Sanctions Screening

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

Annotations already indicate read-only, idempotent, open-world. Description adds cost implication (BYO key for Anthropic), return structure per model, and combined view, going beyond annotations.

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

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

Five well-structured sentences; first sentence covers action and output, second explains models, third return format, fourth use cases. No redundancy.

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

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

With no output schema, description provides return structure, default behavior, and pricing. Covers all key aspects for a 4-param tool with optional key.

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 params. Description adds context: default model name, _apiKey purpose, and context disambiguation, enriching 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?

Clear verb 'Probe', specific resource 'LLMs for visibility', and output 'score (0-100) per model'. Differentiates from sibling tools like scan_competitor_ai_presence by focusing on LLM knowledge scoring for brands.

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

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

States default model and optional Anthropic probe with own key, and lists use cases (AI-marketing audits, pre-launch checks). Lacks explicit when-not-to-use or comparison to similar 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?

Description adds significant context beyond annotations: routes to 5011 tools across 1319 sources, fills arguments, returns structured answer with stable citation URIs. All annotations (readOnly, openWorld, idempotent) are consistent.

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

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

Well-structured with clear purpose, usage guidance, and examples. Slightly verbose but front-loaded with essential 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 tool's complexity (many sources), the description fully covers capabilities, when to use, and provides examples. No output schema but mentions structured answer with citations, sufficient for selection.

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

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

Schema covers all 6 parameters with descriptions (100% coverage). Description adds value by explaining the question parameter and its aliases, but the schema already provides clear semantics.

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

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

The description clearly states the tool's purpose: answering factual questions using structured data from many sources. It specifies the types of data (SEC filings, FDA, FRED, etc.) and distinguishes from siblings like web search and deep_research.

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

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

Explicit guidance on when to use (prefer over web search, start here for most questions) and when to step up (ask_pipeworx_grounded for single answer with evidence, deep_research for broad questions). Includes examples and query patterns.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds valuable behavioral context: source-preference ranking, fallback behavior, and that it is a full working router. 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 four sentences, front-loading the beta status and key difference. Every sentence adds value with no redundancy.

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

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

Given the tool's complexity (beta router with routing changes), the description fully explains its purpose, usage, behavior, and relationship to the stable version. Output schema is not needed as response shape is inherited.

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 mentions 'same arguments' and 'same response shape' but does not add semantics beyond the schema's parameter 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 is a beta version of ask_pipeworx with identical functionality but improved routing, distinguishing it from the stable sibling ask_pipeworx. It specifies the verb 'ask' and the resource 'universal router'.

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

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

The description instructs to use it exactly like ask_pipeworx when wanting the newest routing, implying a comparison context. It does not explicitly exclude any scenarios but provides clear usage 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?

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description adds significant context: it details the exact return format {answer, evidence, confidence, source, fetched_at, refusal_reason:null} and enumerates specific refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error). It also discloses the cost of one extra LLM call, which is behavioral information not in 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 approximately 100 words in two sentences, with no waste. The first sentence clearly states the core purpose and mode. The second sentence elaborates on when to use and alternatives. Every word 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?

Despite no output schema, the description fully specifies the return structure including both success and explicit refusal cases. It covers complex error handling (5 refusal reasons) and provides complete context for an agent to use the tool appropriately with its 5,000+ sibling tools.

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

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

Schema coverage is 100%, with each parameter described as an alias for question. The description adds value by explaining that the tool accepts a question in natural language and lists the aliases explicitly. While baseline is 3 due to full schema coverage, the description's explicit listing of aliases and clarification of usage provides slight extra 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 that this tool is a hallucination-resistant answer mode for high-stakes reads. It explains the process: picks the right tool, fetches data, and extracts the answer using only the tool result. It distinguishes from sibling 'ask_pipeworx' by noting the extra LLM call and the strict extraction behavior.

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 this tool: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts'. Also provides when-not: 'prefer ask_pipeworx for casual lookups'. The description also lists example domains (financial verdicts, legal claims, medical lookups, public statements) to guide usage.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description goes far beyond by detailing fan-out logic, response shapes, resolver contracts, parent event extraction, news fallback, safety short-circuits, and resolution-rule risk. It fully discloses behavioral traits and edge cases.

