Listenbrainz

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds valuable behavioral context: default model is free, Anthropic requires BYO key, returns per-model scores with confidence/signals, and a combined view. 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?

Approximately 80 words, two sentences plus a clear listing of return fields. Front-loaded with the main action and output. Every sentence adds essential information; no fluff.

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

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

Given 4 parameters (all described in schema and description), no output schema, and rich annotations, the description is complete enough. It covers return shape (score, confidence, signals, raw_response per model + combined view), models, and use cases. Slight gap: does not mention pagination or limits, but not needed for a probe 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%, so baseline is 3. The description adds meaning beyond schema: explains that '_apiKey' is passed straight through to api.anhtropic.com, 'context' helps disambiguate common names, and 'models' can be omitted for default. This adds value.

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

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

The description uses a specific verb ('Probe') and resource ('LLMs for what they know about a business/brand/product/topic') and clearly states the output (visibility score 0-100). It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on probing LLM knowledge rather than scanning for 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?

The description explicitly lists use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains default vs. optional model usage. It does not explicitly state when not to use it, but the context is clear and distinguishes from alternatives among siblings.

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

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

The description adds valuable behavioral details beyond annotations: it routes to 3,689 tools, fills arguments, and returns structured answers with stable pipeworx:// citation URIs. No contradiction with annotations.

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

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

The description is comprehensive but somewhat verbose. It is front-loaded with the key preference message and includes examples, which helps structure. A few sentences could be trimmed without losing clarity.

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

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

Given the tool's broad scope and many siblings, the description covers the primary use case and context well. However, it does not differentiate from ask_pipeworx_grounded or other similar tools, leaving a slight gap.

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

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

Schema coverage is 100% and description explains the 'question' parameter accepts multiple aliases and natural language. It adds meaning beyond the schema by describing the query as a request for factual information, but the schema already documents aliases.

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

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

The description clearly states the tool is preferred over web search for factual questions, lists specific domains (SEC filings, FDA data, etc.), and gives concrete examples, making the purpose highly specific and distinguishable.

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 recommends this tool over web search and provides usage triggers ('when user asks...'). It does not explicitly mention when not to use it or compare to sibling tools like ask_pipeworx_grounded, but the guidance is clear for the primary use case.

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 rich context beyond annotations: extraction only from tool result, explicit refusal reasons, return format fields, and extra LLM cost. No contradiction with annotations.

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

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

Front-loaded with key behavior; multiple sentences but each adds value. Could be slightly tighter but remains efficient.

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

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

Given no output schema, the description covers purpose, mechanism, return format, refusal reasons, use cases, cost, and sibling comparison. 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% with all parameters described and aliases listed. Description does not add significant parameter detail beyond what schema provides.

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

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

The description clearly states it's a hallucination-resistant answer mode for high-stakes reads, explains the mechanism (routes, fills args, extracts from tool result), and distinguishes from sibling ask_pipeworx by noting extra LLM call.

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: for answers that will be quoted/cited/acted on (financial, legal, medical), and when not to use: prefer ask_pipeworx for casual lookups.

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

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

The description goes far beyond the annotations (readOnly, openWorld, idempotent, not destructive) by detailing internal logic: fan-out parallelism, classifier dispatch by category, response shapes for each field, resolver contract with confidence levels, parent event extraction, news fallback handling, and safety mechanisms (low-confidence short-circuit, closed market detection, wide-spread warnings). This level of detail is exemplary and fully informs the agent of 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 long but earns its length: every section adds distinct information (classifiers, fan-out examples, response contract, safety). It is front-loaded with the core purpose and then structured with clear headings (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.). While it could be slightly tightened, the complexity justifies the length.

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

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

Given the tool's complexity (multiple fan-out paths, custom response shapes, safety logic), the description covers all essential aspects: input formats, parameter behavior, output structure (market, analysis, evidence), resolver fallback, parent event summary, news error handling, and blocking conditions. No output schema exists, but the description fully compensates by detailing the response shape.

