Medical Codes

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

Annotations already declare read-only, idempotent, and open-world hints. The description adds behavioral context: it probes multiple LLMs, returns per-model data, requires an API key for Anthropic (with cost implications), and the default model is free. This complements the annotations well without contradicting them.

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

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

The description is concise (three sentences) and front-loaded with the primary purpose. Every sentence adds value: purpose, model selection details, return format, and use cases. No unnecessary words or repetition.

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

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

Given the tool has 4 parameters, no output schema, and rich annotations, the description adequately covers return format (per-model score/confidence/signals/raw_response + combined view), cost implications, and use cases. It is complete enough 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 description coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema: 'entity' is the target, 'models' selects which LLMs, '_apiKey' is for Anthropic (passed through), and 'context' disambiguates. This provides functional context that aids correct invocation.

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

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

The description clearly states the tool's purpose: probing LLMs for knowledge about an entity and scoring visibility. It specifies the verb 'probe,' the resource 'LLMs,' and the output (visibility score 0-100). It distinguishes from sibling tools like 'scan_competitor_ai_presence' by focusing on AI-marketing audits and brand checks.

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

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

The description provides clear usage context: AI-marketing audits, pre-launch brand checks, competitive monitoring. It explains default model and optional Anthropic integration with BYO key. However, it does not explicitly mention when not to use or alternatives among siblings, though the use cases are well-defined.

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

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

Annotations already provide readOnlyHint, openWorldHint, etc. The description adds valuable context: routing to sources, returning structured answers with citation URIs, working on all tiers, being fast. No contradictions.

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

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

The description is long but well-organized: starts with key preference, then usage guidelines, examples, and sibling comparisons. Every sentence adds value, though could be slightly trimmed.

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

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

Given the tool's complexity (4,774 tools, many siblings), the description covers purpose, usage, alternatives, examples, and return format (citations). No output schema needed as description suffices.

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

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

Schema covers 100% of parameters, and description does not add significant meaning beyond the schema's 'Your question or request...' definition. 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 explicitly states the tool answers factual questions using structured data from thousands of sources, with examples. It clearly distinguishes from siblings like ask_pipeworx_grounded and deep_research.

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

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

Explicitly says 'PREFER OVER WEB SEARCH' and provides usage criteria like 'what is', 'look up', etc. It also advises when to use alternatives, making when-to-use and when-not-to-use 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?

Goes well beyond annotations by detailing the internal routing, extraction process, exact return fields and refusal reasons, and the extra LLM call cost. 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?

Every sentence serves a purpose: purpose, mechanism, use cases, trade-off. No filler, front-loaded with key information, well-organized into a coherent paragraph.

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 (high-stakes, alternative to ask_pipeworx, 34 siblings) and lack of output schema, the description is thoroughly complete: covers behavior, return structure, failure modes, and usage boundaries.

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

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

100% schema coverage; description adds value by listing all accepted aliases for the 'question' parameter, preventing confusion. No additional semantic depth needed beyond the aliases.

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?

Explicit verb-resource combination: 'Hallucination-resistant answer mode for high-stakes reads.' Clearly distinguishes from sibling ask_pipeworx by emphasizing grounded extraction from tool results only.

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 (quoted/cited/acted-on answers, high-stakes domains) and when not (casual lookups, prefer ask_pipeworx). Also explains the extra cost trade-off.

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 extensive behavioral details beyond annotations: classification, fan-out per category, response shapes, resolver contract, parent event extraction, news fallback, safety checks (low-confidence, closed markets, wide spreads), and resolution-rule risk. Annotations already indicate read-only/idempotent, but description adds rich 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?

While well-structured with section headers (CLASSIFIERS, FAN-OUT, etc.), the description is excessively long, containing many examples and edge cases. Could be trimmed by 30-40% without losing utility.

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

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

Given no output schema, the description fully explains return shapes (market, analysis, evidence), resolver contracts, parent event extraction, news fallback, and safety behaviors. Covers all critical scenarios an agent needs.

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% (3 params fully described). Description adds meaning for include_raw (when to use true vs false) and depth (quick vs thorough), which the schema provides but description enriches with practical guidance.

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

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

The description clearly states it researches a Polymarket bet by pulling Pipeworx data in one call. It specifies valid inputs (slug, URL, question text) and output types (evidence packet, market-vs-model comparison), distinguishing it from sibling tools that focus on edges or arbitrage.