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

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

The description is lengthy but well-structured with capitalized section headers (RESOLVER CONTRACT, PARENT_EVENT EXTRACTOR, etc.). Every sentence adds value, but it could be more concise. The front-loading is effective: purpose and usage in first paragraph.

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

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

Given the tool's complexity (3 parameters, no output schema), the description is exceedingly complete. It covers all aspects: input formats, classification categories, fan-out examples, response shapes, resolver confidence, parent event extraction, news fields, safety mechanisms, and resolution rules. No gaps remain for an agent to misunderstand behavior.

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

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

While schema coverage is 100%, the description adds significant meaning: explains the market parameter accepts slug, URL, or question text; describes the depth parameter's effect (quick vs thorough with default); and explains include_raw's trade-offs (summary vs full payload). This adds value beyond the schema alone.

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

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

The description clearly states the tool's purpose: research a Polymarket bet by pulling relevant Pipeworx data. It specifies the input forms (slug, URL, question text) and the output (evidence packet + market-vs-model comparison). The verb 'research' and resource 'bet' are specific and distinct from sibling tools, though not explicitly differentiated.

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 use cases are given: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. It also describes blocking conditions (low confidence match, market closed) that indicate when results are not trustworthy. However, it does not explicitly mention when NOT to use the tool or compare it to alternative sibling tools like polymarket_edges or polymarket_arbitrage.

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 substantial detail beyond annotations: specifics on data pulled (10-K metrics, FAERS counts), handling of off-calendar fiscal years, results sorted by primary metric, and 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?

Concise at 4-5 sentences. Front-loaded with common query phrases. Every sentence adds value, though slightly dense.

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 data (paired data, citation URIs, sorted by primary metric) and handles edge cases (off-calendar fiscal years). Adequate for a simple comparison 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%. Description adds meaning to 'type' and 'values' parameters with clear examples and behavioral context (e.g., tickers vs drug names, max 5 items).

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 and resources: 'side-by-side comparison', 'parallel call', and gives example queries. It clearly distinguishes itself from siblings by stating it replaces 8-15 sequential lookups.

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

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

Explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Provides context for each type and what data is pulled, though 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 show readOnly, openWorld, idempotent, non-destructive. Description adds account requirement, paid tier, parallel execution, return structure (findings packet with evidence, confidence, gaps, contradictions), hop field, citation_uri, excerpting behavior, and timing estimates. 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 long but well-organized: starts with crucial prerequisites, explains core function, then details, comparisons, and depth options. Every sentence adds value, though a few technical details like 'hop field' could be 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 complexity and lack of output schema, the description fully explains return format, timing, caveats, and depth behaviors, leaving no critical gaps 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 coverage is 100% for the 2 parameters. Description explains each depth value's facet count and added features (gap recovery, contradictions). It clarifies that question can be broad/multi-part, beyond the schema's 'natural language' hint.

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 'research', 'decompose', 'route', and 'return' and clearly states it operates on structured data sources. It distinguishes itself from siblings like ask_pipeworx by emphasizing it is for broad/multi-part questions, not single lookups or news.

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

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

Provides explicit guidance: when to use (broad/multi-part structured data), when not (single lookup → ask_pipeworx, breaking news → ask_pipeworx), and prerequisites (account, paid plan for thorough). Also explains depth modes.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description does not need to restate safety. It adds context about returning 'full input schemas (with curated examples)' and that results are 'ready to call directly, no second schema lookup needed,' which is valuable 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 about 150 words, front-loaded with the core purpose. Every sentence adds value: purpose, usage guidance, domain list, return format richness, and when to call. 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?

Even with no output schema, the description clearly explains what is returned: tool names, descriptions, full input schemas with examples, ready to call. It also mentions default limit and max. For a discovery tool with good annotations, this is complete and actionable.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds examples of natural language queries (e.g., 'analyze housing market trends') and lists aliases, enhancing understanding of the query parameter beyond the schema's dry field descriptions.

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

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

The description starts with a clear verb-resource pairing: 'Find tools by describing the data or task.' It lists specific domains (SEC filings, FDA drugs, etc.) and distinguishes from siblings by being a discovery tool to call first, unlike search_within or deep_research.

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

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

Explicitly states 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This gives clear when-to-use guidance and implicitly advises against calling it if the 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 indicate read-only, open-world, idempotent, non-destructive behavior. The description adds detailed behavioral context: fans out across multiple sources, returns specific fields (cik, company_name, recent_filings with URIs, fundamentals, patents with sunset warning, news with fallback, LEI), and notes soft-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 informative and well-structured with examples upfront, but is slightly long. However, every sentence adds value, so it earns a high score.

