Hex Pm

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

Annotations indicate read-only, idempotent, non-destructive behavior. The description adds behavioral details: it makes API calls to LLMs, costs may be incurred for Anthropic via BYO key, and it returns per-model results (score, confidence, signals, raw_response) plus a combined view. This exceeds annotation info 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, front-loaded with the main purpose, followed by model details and use cases. Every sentence adds value with no redundancy or 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 four parameters, one required, and no output schema, the description adequately details return format (per-model fields and combined view) and use cases. It is sufficient for an agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100%, but the description adds extra meaning: it explains the default model, the purpose of the API key, and how context helps disambiguate. This enriches the parameter definitions 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 probes LLMs for knowledge about a business/brand/product/topic and returns a visibility score (0-100). It specifies the verb (probe), resource (LLMs), and outcome (visibility score). It also distinguishes from siblings by focusing on AI visibility audits, pre-launch checks, and competitive monitoring.

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

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

The description provides clear context on when to use the tool (AI-marketing audits, brand checks). It explains the default model and how to add Anthropic probing with a BYO API key. It does not explicitly state when not to use or name alternative tools, but the context of sibling tools makes the purpose distinct.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. The description adds valuable context: routes to 3,826 tools, fills arguments, returns structured answer with citation URIs. No contradiction.

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

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

The description is well-structured, starting with a directive, then listing supported data types, explaining functionality, and providing examples. Every sentence adds value with 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 the complexity (multiple aliases, no output schema) and rich annotations, the description covers when to use and what to expect. Could be enhanced with return format details, but sufficient for agent understanding.

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 as aliases for 'question'. The description adds examples but no additional parameter-level detail beyond what the schema provides. 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: answering factual questions about current/historical data from many sources, routing to thousands of tools. It explicitly says to prefer over web search, distinguishing it from siblings like deep_research or entity_profile.

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

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

The description provides explicit when-to-use guidance: 'PREFER OVER WEB SEARCH' for many types of queries and gives examples of trigger phrases. It does not explicitly mention when not to use it or alternatives, but the 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?

Adds behavioral details beyond annotations: costs one extra LLM call, returns explicit refusal reasons with five categories, and returns structured evidence. Complements the readOnlyHint and idempotentHint with concrete 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?

Highly concise, using 4 sentences with front-loaded key benefit. Every sentence adds value: purpose, routing behavior, output structure, and usage guidance.

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 the return structure including answer, evidence, refusal reasons, and timing. Covers all necessary context for a high-stakes 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% and already documents all parameters as aliases for 'question'. The description only restates the aliases without adding new semantic meaning beyond the schema.

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

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

The description clearly states the tool's purpose as a hallucination-resistant answer mode for high-stakes reads, with a specific verb 'EXTRACTS' and distinct behavior from its sibling 'ask_pipeworx'.

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

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

Explicitly provides when to use (quoted/cited/acted-on answers, no invention of facts) and when to prefer the cheaper sibling (casual lookups), with cost tradeoff.

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 is exceptionally detailed about behavior, covering market resolution, classifier fan-out, response shapes, safety mechanisms (low-confidence, closed markets, wide spreads), and resolution-rule risk. It adds significant context beyond the annotations (readOnly, idempotent), which only indicate safety and idempotency. No contradictions 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 long but well-structured, front-loading the core purpose and then providing detailed sections on classifiers, fan-out examples, response shapes, and safety. Every sentence adds value, but the length could be slightly reduced. Overall, it is appropriate for 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?

Given the tool's complexity (3 parameters, no output schema), the description is remarkably complete. It covers return value shapes, edge cases (low confidence, closed markets, wide spreads), resolution rules, and even warns agents to inspect match confidence. No output schema exists, so the description fully explains 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?

Schema description coverage is 100%, but the description adds critical context: market can be slug, URL, or question text; depth parameter usage illustrated; include_raw explains when to pass true. The description also details the classifier system and fan-out examples, greatly enriching parameter meaning beyond the schema.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data. It specifies accepted input formats (slug, URL, question text) and output (evidence packet + comparison). The intended use cases are explicitly given, distinguishing it from sibling tools like ask_pipeworx or validate_claim.