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

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

The input schema covers 100% of parameters with descriptions, so the baseline is 3. The description adds semantic value by explaining the default for 'depth' (full-thorough), what 'quick' means (2-3 sources), and the trade-off for 'include_raw' (summarized vs. full payloads with size implications). This enriches agent understanding beyond the schema.

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

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

The description starts with a clear verb-resource pair ('Research a Polymarket bet') and specifies the input formats (slug, URL, question text). It provides three concrete use cases ('should I bet on X', etc.) and lists 16 classifier categories with fan-out examples, making the tool's scope unmistakable. Though it does not explicitly differentiate from siblings like 'polymarket_edges', the function is distinct enough (research vs. edge calculation) to be 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 three use cases ('Use for ...'), giving clear context on when to invoke the tool. It does not mention when NOT to use it or direct the agent to an alternative sibling, but the use cases are well-defined and cover the main scenarios. The lack of exclusions prevents a top score, but the guidance is solid.

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

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

Annotations already provide readOnlyHint, idempotentHint, etc. Description adds key behaviors: correct fiscal year handling, sorting by primary metric, and returns 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?

Description is longer but well-structured with usage instruction, per-type details, and behavior notes. Every sentence adds value, though could be slightly more concise.

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

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

Given tool complexity and no output schema, description covers return format, sorting, fiscal year handling, and data sources. It is complete enough for an agent to use effectively.

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

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

Schema coverage is 100%, but description adds meaning beyond schema: explains what data each type pulls (e.g., 10-K metrics for company, FAERS counts for drug) and provides examples for values 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 it performs side-by-side comparisons of 2-5 companies or drugs, with specific example queries. It distinguishes from sequential lookups and details what data each type retrieves.

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 instructs to prefer this tool over sequential single-pack lookups when comparing entities, and mentions it replaces 8-15 queries. Provides 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 indicate read-only and idempotent behavior. The description adds that the tool returns 'top-N most relevant tools' with full schemas and curated examples, and that results are ready to call directly without a second lookup. 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 main action and provides a bullet-style list of domains. While clear, it is slightly verbose with the domain enumeration and could be tightened without losing meaning.

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

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

Given the tool's role as a meta-discovery tool with rich annotations and schema, the description covers when to use, what it returns, and that it is safe. It lacks details on pagination or ordering but is otherwise complete for an agent to understand and 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 coverage is 100%, so baseline is 3. The description does not add additional meaning beyond what the schema provides for parameters; it mainly reiterates the query concept. No extra semantic depth.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It specifies that it returns tool names, descriptions, and full input schemas, and distinguishes itself from sibling tools by being a meta-discovery 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?

The description explicitly says when to use this tool ('when you need to browse, search, look up, or discover what tools exist') and provides a directive to call it first when many tools are available. It lists domains and implies when not to use (when a specific tool is already known).

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds value beyond these by detailing the tool's behavior: fans out across multiple sources (SEC EDGAR, XBRL, USPTO, news, GLEIF), notes a soft-fail for patents (USPTO API sunset), and explains how results are structured. No contradiction with annotations.

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

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

The description is relatively long but well-structured: starts with examples, then states preference, explains the fan-out, lists return components, and ends with parameter clarification. Every sentence serves a purpose. Slight wordiness in the middle section could be tightened 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, no output schema, and no nested objects, the description thoroughly explains what is returned: CIK, company_name, recent filings with URIs, fundamentals (with specific fields), patents (with sunset note), news via GDELT→GNews, and LEI. It also covers limitations and error conditions (names not supported). Completely adequate for the 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 coverage is 100%. The description adds meaning by explaining that type is currently only 'company', and value must be a ticker or zero-padded CIK. It explicitly states that names are not supported and directs to resolve_entity, providing crucial context beyond the schema's enum and 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 uses specific verbs like 'get' and 'profile', lists concrete examples (e.g., 'research Acme', 'brief me on Tesla'), and specifies the output components (CIK, filings, fundamentals, patents, news, LEI). It clearly distinguishes from sibling tools by stating to prefer this over chaining single-pack lookups and noting that names require resolve_entity first.

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: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Also explains when not to use: names not supported, so use resolve_entity first. Provides clear context for the agent to decide correctly.