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 what the tool returns (specific fields, limits, fallbacks) and how parameters work. It covers data sources, limitations (patents sunset, name unsupported), and recommended usage, making it complete for an agent.

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

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

Schema coverage is 100%, but the description adds significant semantic value: specifies that ticker or zero-padded CIK are accepted, explicitly states names are not supported and directs to resolve_entity, which goes beyond the schema's enum and type 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 begins with explicit example queries and states it is a 'full cross-source profile of a US public company in ONE parallel call', clearly identifying the tool's purpose and distinguishing it from chained single-source 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 advises to 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view' and clarifies that if only a name is available, use resolve_entity first, providing clear when-to-use and alternatives.

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

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

Matches annotations (destructiveHint=true) and adds context about clearing sensitive data. No contradictions. Minor omission of side effects beyond deletion, but annotations already cover destructive 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?

Two sentences, front-loaded with main action. No wasted words. 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?

Simple tool with one parameter, no output schema. Description fully covers purpose, usage, and parameter. Complete for the 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 one parameter 'key' described as 'Memory key to delete'. Description does not add new information 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?

Clearly states the verb 'delete' and resource 'previously stored memory by key'. Distinguishes from siblings '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: 'when context is stale, the task is done, or you want to clear sensitive data'. Mentions pairing with 'remember' and 'recall' as alternatives.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds process steps (fetches, extracts, emits) and output format, providing useful context beyond annotations.

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

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

Three concise sentences plus a bullet list of use cases. No redundant information; every sentence adds value. Front-loaded with main purpose.

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

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

For a simple tool with two parameters, full schema coverage, and no output schema, the description sufficiently explains what the tool does, how it works, and when to use it. The output format is described as 'single text blob', which is adequate.

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

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

Schema already covers both parameters with full descriptions. The description does not add new semantic information beyond what is in the schema, so baseline 3 is appropriate.

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

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

The description clearly states 'Generate a production-ready llms.txt file for any URL', using a specific verb and resource. It distinguishes from siblings by focusing on file generation rather than scanning or 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 use cases (client indexing, own project drafting, competitor auditing). However, it does not specify when not to use or compare directly to sibling tools.

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

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

Annotations already confirm read-only and idempotent behavior. Description adds caller-scoped access and exact return fields, providing complete 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, no fluff. Purpose and return fields in first sentence, usage advice in second. Well 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 no output schema, description compensates by listing all returned fields. Could note lack of pagination or ordering, but overall sufficient for a simple list tool.

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

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

Schema covers the only parameter with a clear description. Tool 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 tool lists the caller's subscriptions, specifies the returned fields, and distinguishes from sibling tools like subscribe and unsubscribe.

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

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

Explicitly provides guidance on when to use this tool: to review current subscriptions before adding more or to find an ID to cancel, helping avoid unnecessary tool switching.

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 limit (5 per identifier per day), no cost, no quota impact. Annotations (all false) don't contradict; description adds 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?

Single paragraph, front-loaded purpose, then usage, then behavior. Every sentence adds value. 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?

Comprehensive for a feedback tool with no output schema. Covers purpose, usage, parameters, rate limits, and quota. 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 covers 100% of parameters. Description adds context for 'type' meanings and advises describing issues in tool/pack terms, which aids parameter 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?

Description clearly states it sends feedback about bugs, features, data gaps, or praise. Distinguishes from sibling tools that are mostly query/info tools.

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

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

Explicitly says when to use (bug, missing tool, praise) and what not to do (don't paste user prompt). Also mentions rate limits and that it's free.

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

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

Annotations already declare read-only, open-world, idempotent, non-destructive. Description adds significant behavioral context: 'Self-aggregating signal', 'no PII', 'cached 5min-1h'. 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?

Single paragraph, front-loaded with output, then use cases, then details. Every sentence adds value. No redundancy.

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

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

Given low complexity, rich annotations, and full schema coverage, the description fully covers purpose, usage, behavior, and parameter semantics. No need for output schema.

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

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

Single parameter 'window' with 100% schema coverage. Description adds semantic meaning: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' Also mentions window options in main text.

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?

