Setlist Fm

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

Discloses that probing is read-only (aligns with readOnlyHint) and returns per-model score/confidence/signals/raw_response plus combined view. No contradictions with annotations; adds value beyond them by explaining response structure and cost implications for Anthropic.

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 compact (3 sentences) and well-structured: action, model details, response format, use cases. No redundant information.

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

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

Covers all key aspects: what it does, models, parameters, response structure, and use cases. No output schema, but description adequately explains return values. Annotations provide safety context, making the description nearly complete for this complexity level.

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

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

Schema description coverage is 100%, so baseline is 3. Description adds minimal extra meaning (default model, example entity), but does not significantly enhance understanding beyond schema definitions.

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

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

Description clearly states the tool probes LLMs for entity knowledge and scores visibility (0-100), using specific verbs and resource. It distinguishes from siblings by focusing on multi-model probing with scoring, unlike related tools like 'scan_competitor_ai_presence'.

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 the default model vs Anthropic with API key, and lists use cases (AI-marketing audits, pre-launch checks). Lacks explicit 'when not to use' or differentiation from sibling tools, but provides clear 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 readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context about exact-match preference to avoid tribute acts and explains the name-to-MBID resolution process, going beyond the annotations.

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

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

The description is exceptionally concise: three sentences each serve a distinct purpose (function, parameter usage, return fields). No redundant or filler content.

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

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

Given the simple two-parameter tool with 100% schema coverage, output schema, and comprehensive annotations, the description fully covers invocation modes and behavior. It is complete for an agent to select and call this tool.

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

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

Schema descriptions cover both parameters, but the description enriches them by explaining that artist_name is resolved to an MBID while mbid is used directly, and notes the exact-match preference—details not present in the schema.

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

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

The description clearly states the tool's purpose with a specific verb ('Fetch') and resource ('setlist.fm artist record'), and it distinguishes itself from siblings like artist_search by emphasizing direct lookup with MBID resolution rather than searching.

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

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

Provides clear guidance on when to pass artist_name (to resolve to MBID) versus mbid (when already known), which informs usage. However, it does not explicitly mention alternatives such as artist_search, so exclusions are absent.

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

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

Annotations declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, which are consistent with a search tool. Description adds value specifying searchable fields and pagination, but does not disclose behavior beyond what annotations already imply.

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

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

Single sentence that efficiently communicates core purpose, search keys, and return type. No redundancy; every word earns its place.

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

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

Given 5 parameters, 0% schema coverage, and output schema existing, description covers search keys and return fields but lacks details on pagination parameters (p, sort). Could be more complete for a complex search 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 parameter coverage is 0%, so description must compensate. It explains three parameters (artistName, artistMbid, artistTmid) but omits 'p' (page) and 'sort' (sort order) entirely, only mentioning 'paginated' generically. Adds meaning for most parameters but incomplete.

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

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

Description clearly states it searches artists by multiple identifiers (name, MBID, Ticketmaster ID) and returns paginated results. This differentiates it from siblings like 'artist' (likely single lookup) and 'artist_setlists' (setlists for known artist).

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 lacks explicit when-to-use or when-not-to-use guidance. While the search functionality is obvious, it does not differentiate from closely related siblings like 'artist' or 'setlist_search' which might search by different criteria. Implicit usage but no exclusions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds behavioral nuance beyond that: artist_name is resolved to a MusicBrainz ID with exact-match preference, and it discloses the return fields (date, venue, city, full track lists). This enriches the agent's understanding of the tool's behavior.

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

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

The description is two dense sentences, front-loaded with purpose and a practical example. It avoids redundant restatement of the schema and earns every phrase — even the tribute-act nuance clarifies behavior without verbosity.

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 rich schema (100% coverage) and annotations (read-only, idempotent, open-world), the description is complete for the tool's complexity. It covers the core use case, parameter relationship, resolution behavior, and return contents. The output schema and annotations handle the rest, and no critical gaps remain.

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

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

Schema coverage is 100%, so the schema already documents all three parameters. The description adds meaning by explaining how artist_name maps to an MBID and when to prefer one parameter over the other, which goes beyond the schema's basic 'either this or mbid is required' wording. This lifts it above the baseline of 3.

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

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

The description opens with 'A band's concert history, newest first' — a specific verb-resource pairing with clear ordering. It then gives a concrete query example ('what did Radiohead play at their most recent show') and names distinct lookup modes (artist_name vs mbid), effectively distinguishing it from sibling tools like setlist_search or venue_setlists.

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 for' with a concrete scenario, and it explains when to pass artist_name vs mbid (including the exact-match preference to avoid tribute acts). It does not name alternative tools or state when not to use it, but the context is clear enough for basic selection.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral context: it routes to sub-tools, fills arguments automatically, returns structured answers with stable citation URIs, and notes it works on every tier with one fast call. No contradiction with annotations.

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

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

The description is front-loaded with the key directive 'PREFER OVER WEB SEARCH', then efficiently covers domains, examples, and escalation paths. Despite its length, every sentence carries decision-relevant information and is well organized, making it easy to scan.

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?

Even without an output schema, the description explains the return format (structured answer with pipeworx:// citation URIs), the scope of questions it handles, tier compatibility, and when to escalate to alternatives. This is comprehensive for a read-only query 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 fully documents the required 'question' parameter and its aliases (100% coverage), so the baseline is 3. The description goes beyond the schema by giving concrete example questions and explaining accepted phrasings like 'what is', 'look up', and 'find', which enriches the meaning of the parameter.

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

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

The description clearly states that ask_pipeworx routes natural-language questions to one of 5,529 tools across verified sources and returns structured answers with citation URIs. It distinguishes itself from siblings like ask_pipeworx_grounded and deep_research by positioning itself as the default entry point.

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

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

The description explicitly says 'PREFER OVER WEB SEARCH', 'START HERE for most questions', and 'Step up only when needed', with named alternatives for grounded answers or broad multi-part queries. It also provides trigger phrases and concrete examples, making the when-to-use decision very clear.