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 for' statements and numerous examples of when to invoke the tool for different bet categories. It implies appropriate usage but does not include explicit 'do not use' scenarios or direct comparisons to siblings. Still, the guidance is substantial and 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. Description adds that it pulls latest 10-K data for companies (with off-calendar fiscal year handling) and FAERS data for drugs, sorts results by primary metric, and returns citation URIs. 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 slightly lengthy but front-loaded with user query examples; every sentence adds value, justifying its length for the complexity of the 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?

No output schema, but description explains return includes paired data and citation URIs per entity. It covers both entity types well; could be more explicit about output structure but sufficient 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 has 100% coverage with descriptions for both parameters. Description enriches by specifying exact data sources and metrics for each entity type (company: revenue, net income, cash, long-term debt; drug: adverse-event counts, etc.), and notes sorting behavior.

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

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

The description uses concrete query examples like 'Compare X and Y' and 'X vs Y', and clearly states it performs side-by-side comparison of 2-5 companies or drugs. It distinguishes from sibling tools like entity_profile by emphasizing multi-entity comparison.

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

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

Explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and notes it replaces 8-15 sequential lookups, providing clear when-to-use guidance and preference over 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?

Description reveals key behaviors: parallelism, citation format (pipeworx://), gap handling ('explicit gaps[]'), and latency (15-60s). Annotations confirm readOnly and idempotent, 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?

Long but front-loaded with purpose. Every sentence adds value, though could be slightly more concise. Appropriate given tool complexity.

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

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

No output schema, so description must explain returns. It details structured findings packet, pre-verified facts, gaps, and citations. Also covers prerequisites and latency. 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 covers both parameters (100% coverage). Description adds context: depth tiers with counts (quick=3, etc.) and paid plan requirement; question accepts broad input. Adds 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?

The description clearly states the tool's purpose: 'Grounded multi-source research in ONE call.' It details decomposition, parallel routing, evidence extraction, and distinguishes from sibling tools like ask_pipeworx.

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

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

Explicit usage guidance: 'Use for broad or multi-part questions... use ask_pipeworx for single lookups.' Also notes account requirements and depth tiers, including paid plan for 'thorough'.

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, idempotentHint=true, destructiveHint=false. The description adds behavioral context by stating the tool returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples)' and that each result is 'ready to call directly'. 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 a single, well-structured paragraph of about 5 sentences. It front-loads the purpose, then provides usage guidance, then details on return values. 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 no output schema, the description adequately explains what is returned: 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples)'. It also mentions the limit parameter and that results are callable. Could mention default N or pagination, but the limit parameter covers 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% with all 6 parameters described. The description adds value by listing example tasks and stating that 'query' accepts natural language and aliases like task, q, description, search. This clarifies usage beyond the 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?

The description clearly states 'Find tools by describing the data or task' and lists specific domains. It distinguishes itself from sibling tools by explicitly saying 'Call this FIRST when you have many tools available and want to see the option set', making it 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 gives explicit guidance: 'Use when you need to browse, search, look up, or discover what tools exist for: [domains]' and 'Call this FIRST...'. It provides positive usage context but does not explicitly state when not to use it, though the positive guidance 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 indicate read-only, idempotent, and not destructive. The description adds behavioral traits like the parallel fan-out nature, soft-fall for USPTO, and returning specific fields, which goes beyond the annotations without contradicting them.

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

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

The description is detailed but well-structured, starting with example queries and then listing sources and return fields. Every sentence contributes value, though it could be slightly more concise by grouping related 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 complex tool with no output schema, the description thoroughly covers all sources, return fields, edge cases (soft-fail, name handling), and preconditions. It is complete and leaves no major gaps for the agent to understand its capabilities.

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

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

Input schema has 100% coverage, fully describing both parameters. The description reinforces that names are not supported and value should be ticker or CIK, adding marginal value. Baseline 3 is appropriate as schema carries the burden.

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

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

The description clearly states the tool generates a full cross-source profile for US public companies, listing specific sources and returned fields. Example queries make the purpose immediately recognizable, and it distinguishes itself from chaining single 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 states to prefer this tool over chaining single lookups for holistic views, and clarifies that names are not supported, directing users to resolve_entity first. This provides clear when-to-use and when-not-to-use guidance.

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

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

Description adds context about clearing sensitive data beyond annotations, which already indicate destructive action. 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 are front-loaded with purpose and usage, 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 the single parameter and no output schema, the description fully covers purpose, usage, and pairing, 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?

With 100% schema coverage, the description merely restates the key field, adding no new semantic value 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?