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

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

Description mentions 'Delete' which matches destructiveHint annotation, and adds context about clearing sensitive data, but doesn't elaborate on authorization or irreversibility 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 clear, front-loaded sentences with no wasted words.

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

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

Tool is simple with one required param; description covers use cases and pairing, making it complete for the complexity.

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

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

Schema coverage is 100%, and description adds no extra meaning beyond the schema's 'Memory key to delete'.

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

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

The description uses specific verb 'Delete' and resource 'memory by key', clearly distinguishing from sibling tools 'remember' and 'recall'.

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

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

Explicitly states when to use ('context is stale', 'task done', 'clear sensitive data') and hints at alternatives ('Pair with remember and recall').

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds that it fetches the page and extracts title/description/key links, which aligns with annotations and provides useful behavioral context.

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

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

The description is two sentences plus a list of use cases, front-loaded with the main purpose. It is efficient but the use case list could be slightly more streamlined.

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

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

Given the tool's simplicity (2 params, no output schema), the description fully explains input, process, and output format (single text blob for site-root/llms.txt). No gaps.

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

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

Schema coverage is 100% with descriptions for both parameters. The description reiterates the URL purpose and adds default and max for max_links, adding marginal value over 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 generates a production-ready llms.txt file for any URL, which distinguishes it from sibling tools like ai_visibility_check or 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?

The description provides explicit use cases (getting client site indexed, drafting for own project, auditing competitor), but does not mention when not to use or 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 readonly/idempotent/destructive hints. Description adds that it returns specific fields and defaults to active only, adding value beyond annotations.

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

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

Two sentences, no wasted words, purpose front-loaded. Efficient and clear.

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

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

Enumerates return fields (no output schema) and default behavior. For a simple list tool with one optional parameter, this is fully complete.

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

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

Schema coverage is 100% with one parameter well-described. Description does not add extra meaning beyond schema, so baseline 3 is appropriate.

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

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

The description clearly states the verb 'List', the resource 'subscriptions', and scope 'caller's active subscriptions'. It enumerates return fields, distinguishing it from siblings like subscribe/unsubscribe.

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

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

Explicitly advises using before adding more subscriptions or to find an id to cancel, connecting to sibling tools. Lacks when-not-to-use 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?

Beyond readOnlyHint=true, idempotentHint=true, etc., the description adds 'Open music listening data. Keyless for public profiles.' This informs about authentication and data openness, which annotations don't cover. 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?

Two sentences, no fluff, front-loaded with purpose. Every part adds value.

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

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

No output schema, and description only says 'currently-playing track, if any' without hinting at return structure (e.g., track name, artist, album). Agent cannot predict the output's fields, reducing utility.

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 covers the single parameter with example username. Baseline 3 as schema does the work; description adds no extra meaning.

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

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

Description clearly states 'currently-playing track' and 'right now', specifying the resource (ListenBrainz user's current listen) and verb (get/retrieve). This differentiates from sibling tools like user_listens (history) and top_artists (aggregates).

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 by saying 'right now' and 'if any', but no explicit when-to-use or alternatives. Siblings like user_listens are not mentioned, leaving the agent to infer from 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?

Discloses rate limits (5 per identifier per day), that it's free, and doesn't count against tool-call quota. Also explains team digests and roadmap impact, beyond the annotations which are all false.

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 purpose, use cases, and constraints. It is slightly verbose but each sentence adds value. Could be trimmed slightly without losing 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 aspects: when to use, what to include, constraints (rate limit, quota), and expected behavior. With no output schema, it fully explains the tool's operation.

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

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

While schema coverage is 100%, the description adds meaning: explains enum values for 'type', describes optional context fields, and gives message formatting guidance (1-2 sentences, 2000 chars max).

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

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

The description clearly states the tool's purpose: sending feedback to Pipeworx. It specifies the types (bug, feature, data_gap, praise) and distinguishes from sibling tools like ask_pipeworx or discover_tools by focusing on reporting issues or suggestions to the team.

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

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

Explicitly tells when to use: for bugs, feature requests, data gaps, or praise. Also provides a clear exclusion: don't paste end-user prompts. This guides the agent effectively.