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

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

Annotations already cover read-only/idempotent safety; the description adds valuable behavioral context about the beta nature, the absence of an active candidate as of a specific date, and that it currently matches ask_pipeworx exactly. It also discloses that results feed into a comparison process for routing improvements. 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 multi-sentence but every clause earns its place: beta status, current equivalence, usage instruction, and experimental purpose. It is front-loaded with the core identity and avoids redundant phrasing, though it could potentially be tightened without losing meaning.

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

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

Given the tool's experimental nature and the absence of an output schema, the description is thorough: it explains what the tool is, when to use it, its current state, how it relates to the stable router, and that the response shape is identical to ask_pipeworx. This gives an agent sufficient context to invoke and interpret results.

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

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

Schema coverage is 100%: all six parameters are aliases for question with descriptions. The description mentions 'same arguments' but adds no parameter-specific detail beyond what the schema already provides, so baseline 3 applies.

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

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

The description clearly identifies this as the beta version of ask_pipeworx, a universal router with the same 5,529 tools, arguments, and response shape. It distinguishes itself from the stable sibling by being the experimental edge with candidate routing improvements, and explicitly states it is a full working router.

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

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

The description gives explicit usage guidance: 'Use it exactly like ask_pipeworx when you want the newest routing' and explains that results are compared against the stable router. It notes the current equivalence to ask_pipeworx when no candidate is active. It lacks explicit when-not-to-use guidance relative to ask_pipeworx_grounded, but the primary use case 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?

The description richly discloses behavioral traits beyond the annotations: refusal reasons, exact return fields including evidence and confidence, extra LLM call cost, and the strict grounding constraint. This complements the readOnly and idempotent hints 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 front-loaded with purpose, then details usage, behavior, and return structure. Every sentence carries meaningful information, balancing density with readability. It avoids filler and repetition of schema or annotation 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 tool's complexity and lack of an output schema, the description provides a complete picture: how it routes, what it returns, all refusal reasons, and when to use it. No critical aspect is left unexplained.

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% description coverage across all parameters (all aliases of 'question'), so the description does not need to add parameter details. The description's mention of routing and fetching implies how the question is used, but this is behavioral context, not parameter semantics. Baseline 3 is appropriate.

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

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

The description clearly states a specific purpose: a hallucination-resistant answer mode that routes to tools and extracts answers only from tool results. It explicitly distinguishes itself from the sibling 'ask_pipeworx' by positioning as the grounded variant for high-stakes reads.

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

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

Provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on' with concrete examples (financial, legal, medical). It also names the alternative 'ask_pipeworx' and gives a cost-based preference cue, making the decision process 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?

The description goes far beyond annotations by detailing safety (low-confidence short-circuit, closed market handling), response shapes (resolver contract, parent event extractor, news fallback), and resolution rule risk. All disclosures are consistent with readOnlyHint=true and idempotentHint=true, with 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 in the first sentence and follows a logical structure from usage to response details. While it is very long (over 600 words), every section adds necessary behavioral context for a complex tool. Minor verbosity exists but is justified by the tool's complexity.

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

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

With no output schema, the description compensates thoroughly by covering response shapes, resolver contract, parent events, news fields, edge cases (low-confidence, closed markets, wide spreads), and resolution rules. All critical aspects for agent decision-making are addressed, making it very complete.

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

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

Schema coverage is 100%, and the description does not add significant new meaning beyond the parameter descriptions already present in the input schema. The description provides examples and context but essentially repeats the schema details, so a 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 states clearly that the tool researches a Polymarket bet by pulling Pipeworx data, with specific verbs 'research' and 'pulling'. It lists multiple input forms (slug, URL, question text) and concrete use cases ('should I bet on X'). The tool distinguishes itself from siblings like polymarket_edges by focusing on a single bet's evidence package.

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 ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and mentions the breadth of classifiers and fan-out examples, giving agents clear context. However, it does not explicitly state when to choose this tool over siblings like polymarket_edges or polymarket_arbitrage, leaving some inference required.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, covering safety and non-destructive behavior comprehensively. The description adds value by specifying the return shape (name, state, country, coordinates) and its role in lookup, complementing rather than contradicting the annotations.

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

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

Two concise sentences, zero wasted words. The first sentence states purpose and output, the second gives the practical use case. Front-loads the action and result, then provides context.

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?

Description covers purpose, output shape, and use case; output schema exists so return values don't need explanation. The undocumented `p` parameter is a gap, and there's no explicit statement about result limits or matching behavior, but overall the description is sufficient for a lookup tool with rich annotations and output schema.

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

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

Schema coverage is 67% with descriptions for `name` (with example) and `country` (with format constraint 'DE not Germany'). The description adds the resolution-use-case context, but the `p` parameter remains undocumented in both schema and description. The description mostly reinforces what the schema already communicates, hence baseline 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?

Description clearly states verb+resource ('Search setlist.fm for cities by name or country'), specifies what's returned (city name, state, country, geolocation coordinates), and distinguishes its purpose from venue/setlist searches. Also differentiates from the 'countries' sibling by saying to call it for the country code list.

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?

States a concrete use case ('resolving city identifiers before searching setlists or venues') and implies usage context. It doesn't explicitly say when NOT to use it or name alternative tools for the main search scenario, but the resolved-identifier purpose and the explicit reference to `countries` provide reasonable guidance.

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

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

Annotations already indicate safe, idempotent read operation. Description adds valuable behavioral details: off-calendar fiscal year handling, sorting by primary metric, 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?

Packed with actionable info, front-loaded with trigger phrases. Every sentence adds value, though slightly verbose. Well-organized.

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

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

No output schema, but description covers return value basics (paired data, citation URIs, sorted). Sufficient for a comparison tool with good annotations.

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

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

Schema has 100% coverage, but description enriches by explaining what data each type pulls (e.g., 'LATEST 10-K revenue + net income + cash + long-term debt' for companies). Provides context beyond schema enum values.

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

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

Description clearly states it provides side-by-side comparison of 2-5 companies or drugs, with specific data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs). It distinguishes from siblings by noting it replaces 8-15 sequential 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?

Explicitly lists trigger phrases like 'compare X and Y', 'which is bigger', 'head to head'. Advises to 'ALWAYS PREFER over sequential single-pack lookups', giving clear when-to-use guidance.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds context by specifying the data includes ISO codes and names, but does not contradict annotations. It is transparent enough for a simple list tool.