States 'Returns the top tools, top packs, and total call volume over a recent window' with specific verbs and resource. Distinguishes from sibling tools like 'ask_pipeworx' and 'discover_tools' by focusing on trending/usage statistics.

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 three explicit use cases (discovering hot data sources, confirming popular tool, seeing use case alignment). Implies context for when to use, but does not explicitly mention when not to use or direct 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 declare readOnlyHint, idempotentHint, and openWorldHint; description adds extensive behavioral context: internal checks (monotonicity, partition sum, semantic similarity), fill check against CLOB depth, and conditions for null signals. 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?

Well-structured with clear sections, front-loaded purpose, and detailed parameter explanations. Slightly lengthy but every sentence contributes meaningful information; no redundancy.

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

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

Completely describes the tool's behavior across all modes, including algorithm details, response structure (opportunities, partition_check, fill_check), and edge cases (placeholder filtering, low similarity reporting). No output schema exists, so the description carries full burden and meets it thoroughly.

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

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

Schema coverage is 100%, and the description greatly expands on parameter meanings with concrete examples and detailed behavior for each mode (event slug format, topic seed question, cross-event scanning description), adding significant 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?

Clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. Distinguishes from sibling tools like polymarket_fill_risk by specifying its unique algorithm and usage contexts.

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

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

Explicitly describes when to use each invocation mode: no args for trending scan, event for specific market, topic for cross-event scanning. Provides recommendations and warns against trading when realizable edge is ≤0, directing to polymarket_fill_risk for custom sizing.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description discloses extensive behavioral details: cache behavior (1h KV-level), response structure (by_segment, diagnostics), edge calculation methodology (lognormal, GDELT, partition overround), slippage assumptions, and limitations (Fed note). 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 excessively long (500+ words) with detailed model formulas and segment explanations that could be abbreviated or referenced externally. It is well-structured with section labels (MODEL_DRIVEN, etc.) and front-loaded purpose, but every sentence does not earn its place given the verbosity.

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

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

Despite no output schema, the description fully specifies response structure (by_segment with sub-segments, fed_candidates, _diagnostics), key fields (edge_pp_net, kelly_fraction), and all filters. It covers caching, limitations, and knob purposes, making it complete for an agent to understand behavior and results.

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

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

Schema coverage is 100% with detailed parameter descriptions. The tool description adds general context for parameter groups ('tradeable-edge knobs') but largely reiterates schema descriptions. Some parameters get marginal extra context (e.g., min_partition_leg_kelly explains interaction with min_kelly). 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 scans Polymarket markets for edge opportunities where Pipeworx data diverges from market price, positioning it as a discovery tool for 'what should I bet on today'. It distinguishes from siblings like polymarket_arbitrage and polymarket_edge_tracker by focusing on top markets and model-driven opportunity detection.

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

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

The description provides explicit context for use ('what should I bet on today') and details the three opportunity segments (model-driven, structural arbitrage, concentrated longshot), guiding when each applies. It lacks explicit when-not-to-use guidance but implies alternatives through segment descriptions and tradeable-edge knobs.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false, which the description complements with details on data source (daily snapshots), limits (60-day TTL, snapshot start date), and that decay is from daily closes of edge_pp_net (not intraday). No contradiction; description adds meaningful behavioral context beyond annotations.

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

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

Description is front-loaded with the core purpose and effectively structured with args, RESPONSE, and LIMITS sections. While verbose, every sentence adds value and does not wastewords. Slightly long but 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?

Despite no output schema, the description fully covers the return structure: tracked array with time-series, first_seen, trend, decay_pp_per_day; expired array with lifespan_days; snapshot_dates array. It also addresses TTL limits and data gaps, making the tool self-explanatory 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?

Both parameters have 100% schema coverage. Description adds context: 'days' is lookback with default 14 and max 30 (schema says clamp 2-30); 'window' is snapshot family with default '1wk'. This explains their role in the tool's logic, going beyond basic schema types.

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 'edge persistence and decay telemetry' and answers 'how long has this edge existed and is it shrinking?'. The verb-resource pair is specific to analyzing edge time-series, distinguishing it from sibling 'polymarket_edges' which likely provides raw snapshots.

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

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

The description explains the use case (comparing fresh vs. old edges) and the response structure (tracked, expired, snapshot_dates), implying when to use it for decay analysis. It does not explicitly state when not to use or name alternatives, but the sibling context provides implicit differentiation.