Clearly states the tool deletes a memory by key, distinguishing it from siblings like 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 lists when to use (stale context, task done, clear sensitive data) and mentions pairing, but doesn't specify 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 provide readOnlyHint, idempotentHint, and destructiveHint. The description adds context by explaining the process: fetches page, extracts metadata, emits markdown. It does not contradict annotations and provides useful behavioral detail.

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: first sentence states core action, second explains process, third lists use cases. No unnecessary words. Front-loaded with key information.

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

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

Given the tool's simplicity (2 parameters, no output schema), the description covers purpose, process, output format, and use cases comprehensively. It is complete for an agent to decide when and how to use 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 description coverage is 100% for both parameters (url and max_links). The description does not add significant meaning beyond the schema, but it provides context for why max_links might be adjusted. Baseline of 3 is appropriate.

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

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

The description clearly states the verb 'generate', the resource 'llms.txt file', and the purpose for AI crawlers. It avoids tautology and distinguishes from sibling tools like 'ai_visibility_check' and 'scan_competitor_ai_presence' by focusing on file generation.

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

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

The description explicitly lists three use cases: getting a client's site indexed, drafting for own project, auditing competitor. It implicitly guides when to use this tool over siblings, though it does not explicitly state when not to use it.

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

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

Annotations already indicate readOnlyHint=true, so description adds value by listing return fields and noting default active filter. 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 concise sentences, front-loaded with purpose and return fields, 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?

Lists all return fields, describes parameter, aligns with annotations. No gaps given simplicity.

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?

Description does not add much beyond schema for 'include_inactive'; schema already explains it. Schema coverage 100%, baseline 3 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?

Clear verb 'list' and resource 'subscriptions', specifies scope as 'caller's active subscriptions'. Distinguishes from siblings like subscribe/unsubscribe by being a read-only listing.

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: 'review what you're monitoring before adding more or to find an id to cancel', implying when not to use and suggesting alternatives.

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

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

Adds rate limit and free-call info beyond annotations. No contradictions with annotations (readOnlyHint false is appropriate for a mutation). Could mention idempotency, but annotations already flag idempotentHint 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?

Four well-structured sentences, no wasted words, key information front-loaded.

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 feedback tool with simple schema and no output schema, description covers purpose, usage, parameter guidance, and constraints (rate limit, quota). 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 100% of parameters, but description adds value by elaborating enum options and clarifying optional context 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 specifies verb 'tell' and resource 'Pipeworx team', clearly indicating it's for sending feedback. Differentiates from sibling tools by being the only feedback mechanism.

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 (bug, feature, data_gap, praise) and provides guidance on what to include and not include (don't paste user prompt). Lacks explicit when-not-to-use, but coverage 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 indicate safe read-only operation. Description adds valuable context: self-aggregating signal, derived from CF analytics-engine, no PII, caching behavior (5min-1h). This goes well 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 paragraphs are well-structured: first line states purpose, then bulleted use cases, then technical details. No redundant sentences; every sentence adds value. Front-loaded with core result.

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 optional parameter, no output schema), the description is fully complete. It explains what is returned (top tools, packs, total call volume) and the parameters clearly. No missing information.

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

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

The only parameter 'window' has complete schema coverage (100%) with enum and description. The description adds further nuance: shorter windows surface hot now, longer windows show steady-state demand, providing meaning beyond the schema.

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

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

The description clearly states what the tool does: returns top tools, top packs, and total call volume over a recent window. It uses specific verbs and resource description, and is well-distinguished from sibling tools which don't offer trending analysis.

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

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

Explicitly lists three concrete use cases for when to use this tool, such as discovering hot data sources or confirming canonical choices. Does not include when-not-to-use or direct alternatives, but the use cases provide sufficient context for 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 indicate readOnly, openWorld, idempotent, and non-destructive, which the description supports. It adds rich behavioral context: algorithms (monotonicity violations, partition-sum checks, Jaccard similarity threshold), filler detection, and fill check against live CLOB depth. 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 dense but well-structured: starts with a requirement, then explains modes, then detailed features. While it is lengthy, the complexity of the tool justifies the detail. Minor reduction possible without losing clarity.

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

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

No output schema, so the description explains the response structure (opportunities array, partition_check object) and fill check. Covers what the tool does, how to use it, and expected outputs. Missing some edge cases but adequate for a complex tool.

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

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

Schema description coverage is 100% (both parameters documented). The description adds significant value beyond schema by explaining the semantics of 'event' and 'topic' with examples and how they trigger different analysis modes.