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

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

Explicitly states use cases ('should I bet on X', 'what does the data say', 'is there edge') and provides examples. Does not explicitly name alternatives or when to avoid, but the context is clear enough for correct 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 indicate read-only, open-world, idempotent, non-destructive behavior. The description adds substantial value by detailing data sources (SEC EDGAR for companies, FAERS for drugs), handling of fiscal years, and inclusion of citation URIs. No contradictions.

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

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

The description is dense with useful information but somewhat lengthy. It front-loads trigger phrases and key action, but could be slightly trimmed without losing value. Still, every sentence serves a purpose.

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

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

Despite no output schema, the description explains output includes paired data and citation URIs. It covers entity types, constraints, sorting behavior, and the replacement of 8–15 sequential calls. Fully complete for its 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 the schema already documents parameters. The description adds meaningful context for 'values': for company use tickers/CIKs, for drug use names, and specifies min/max items. This exceeds baseline expectations.

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

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

The description starts with multiple trigger phrases ('Compare X and Y', 'X vs Y', etc.) and clearly states it performs side-by-side comparison of 2–5 companies or drugs. It distinguishes itself from siblings by explicitly recommending it over sequential single-pack lookups.

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

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

Provides explicit guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Also includes concrete examples like 'which is bigger' and 'rank these companies', making usage clear.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint = false. Description adds significant behavioral context: parallel decomposition across 4,774 tools, 15-60s response time, gaps[] for unanswered facets, and that it never invents. No contradictions.

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

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

Description is detailed but well-structured with bolded keywords and logical flow. Front-loads account requirement and alternatives. Each sentence serves a purpose; slightly verbose but justified given tool 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?

No output schema, so description fully explains return format: 'findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[]'. Also covers expected response time, account tiers, and tool limitations. Complete given the tool's complex behavior.

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

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

Schema coverage is 100% (both parameters described). Description adds value beyond schema: explains depth enum meanings (quick=3, standard=5, thorough=8) and mentions default and paid plan restriction. Also clarifies question parameter 'natural language' and that broad questions are fine.

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

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

The description clearly states the tool's purpose: 'Grounded multi-source research across Pipeworx's 1242 STRUCTURED data sources' returning a findings packet. It distinguishes from sibling tools like ask_pipeworx (single lookup) and mentions alternatives for current news, making 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?

Explicit usage guidance: 'Best for broad/multi-part questions over structured data' with counter-examples (breaking news, current events) and explicit alternatives (ask_pipeworx, ask_pipeworx_grounded). Also states account requirements and when the tool returns empty gaps.

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

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

Annotations indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds that each result is 'ready to call directly, no second schema lookup needed,' which is a useful behavioral trait beyond the annotations. No contradictions.

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

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

The description is front-loaded with the core purpose and usage context. While it lists many domains and includes details about the return format, it is not overly verbose. It could be slightly more concise, but it effectively communicates key information.

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

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

Despite lacking an output schema, the description thoroughly explains what the tool returns: 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly.' It also advises to call it first for discovery. This is complete for a 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% with all parameters described. The description mentions that 'query' accepts natural language and lists aliases, but these details are already in the schema descriptions. The description does not add significant meaning beyond what the schema provides, so baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists specific domains (SEC filings, financials, etc.) and explains that it returns the top-N relevant tools with full schemas, distinguishing it from sibling tools that likely focus on specific tasks or queries.

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

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

The description explicitly says to use this tool 'when you need to browse, search, look up, or discover what tools exist' and instructs to 'Call this FIRST when you have many tools available and want to see the option set.' It provides clear context for when to use it, though it could be more explicit about when not to use it.

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

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds valuable detail: it returns specific fields (CIK, filings, fundamentals, patents, news, LEI), notes that the patents API may soft-fail due to sunset, and clarifies input format. This exceeds what annotations alone 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 front-loaded with example queries and well-structured, covering purpose, input, and output in logical sections. While somewhat lengthy, every sentence adds value; minor redundancy could be trimmed.

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

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

Given no output schema, the description adequately lists return fields and sources. It mentions limitations (patents may fail) and input requirements. Missing details like error handling or rate limits are minor given the complexity, and 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%, but the description adds meaning beyond schema descriptions by giving concrete examples ('AAPL', '0000320193'), explicitly stating that names are not supported, and distinguishing between ticker and CIK format.