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

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

Annotations (readOnlyHint, idempotentHint, etc.) already indicate safe read behavior. The description adds rich behavioral context: it explains that the tool walks the order-book ladder, returns fill details, and most importantly warns about forced directional risk from partial basket fills. 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, starting with the core purpose, then detailing modes in separate sections. Every sentence adds value; however, some redundancy exists (e.g., repeating enum values). Could be slightly more concise but still 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?

Since there is no output schema, the description fully explains return values for both modes (top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict for single; theoretical_sum, realizable_sum, capture_ratio, profit_usd, per-leg fill detail, thin_legs, max_clean_notional_usd, forced_directional_risk for basket). This is comprehensive and leaves no 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 description coverage is 100%, so baseline is 3. The description adds some meaning (e.g., size_usd in basket mode as 'settlement notional S — shares per leg') but does not significantly extend beyond the schema's own descriptions for most parameters. Adequate but not a standout.

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

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

The description explicitly states the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It clearly distinguishes between single-market and basket modes, and the verb 'check' combined with specifics makes it distinct from siblings like polymarket_arbitrage.

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

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

The description provides explicit when-to-use guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains why (theoretical overround not capturable, partial fills cause directional risk), giving clear context for 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 declare readOnlyHint, openWorldHint, idempotentHint true, destructiveHint false. The description goes far beyond annotations by detailing compatibility_warning conditions (shape mismatch, semantic mismatch), temporal_alignment constraints, and skipped leg counters. No contradiction exists.

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

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

The description is dense but well-organized: purpose first, then modes, response structure, safety fields. Every sentence adds value, and the length is justified by the tool's complexity. No fluff or repetition.

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

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

Despite having no output schema, the description thoroughly explains the response structure (leg-by-leg prices, matched spreads, safety fields). It covers edge cases (compatibility warnings, temporal alignment) and gives realistic expectations about tradeability. Complete for a data-comparison 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 each parameter is already documented. The description adds value by explaining the two interaction modes (topic vs explicit), listing the 10 pre-mapped topics, and clarifying that explicit parameters override the topic-mapped side.

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

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

The description opens with a clear verb+resource: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It distinguishes itself from sibling tools like polymarket_arbitrage by focusing on cross-venue comparison rather than single-venue arbitrage.

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

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

The description explains two modes (topic shortcuts vs explicit tickers) and warns that most pre-mapped topics return compatibility warnings. It implies when to use each but does not explicitly contrast with siblings or state when not to use; a brief mention of alternatives would push to 5.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds scoping to user identifier and pairing behavior, fully aligning with annotations and providing additional useful 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 concise, front-loaded with the primary action, and structured logically. 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 explains return behavior (retrieve value or list keys). It covers essential context but omits error handling or expected output format.

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 with a clear description. The description adds key insight: omit key to list all keys. This goes beyond schema but is not richly detailed (no examples of return format).

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

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

The description clearly states the tool retrieves a value previously saved via remember or lists all keys when omitted. It explicitly distinguishes from sibling tools (remember, forget).

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

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

The description explains when to use (look up stored context) and scoping (user identifier). It suggests pairing with remember/forget but lacks explicit when-not or alternative tools for similar retrieval patterns.

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

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds value by explaining the mark_read option's effect on subsequent calls and noting that polling works fine, complementing the annotations without contradiction.

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

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

The description is concise at three sentences, covering purpose, return fields, filter options, a behavioral note, and an alternative endpoint without any redundant or 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 no output schema, the description adequately explains return values (source, citation_uri, raw payload) and covers filtering and mark_read behavior. It lacks details on pagination beyond the limit parameter, but is otherwise 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 descriptions for each parameter. The description adds slightly to the mark_read parameter by explaining its effect on subsequent calls, but the overall added value beyond the schema is modest.

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

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

The description clearly states it pulls fired events from subscription feed, returns recent alerts with specific fields, and distinguishes itself from sibling tools like list_subscriptions by focusing on reading alerts rather than managing 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 clear context on when to use this tool (to get recent alerts) and mentions an alternative endpoint (GET registry.pipeworx.io/alerts.json) for scripts/dashboards, but 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 readOnly, openWorld, idempotent, non-destructive. The description adds details on data sources (SEC, GDELT/GNews, USPTO), fallback mechanism, rate-limiting handling, soft-failure for USPTO, and return structure (changes[], total_changes, citation URIs). No contradictions.