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

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

Two sentences, no wasted words. The purpose is front-loaded, and every sentence adds value.

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

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

The tool is simple with no parameters and has an output schema. The description fully explains what the tool returns and how to use it, making it complete for an agent.

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

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

No parameters exist, so baseline 4 applies. The description does not need to add parameter semantics.

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 returns the full list of countries with ISO codes and names from setlist.fm. It distinguishes itself from sibling search tools by explicitly mentioning its use for filtering setlist or venue searches.

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 guides the agent to use this tool before filtering setlist or venue searches, providing clear when-to-use context and implying it is a prerequisite step.

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 substantial behavior beyond annotations: account/payment requirements, latency ranges, gap handling (never invented), citations with fetchable pipeworx:// URIs, hop fields, contradictions[] scanning, and semantic excerpting. The description complements the readOnlyHint/idempotentHint annotations without contradicting them; openWorldHint is consistent with external structured sources.

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

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

The description is a single dense paragraph with heavy use of parentheses, dashes, and multi-clause sentences. Front-loaded with the critical account warning, but the volume and lack of section breaks make it somewhat difficult to scan. Each sentence earns a place, though the structure could be improved.

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 tool with no output schema, the description thoroughly covers return structure (findings packet, gaps[], contradictions[], hop, citation_uri), auth needs, latency, and alternative tools. It is nearly complete; a minor gap is not stating exact failure behavior when signed out, but the overall guidance is sufficient.

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

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

Schema already covers both parameters with detailed descriptions, and the description adds further nuance: example questions for 'question' and more granular behavior differences between depth levels (e.g., second-hop iteration, latency implications). The increment is meaningful but not huge given the thorough schema.

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

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

The description clearly states the tool's purpose with a specific verb ('research'), a defined resource ('Pipeworx's 1455 STRUCTURED data sources'), and a detailed mechanism (decomposition into facets routed to 5,529 tools in parallel). It explicitly distinguishes from siblings: 'For a single lookup use ask_pipeworx' and 'For BREAKING or colloquial CURRENT-NEWS topics, prefer ask_pipeworx'.

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

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

Provides explicit when-to-use and when-not-to-use guidance, including best-fit scenarios ('broad/multi-part questions over structured data'), alternatives ('For a single lookup use ask_pipeworx'), and exclusions (breaking/current news). It also notes the account prerequisite and directs signed-out users to ask_pipeworx.

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

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

Beyond annotations (readOnly, idempotent, non-destructive), the description discloses that it returns top-N tools with schemas and examples, ready to call, and no side effects. It adds context about the function's behavior without contradicting annotations.

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

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

The description is well-structured and front-loaded with key information, but is somewhat lengthy due to listing many domains. However, every sentence contributes value, making it appropriate for the tool's complexity.

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

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

Given the rich schema and annotations, the description covers purpose, usage, return format (top-N with schemas), and aliases. It does not mention pagination or sorting, but these are implied by the limit parameter and relevance ranking, which is sufficient.

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

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

Schema coverage is 100%, providing baseline of 3. The description adds value by noting that task, q, description, and search are aliases for the query parameter, and includes examples. This goes beyond the schema's individual field descriptions.

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

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

The description clearly states the tool finds tools by describing data or task, lists specific domains, and distinguishes itself from siblings like deep_research by being a preliminary discovery step. It explicitly says 'Call this FIRST' which reinforces its role.

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 need to browse, search, look up, or discover what tools exist' and provides a direct instruction: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This gives clear when-to and when-not-to guidance relative to siblings.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds critical behavioral context: it fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF; notes patents API may soft-fail; explains parallel execution; and details return fields. No contradiction with annotations.

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

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

The description is detailed but front-loaded with example queries. Every sentence adds value (sources, fields, limitations). Minor redundancy (mentioning 'soft-fails' and 'not supported' twice) prevents a perfect score, but overall efficient.

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

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

Given the tool's complexity (multiple sources, specific return fields, no output schema), the description is thorough. It covers inputs, outputs, data sources, limitations (patents API sunset), and prerequisites (resolve_entity for names). No gaps remain 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%, so baseline is 3. But the description significantly enriches understanding: explains that 'value' can be ticker or zero-padded CIK, explicitly states names are not supported, and provides example values ('AAPL', '0000320193'). This goes well beyond the schema's descriptions.

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

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

The description clearly states the tool's purpose: providing a full cross-source profile of a US public company. It lists specific use case examples ('Tell me about X', 'research Acme', etc.) and distinguishes from sibling tools like resolve_entity and compare_entities, making its role 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 advises when to use (holistic view) and when not to ('Names not supported — use resolve_entity first'). Also states 'ALWAYS PREFER over chaining single-pack lookups', providing clear guidance relative to alternatives.

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

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

Annotations already provide destructiveHint=true and idempotentHint=true, so the description's 'delete' aligns. Adds context about clearing sensitive data but doesn't elaborate 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 sentences, concise and front-loaded with the primary purpose. No wasted words.

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

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

For a simple, one-parameter tool with annotations, the description sufficiently covers use cases and sibling relations. Minor omission: no mention of success/failure behavior, but it's not critical.

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

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

Schema coverage is 100%, and the description mentions 'by key' but adds no further meaning beyond the schema's parameter definition.

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

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

The description clearly states the action 'delete' and the resource 'a previously stored memory by key'. It distinguishes from sibling tools by mentioning 'remember' and 'recall' as paired operations.

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

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

Provides explicit use cases: when context is stale, task is done, or clearing sensitive data. No explicit 'when not to use' but context is sufficient for a simple tool.

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

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

The description discloses the process of fetching the page, extracting title/description/key links, and emitting markdown format. Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint; the description adds context about the output format and actions 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 three sentences long, front-loading the core action in the first sentence, followed by process and use cases. Every sentence adds value with no redundancy.

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

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

Given 2 parameters, no output schema, and presence of annotations, the description covers purpose, process, output format, and use cases. Minor gaps exist (e.g., error handling, prerequisites like site accessibility), but for this simple tool it is largely 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 both parameters described. The description adds minimal extra meaning beyond the schema, stating the URL as 'site to summarize' and mentioning max_links default/max values, but does not significantly expand on schema details.