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

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

The description clearly states the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call.' It lists example queries ('Tell me about X', 'research Acme', etc.) and explicitly distinguishes from chaining individual tools, making its use unambiguous.

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

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

The description provides explicit guidance: 'ALWAYS PREFER over chaining...when the user asks for a holistic view.' It also states when not to use (if only a name is given, use resolve_entity first) and notes alternatives (chaining single-pack lookups).

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. The description adds context about clearing sensitive data and staleness, but does not explicitly state that deletion is irreversible or that it affects long-term memory persistence. No contradiction with annotations.

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

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

Two sentences, front-loaded with the primary action, no extraneous information. Every sentence adds value.

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

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

For a simple tool with one parameter and clear annotations (destructiveHint, idempotentHint), the description covers purpose, usage guidance, and context. No output schema needed, and 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?

The input schema has 100% coverage with a description for the single parameter 'key' ('Memory key to delete'). The description does not add additional meaning beyond the schema's description, 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 'Delete a previously stored memory by key,' specifying the verb (delete) and resource (memory by key). It distinguishes from siblings like 'remember' and 'recall,' which are listed for context.

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

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

The description explicitly states when to use: 'context is stale, the task is done, or you want to clear sensitive data.' It also provides alternative tools ('remember' and 'recall') and context for pairing.

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, non-destructive behavior. The description adds behavioral detail: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This aligns with annotations and provides useful context about the process.

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

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

The description is a single, well-structured paragraph. It front-loads the main action, explains the output, and ends with concrete use cases. Every sentence serves a purpose with no unnecessary words.

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

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

For a simple tool with two parameters and no output schema, the description is complete. It covers what the tool does, when to use it, and the output format ('a single text blob ready to drop at site-root/llms.txt'). No additional information is needed.

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

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

Schema coverage is 100% with detailed descriptions for both parameters. The tool description does not add significant new information about parameters beyond stating the overall purpose. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the tool's function: 'Generate a production-ready llms.txt file for any URL'. It specifies the output format and the resource involved, distinguishing it from sibling tools which are unrelated.

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: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' While it does not list alternatives or exclusions, the contexts are clear and helpful.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds that it returns active subs by default and lists return fields, but does not add major behavioral insights beyond annotations.

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

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

Two short, front-loaded sentences: first covers purpose and output, second provides usage guidance. 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 one parameter, robust annotations, no output schema, and description listing return fields, the description is 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?

Only one parameter (include_inactive) already fully described in schema with description. Description does not add additional parameter-level meaning beyond schema coverage of 100%.

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

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

Clearly states 'List the caller's active subscriptions' with verb 'list' and resource 'subscriptions'. Distinguishes from siblings (subscribe, unsubscribe) by focusing on enumeration. Returns specific fields enumerated.

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

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

Explicitly says use to review before adding more subscriptions or to find an id to cancel, providing clear when-to-use context and implying 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 rate-limiting ('Rate-limited to 5 per identifier per day') and billing implications ('Free; doesn't count against your tool-call quota'). These add value beyond the annotations, which only indicate read/write/destructive traits. 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?

Every sentence adds value. Front-loaded with purpose, followed by usage scenarios, then exclusions, then operational details. No fluff. Efficient and well-structured.

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

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

Given the tool's simplicity (3 params, no output schema), the description covers all necessary aspects: what feedback is expected, how to format it, behavioral constraints, and what happens to it. 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%, but description adds contextual guidance: explains the enum values in detail, specifies message length (1-2 sentences, 2000 chars max), and clarifies the context object's purpose. Also advises against pasting end-user prompts, which is not in the schema.

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

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

Description clearly defines the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It uses specific verbs and resources, and the categories (bug, feature, data_gap, praise) distinguish it from sibling tools that are about querying or information retrieval.

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 each feedback type: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' Also provides a negative guideline: 'don't paste the end-user's prompt.'

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 that data is self-aggregating, derived from CF analytics-engine, no PII, and cached 5min-1h depending on window. Adds significant behavioral context beyond annotations, which already indicate read-only, idempotent, non-destructive.

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?

Highly concise: one sentence for primary output, bullet list for use cases, and a note on derivation/caching. Front-loaded with key information. No redundancy.