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

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

The description is comprehensive and well-structured with examples, but slightly verbose. It could be trimmed without losing key information, but it remains clear and organized.

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

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

Despite no output schema, the description explains the return structure. It covers all parameters, data sources, fallback behavior, and points to an alternative tool. For a complex tool, this is complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds practical usage tips like 'Use "30d" or "1m" for typical monitoring' and clarifies that 'value' accepts ticker or zero-padded CIK, going 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 explicitly states the tool provides a 'change feed for a company in the last N days/weeks/months' with concrete example queries, and distinguishes itself from the 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?

Provides explicit guidance on when to use this tool (for dynamic change queries within a time window) and when to use 'entity_profile' (for static profile regardless of window). Also notes fallback behavior between GDELT and GNews.

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 scoping by identifier, persistence differences (authenticated vs anonymous), and retention period (24 hours for anonymous). Annotations provide idempotentHint=true, and description adds useful behavioral context 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?

Concise, front-loaded description with three sentences that cover purpose, usage, and behavior without superfluous detail.

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

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

Given the simple tool with complete schema and clear context (paired with recall/forget), the description is fully 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 covers both parameters with descriptions. The description adds example keys and notes that value is 'any text,' but baseline is 3 due to high schema coverage.

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 provides specific examples and distinguishes from sibling tools recall and forget.

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

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

Explicitly says 'Use when you discover something worth carrying forward' and pairs with recall and forget, offering clear guidance on when to use.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds value by revealing that 'each call cascades through several lookup endpoints internally' and replaces 2–3 manual lookups. No contradiction with annotations; all statements are consistent.

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

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

Front-loaded with a clear purpose statement and examples. Information is packed efficiently without redundancy. Could be slightly more structured (e.g., bullet points for types), but current paragraph is concise and scannable.

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

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

Given no output schema, the description fully covers return values and input semantics for both entity types. It also explains internal behavior (cascading lookup) and the tool's role in the workflow. Adequately prepares an agent for correct invocation without missing critical details.

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%, providing baseline 3. The description enriches by detailing exact return fields for each type (ticker, CIK, company_name + citation for company; RxCUI, ingredient, brand + citation for drug) and acceptable input formats (ticker, CIK, name for company; brand or generic name for drug). This adds meaning beyond the schema.

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

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

Description uses specific verbs and resources: 'resolve a name to the canonical/official identifier'. It provides concrete query examples ('What's the ticker for…', 'find the CIK for…') and lists supported types (company, drug) with associated identifiers. The instruction 'Use FIRST' and the detail that it replaces 2–3 manual lookups clearly distinguishes its role from sibling tools.

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

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

Explicitly states 'Use FIRST whenever you have a name but need an ID', giving clear context for when to invoke. It details what each supported type returns and accepts, and notes that internal cascading replaces manual lookups. Does not explicitly list alternatives or when-not-to-use, but the context is sufficiently guiding.

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 safe, idempotent, non-destructive. Description adds valuable detail on what data is returned (full record with aliases, addresses, IDs, etc.), exceeding annotation coverage.

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

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

Single sentence with purpose and field list, no fluff. Front-loaded with action and resource.

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 lists many return fields, compensating well. Covers key details; minor omission on conflict resolution if both params given.

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

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

Schema coverage is 100% with good descriptions. Description adds format examples and relationship to sanctions_screen, slightly enhancing understanding beyond schema.

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

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

Description clearly states the tool retrieves the full Consolidated Screening List record for one entry using uid or entity number. Distinguishes from sibling tools like sanctions_screen (list) and sanctions_lists.

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

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

Explicitly mentions use after sanctions_screen results, providing clear context. Could include when not to use but sufficient for typical workflow.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by specifying that the output includes entry counts and last sync time, providing behavioral detail 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?

Two concise sentences: the first defines the action and output, the second gives a concrete usage example. Front-loaded and efficient 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 zero-parameter tool lacking an output schema, the description explains the output (list of lists with counts and timestamps) and a typical use case, making it fully complete given the 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?

There are no parameters; the schema coverage is 100% (empty). The description does not need to add parameter information and clarifies the tool's purpose sufficiently.