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 itself from sibling tools like polymarket_edges and polymarket_fill_risk by focusing on arbitrage detection across events.

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 requires one of 'event' or 'topic' and explains when to use each mode: event mode for a specific market, topic mode for cross-event scanning. Provides examples and notes that cross-event mode catches patterns missed by single-event.

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

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

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds extensive behavioral context: caching strategy (1h KV-level), model families (crypto_price, news_momentum, etc.), response structure (by_segment, diagnostics), and edge calculation details (edge_pp_net, kelly_fraction). It also explains tradeable-edge knobs and their effects on filtering. 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 quite long and dense, containing detailed algorithmic explanations. While it is front-loaded with the main purpose, it could be more structured (e.g., breaking into sections). Some sentences are complex and may be hard to parse quickly. Still, every sentence adds value, so it is not excessively verbose.

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 (9 parameters, multiple model segments, diagnostic output) and lack of output schema, the description is remarkably complete. It explains the response structure (by_segment, diagnostics), caching, knob effects, and even includes edge cases (Fed bets, stale segments). An agent can fully understand how to invoke and interpret the tool without additional context.

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

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

Schema coverage is 100% with detailed descriptions for all 9 parameters. The description adds extra meaning by explaining how parameters like min_partition_leg_kelly interact with the partition_overround logic and why min_kelly doesn't apply to partitions. It also contextualizes knobs as 'tradeable-edge filters' and provides practical guidance (e.g., slippage_pp default reasoning). This goes 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: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It specifies the verb (scan/return), resource (Polymarket markets), and the unique value (disagreement with Pipeworx data). This distinguishes it from siblings like polymarket_arbitrage, which likely focus on different calculations.

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 frames the tool as 'Built for "what should I bet on today"' and describes the use case of discovering opportunities. It explains the response segments and knobs for filtering. However, it does not explicitly state when not to use this tool or compare it against alternatives like polymarket_arbitrage, leaving some ambiguity.

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

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

Adds significant context beyond annotations: explains source (daily snapshots), response structure (tracked, expired, snapshot_dates), data gaps due to cache-miss, and decay calculation. No contradiction with idempotent/readOnly 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?

Well-structured, front-loaded with key question, then param details, response format, and limits. Some redundancy (e.g., 'default 14' already in schema) 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 no output schema, the description thoroughly explains the response structure and nuances. Covers tracked, expired, and snapshot_dates arrays with field details. Limits are also addressed.

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 value by clarifying defaults (14 days, '1wk') and constraints (max 30 days). Provides more context than schema alone.

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

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

The description clearly states the tool provides 'edge persistence and decay telemetry' and answers a specific question. It distinguishes from siblings like 'polymarket_edges' by focusing on historical trends rather than current edges.

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

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

Implies use when historical edge persistence matters, contrasting fresh vs. aged edges. Mentions limits (60-day TTL, snapshot start date). However, does not explicitly state when not to use or point 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 indicate readOnly, openWorld, idempotent, non-destructive. The description adds behavioral details: checks order book depth, returns verdict (clean|degraded|cannot_fill), and warns about forced directional risk and thin legs. No contradiction with annotations.

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

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

The description is dense and front-loaded with the core purpose, but could benefit from more structured formatting (e.g., bullet points for modes). However, every sentence adds value and there is no wasted text.

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 (two modes, many output fields) and no output schema, the description is comprehensive: it lists return fields (top_of_book, vwap_fill_price, slippage_pp, etc.), explains verdict semantics, covers edge cases like thin legs and partial fills, and addresses risks. 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 description coverage is 100%, but the tool description adds significant meaning: explains mode selection (market vs event), default side logic for basket, interpretation of size_usd (spend vs settlement notional), and clamp constraints (10-1,000,000). This enhances understanding beyond the schema alone.

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

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

The description clearly states the tool performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes and explicitly contrasts with sibling tools like polymarket_arbitrage and polymarket_edges, specifying when this tool should be used.

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: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains why (theoretical overround on thin books not capturable, partial fills cause unhedged positions), covering both when and why 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?

The description extensively covers behavior beyond annotations, such as two operational modes, compatibility_warning conditions, temporal alignment checks, and skipped cross-type/subtype counters. It adds significant transparency about when spreads are meaningful.