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

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

Fully covers all needed context: purpose, usage, behavioral traits, parameter semantics. No output schema needed as return values are described. Annotations 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% and the description in schema explains tradeoffs for each window. Main description also mentions windows, adding 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?

Description clearly states the tool returns top tools, top packs, and total call volume over a window. Differentiates from sibling tools like discover_tools and ask_pipeworx by specifying the trending nature.

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

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

Explicitly lists three concrete use cases (discovering hot data sources, confirming canonical choice, seeing use case alignment). Also explains window tradeoffs but does not provide explicit when-not scenarios.

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

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

Discloses extensive behavioral details beyond annotations: internal checks (monotonicity, partition), partition filter for placeholder slugs, fill check against live order book, semantic anchor using Jaccard similarity. No contradiction with annotations (read-only, idempotent).

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

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

The description is lengthy but well-organized, starting with a general overview then detailing each mode. Every sentence adds value, though some technical details could be condensed without loss.

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

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

No output schema, but the description thoroughly explains the return structure (opportunities array with fields, partition_check object). Covers edge cases, fill verification, and relationship to other tools.

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

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

Schema description coverage is 100% and the description adds significant meaning: explains the difference between event and topic modes, provides example slugs, describes no-arg behavior, and details the scanning logic.

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

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

Clearly specifies the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. Distinguishes between two operational modes (event and topic) and a no-arg scan, making it distinct 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?

Explicitly describes when to use each parameter: call with no args for trending scan, pass 'event' for a specific market, pass 'topic' for cross-event scanning. Provides warnings about when not to trade (realizable edge ≤ 0) and references sibling tool 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 already declare readOnlyHint, openWorldHint, etc. The description adds substantial value by detailing model families (crypto_price, news_momentum, etc.), response structure (by_segment, fed_candidates, diagnostics), caching behavior (1h at KV level), and edge cases (Fed bets excluded). No contradictions with annotations.

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

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

While front-loaded with purpose, the description is quite verbose (multiple paragraphs with deep model details). It uses numbered lists and bold terms for structure but could be more concise for agent consumption.

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 (9 parameters, no output schema, three segments, caching, diagnostics), the description covers everything essential: what, how, response format, parameter knobs, caching, and edge cases (empty segments, Fed bets). It is contextually complete for agent decision-making.

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 parameter descriptions. The tool description reinforces those but also adds context like slippage rationale, tradeable-edge filter explanations, and per-leg Kelly knobs, enhancing meaning beyond the schema baseline of 3.

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

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

The description clearly states the tool scans top Polymarket markets for opportunities where Pipeworx data disagrees with market price, explicitly positioning it for 'what should I bet on today'. This specific verb-resource-scope effectively distinguishes it from siblings like polymarket_arbitrage or polymarket_edge_tracker.

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

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

The description provides clear usage context, including built intent, knob configurations, and diagnostics for empty segments. However, it does not explicitly state when not to use this tool versus alternatives, lacking direct exclusions or sibling 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 indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds context beyond annotations by explaining the snapshot mechanism (daily snapshots, cache-miss triggers), history bounds (60-day TTL, from enablement), and decay computation (daily closes, not intraday). This enriches behavioral understanding without contradiction.

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

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

The description is well-structured with a front-loaded purpose, detailed output breakdown, and limitations section. While somewhat lengthy, every sentence adds value and the organization aids readability. It avoids redundancy given the schema coverage.

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 specifies the return format: tracked[] with edge_pp_net time-series, first_seen, trend, decay_pp_per_day; expired[] with lifespan_days; snapshot_dates[]. It also covers limitations like TTL and data gaps. This is comprehensive for a read-only telemetry 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?

The input schema has 100% coverage with clear descriptions for both 'days' and 'window'. The description reiterates defaults and ranges but does not add new semantic meaning beyond the schema. Since the schema already conveys the parameters' meaning, the description's contribution is minimal.

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: providing edge persistence and decay telemetry from daily snapshots. It answers a specific user question about how long an edge has existed and distinguishes between fresh and old edges. It also lists the output components (tracked, expired, snapshot_dates), effectively differentiating from sibling tools like polymarket_edges.

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

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

The description implies usage for analyzing edge persistence over time and provides limitations (60-day TTL, daily closes). However, it does not explicitly state when to use this tool versus alternatives like polymarket_edges, nor does it give exclusion criteria. The guidance is implied but not direct.