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 lists the 12 US restricted-party lists with entry counts and data freshness, distinguishing it from siblings like sanctions_entry and sanctions_screen which deal with specific entries or screening.

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 a specific use case (cite coverage in a compliance report) and enumerates the lists, but does not explicitly state when not to use the tool or mention alternatives.

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

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

Annotations already declare readOnlyHint and idempotentHint, but the description adds behavioral details beyond that: fuzzy name matching, relevance score, matched aliases, programs, addresses, and which list each hit is on. It also notes daily updates and ~26k entries. 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 well-structured, front-loading the main purpose and list details. It is informative without being verbose, though slightly long due to listing lists and use cases. 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 sufficiently explains the return format (matches array with relevance, aliases, programs, addresses, lists) and states behavior when no matches. Completeness is high for a screening tool with good annotations.

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

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

Schema coverage is 100% as all 5 parameters have descriptions. The description adds contextual meaning like 'Fuzzy name matching with a 0-1 relevance score' but does not significantly enhance parameter semantics beyond what the schema already 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 screens a person, company, vessel, or aircraft against the US Consolidated Screening List for compliance purposes. It lists specific sanctions lists and use cases (KYB/KYC/AML/export control), distinguishing it from related tools like sanctions_entry or sanctions_lists.

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 for KYB / KYC / AML / export-control compliance checks,' providing clear usage context. It does not mention when not to use or alternatives, but the specificity and mention of returning empty matches when clear offer practical guidance.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false. The description adds significant behavioral context: it probes each entity with ai_visibility_check, returns a ranked list with score/confidence/signal density, and treats the first entity as the subject. This goes beyond what annotations provide.

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

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

The description is four sentences, front-loaded with the main purpose, then method, then use case, then output. Every sentence earns its place with no redundancy.

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

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

Given the tool's moderate complexity (4 parameters, no nested objects, no output schema), the description sufficiently explains inputs (entities order, context role), process (probes via ai_visibility_check), and output format (ranked list with metrics).

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%. The description adds extra semantics by clarifying that the first entity is the 'subject' for narrative and that context is shared across probes. This adds 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?

The description clearly states the tool compares AI visibility across multiple entities side-by-side, uses ai_visibility_check, ranks by score, and surfaces most/least recognized. This distinguishes it from sibling tools like ai_visibility_check (single entity).

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 a concrete use case for competitive AI-marketing audits and implies that for single-entity checks, one should use ai_visibility_check. However, it lacks explicit when-not-to-use guidance or enumerated alternatives.

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

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

Discloses fan-out across two sources, return structure, and bundlephobia's potential 5–30s delay. Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false; description 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?

Description is relatively long but well-structured with front-loaded purpose and detailed breakdown. Every sentence adds value, though minor tightening possible.

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

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

Given the tool's complexity and absence of output schema, the description comprehensively covers input, output fields, failure modes, and ecosystem constraints.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by clarifying scoped package acceptance and default version behavior, earning a 4.

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 is a composite check for npm packages (specific verb+resource) and distinguishes itself from sibling tools like 'deps.dev:version' by focusing on npm and including bundlephobia data.

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 ('is X safe / popular / small'), when not to use (only npm in v1), and provides alternatives for other ecosystems. Also describes graceful degradation on bundlephobia timeout.

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

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

Beyond annotations (readOnly, idempotent, non-destructive), the description adds valuable behavioral details: returns passages with offsets and scores, uses BGE embeddings with cosine similarity over 500-char windows, caps at 200K chars with truncation flagging. There is 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 a single well-structured paragraph with no wasted words. Each sentence serves a purpose: core action, usage context, pairing with sibling, and technical details. Perfectly concise.

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

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

Given no output schema, the description fully explains return values (passages with offsets and similarity scores) and includes technical constraints (embeddings, window size, char limit). Also references sibling tool for grounding, providing complete 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 description coverage is 100%, providing baseline of 3. The description adds extra value: states the text max size (~200K chars), limit range with default, and gives concrete examples for the query parameter, improving understanding beyond 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 semantic search inside a fetched record, using specific verbs and resources. It distinguishes from siblings like ask_pipeworx_grounded by explaining when to use this tool (when record is too large for the prompt) and how they pair together.

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: 'Use when the record is too big to cram into the prompt.' It also explains the workflow synergy with ask_pipeworx_grounded, providing clear context for decision-making.