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

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

The description is lengthy but well-structured with clear sections for modes, response fields, and safety fields. It is front-loaded with purpose and efficiently uses every sentence to convey important details.

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

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

Given the tool has 3 input parameters, no output schema, and rich annotations, the description covers all critical aspects: modes, response structure (top_spreads_pp, compatibility_warning, temporal alignment), and usage caveats. It is fully adequate for the tool's complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining the pre-mapped topic shortcuts, providing example values, and describing how explicit parameters override the topic-mapped side, which enriches 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, with two distinct modes (pre-mapped topics and explicit tickers/slugs). It distinguishes itself from sibling tools like polymarket_arbitrage by focusing on spread across venues.

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

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

The description provides clear context on when to use the tool, including warnings that most pre-mapped topics currently return compatibility warnings. However, it does not explicitly mention when not to use it or suggest alternative sibling tools.

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

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

Annotations already indicate read-only and idempotent behavior; description adds context on scoping to user identifier and pairing with other tools, enriching 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?

Three sentences, front-loaded with core function, no wasted words, efficient structure.

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

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

Complete for a simple retrieval tool: explains input, behavior, scoping, and relationships; no gaps given annotations and single parameter.

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 key parameter with description; description adds that omitting key lists all keys and provides examples, adding value beyond schema.

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

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

The description clearly states the tool retrieves a previously saved value or lists all keys, distinguishing it from sibling tools 'remember' and 'forget'.

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

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

Explicitly says to use for looking up stored context, gives examples, and notes pairing with remember/forget, providing 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, openWorldHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral context: the mark_read parameter's effect on subsequent calls, the return format, and the ability to filter by type and since. No contradictions with annotations.

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

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

The description is concise and well-structured, front-loading the purpose, then key return details, then parameter usage, then polling advice, then alternative access. Every sentence adds value without redundancy. 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 5 optional parameters, no output schema, and detailed annotations, the description covers all necessary aspects: return fields, filtering, mark_read behavior, polling suitability, and alternative access. It is complete for an agent to correctly select and invoke the tool.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds meaning beyond schema: gives an example for type ('sec_8k'), explains mark_read's effect on read status, and clarifies the since parameter expects an ISO timestamp. This compensates for any potential ambiguity 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 pulls fired events from the subscription feed, specifies the resource (subscription feed) and verb (pull/returns), and distinguishes from sibling tools like list_subscriptions or subscribe by focusing on alerts. It also provides concrete return fields (source, citation_uri, raw payload).

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 mentions the tool is suitable for polling and provides an alternative API endpoint for scripts/dashboards. It implies usage for retrieving alerts with optional filters but does not explicitly state when not to use it or compare to sibling tools like search_within or validate_claim. The guidance is clear but not exhaustive.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds rich detail: data source fan-out, fallback logic (GDELT→GNews), USPTO soft-fail, return structure with citations. 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?

Slightly long but front-loaded with natural language examples. Every sentence adds essential info; minor room for tightening but highly 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?

Despite no output schema, description explains return structure (changes[] grouped, count, citation URIs). Covers all complexities (multiple sources, fallback, parameter formats) for a tool with 3 required params.

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 value: examples for 'since' (ISO and relative), clarifies 'type' is only 'company', and explains 'value' accepts ticker or CIK.

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

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

Description clearly states the tool provides a 'change feed for a company' with specific sources (SEC, GDELT, USPTO) and explicitly distinguishes from sibling 'entity_profile' for static profiles.

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?

Includes explicit usage examples ('what's new', 'latest') and a direct alternative ('Use entity_profile instead when you want the static profile'), guiding when to use this tool vs. 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 indicate the tool is idempotent and not destructive. The description adds valuable context: key-value storage scoped by identifier, persistence details (authenticated users get persistent memory, anonymous sessions 24h). No contradictions.

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

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

The description is four sentences, each adding meaningful information. It is structured well but could be slightly more concise. No unnecessary words.

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

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

Given simple parameters (2 required string fields) and no output schema, the description covers purpose, usage, behavior, and does not miss crucial details. Annotations cover safety. Complete enough for effective agent use.

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

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

Schema coverage is 100% with descriptions for both 'key' and 'value'. The description adds minimal parameter-specific semantics beyond the schema (e.g., example value patterns). 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: 'Save data the agent will need to reuse later'. It specifies the verb 'save' and resource 'data', and distinguishes it from sibling tools like 'recall' and 'forget' by explaining the full lifecycle.

