed list, and graceful degradation. Annotations indicate readOnly, openWorld, idempotent, non-destructive, and 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?

The description is packed with information, well-organized, and front-loads the main purpose. While slightly long, every sentence serves a purpose, making it efficient for an AI agent.

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

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

Despite no output schema, the description details the return structure: summary block fields, per-advisory detail, links, and alternative versions. This is thorough for a composite tool with two services, covering all necessary context.

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

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

Schema coverage is 100% with both parameters described. The description does not add significant value beyond the schema; it essentially restates the parameter descriptions. Baseline 3 is appropriate.

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

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

The description clearly states it's a composite check for npm packages combining deps.dev and bundlephobia, with specific verb 'check' and resource 'npm package'. It distinguishes itself from siblings by focusing on npm dependency analysis, unlike other tools like bet_research or compare_entities.

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

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

The description explicitly says 'Use whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. It also specifies the NPM ecosystem only and mentions partial fallback and alternatives for other ecosystems (deps.dev:version directly).

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

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

Annotations already provide readOnlyHint, idempotentHint, etc. Description adds value by disclosing chunking strategy (500-char overlapping windows), embedding model (BGE-base-en), truncation at 200K chars with flagging, and inclusion of character offsets. This goes beyond annotations, though response structure is not detailed.

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

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

Description is concise (~120 words) with front-loaded purpose, followed by usage guidance, examples, and technical details. No redundant or unnecessary sentences.

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 all key aspects: what it does, when to use, pairing with sibling, input schema with examples, technical details (chunking, truncation, offsets), and output description (top-N passages with offsets and scores). No output schema, but description adequately explains return values.

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

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

Schema coverage is 100% with good descriptions. Description adds concrete examples (e.g., SEC 10-K body, 'supply-chain risk') and clarifies the role of query as a natural-language request, making parameters more actionable.

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, with specific verb 'search' and resource 'inside a fetched record'. It distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded and contrasting usage scenario 'when the record is too big to cram into the prompt'.

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

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

Explicitly states when to use (record too big for prompt) and pairs with sibling ask_pipeworx_grounded. It also implies when not to use (if record fits in prompt). Provides clear context for decision-making.

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 show readOnlyHint=false, destructiveHint=false, idempotentHint=true. The description confirms it creates a subscription, adding many behavioral details: requires OAuth, delivery channel specifics, phone verification, 10/day SMS cap, webhook signing secret returned once, auto-disable after 10 failures. This is rich 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 thorough but somewhat lengthy. It front-loads the purpose and then systematically covers types and delivery. Every sentence adds value, but the level of detail could be trimmed slightly for agents needing quick comprehension. Still well-organized.

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

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

Despite no output schema, the description states 'Returns the new subscription id.', which is sufficient. It covers prerequisites, all supported types, delivery options, and limitations (phone verification, SMS cap, webhook auto-disable). For a subscription creation tool, it is highly 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 coverage, the description provides detailed examples for each type (e.g., sec_8k: {ticker:"AAPL", items?:["5.02","1.01"]}) and explains delivery object fields in depth. This adds significant meaning beyond the schema's terse property 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: 'Create a proactive monitoring subscription to a live-data event stream.' It specifies the action (subscribe), the resource (alerts), and distinguishes from siblings like list_subscriptions and unsubscribe. The supported types are listed, providing concrete examples.

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

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

The description provides usage context: requires Pipeworx OAuth, can't be used with anonymous/BYO accounts. It implies when to use alternative tools like recent_alerts for pulling the always-on feed. However, it doesn't explicitly state when not to use this tool vs. others, leaving room for slight ambiguity.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds value by explaining the output structure (category-bucketed examples with exact tool+argument shapes) and that it draws from a live catalog, which the annotations don't cover.

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

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

The description is slightly long but front-loaded with the most important information: the onboarding purpose and when to use it. Every sentence contributes to the agent's understanding, but it could be slightly more concise without losing meaning.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description is thorough. It covers the purpose, usage context, parameter behavior, and output format, leaving no ambiguity. Combined with strong annotations, the tool definition is fully self-contained.