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 value by disclosing that the data is self-aggregating, contains no PII, and is cached for 5min-1h. This behavioral context goes 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 concise: 4 sentences that front-load the main function and then list use cases and behavioral notes. No redundant information; every sentence earns its place.

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

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

Given the tool's simplicity (one parameter, no output schema), the description adequately explains the return value structure (top tools, packs, call volume) and caching. It could be slightly more specific about output format, but it is sufficient for an agent to understand what to expect.

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

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

The schema already provides a complete description for the single parameter (window) with enums and guidance. The tool description adds a note about caching depending on window, but that is minor. With 100% schema coverage, a score of 3 is appropriate as the description does not significantly enhance parameter 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?

The description clearly states the tool returns top tools, packs, and call volumes over a window. It uses specific verbs and resources, and the phrase 'What other AI agents are calling on Pipeworx right now' makes the purpose immediately understandable. It distinguishes from siblings like 'discover_tools' by focusing on real-time popularity.

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

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

The description provides three explicit use cases (discovering hot data sources, confirming canonical tool, seeing alignment) and mentions caching behavior. It does not state when not to use the tool or suggest alternatives, but the context is clear for when it is appropriate.

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

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds substantial behavioral details: required parameters, monotonicity and partition-sum checks, Jaccard similarity filter, partition placeholder filter, and response structure. This goes beyond the annotations.

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

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

The description is well-structured, starting with the requirement, then explaining both modes, followed by semantic and filter details. It is front-loaded with critical information. While slightly verbose, it remains clear and efficient with no extraneous 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 has no output schema, so the description must explain return values. It does so by describing the response structure: 'opportunities[]' with fields like gap_pp, suggested_trade, reasoning, and partition_check for event mode. It also covers conditions like Jaccard similarity and placeholder filtering. The description is thorough given the tool's complexity.

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

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

Schema description coverage is 100%, with each parameter having a schema description. The tool description elaborates further: examples of event slugs and topic seeds, recommended usage for each mode, and how the modes work. This adds meaningful context beyond the schema.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes two modes (event and topic) with concrete examples. Among siblings, this tool's function is distinct from polymarket_edges and polymarket_kalshi_spread.

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 recommends when to use each parameter: 'event (recommended for a specific market)' and 'topic (for cross-event scanning).' It notes that calling with no args fails and explains that cross-event mode catches patterns missed by single-event mode. While it doesn't explicitly list when not to use the tool, it provides clear usage 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?

The description extensively details the three model families, response segments, fields like edge_pp_net and warnings, caching behavior, and the tradeable-edge knobs. This adds significant value beyond the annotations, which only indicate read-only and idempotent nature.

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

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

While the description is long, it is well-structured with clear sections for each model family and parameters. Every sentence adds value, though some could be more concise.

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

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

For a tool with 9 parameters and no output schema, the description fully explains the output structure, segments, diagnostics, and trade-off knobs. No gaps are evident.

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

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

Schema coverage is 100%, so baseline is 3. The description adds extra context to parameters like slippage_pp (justifying default 0.3) and min_partition_leg_kelly (explaining its role). 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 clearly states the tool scans Polymarket markets for opportunities where Pipeworx data disagrees with market price, positioning it for 'what should I bet on today'. However, it does not explicitly differentiate from sibling tools like polymarket_arbitrage, which could cause confusion.

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

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

The description provides clear context for when to use the tool (discovering opportunities without paging hundreds of markets), but does not specify when not to use it or mention 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 indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds substantial behavioral context: compatibility_warning conditions (skipped_cross_type, skipped_cross_subtype), temporal alignment field, and counters. It explains when spreads are meaningless and why pre-mapped topics may not yield tradeable spreads. 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 comprehensive (~500 words) but well-structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS). It front-loads the core purpose and avoids unnecessary repetition. While slightly long, the complexity of the tool warrants the detail; every sentence contributes to understanding.