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 guidance on when to use: 'when you discover something worth carrying forward' and contrasts with 'recall' and 'forget' for retrieval and deletion. It lacks explicit when-not-to-use instructions but is otherwise 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 indicate read-only, open-world, and idempotent behavior. The description adds that each call cascades through several lookup endpoints internally and that using this tool replaces 2-3 manual lookups. No contradictions with annotations exist.

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

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

The description is front-loaded with examples and clear purpose, then structures details per type. While slightly verbose, every sentence adds value. It is well-organized but 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?

Given the tool's simplicity (2 parameters, 100% schema coverage, clear annotations, no output schema), the description fully covers purpose, usage, parameter details, internal behavior, and return information including citation URIs. No gaps are apparent.

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 enriches the meaning beyond the schema: it provides specific examples for the 'value' parameter (ticker, CIK, name for company; brand/generic for drug) and notes auto-disambiguation for company input. The type parameter enum is documented, but the description adds context on what each type returns.

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 concrete examples of user queries and clearly states the tool's purpose: resolving a name to a canonical/official identifier. It specifies that this tool should be used first when needing an ID, and it distinguishes itself from sibling tools by clarifying that other tools require these IDs as input. The supported types (company, drug) are listed with detailed return values.

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 FIRST whenever you have a name but need an ID,' providing clear when-to-use guidance. It also explains what each entity type returns and acceptable input formats. However, it does not explicitly exclude scenarios when an ID is already known or compare with sibling tools like entity_profile or 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?

Annotations already mark it as read-only, open-world, and idempotent. The description adds context by explaining the internal process: it probes each entity, ranks by score, and surfaces the most/least recognized. 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 sentences, front-loaded with the main action, no redundant information. Every sentence adds value.

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

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

Given the complexity (multi-entity, multiple models, no output schema), the description explains return fields (score, confidence, signal density) and the internal call to ai_visibility_check. It covers key aspects, though more detail on model selection could help.

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 value by noting that the first entity is treated as the '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 has a specific verb ('compare AI visibility'), a resource ('multiple entities'), and distinguishes from sibling tools by naming the underlying function (ai_visibility_check) and the competitive audit use case.

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

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

It clearly states when to use the tool ('competitive AI-marketing audits') and gives an example question, but does not explicitly state when not to use it or provide alternatives among siblings.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed will be listed. Does not disclose rate limits or auth requirements, but overall good transparency.

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 is well-structured with details about ecosystem, alternatives, and failure behavior. Could be slightly more concise, but every sentence adds value.

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

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

Despite no output schema, the description thoroughly explains the return structure: summary block, per-advisory detail, links, and alternative versions. Covers ecosystem constraints, error handling, and partial failures. Complete for a complex composite tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds that scoped packages are accepted and version defaults to latest, which provides minor additional meaning beyond the schema.

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

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

The description clearly states it is a composite check for npm packages, fanning out to deps.dev and bundlephobia to answer safety, popularity, and size questions. It distinguishes itself from sibling tools by specifying NPM ecosystem only and noting alternatives for other ecosystems.

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 to use when an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. Also states NPM only and directs users to deps.dev:version for PyPI/Maven/Cargo/Go. Provides clear when-to-use and when-not-to-use guidance.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. The description adds embedding model (BGE-base-en), similarity metric (cosine), window size (500-char overlapping), character cap (200K chars), truncation behavior, and output contents (passages with offsets and scores).

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

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

Three well-structured sentences: (1) purpose, (2) usage benefit and output, (3) technical details. No wasted 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?

Given rich annotations (readOnly, openWorld, idempotent) and no output schema, the description covers algorithm, limits, pairing, and output characteristics. Complete for intended 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. Description provides examples for the query parameter and mentions limit of top-N passages but does not specify default or range for limit beyond what schema says. Moderate added value.

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

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

The description clearly states it performs semantic search inside a fetched record, with specific examples (SEC 10-K, article). It distinguishes itself from siblings by mentioning it is used when the record is too large for the prompt and pairs with 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?

Explicitly says 'Use when the record is too big to cram into the prompt' and provides concrete scenarios. Also mentions pairing with ask_pipeworx_grounded for grounded responses, 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 indicate readOnlyHint=false and idempotentHint=true, which match the description's write operation. The description discloses behavioral traits beyond annotations: returns new subscription id, limitations (anonymous/BYO cannot persist), SMS 10/day cap, webhook auto-disable after 10 failures, and webhook signing secret returned once. 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 moderately long but every sentence adds value. It is front-loaded with the main purpose. Minor verbosity in the delivery section could be trimmed, but it remains clear and well-structured.