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, destructiveHint. The description adds behavioral context: it checks order-book depth, walks the ladder, and returns a verdict (clean|degraded|cannot_fill). It also warns about partial basket fill risk. Does not contradict annotations, though could mention data freshness or rate limits.

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

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

The description is well-structured with sections for single-market and basket, front-loads purpose, and uses bold/ALL CAPS for emphasis. While every sentence is informative, it is somewhat dense and could be slightly more concise without losing detail.

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

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

Given no output schema, the description fully covers return values for both modes (single-market fields, basket fields like capture_ratio, thin_legs, etc.). It also explains the risk of partial fills and directional exposure, making it complete for a tool with 4 parameters and moderate 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 description coverage is 100%, but the description enriches each parameter with mode-specific behavior: side explains defaults for basket vs single-market, size_usd clarifies spending vs proceeds vs settlement notional. This adds significant meaning beyond the schema.

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

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

The description clearly states it is a realizable-vs-theoretical edge check against live CLOB order-book depth. It distinguishes two modes (single-market and basket) and lists specific return fields (top_of_book, vwap_fill_price, slippage_pp, etc.), making it easy to differentiate from sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

The description explicitly says 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It explains why (theoretical overround on thin books not capturable, partial basket fills convert arb into unhedged directional position), providing clear usage context and referencing 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?

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description details safety fields, compatibility warnings, temporal alignment, and the fact that real spreads are rarer. 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 detailed and structured with sections (TWO MODES, RESPONSE, SAFETY FIELDS), which aids readability. However, it is somewhat verbose; could be slightly more concise while retaining key information.

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

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

Given the complexity of cross-venue spreads, the description covers all essential aspects: modes, response structure, safety fields, temporal alignment, and warnings. No output schema exists, so the description adequately fills the gap.

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

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

Schema coverage is 100%, so baseline 3. The description adds value by explaining the two modes (topic shortcuts and explicit tickers), providing examples, and clarifying override behavior.

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

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

The description clearly states the tool computes cross-venue spreads between Kalshi and Polymarket for the same resolving question, and distinguishes it from siblings like polymarket_arbitrage. It specifies the resource and action with specific verb and domain.

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

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

Explicitly describes when to use (comparing spreads between venues) and when not (when compatibility_warning fires, no arb exists). Also warns that pre-mapped topics often return warnings, guiding the agent on realistic expectations.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, but description adds scoping info (anonymous IP, BYO key hash, account ID) and the listing behavior when key is omitted. No contradictions.

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

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

Three sentences, front-loaded with primary action, then usage guidance, then scoping and pairing. No redundant text; every sentence adds value.

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

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

For a single-parameter read-only tool, description covers purpose, usage, scoping, and relationship to siblings. No gaps given lack of output schema; 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?

Input schema has one optional parameter with description. Description reinforces that omitting key lists all keys and relates to 'remember' for saving, adding value beyond schema.

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

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

Description clearly states the tool retrieves a saved value or lists all keys, distinguishing it from sibling tools like 'remember' (save) and 'forget' (delete). The verb 'retrieve' and resource 'memory' are specific.

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

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

Explicitly gives use cases (look up stored context like ticker, address, notes) and mentions pairing with remember/forget. Does not explicitly state when not to use, but context is clear.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable context: the mark_read parameter flags events as read, altering subsequent calls; polling is safe. This goes beyond annotations without contradicting them.

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

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

The description is concise with three sentences, front-loading the main purpose. Each sentence serves a clear role: purpose, return structure, parameter effects, and alternative access. No unnecessary words.

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

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

Given the tool has no output schema and 5 optional parameters, the description covers essential behavioral and structural aspects: what alerts contain (source, citation_uri, payload), parameter effects, and safe polling. It is complete for a read-only, idempotent 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%, providing baseline parameter descriptions. The description enriches semantics by explaining the effect of mark_read ('the next call only shows newer ones') and giving an example for type ('sec_8k'). This adds context beyond the schema.

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

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

The description clearly states it pulls fired events from the subscription feed, using specific verbs ('Pull fired events') and specifying the resource ('subscription feed'). It distinguishes from sibling tools like 'list_subscriptions' by focusing on alerts rather than subscription metadata.

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

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

The description implies usage for retrieving recent alerts but does not explicitly state when to use this tool over siblings. It provides an alternative access method via GET registry.pipeworx.io/alerts.json, which offers partial guidance but no clear exclusion criteria for other tools.