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

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

Beyond annotations, the description adds rich behavioral context: return of new subscription id, authentication requirement, type-specific param structures, delivery channel options with constraints (phone verification, 10/day SMS cap, webhook auto-disable after 10 failures). All annotations (readOnlyHint=false, destructiveHint=false) are consistent with the description's portrayal of a non-destructive creation operation.

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 yet front-loaded with the main purpose. It is well-structured with type examples and delivery details. While lengthy, every sentence adds unique value; minor reduction possible but overall efficient.

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

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

Given the tool's complexity (multiple types, delivery channels, constraints), the description covers all necessary context: account requirement, type support with examples, delivery options and their quirks, rate limits, and auto-disable behavior. No output schema, but return value (subscription id) is noted.

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 significantly enriches each parameter. It provides concrete examples for each type, details param structure per type, and thoroughly explains delivery fields (email, SMS, webhook) including constraints and behavior. This exceeds 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?

The description starts with 'Create a proactive monitoring subscription to a live-data event stream', providing a specific verb and resource. It clearly distinguishes the tool from siblings like list_subscriptions, unsubscribe, and recent_alerts 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 states the requirement for a Pipeworx OAuth account and lists supported subscription types with examples. It implies when to use (subscribing) versus alternatives, but does not explicitly exclude scenarios or compare with 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 declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds context: returns category-bucketed example questions drawn from a live catalog, which is consistent with annotations and provides behavioral insight beyond the structured fields.

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 front-loaded with natural language queries and structured examples. Every sentence adds value for the onboarding use case. Slightly verbose but justified by the need to cover many 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 the tool's role as an onboarding entry point with no output schema, the description fully covers what the tool does, when to use it, what parameters do, and what the response looks like (category-bucketed questions). 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% (topic parameter with enum-like examples). The description repeats that parameter and adds that omitting it gives a cross-category spread, which adds minor value. Baseline 3 is appropriate.

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

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

The description clearly states it's an onboarding entry point for generating example questions, lists exact use cases, and distinguishes from siblings like ask_pipeworx and entity_profile by noting it's the first tool to use when you don't know what Pipeworx can do.

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and explains optional topic parameter to focus results. Could improve by stating when not to use (e.g., if you already know the data source), but overall clear.

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

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

Description adds behavioral detail beyond annotations: 'row is deactivated (not deleted)' and that historical events remain via 'recent_alerts'. Annotations already indicate idempotent and non-destructive, but description enriches understanding.

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: first states primary action, second adds critical constraints and side effects. No wasted words, front-loaded with essential information.

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

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

For a simple tool with one parameter, no output schema, and good annotations, the description fully addresses what the tool does, its constraints, and its impact (deactivation, not deletion). References 'recent_alerts' for historical context, 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?

Only one parameter 'id' with schema description 'Subscription id (uuid) returned by subscribe.' Schema coverage is 100%, so baseline is 3. Description adds no further semantic detail about the parameter.

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

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

Description uses specific verb 'Cancel' and resource 'subscription by id', clearly distinguishing from sibling tools like 'subscribe' (creation) and 'list_subscriptions' (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 states ownership enforcement ('you can only cancel your own subscriptions'), guiding the agent on when the tool is applicable. Does not explicitly mention when not to use or name alternatives, but context is clear given 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 provide safety and idempotency hints. The description adds behavioral details: the two processing paths (structured EDGAR+XBRL for financial claims, grounded pipeline for others), return values (verdict, value, citation, reasoning), and the fact that it replaces multiple sequential calls. 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 somewhat lengthy but well-structured. It starts with trigger phrases and examples, then explains the two processing paths, and finally describes the return value. Each sentence adds value, though it 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 the tool has 2 parameters, no output schema, and moderate complexity, the description covers the functional behavior, input semantics, and return format adequately. It explains different scenarios and provides enough context for correct invocation.

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

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

Schema coverage is 100%. The description adds meaning beyond the schema: explains that 'claim' is a natural-language factual statement, and that 'tolerance_pct' overrides the default tolerance derived from the claim wording. This provides useful context for usage.

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: natural-language claim verification against authoritative sources. It specifies the verb ('verify'), resource ('claim'), and scope (company-financial vs other). It distinguishes from siblings by claiming to replace 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?

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates processing paths for company-financial vs other claims, providing clear context. While it doesn't explicitly state when not to use, the context is sufficient.

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