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

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

Given the absence of an output schema, the description thoroughly covers the response structure: leg-by-leg prices, matched spread with top_spreads_pp, and all safety fields (compatibility_warning, temporal_alignment, skipped counters). It addresses edge cases and provides enough context for an agent to interpret results 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 all three parameters described. The description adds value by listing the 10 topic shortcuts, explaining that explicit tickers override topic-mapped sides, and providing example values. This enhances the schema descriptions, justifying a score above the baseline.

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

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

The description explicitly states 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It clearly identifies the tool's purpose, distinguishes it from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on cross-venue comparison, and details the two modes of operation.

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

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

The description provides explicit guidance on when to use the tool (e.g., for comparing probability spreads), when not to (e.g., when compatibility_warning fires due to non-equivalent bet shapes or temporal misalignment), and explains the two modes (topic shortcuts vs. explicit tickers) with clear examples. It warns that most pre-mapped topics return warnings, setting realistic expectations.

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

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral context (omitting key lists all keys, scoping by identifier) 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?

Two efficient sentences that front-load the primary action ('Retrieve a value...') and context for use, with no wasted words.

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

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

For a simple retrieval tool with no output schema, the description covers retrieval, listing, scoping, and pairing with siblings, making it self-contained.

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

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

Schema coverage is 100%, so the parameter 'key' is fully documented. The description adds clarity by explaining that omitting the key lists all saved keys, complementing 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 retrieves values saved via 'remember' or lists all keys when omitted. It distinguishes from sibling tools 'remember' and 'forget' by explicitly mentioning them.

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

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

It explicitly says when to use (look up stored context) and provides guidance on alternatives ('Pair with remember to save, forget to delete'). Also specifies scoping ('Scoped to your identifier').

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds specifics: returned fields (source, citation_uri, payload), mark_read side effect (flags read for next call), and that polls work fine. 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?

A single paragraph of 4 sentences, each earning its place. Front-loaded with purpose, then details, then alternative. No fluff.

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

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

For a tool with 5 optional parameters, no output schema, and rich annotations, the description covers the functionality comprehensively: what it returns, how to filter, mark_read effect, polling suitability, and the alternative HTTP endpoint.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds value by explaining mark_read behavior ('flag returned events read so the next call only shows newer ones') and exemplifying filter type ('e.g. "sec_8k"').

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 ('Pull') and resource ('fired events from your subscription feed'), clearly differentiating from siblings like 'list_subscriptions' which lists subscriptions, not alerts.

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

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

Provides clear context on filtering by type and since, using mark_read, and polling. Mentions an alternative endpoint for scripts/dashboards, giving guidance on when to use the HTTP API instead.

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

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description details multi-source fan-out, GDELT→GNews fallback, USPTO soft-fail, and output structure. Adds significant behavioral context.

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

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

Informative single paragraph, but dense. Could benefit from bullet points for readability, but still concise and front-loaded with examples.

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 complexity (multiple sources, fallback, parameter formats) and no output schema, the description covers all aspects: sources, parameter options, output structure, and sibling differentiation.

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

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

Schema coverage is 100%, but description enriches with examples for `since` (ISO and relative shorthand) and explains the `value` parameter as ticker or CIK. Adds practical usage beyond schema.

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

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

The description clearly states the tool's purpose: 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It provides example queries and distinguishes from sibling tool entity_profile.

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

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

Explicitly lists when to use (e.g., 'What's new with X') and when not to use ('Use entity_profile instead when you want the static profile'). Provides a concrete alternative.

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

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

Adds behavioral details beyond annotations: scoped by identifier, persistent vs anonymous, 24-hour retention. 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?

Four sentences, front-loaded with purpose, guidelines, then behavior. No filler, every sentence earns its place.

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

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

Tool is simple (2 params, no output schema); description fully explains persistence behavior, auth distinction, and pairing with siblings, making it 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 covers both params at 100% with examples; description adds usage context (values can be findings, addresses, etc.) 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?

Clearly states verb 'Save data' and resource 'key-value pair', distinguishes from siblings recall and forget by specifying reuse across conversations/sessions.

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

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

Explicitly says when to use: 'discover something worth carrying forward', and pairs with recall/forget for lifecycle, but no explicit 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 the tool as readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds behavioral context beyond annotations by stating that 'each call cascades through several lookup endpoints internally' and that the tool is 'auto-disambiguated', which is valuable for understanding its internal behavior. No contradictions with annotations were found.