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

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

The description clearly states the tool generates 'a production-ready llms.txt file for any URL' with a specific purpose for AI crawlers. It distinguishes from siblings like ai_visibility_check by focusing on file generation rather than visibility checking.

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

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

The description lists three explicit use cases (client indexing, own project drafting, competitor auditing), providing clear context. However, it does not specify when NOT to use the tool or explicitly compare to alternatives.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, covering safety. Description adds value by specifying default behavior (active only) and return fields, but does not disclose auth requirements 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?

Two sentences, front-loaded with action, no superfluous words. 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, description lists all returned fields. It addresses default behavior (active only). Missing pagination details, but for a tool with one optional parameter, this is reasonably complete.

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

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

Schema coverage is 100% and the schema adequately describes the single parameter 'include_inactive'. Description adds no additional parameter-level meaning beyond the schema, consistent with baseline 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?

Description clearly states 'List the caller's active subscriptions' with specific verb and resource. It also enumerates returned fields, providing clarity. While not explicitly distinguishing from siblings like 'subscribe' or 'unsubscribe', the purpose is 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 suggests use cases: 'review what you're monitoring before adding more' and 'find an id to cancel'. This provides clear context for when to invoke the tool.

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

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

With all annotations false, the description carries the full behavioral burden and excels: it discloses rate limiting (5 per identifier per day), free/no-quota status, the claim_token workflow for tracking, daily human review, and roadmap impact. It also explains the no-account fallback, which is not derivable from the schema or 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 longer than average, but nearly every sentence earns its place by answering a likely usage question or edge case. It is front-loaded with the core purpose and then branches into when-not-to-use, claim_token mechanics, rate limits, and workflow impact. Slight redundancy in the 'different MCP server' explanation could be trimmed, but the structure is logical and readable.

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

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

Given 4 parameters (one nested object), no output schema, and no helpful annotations, the description fully compensates: it explains the return token behavior, what types of feedback are valid, how to formulate messages, and the operational constraints. The agent can reliably decide when to invoke this tool and what to expect from it.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds meaningful beyond-schema semantics: it clarifies the claim_token lifecycle (pass it later to read resolution status), instructs users to describe issues in terms of Pipeworx tools/packs rather than pasting user prompts, and reinforces message specificity. This elevates it above baseline.

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

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

The description opens with a specific verb and resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It clearly distinguishes this tool from siblings by positioning it as the channel for reporting issues or praise about Pipeworx tools specifically, unlike ask_pipeworx or research 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?

Usage guidance is explicit and comprehensive: it names concrete scenarios (bug, feature/data_gap, praise) and provides a clear exclusion ('if the tool came from a different MCP server... file it with that server instead'). It also tells users how to attribute feedback to specific Pipeworx tools/packs, eliminating ambiguity about scope.

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

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. Description adds critical behavioral traits: caching (5min-1h), no PII, derived from CF analytics-engine. 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 a focused paragraph with main output, then bullet-like use cases, then derivation detail. All sentences add value, no fluff. Could be slightly more compact 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?

Given the tool's simplicity (1 param, no output schema), the description provides sufficient context: purpose, usage windows, caching, data source, derived nature. 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 already describes the window parameter with enum and description. Description adds semantic guidance on when to use each window (hot vs steady-state), exceeding the baseline 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 returns top tools, packs, and call volume over a window, with specific use cases. It distinguishes itself from sibling tools like discover_tools by focusing on trending usage from other AI agents.

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

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

Explicitly lists three use cases (discovering hot data sources, confirming canonical tools, checking alignment). Does not include alternatives, but the context is self-contained and highly actionable.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds substantial behavioral context: fill check against live CLOB depth, partition filter dropping placeholder slugs, semantic anchor requiring Jaccard similarity >=0.30, and partition check with placeholder fraction threshold. These details go beyond annotations and are neither contradictory nor redundant.

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 verbose (over 500 words) with multiple paragraphs covering technical details like monotonicity, Jaccard similarity, and fill check. While front-loaded with the core purpose, it could be more concise by grouping related details. However, the structure flows logically from introduction to modes to internal checks to response 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?

Given no output schema and two parameters, the description is highly complete. It explains input modes, internal logic (monotonicity, partition sum, semantic anchor, placeholder filter), response structure (opportunities array, partition check, fill check), and even a pointer to polymarket_fill_risk for custom sizing. An AI agent would have enough context to select and invoke the tool correctly.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds significant meaning: explains event mode uses slug/URL and walks child markets, topic mode searches related events and flattens markets. It provides concrete examples ("fed-decision-may-2026", "Fed rate decision") and explains behavioral differences. This adds value beyond the schema, but some parameter semantics are already covered in schema descriptions.

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

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

The description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes three modes (no args, event, topic) with specific verbs and resources, setting it apart from sibling tools like polymarket_edges or polymarket_fill_risk. The verb 'find' and resource 'arbitrage opportunities on Polymarket' are 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?

The description provides explicit when-to-use guidance: call with no args for trending scan, pass event for single-event partition check, pass topic for cross-event scanning. It recommends event mode for specific markets and warns about fill check conditions. It also mentions when not to trade (realizable_edge_pp <= 0). Although sibling alternatives like polymarket_fill_risk are mentioned for custom sizing, the description does not contrast with siblings like polymarket_edges, but the mode guidance is comprehensive enough.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds substantial behavioral details: caching policy (1h at KV level), response structure (by_segment, _diagnostics), model families and their computation (crypto_price with FRED log-returns, news_momentum with GDELT volume ratio, partition_overround with sport-specific α), and edge calculation (net of slippage, 24h-move warning). 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 comprehensive but somewhat dense, packing model details, output fields, and knobs into long paragraphs. It is front-loaded with purpose but could benefit from more structured formatting (e.g., bullet points for knobs or segments). However, every sentence earns its place; no extraneous content exists.

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 9 parameters, no output schema, but rich annotations, the description is exceptionally complete. It explains the three response segments (model_driven, structural_arbitrage, concentrated_longshot), diagnostic outputs (funnel counters, filter_skips), caching behavior, and special handling of Fed bets. An agent has all necessary context to invoke the tool correctly and interpret results.