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

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

Annotations already declare readOnlyHint/idempotentHint. Description adds fan-out logic, fallback mechanism, grouped return structure, and citation URIs. Still adds value beyond annotations, but could mention data freshness boundaries.

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?

Packed with information but front-loaded with example queries. A bit dense but every sentence adds value. Could be slightly more structured with bullet points.

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 complex multi-source tool with fallbacks and no output schema, description covers sources, parameter details, return structure, and alternative tool. No gaps left.

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 enhances: explains since accepts ISO or relative shorthand with examples and typical monitoring suggestion; clarifies value as ticker or CIK; notes type only supports company.

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 defines the tool as a change feed for a company over a recent window, fanning out to SEC EDGAR, GDELT/GNews, and USPTO. It lists example queries and distinguishes from entity_profile 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?

Provides explicit when-to-use (change feed for window) and when-not-to-use (use entity_profile for static profile). Explains fallback behavior (GDELT preferred, GNews on rate limits) and USPTO status.

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

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

Annotations provide idempotentHint=true and non-write-safe. Description adds context about session scoping, authentication persistence, and 24-hour retention for anonymous sessions, going beyond annotations.

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

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

Four short sentences, each adding unique value: purpose, usage, behavioral details, and companion tools. No wasted words.

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

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

For a simple memory tool with 2 required params and no output schema, the description fully covers what the tool does, when to use, and key behavioral traits (scoping, persistence). 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 has 100% coverage with descriptions for both key and value. Description only adds examples, not substantial new meaning. 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?

Clearly states 'Save data the agent will need to reuse later.' and provides specific examples (tickers, addresses, preferences), distinguishing it from sibling tools 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?

Explicitly says 'Use when you discover something worth carrying forward' and mentions alternatives: 'Pair with recall... forget to delete.' Also explains scoping and persistence rules.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds significant behavioral details: internal cascade of lookups, auto-disambiguation, and specific return fields for each type (ticker, CIK, citation URI for company; RxCUI, ingredient, brand for drug). 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 well-structured, starting with user-facing examples, then purpose, usage, and details. It is informative but somewhat lengthy; however, every sentence adds value. A slight trim could improve conciseness.

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

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

Given no output schema, the description does a good job covering return details for both entity types. It mentions auto-disambiguation and the fact it replaces multiple lookups. Missing information about error handling or what happens when an entity is not found, but overall complete for a lookup tool.

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

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

Schema coverage is 100%, but the description goes beyond by providing concrete examples for both parameters (e.g., 'AAPL', '0000320193', 'ozempic') and explaining what the tool returns for each type. This adds meaning beyond the schema alone.

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

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

The description clearly states the tool resolves user-spoken names to canonical identifiers, with specific examples (ticker, CIK, RxCUI). It also explicitly distinguishes from siblings by positioning itself as the first step for obtaining IDs needed by other tools.

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

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

Provides explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It also mentions supported types (company, drug) and the fact it replaces manual lookups. However, it does not explicitly state when not to use it or mention direct 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 readOnlyHint, idempotentHint, openWorldHint, and non-destructive. The description adds behavioral details: probes each entity with ai_visibility_check, ranks results, and returns score, confidence, signal density. No contradictions.

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

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

The description is four sentences with no filler. It front-loads the core action and progressively adds details. Every sentence serves a purpose.

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

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

Despite no output schema, the description explains return structure (ranked list with score, confidence, signal density). It covers entity count limits, model options, and context usage. Fully meets needs 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 coverage is 100%, but the description adds valuable context: first entity is treated as subject, context disambiguates common names, models array includes optional Anthropic key usage. This goes beyond schema descriptions.

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

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

The description clearly states that the tool compares AI visibility across multiple entities side-by-side, using ai_visibility_check internally, and ranks them. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic).

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 indicates use for competitive AI-marketing audits and provides a concrete query example. It implies when not to use by mentioning reliance on ai_visibility_check, but does not explicitly exclude single-entity checks. Sibling list includes ai_visibility_check, reinforcing the alternative.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds behavioral details: fans out to two external services, bundlephobia's first measurement can take 5-30 seconds, partial failures degrade gracefully with sources_failed listed. 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 packed with useful information and is front-loaded with the core purpose. While slightly verbose, every sentence contributes to understanding the tool's behavior, limitations, and return values. Could be tightened slightly, but still efficient.