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 that packs examples, usage guidance, and output details without redundancy. It is front-loaded with example queries and the key instruction 'Use FIRST.' However, it could be slightly more structured (e.g., bullet points or separate sections for each type) to improve skimmability, though it remains appropriately sized and informative.

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

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

Given that there is no output schema, the description fully compensates by detailing the return values for each type, including citation URIs. It covers input formats, supported types, and expected outputs, making the tool's behavior transparent and complete for an agent to select and invoke correctly. The context is rich enough to replace what an output schema would provide.

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

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

With 100% schema description coverage, the baseline is 3, but the description provides substantial additional meaning. For the 'type' parameter, it explains the two enum values and their return outputs (ticker + CIK for company; RxCUI for drug). For 'value', it offers concrete input examples (ticker, CIK, name for company; brand/generic name for drug) and mentions auto-disambiguation, greatly enhancing the schema's information.

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: 'resolve a user-spoken NAME to the canonical/official identifier other tools require as input.' It provides concrete examples like 'What's the ticker for…' and explicitly distinguishes it from siblings by specifying that it supplies identifiers needed by other tools, fulfilling the requirement of a specific verb+resource and sibling differentiation.

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 FIRST whenever you have a name but need an ID.' It also details supported types (company, drug) and what each returns, helping the agent understand when to invoke this tool versus alternatives like entity_profile. Additionally, it notes that using resolve_entity replaces 2-3 manual lookups, reinforcing its role as the primary lookup tool.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds behavioral details: it probes each entity with `ai_visibility_check`, returns a ranked list with specific fields (score, confidence, signal density), and notes the requirement for an Anthropic API key when using specific models. 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?

Three sentences, no wasted words. The first sentence clearly states the main action, the second adds detail on how it works, and the third provides the use case. Front-loaded and efficient.

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

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

Given the tool has 4 parameters and no output schema, the description adequately covers purpose, parameter semantics, and use case. It mentions the output is a ranked list with score, confidence, and signal density. A slightly more explicit description of the output format would raise this to 5.

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 all 4 parameters. The description adds meaning beyond the schema: it explains that the first entity in the array is treated as the 'subject' for narrative purposes, and clarifies the role of the optional `models` and `_apiKey` parameters.

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

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

The description uses specific verbs ('Compare', 'Probes', 'ranks') and clearly identifies the resource (AI visibility across multiple entities). It distinguishes from the sibling tool `ai_visibility_check` (which probes a single entity) by emphasizing side-by-side comparison and ranking.

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

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

The description explicitly states the tool is 'useful for competitive AI-marketing audits' and provides a concrete example. While it does not explicitly state when not to use it, the context is clear enough for appropriate selection among siblings.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, indicating safe and repeatable behavior. The description adds important behavioral context beyond annotations: bundlephobia's first measurement can take 5-30 seconds, and failures degrade gracefully with sources_failed listing timeouts. This helps agents manage expectations and handle partial 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 front-loaded with the core purpose and composite nature in the first sentence. Subsequent sentences add necessary details (usage, ecosystem scope, failure behavior) with no fluff or redundancy. Every sentence earns its place.

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

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

Despite no output schema, the description fully explains the return structure (summary block with specific fields, per-advisory detail, links, recent alternative versions, and sources_failed). It covers the tool's complexity, including partial failures and ecosystem limitations, providing all information needed for an agent to use the tool correctly.

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

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

The input schema already has 100% description coverage for both parameters (package and version), including that version defaults to latest. The description confirms scoped packages are accepted, which is already in the schema. The description adds minimal new semantic meaning beyond what the schema provides, 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 starts with a clear verb+resource+scope: "Composite 'should I add this npm package to my project' check in ONE call" and specifies the two data sources (deps.dev and bundlephobia) and the metrics checked. It distinguishes itself from siblings by explicitly limiting scope to "NPM ecosystem only in v1" and mentioning other ecosystems fall under a different tool.