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

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

Input schema coverage is 100%, providing a baseline of 3. The description goes far beyond by explaining how each parameter functions: 'tradeable-edge filter' for max_spread_pp and min_liquidity, interaction between min_kelly and min_partition_leg_kelly, and rationale for defaults (e.g., slippage_pp notes zero fees but typical 20-50bp). This extra semantics greatly assists an agent in parameter selection.

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

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

Description clearly states 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price', with a specific verb ('scan') and resource ('Polymarket markets'). The sibling tool 'polymarket_edge_tracker' is related but the description's focus on discovery and model-driven vs structural arbitrage distinguishes it effectively. The purpose is unambiguous and tied to a concrete use case ('what should I bet on today').

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 context for when to use the tool (discovery of betting opportunities) and includes detailed information about knobs and filters (min_liquidity, max_spread_pp) that guide usage. However, it does not explicitly state when NOT to use this tool versus its siblings (e.g., polymarket_arbitrage, polymarket_edge_tracker), relying on implicit differentiation through content.

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?

Exceeds annotations by detailing data limits (60-day TTL, daily closes), response structure (tracked, expired, snapshot_dates), and edge sign convention (negative = SELL YES). 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?

Front-loaded with core purpose, then logically details response structure and limitations. Every sentence contributes necessary context for a complex tool.

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 explains output format (tracked, expired, snapshot_dates with key fields) and data limitations (TTL, daily closes, gaps). No output schema exists, so description carries full burden and meets it.

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

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

Schema coverage is 100%, so description adds minimal extra value beyond defaults and clamping range. Parameters are adequately described in schema, meeting baseline.

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

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

Clearly states it provides telemetry on edge persistence and decay, differentiating from siblings like polymarket_edges by focusing on historical evolution rather than current snapshots. The description explicitly contrasts fresh vs. old 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?

Implies usage for edge persistence analysis by asking 'How long has this edge existed and is it shrinking?' but does not explicitly exclude alternatives. Sibling tools listed provide context for differentiation.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable beyond-annotation context: the tool returns fill details, verdicts, and warns about directional risk from partial fills. 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 with sections ('SINGLE-MARKET', 'BASKET'), bullet points, and clear defaults. While verbose, every sentence is informative and earns its place. Could be slightly more concise but front-loads the core purpose effectively.

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 all return fields for both modes (e.g., top_of_book, vwap_fill_price, slippage_pp, capture_ratio, thin_legs). It addresses edge cases like forced directional risk and max clean notional, making it complete for a complex arbitrage-checking tool.

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

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

Schema description coverage is 100% with basic descriptions for all 4 parameters. The tool description adds significant context: explains mode-specific behavior, default side auto-detection in basket mode, and the dual interpretation of size_usd (spend vs. proceeds vs. settlement notional). This goes beyond what the schema provides.

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

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

The description clearly specifies the tool's function as a 'realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes two modes (single-market and basket). It differentiates from sibling tools like polymarket_arbitrage and polymarket_edges by being a pre-trade risk check rather than a signal generator.

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 THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Provides reasoning about partial basket fills converting arbs into unhedged positions, giving clear decision criteria.

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

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

The description goes far beyond the annotations by detailing safety fields, compatibility warnings, skipped cross types, and temporal alignment. It discloses edge cases and limitations, ensuring the agent understands the behavior thoroughly.

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 clear lead sentence and logical organization into modes, response, and safety fields. However, it is verbose and could be trimmed without losing essential 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 the tool and the absence of an output schema, the description comprehensively covers input parameters, response structure, safety checks, and potential pitfalls. It leaves little ambiguity for the agent.

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

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

Schema coverage is 100% and both the schema and description provide examples and detailed parameter descriptions. The description adds context about modes but does not significantly enhance parameter semantics beyond the schema.

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes from siblings like polymarket_arbitrage by focusing on cross-venue differences.

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

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

The description provides extensive context on how to use the tool, including two modes and caution that pre-mapped topics are not always tradeable. However, it does not explicitly contrast with alternatives or specify when not to use it.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable context: scoping by identifier, listing behavior without key, and relationship with remember/forget. No contradictions.

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

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

Two sentences, front-loaded with the action. Every sentence provides essential information: operation, condition, scope, and related 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 retrieval tool with one optional parameter and no output schema, the description thoroughly covers usage, scoping, relationships, and when to use. Annotations further reinforce safety.

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

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

Schema coverage is 100% with a clear description of the 'key' parameter. The description's mention of the parameter ('omit the key argument to list all keys') echoes the schema, adding little new meaning beyond purpose context. 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 the action (retrieve/list) and resource (saved values/keys). It names sibling tools (remember, forget) to distinguish itself, and specifies exact behavior with and without the key argument.

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

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

Provides explicit context for when to use (look up previously stored context) and mentions scoping (by identifier). It also pairs with sister tools (remember, forget), but doesn't explicitly state when not to use or list alternative tools.

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

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

Annotations already indicate safe, idempotent read. Description adds that mark_read modifies state (flags read), mentions polling works, and shows the data structure. No contradictions.

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

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

Four focused sentences with front-loaded purpose. Every sentence adds value: purpose, data fields, filtering options, mark_read effect, and alternative access method. 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?

Covers input, output fields, filtering, and side effects. Lacks explicit mention of limit and unread_only, but schema covers them. With no output schema, description is sufficiently complete for an agent.

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

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

Schema covers 100% of parameters. Description adds examples (type 'sec_8k'), clarifies ISO timestamp, and explains mark_read behavior, going beyond schema details.

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 'Pull fired events from your subscription feed' and specifies the returned data (source, citation_uri, payload). It distinguishes itself from sibling tools by focusing on reading recent alerts, unlike list_subscriptions or subscribe.

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 filtering guidance (type, since), explains mark_read usage, and mentions an alternative REST endpoint. Lacks explicit when-not-to-use but context is clear.

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

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

Annotations declare read-only, idempotent, non-destructive. The description adds behavioral details: parallel call to multiple sources, fallback from GDELT to GNews, soft-fail for patents, and return format with grouped changes and URIs.

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 example queries, then detailing sources, parameter information, and alternative tool. Every sentence adds value without redundancy.