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

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

Despite no output schema, the description thoroughly explains what is returned (summary block with fields, per-advisory detail, links, recent versions). It also covers ecosystem limitation, timeout behavior, and error handling. For a composite tool with two external sources, 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 coverage is 100%, so baseline is 3. Description adds value by clarifying that 'package' accepts scoped names (e.g., @types/node) and that 'version' defaults to latest. This enriches the schema descriptions with practical usage context.

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

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

The description clearly states it performs a composite check for npm packages, answering questions about safety, popularity, and size. It distinguishes itself from sibling tools by focusing on npm ecosystem packages, making its purpose specific and 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?

Explicitly specifies when to use: when an agent asks about npm package attributes like safety, popularity, or size. Also provides clear exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' This guides correct invocation.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds context about being keyless and offline, which is useful beyond annotations. No contradictions.

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

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

The description is concise: two sentences with no unnecessary words. It front-loads the purpose and includes a helpful example, making it 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?

For a search tool with good annotations, the description is complete. It covers purpose, input example, and output format (even without an output schema). No missing aspects for effective use.

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

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

Schema coverage is 100% with both parameters described. The description provides an example of query usage but does not add significant extra meaning beyond the schema descriptions. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool searches ICD-10-CM diagnosis/billing codes by keyword or code, with a specific example showing input and output format. It distinguishes from sibling tools like search_loinc (lab codes) by specifying the coding system.

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

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

The description implies usage for finding ICD-10 codes, with 'keyless, offline' indicating no authentication needed. However, it does not explicitly state when not to use this tool versus alternatives like search_loinc, though the domain is clear.

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

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds minimal extra behavioral context (keyless access, example return format) but does not elaborate on pagination, rate limits, or edge cases.

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

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

Two succinct sentences with no wasted words. The main action is front-loaded, and the example efficiently communicates input and output format.

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 search tool with two parameters and no output schema, the description covers core functionality and provides a useful example. It could be improved by noting result ordering or default behavior, but 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 coverage is 100%, so baseline is 3. The description adds value by clarifying the query parameter through the example ('hemoglobin a1c') and indicating it returns LOINC number and long common name. The limit parameter is adequately described in 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 'Search LOINC codes' with scope 'lab tests & clinical observations' and provides a concrete example. While the resource is specific, it does not explicitly distinguish from sibling tools like search_icd10 or search_medical_terms.

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 phrase 'by keyword (keyless)' implies free-text search usage, and the example gives context. However, there is no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds no behavioral context like rate limits, result format, or what happens on empty results.

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

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

Single sentence, front-loaded with action and resource, no redundant words. Every part adds value.

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

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

Missing output schema means description should explain return format (e.g., what fields are returned). Description omits this, and also doesn't mention limit default or pagination.

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

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

Schema covers all parameters fully (100% coverage). Description adds minor parenthetical explanations for system values but does not add significant meaning beyond the schema.

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

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

Description clearly states verb (search), resource (clinical term lists), and specific system options (conditions, procedures, drugs) which differentiates from sibling tools like search_icd10 and search_loinc.

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

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

Description lists system options but does not explicitly state when to use this tool versus siblings (e.g., for ICD-10 or LOINC codes). The 'keyless' hint is present but not a full guideline.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds embedding model (BGE-base-en), window size (500 char overlapping), char cap (200K) with truncation behavior, and offset info. This goes well beyond annotations and has 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?

Concise yet packed with essential information. Front-loaded with purpose; every sentence adds value. No waste.

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

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

Given no output schema, description explains return values (passages with offsets and scores) and behavioral caps (200K chars, truncation flag). It also mentions pairing with a sibling tool, making the context complete.

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

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

Schema coverage is 100% with adequate descriptions. The description reinforces by explaining 'text' as the fetched record, 'query' with natural-language examples, and 'limit' default. It adds context like char cap but does not significantly expand 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 'Semantic search INSIDE a fetched record' with specific verb and resource. It differentiates from siblings by explicitly naming ask_pipeworx_grounded as a companion tool, and highlights unique benefits like saving context and returning offsets.

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

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

Explicitly states when to use: 'Use when the record is too big to cram into the prompt.' Provides workflow guidance with ask_pipeworx_grounded, indicating complementary usage. No contradictory or missing usage advice.