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

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

Explicit usage guidance is provided: "Use whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'". It also tells when not to use by stating alternative tools for other ecosystems: "PyPI / Maven / Cargo / Go fall under deps.dev:version directly." Partial failure behavior is described, helping agents know what to expect.

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 technical details beyond annotations: BGE-base-en embeddings, cosine similarity, 500-char windows, 200K char cap with truncation flag, and return of offsets/scores. Annotations alone do not provide this depth.

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 compact (three sentences) with no redundant information, front-loaded with the core action, and each sentence adds value.

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

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

Despite lacking an output schema, the description fully details return values (passages with offsets and scores), explains pairings, and covers edge cases (truncation), making it complete for a complex tool.

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

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

Schema coverage is 100%, but the description adds meaningful usage examples for the query parameter and contextual constraints like max chars for the text parameter, enhancing understanding.

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

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

The description clearly states 'Semantic search INSIDE a fetched record' with concrete examples (SEC 10-K, article, long tool result) and differentiates from sibling ask_pipeworx_grounded, making the tool's purpose unambiguous.

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

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

Explicitly advises using the tool when the record is too big for the prompt and pairs it with a sibling, but does not include explicit when-not-to-use scenarios.

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

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

Beyond annotations (readOnlyHint false, idempotentHint true, destructiveHint false), the description adds behavioral details: it creates a subscription, returns id, has rate limits on SMS (10/day), and webhook auto-disables after 10 failures. 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 and front-loaded with the main purpose. While it is dense, every sentence adds necessary information. Slightly verbose but justified by the 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?

For a tool with 3 parameters and no output schema, the description covers all aspects: supported types with examples, delivery options with constraints, rate limits, and behavioral notes like webhook auto-disable. It provides complete guidance for an AI agent.

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

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

Schema coverage is 100%, but the description adds significant value with detailed examples for each subscription type and delivery channel, including constraints like phone verification and webhook signing. This goes far beyond the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: 'Create a proactive monitoring subscription to a live-data event stream.' It uses a specific verb (Create) and resource (subscription), and distinguishes itself from siblings like unsubscribe and list_subscriptions by focusing on creation.

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 (to set up proactive monitoring) and prerequisites (requires Pipeworx OAuth account). It also explains that anonymous and BYO accounts cannot persist subscriptions, providing clear guidance on 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, and destructiveHint=false, ensuring safety. The description adds significant context: the output is category-bucketed examples with exact tool+argument shapes, and behavior for empty vs. topic argument. 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 somewhat lengthy but front-loads key usage questions. Every sentence contributes meaning, though some repetition (e.g., listing categories twice) could be trimmed without loss.

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

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

Given its role as an onboarding tool with a single optional parameter and no output schema, the description covers the purpose, usage variants, output format, and relationship to siblings. 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 coverage is 100% with one optional parameter described. The description adds value by explaining the effect of omitting the parameter (full spread) and listing accepted topic values beyond the schema's terse list.

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 defines the tool as the onboarding entry point for discovering what questions Pipeworx can answer, providing example queries and categories. It distinguishes itself from sibling tools like ask_pipeworx or entity_profile by focusing on meta-guidance and capability exploration.

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

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

Explicitly states 'Use this FIRST when you do not yet know what Pipeworx can do for you' and details when to pass a topic vs. omit. Does not explicitly list when not to use it, but the context makes the decision 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 provide readOnly, idempotent, openWorld, non-destructive hints. Description adds valuable behavioral context: source (ListenBrainz/MetaBrainz/MusicBrainz), keyless access (no API key required), and that it returns most-listened artists across all users. This goes beyond annotations by clarifying data provenance and auth requirements.

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 that front-load the tool's purpose, source, and access model. No unnecessary words, every sentence adds value.

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

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

For a simple read tool with good annotations and full schema coverage, the description covers key aspects (source, scope, authentication). It could mention that results are ordered by listens (implied by 'top'), but not a major gap. Missing output schema not critical here.

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

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

Schema covers both parameters fully with descriptions. Description does not add extra meaning beyond what is already in schema (count defaults/max, range options). The 'Keyless' note is not parameter-specific. So baseline 3 is appropriate.