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

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

Given full parameter descriptions, annotations covering safety, and detailed explanation of sources, fallback, and return format, the description is fully complete for agent invocation.

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

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

Schema coverage is 100%, and the description adds meaningful guidance: for 'since' it gives examples and typical values, for 'type' it clarifies only 'company' supported, for 'value' it explains ticker/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 provides a change feed for a company (SEC filings, news, patents) with explicit example queries. It distinguishes from the sibling tool 'entity_profile' by specifying when to use that instead.

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

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

The description provides explicit usage examples and guidance on when to use entity_profile instead. It explains fallback behavior and parameter usage, but does not explicitly state when not to use this tool.

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

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

Adds context beyond annotations: scoping by user identifier, persistence for authenticated vs anonymous sessions (24 hours). Annotations already hint idempotent and non-destructive; description confirms and expands.

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 with no redundancy; first sentence states purpose, second gives usage guidance, third adds behavioral context. Efficient and well-organized.

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

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

Despite no output schema, description covers storage mechanism, scoping, persistence duration, and mentions companion tools recall and forget, making it sufficiently complete for a simple write 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 has 100% coverage with descriptions for key and value; description adds examples and clarifies they form a key-value pair, enhancing meaning.

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

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

Description clearly states it saves data for later reuse, specifies key-value pair storage, and distinguishes from sibling tools recall and forget by naming them directly.

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

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

Explicitly says when to use (when discovering something worth carrying forward) and pairs with recall/forget, but does not explicitly state when not to use or list alternative tools beyond those.

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

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

Beyond annotations (readOnly, idempotent, non-destructive), the description adds behavioral details: cascading through multiple endpoints, graceful degradation of LEI/FIGI enrichment, explicit labelling of source and unresolved identifiers, and the note that it replaces multiple manual lookups. This is excellent behavioral disclosure.

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

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

The description is long but every sentence adds value: examples, supported types, degradation behavior, and output semantics. It is well-structured with clear sections, though it could be slightly tighter.

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

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

Given no output schema, the description covers return value semantics (source labels, unresolved field) and edge cases (degradation). It leaves little ambiguity about the tool's behavior, making it highly 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 already covers both parameters fully. The description adds meaningful examples ('AAPL', '0000320193', 'ozempic') and clarifies that company input accepts ticker, CIK, or name, while drug input accepts brand/generic. This enhances beyond the schema's enum and string description.

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

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

The description clearly states the tool resolves user-spoken names to canonical official identifiers, with specific example queries and supported types. It distinguishes itself as the first step when an ID is needed but only a name is known, aligning with its role among 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?

Provides explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It elaborates on what inputs are accepted for each type, making it clear when to use. It doesn't explicitly name alternatives, but the context is strong.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds behavioral context: internal use of ai_visibility_check, ranking, and treating first entity as subject. 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 three sentences with no wasted words. Purpose is front-loaded, followed by mechanism and use case/output format. Perfect 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?

The tool has 4 parameters (1 required), no output schema. The description explains the output (ranked list with score, confidence, signal density) and mentions internal probe tool. Minor gaps: no mention of error handling or caching, but overall complete given annotations.

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

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

Schema description coverage is 100%, so baseline is 3. The description does not add significant meaning beyond the schema, except noting that the first entity is treated as 'subject' for narrative, 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?

The description clearly states the tool does a side-by-side comparison of AI visibility across multiple entities. It uses specific verbs: 'compare', 'probes', 'ranks', 'surfaces', and distinguishes from sibling like ai_visibility_check (single probe) by being a batch version.

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 for competitive AI-marketing audits with an example question. However, it does not explicitly state when not to use it or mention alternatives like compare_entities.

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

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

The description adds behavioral context beyond annotations: partial failures degrade gracefully, bundlephobia first measurement can take 5-30 seconds, and sources_failed will list timeouts. No contradiction with annotations (all safe, read-only hints).

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

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

Every sentence adds value. The description is front-loaded with the core purpose and structured to list sources, use cases, return fields, and edge cases. No wasted words.

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

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

Given the tool's complexity (multiple sources, error handling, version defaults) and no output schema, the description fully documents return fields, partial failure behavior, and ecosystem limitations. 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 detailed descriptions. The description adds no new semantic info beyond what's in the schema; it merely repeats it. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: a composite check for deciding whether to add an npm package, aggregating data from deps.dev and bundlephobia. It distinguishes itself from siblings by being a single-call aggregate check.

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

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

Explicit usage guidance is provided: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also specifies that it's NPM-only in v1 and directs to deps.dev:version for other ecosystems, covering when not to use.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds critical behavioral details: embedding model (BGE-base-en), similarity metric (cosine), window size (500-char overlapping), character cap (200K), and truncation behavior. No contradiction with annotations.

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

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

The description is concise, front-loaded with the core purpose, then use case, then technical details. Every sentence adds value with no unnecessary words. It is well-structured for quick comprehension.

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

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

Given the tool's complexity (3 parameters, no output schema, rich annotations), the description covers all important aspects: usage context, parameter semantics, behavioral details, and integration with other tools. It is complete enough for an agent to invoke correctly.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds extra meaning by providing example queries, explaining the limit default, and clarifying the text parameter's max length. This goes beyond the schema but does not justify a 5 since it does not describe each parameter exhaustively.

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

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

The description uses a specific verb ('Semantic search INSIDE a fetched record'), clearly identifies the resource ('a fetched record'), and distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded. It is precise 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?

The description explicitly states when to use the tool ('Use when the record is too big to cram into the prompt'), explains the benefit ('saves context, returns only the passages that matter'), and suggests an alternative ('Pairs with ask_pipeworx_grounded'). This provides clear guidance for an agent.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is clear. Description adds that it returns artist, event date, venue, city, and full track list, providing useful behavioral context beyond annotations.

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

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

Two sentences, front-loaded with the key action. Every sentence is valuable and there is no extraneous information.

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

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

Given the tool's low complexity (one required parameter, simple read operation) and presence of an output schema, the description provides all necessary information to understand what the tool does and what it returns.