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

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

The description adds substantial behavioral context beyond annotations, including account requirements, delivery channel specifics (always-on feed, optional email/sms/webhook), SMS cap, webhook auto-disable after 10 failures, and type-specific parameter examples. Annotations indicate openWorldHint and idempotentHint, which are consistent.

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

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

The description is well-structured and front-loaded with the core action, but slightly verbose. Every sentence adds value, though some details could be more concise.

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

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

The description covers all necessary aspects: prerequisites, types, parameters, delivery options, limitations, and return value (new subscription id). It is complete for a tool with no output schema.

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

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

The description adds significant value beyond the schema, providing type-specific examples and delivery channel details. Schema coverage is 100%, but the description enriches understanding with concrete use cases.

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

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

The description clearly states the tool creates a proactive monitoring subscription to live-data event streams, with specific verb 'Create' and resource 'subscription'. It distinguishes from sibling tools like list_subscriptions and unsubscribe.

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

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

The description explains when to use the tool and provides requirements (Pipeworx OAuth account, not for anonymous/BYO). It lists supported types and parameters, but does not explicitly mention when not to use or alternatives beyond the implicit context.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds value by detailing the return format (category-bucketed examples with tool+argument shape), and how argument omission vs. inclusion affects the output ('call with no arguments for the full spread, or pass topic to focus'). No contradictions with annotations.

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

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

The description is concise and front-loaded with key information: use case examples, tool purpose, output description, parameter guidance, and explicit usage instructions. Every sentence serves a purpose with no filler.

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

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

Given the simple schema (1 optional param, no output schema) and rich annotations, the description comprehensively covers what the tool does, when to use it, how to call it, what it returns, and how it relates to other tools. It leaves no critical gaps for an agent to interpret.

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

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

Schema coverage is 100% with one optional parameter 'topic' described. The description adds context by listing example topic values and clarifying that omitting the parameter yields a full spread of categories. This goes beyond the schema's listing, though examples are already in the schema description.

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

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

The description clearly states it is the 'onboarding entry point' for an agent that just connected, and it 'returns category-bucketed example questions' with exact tool+argument shape. It distinguishes itself from siblings like 'ask_pipeworx' and 'discover_tools' by positioning as a first-step tool to learn what Pipeworx can do.

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).' This provides clear when-to-use guidance and references specific alternatives.

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

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

Adds context beyond annotations: 'The row is deactivated (not deleted) so its historical events stay available via recent_alerts.' Aligns with destructiveHint=false and idempotentHint=true.

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 waste: first sentence states action and key constraint, second explains effect and provides cross-reference. Front-loaded with purpose.

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

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

Complete for a simple tool: covers ownership enforcement, side effect (deactivation), and availability of historical events. No output schema needed; cross-reference to recent_alerts adds 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 100% so baseline 3. Description references id but does not add extra meaning beyond schema description 'Subscription id (uuid) returned by subscribe.'

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

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

Clearly states 'Cancel a subscription by id' which specifies verb and resource. Distinguishes 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 notes ownership enforcement ('you can only cancel your own subscriptions') and explains behavior (deactivated not deleted). Does not list alternatives but provides clear context for when to use.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds meaningful behavioral context: it uses SEC EDGAR and XBRL, replaces 4-6 sequential calls for efficiency, and returns a verdict with citation and delta. No contradictions.

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

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

The description is well-structured, starting with intuitive trigger phrases, then stating the use case and scope, and finally detailing output. Each sentence contributes essential information without redundancy, making it both comprehensive and efficient.

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

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

Despite lacking an output schema, the description fully explains the return values (verdict types, structured form, citation, delta). It covers input requirements, domain limitations, and the tool's role in streamlining multi-step processes, providing sufficient context for correct invocation.

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

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

The input schema has full coverage (100%) with a description for the 'claim' parameter. The tool description enhances this by providing concrete examples and clarifying the natural-language format expected, adding value beyond the schema alone.

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: verifying natural-language claims about company finances against authoritative sources. It provides specific example query patterns and lists the verdict types returned, making the purpose unambiguous and distinct 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 says 'Use whenever the agent needs to check whether something a user said is factually correct' and notes the current scope (company-financial claims only). While it does not enumerate alternatives or when-not-to-use for non-financial claims, the scope limitation is clear enough to guide appropriate usage.

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