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

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

Description clearly states it retrieves sitewide top artists across all users for a time range, using specific verb 'top' and resource 'artists'. Distinguishes from sibling user_top_artists by specifying 'sitewide' and 'Keyless'.

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 general listening trends without authentication, but no explicit guidance on when to use versus user-specific or other tools. Could be improved by noting when to choose over user_top_artists or other analytics 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 indicate readOnlyHint=false, destructiveHint=false, idempotentHint=true. The description adds valuable behavioral details: ownership enforcement, deactivation vs deletion, and preservation of historical data. 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?

Three sentences, concise and front-loaded. Every sentence provides essential information without waste.

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

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

For a simple cancellation tool with no output schema, the description covers the main effect and side effects. It could mention the return value (e.g., nothing or confirmation), but overall it is sufficiently complete for the complexity.

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

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

Schema coverage is 100% (one parameter 'id' with description 'Subscription id (uuid) returned by subscribe'). The description reiterates this but adds no new semantic information beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states 'Cancel a subscription by id', specifying the verb (cancel) and resource (subscription by id). It distinguishes from siblings like 'subscribe' and 'list_subscriptions' by focusing on cancellation.

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 mentions ownership enforcement ('you can only cancel your own subscriptions') and explains the effect (deactivation, not deletion). It also notes that historical events remain available via 'recent_alerts', providing context for when to use this tool. Lack of explicit when-not-to-use is a minor gap.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds useful context: 'open music listening data, MusicBrainz-linked' and 'keyless for public profiles,' enhancing transparency beyond annotations.

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

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

Two sentences, front-loaded with key action and ordering. 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?

With no output schema, the description briefly describes the output ('recent listens (track scrobbles), newest first'), which suffices for a simple retrieval tool. Adequate given rich annotations and sibling context.

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

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

Both parameters are fully documented in the input schema (100% coverage). The description does not add additional meaning or examples beyond what the schema provides.

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

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

The description clearly states the action (retrieves recent listens), the resource (ListenBrainz user's listens), and the ordering (newest first). It distinguishes from sibling tools like now_playing and top_artists.

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?

Mentions 'keyless for public profiles,' implying public access. Does not explicitly state when to use vs alternatives, but context is clear enough.

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

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

Annotations already indicate read-only, idempotent, open. Description adds key details: keyless for public profiles and potential unavailability of stats. 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?

Three concise sentences covering purpose, data source, and availability caveat. Front-loaded and efficient.

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

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

No output schema, so description should provide more detail on return structure. Only hints at 'top artists' but not fields like artist name, count, MusicBrainz ID. Adequate but leaves ambiguity.

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

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

Schema coverage is 100% meaning parameters are well-documented in schema. Description does not add significant extra meaning beyond the schema descriptions.

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

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

Clearly states it retrieves a user's top artists over a time range, distinguishing from global tools like 'top_artists'. Mentions data characteristics and access.

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?

Implicitly specifies when to use (for a specific user) but does not explicitly exclude alternatives or state when not to use. Sibling tools suggest global scope but no direct comparison.

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

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

Annotations provide readOnly, openWorld, idempotent, not destructive. Description adds detailed behavioral context: returns verdict types, extracted structured form, actual value with citation, percent delta, and mentions it replaces 4–6 sequential calls. No contradictions.

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

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

Well-structured with front-loaded examples. Slightly verbose, but every sentence adds value. Could be tightened, but it's effective.

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

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

Given the simple schema (1 param, no output schema), the description thoroughly explains inputs, outputs, and domain. Covers verdict types, citation, and delta. 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 has one parameter 'claim' with 100% coverage. Description adds nuance by explaining the nature of claims (natural-language, factual) and domain specificity, which helps the agent construct better inputs.

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 identifies the tool as a natural-language claim verifier against authoritative sources, provides example input patterns, and distinguishes it from sibling tools by noting it replaces multiple sequential calls.

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

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

Explicitly states when to use: 'whenever the agent needs to check whether something a user said is factually correct.' Specifies domain (company-financial claims). Lacks explicit when-not or alternative tools, but the domain limitation provides implicit exclusion.

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