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

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

Schema description coverage is 0%, but the single parameter setlist_id is self-explanatory from the name and context. The description mentions it and its format, adding minimal 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 'Fetch' and resource 'a single setlist by its setlist.fm setlist_id', which distinguishes it from sibling tools like setlist_search, artist_setlists, and venue_setlists.

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

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

Description indicates when to use: when you have a specific setlist ID. It does not explicitly state when not to use or list alternatives, but the context of sibling tools provides that guidance.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is fully covered. The description adds valuable behavioral context: pagination (returns paginated results) and the output shape (artist, venue, date, tracks). The date-format warning in the schema's 'date' param also adds behavioral detail, though that lives in the schema rather than description. The description contributes pagination and result-shape info beyond what annotations provide.

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

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

The description is a single efficient sentence that front-loads the purpose. No wasted words. It logically lists search criteria and the result shape. It could arguably add usage guidance but the sentence is appropriately concise for its content.

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

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

The tool is moderately complex (13 parameters, all optional, with ambiguous field relationships like cityId vs cityName and artistMbid vs artistName). The description adds the result shape and pagination, but given the 8% schema coverage and many sibling search tools, it leaves meaningful gaps: no guidance on which parameter combination to prefer, no explanation of MBID vs name lookups, and no format notes for year/state/country. It's adequate but not complete for a tool with this parameter surface.

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 only 8% (only the 'date' param has a description). With 13 parameters and nearly zero schema descriptions, the description must compensate, but it only lists the search fields by name without explaining their formats, semantics, or interactions (e.g., how artistName relates to artistMbid, or what cityName vs cityId mean for lookup). The only meaningful parameter detail (date format warning) lives in the schema, not the description. For a 13-param tool at 8% coverage, this is a significant gap.

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

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

The description states a specific verb (search) plus resource (setlist.fm setlists) and enumerates the search fields (artist name/MBID, date, year, city, country, state, tour, venue). Purpose is clear. It doesn't explicitly distinguish from sibling tools like setlist, artist_setlists, or venue_setlists, but 'setlist_search' is distinct enough by name, and the description accurately maps to that broad-search scope.

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

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

The description implies this is the general search entry point (searching by many criteria), which contrasts with siblings like artist_setlists and venue_setlists that search by a single entity. However, it never explicitly states when this tool should be used versus those alternatives, nor when NOT to use it. The contextual guidance is implied rather than explicit.

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 idempotent and non-destructive behavior; description adds context about return value (subscription id), persistence requirements, and delivery channel limitations (SMS verification, daily cap, webhook signing). 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 dense but well-organized, front-loading the main purpose. Uses effective inline enumeration for types and delivery channels. Could benefit from more structural separation (e.g., paragraphs), but remains clear and 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?

Given the tool's complexity (3 parameters, nested objects, 5 types), the description covers types with examples, delivery channels with constraints, and authentication requirement. Lacks mention of rate limits beyond SMS or pagination, but is sufficient for a creation tool. No output schema, but return value is described.

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

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

Schema has 100% description coverage for all parameters. The description significantly expands on each, providing concrete examples for 'type' and 'params' (e.g., 'sec_8k' with items codes, 'polymarket_edge' with topic) and detailed behavior for 'delivery' (e.g., webhook HMAC signing). Adds substantial 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 verb 'Create' and the resource 'proactive monitoring subscription to a live-data event stream'. It distinguishes itself from sibling tools like 'list_subscriptions' and 'unsubscribe' by being the creation tool.

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

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

Description specifies when to use (proactive monitoring) and prerequisites (requires Pipeworx OAuth account). It details delivery channels and constraints, but does not explicitly contrast with other tools like 'recent_alerts'.

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

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

Beyond annotations (readOnly, openWorld, idempotent, non-destructive), the description adds rich behavioral context: it returns example questions with tool+argument shapes, can be called with no arguments for full spread or filtered by topic, and draws from a live catalog. It also notes it's the entry point for onboarding.

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 concisely structured, front-loaded with varied phrasing of the user need, and efficiently covers purpose, usage, and expected output without unnecessary verbiage. Every sentence adds value.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description is fully complete. It explains when to use, how to invoke, what the output contains, and how it fits into the broader tool ecosystem, leaving no meaningful 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?

Although schema coverage is 100% (one parameter, topic), the description adds significant meaning by listing specific topic options (finance, pharma, etc.) and explicitly stating that omitting the parameter gives a cross-category spread. This goes beyond the schema's basic description.

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

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

The description clearly states the tool returns category-bucketed example questions to help users get started. It lists specific categories and explicitly distinguishes itself as an onboarding entry point, setting it apart from siblings like ask_pipeworx or discover_tools.

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

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

The description explicitly advises to use this tool FIRST when unfamiliar with Pipeworx, and explains how to call it with or without the topic argument. It also mentions it can teach how to call meta-tools, providing clear when-to-use guidance relative to alternatives.

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

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

Explains that the row is deactivated (not deleted) to preserve historical events via recent_alerts. Annotations already indicate non-destructive and idempotent mutation; description adds valuable detail beyond annotations.

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

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

Two concise sentences, each earning its place. First sentence states purpose clearly, second adds behavioral detail. 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 one-parameter tool with no output schema, the description fully covers what the tool does, ownership constraints, and post-effect (deactivation). 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?

One parameter 'id' with full schema coverage. Schema describes it as a UUID from subscribe. Description reinforces 'by id' but adds no new semantic information beyond the schema.

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

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

The description clearly states the action 'Cancel a subscription by id', specifying the verb (cancel) and resource (subscription by id). It 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?

The description explicitly states ownership enforcement ('you can only cancel your own subscriptions'), providing clear context on when to use without mentioning alternatives or when-not-to-use.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, indicating a safe, repeatable read. Description adds returned fields but no additional behavioral details (e.g., rate limits, auth).

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 that is front-loaded and descriptive. Every word adds value; 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 one-param tool, presence of output schema, and informative annotations, the description is sufficiently complete. Slight gap: no mention of optional parameters if any exist, but none are defined.

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 no description for user_id (0% coverage). Description mentions 'by user_id' and provides an example, adding basic context but no format or constraints.

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

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

Clearly states the tool fetches a setlist.fm user profile by user_id and lists returned fields. Distinguishes from sibling tools like user_attended.