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

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

Given the tool's complexity (3 parameters, nested objects, no output schema), the description is remarkably complete. It explains the return value, all subscription types with parameters, delivery channels with constraints, and authentication requirements. 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%, so baseline is 3. The description adds significant meaning by providing concrete examples for each subscription type (e.g., 'sec_8k: {ticker:"AAPL", items?:["5.02","1.01"]}') and detailed delivery options (e.g., webhook HMAC signing). This goes beyond the schema's brief descriptions.

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

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

The description clearly states 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' It uses a specific verb ('Create') and identifies the resource ('subscription'), distinguishing it from sibling tools like 'unsubscribe' 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 provides explicit context on when to use (proactive monitoring), required authentication (Pipeworx OAuth account), and notes that anonymous/BYO cannot persist subscriptions. It does not directly compare to alternative tools but gives thorough type-specific parameters and delivery channels.

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 context that output is drawn from a live catalog and includes exact tool+argument shapes, which is valuable beyond the schema. 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 well-structured with upfront example queries clarifying intent. Each sentence adds value without redundancy. Slightly verbose but justified by the complexity of explaining the tool's output and use cases.

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 an onboarding tool with no output schema, the description thoroughly explains what is returned (category-bucketed questions with tool shapes), how to invoke it (no args or topic), and its purpose (first use). No gaps remain given the tool's simplicity.

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 one optional parameter with enum-like description. The description adds pragmatic usage guidance: 'call with no arguments for full spread, or pass topic to focus', which helps the agent understand how the parameter affects behavior.

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

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

The description clearly states this is an onboarding entry point that returns category-bucketed example questions with tool+argument shapes. It uses specific verbs ('returns', 'call') and distinguishes from siblings by positioning as the FIRST tool to use when orientation is needed.

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 FIRST when you do not yet know what Pipeworx can do for you'. Also specifies alternative usage: no arguments for full spread, or pass topic to focus. Provides example argument values and indicates it teaches how to call meta-tools.

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

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

Beyond annotations (destructiveHint=false, idempotentHint=true), description adds specifics: ownership enforcement and that rows are deactivated (not deleted) preserving historical events, enhancing understanding of side effects.

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 streamlined sentences with no redundancy. First sentence gives action and key constraint, second explains behavioral outcome. 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?

For a simple 1-param tool with no output schema, the description fully covers purpose, constraints, and effect. No 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 description coverage is 100% and description adds minimal extra meaning beyond stating 'by id'. Baseline 3 is appropriate as params are well-documented in schema.

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

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

Description clearly states the action 'Cancel a subscription by id', with a specific verb and resource. 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?

Provides clear context: ownership is enforced (only your own subscriptions) and effect is deactivation not deletion, which guides when to use. Lacks explicit alternatives or when-not scenarios, but sufficient for most use cases.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the behavioral safety profile is fully covered. The description adds value by detailing the return structure (verdict, extracted form, actual value with citation, percent delta) and notes it replaces multiple sequential calls, providing additional 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 moderately lengthy but every sentence contributes (examples, purpose, domain, return, efficiency benefit). It is front-loaded with query patterns, making the tool's purpose immediately obvious. 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 tool with one parameter and good annotations, the description is complete. It explains input format, supported claims, return values, and efficiency gain. The only minor gap is not mentioning that unsupported company claims are handled (but 'unsupported' verdict is listed), so it is nearly 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% (only one parameter 'claim' is fully described). The description reinforces meaning with examples (e.g., 'Apple's FY2024 revenue was $400 billion') and specifies the domain, adding value beyond the schema's brief description. This warrants a score above the baseline of 3.

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

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

The description specifies a clear verb ('verify') and resource ('factual claims against authoritative sources'). It provides example query patterns and explicitly delimits the domain (company-financial claims for public US companies), distinguishing it from sibling tools like 'bet_research' or 'compare_entities'.

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

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

The description states 'Use whenever the agent needs to check whether something a user said is factually correct.' While it does not explicitly list when not to use, the domain restriction (company-financial claims) serves as an implicit exclusion. The guidance is clear and context-appropriate given no direct sibling claim-verification tool.

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