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

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

Schema coverage is 100% for the single optional parameter. The description enhances understanding by explaining that omitting topic gives the full spread while providing a specific topic focuses the results, and lists example values (finance, pharma, etc.).

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 the onboarding entry point for an agent to learn about Pipeworx's capabilities. It uses specific verbs ('returns category-bucketed example questions') and distinguishes it from siblings like ask_pipeworx and entity_profile by positioning it as the first tool to call when unsure of what to ask.

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

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

Explicitly tells the agent to 'Use this FIRST when you do not yet know what Pipeworx can do for you' and explains when to omit or specify the topic parameter. While it doesn't explicitly list when not to use it, the context is clear and it provides good guidance on invocation timing.

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

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

Beyond annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true), the description adds that rows are deactivated not deleted, preserving historical events, and clarifies ownership enforcement.

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

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

Two concise sentences with no wasted words, front-loaded key action and constraints.

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

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

Given the tool's simplicity (1 param, no output schema, clear annotations), the description fully covers side effects, ownership, and alternative tool usage, leaving 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 a clear 'id' description. The description adds 'by id' but 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?

Description clearly states 'Cancel a subscription by id.' with a specific verb and resource, distinguishing it from sibling tools 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?

Explicitly mentions ownership enforcement ('you can only cancel your own subscriptions') and explains the deactivation behavior, providing guidance on when to use and alternatives like 'recent_alerts'.

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

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

Annotations already mark the tool as read-only, idempotent, and nondestructive. The description goes beyond by detailing the internal logic (SEC EDGAR + XBRL for financial claims, grounded pipeline for others) and the return structure (verdict, actual value with citation, reasoning). This provides actionable behavioral context 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 moderately long (4-5 sentences) but each sentence adds distinct value: examples for triggering, purpose, internal routing, return structure, and efficiency note. It is front-loaded with the most critical information. 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 complexity of the tool (handling two distinct verification pipelines) and the absence of an output schema, the description fully covers return values (verdict, actual value, citation, reasoning), internal behavior, and the distinction between claim types. No further context is needed for an agent to select and invoke the tool correctly.

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

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

Schema coverage is 100%, so the schema documents both parameters. The description adds value by explaining the `tolerance_pct` default behavior (implied by wording, capped at 5) and its specific use for hallucination detection (set 1-2). The `claim` parameter's natural-language nature is reinforced with 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 begins with concrete natural-language triggers ('Is it true that...', 'fact check') and explicitly states it performs claim verification against authoritative sources. It distinguishes between company-financial claims (SEC EDGAR fast path) and other claims (grounded pipeline), making the purpose precise and differentiated from sibling tools.

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

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

The description explicitly advises 'Use whenever the agent needs to check whether something a user said is factually correct.' It further clarifies the internal routing (structured vs. grounded) and notes that it replaces 4-6 sequential calls, implicitly guiding when to prefer this over multi-step alternatives. However, it does not explicitly list when not to use the 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 declare readOnly, idempotent, non-destructive. The description adds key context: 'Keyless/offline' (no side effects), 'Spaces/dashes ignored' (input processing), and 'Validates the number, not the device' (scope limitation).

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 efficient sentences, front-loaded with the core purpose. Every sentence adds value: validation method, input handling, output details, and scope clarification.

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 validation tool with one parameter and no output schema, the description fully explains input handling, validation logic, and the components of the result (validity, TAC, serial, check digit). It also clarifies what the tool does not do.

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, but the description adds useful detail: allowed formats (15/16 digits), that spaces/dashes are ignored, and what the validation checks (Luhn). This goes 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 clearly states it validates IMEI/IMEISV with specific validation rules (Luhn check for IMEI) and explains what it returns (validity, TAC, serial, check digit). It distinguishes from device validation by explicitly saying 'Validates the number, not the device.'

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 'Keyless/offline' indicating no authentication needed, and clarifies it does not validate the device. However, it does not explicitly differentiate from sibling tool 'imei_check_digit', though the purpose is distinct enough.

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