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?

States basic usage (fetch by user_id) but provides no guidance on when to use this tool versus alternatives like user_attended or search 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, openWorldHint, and destructiveHint=false, so the safety profile is clear. The description adds value by noting pagination and the specific return fields (date, artist, venue, track list), which are not in the input schema. This provides behavioral context beyond annotations.

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

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

The description is a single sentence that is front-loaded with the action and resource. Every word contributes to purpose and usage. No redundant or extraneous information.

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

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

For a simple two-parameter tool with a rich output schema and comprehensive annotations, the description covers the core purpose, key parameter, pagination, and return fields. The only gap is the missing documentation of the p parameter, but overall it is sufficiently complete for an agent to use correctly.

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

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

Schema description coverage is 0%, so the description must compensate. It clearly describes the required parameter user_id and implies pagination via 'paginated list', but the optional parameter p (presumably page number) is not explicitly documented. The description adds some meaning but is incomplete.

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 'Fetch paginated list of concerts a user has marked as attended, by user_id'. The verb 'Fetch' and resource 'concerts a user attended' are specific, and the tool is easily distinguished from siblings like 'user' (general user info) or 'setlist' (single setlist).

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 implicitly indicates usage via the parameter user_id but does not provide explicit guidance on when to use this tool versus alternatives like 'user' or 'setlist_search'. No exclusions or counter-indications are mentioned.

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

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

The description adds substantial behavioral context beyond the read-only/idempotent annotations: it explains the grading math, the automatic fallback routing, and—critically—clarifies the semantic difference between 'could_not_verify' (check did not happen) and 'unsupported' (no source). This is exactly the kind of nuanced disclosure an agent needs to avoid misinterpreting 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?

The description is long but densely packed and well-structured. The trigger phrases are front-loaded, followed by usage, processing details, output summary, and an 'IMPORTANT' caller note. Every sentence provides distinct value, and the format is highly scannable.

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

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

For a tool with no output schema, the description effectively covers the return values (verdict types, cited value, reasoning) and error semantics. It also explains the routing logic and tolerance behavior, leaving no major gaps for an agent to successfully invoke and interpret results.

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

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

Although the schema already covers both parameters well, the description enriches them: it provides examples for 'claim', explains the default tolerance behavior, and explicitly recommends setting tolerance_pct to 1–2 for hallucination detection. This adds actionable guidance beyond the schema.

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

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

The description clearly states the tool's purpose: verifying natural-language factual claims against authoritative sources. It includes common user phrasings, explicitly differentiates between company-financial and other claims, and mentions the verdict outputs, making it distinct from sibling tools like ask_pipeworx_grounded.

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

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

It explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and goes on to detail the behavior for both financial and non-financial claims. This provides unambiguous when-to-use guidance and notes that it replaces multiple sequential calls, implying it should be preferred over composing those steps manually.

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 that it returns specific fields, but it does not disclose any additional behavioral traits such as rate limits, authentication, or potential side effects. Annotations cover safety, so minimal added value.

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?

One sentence, no waste. Front-loaded with verb and resource, followed by output details. Perfectly 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?

Tool is simple (1 param, no nested objects) and has an output schema. The description covers what is returned. Slightly incomplete due to lack of parameter format details, but overall adequate for a fetch-by-ID 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 has 1 parameter 'id' with 0% description coverage. The description mentions 'venue id' but does not specify format, example, or constraints. For a single parameter, more detail (e.g., 'UUID of a venue from setlist.fm') would improve semantics.

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

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

The description clearly states the action ('Fetch'), the resource ('setlist.fm venue record'), the method ('by venue id'), and the output (venue name, city, country, coordinates, URL). It distinguishes from sibling tools like venue_search and venue_setlists by being a direct fetch by ID.

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 when you have a venue ID, but provides no explicit guidance on when to use this tool versus alternatives (e.g., venue_search, venue_setlists). No 'when not to use' or mention of 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 declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the pagination detail ('paginated list') and the result schema (name, city, country), which complements the annotation-covered safety traits.

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

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

Two sentences, no filler. It states the purpose, the searchable fields, and the return shape efficiently, front-loading the core function.

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 7 parameters and only 29% schema coverage, the description could clarify more. It covers the high-level search surface but doesn't explain the cityId/cityName/state/stateCode relationships or the pagination parameter (p). Given output schema exists and annotations are strong, this is adequate but not exhaustive.

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 low at 29%, so the description must carry weight. While the description enumerates search criteria (name, city, country, state), most parameter details live in the schema. The description adds that at least one criterion is effectively needed (implied by search), and the schema itself documents the country format. However, cityId vs cityName vs stateCode vs state distinctions are not clarified, leaving ambiguity.

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 'Search setlist.fm for venues by name, city, country, or state' with a specific verb (search), resource (venues), and search criteria. It also distinguishes from siblings like venue, venue_setlists, setlist_search by naming the search operation and its result fields.

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 identifies what fields can be used (name, city, country, state) and what results are returned, giving clear context. It doesn't explicitly state when not to use it versus venue/venue_setlists, but the tool name and description make the search-vs-direct-lookup distinction reasonably apparent.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds that results are paginated and includes output content (date, artist, track lists), providing useful behavioral context beyond annotations without contradiction.

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

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

The description is a single, well-structured sentence that front-loads the purpose and key details (paginated, venue ID, output content). Every word adds value; no unnecessary 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 tool has two parameters, an output schema (not shown), and annotations cover safety, the description provides sufficient context: it specifies the input (venue ID), pagination, and output content (date, artist, track lists). It is complete enough for an agent to use correctly.

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

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

The input schema has 0% description coverage, so the description must compensate. It explains the 'id' parameter (venue ID) and hints at pagination via 'paginated', but does not explicitly define the 'p' parameter (likely page number). This is adequate but not thorough.

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 fetches paginated setlists for a specific venue by ID. It specifies the resource (venue setlists) and the action (fetch), and the inclusion of the venue ID differentiates it from sibling tools like setlist_search or setlist.

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 does not provide guidance on when to use this tool versus alternatives such as setlist_search, venue, or other related tools. It lacks 'when to use' or 'when not to use' context, leaving the agent to infer usage without explicit differentiation.

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