Greynoise

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds behavioral context: default model is free, Anthropic requires user-payed API key, returns per-model scores and combined view. No contradiction with annotations.

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

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

The description is concise at four sentences, front-loading the core action and results. Every sentence adds useful information 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?

For a tool with 4 parameters, no output schema, and many siblings, the description covers purpose, parameters, use cases, and return format. It lacks details on scoring algorithm but is adequate for selection and 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% with good parameter descriptions. The tool description adds value by clarifying default model behavior, BYO key requirement, and context disambiguation, 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 clearly states the tool probes LLMs for knowledge about a business/brand/product/topic and scores visibility per model. The verb 'probe' and resource 'visibility' are specific, and the use cases (AI-marketing audits, pre-launch checks, competitive monitoring) distinguish it from potential siblings.

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

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

The description provides clear usage context with explicit use cases and explains the default model and how to add Anthropic with a BYO key. However, it does not explicitly mention when not to use this tool versus alternatives like scan_competitor_ai_presence.

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?

Despite no annotations, the description discloses that the tool internally selects and invokes other tools, returning results. This explains its behavior beyond a simple query, though it does not discuss limitations or side effects.

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

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

The description is very concise, with two sentences and examples front-loaded. Every sentence adds value, and there is 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 flexibility and lack of output schema, the description adequately explains its function. However, it could briefly mention the nature of the result (e.g., 'returns answer in plain text') for completeness.

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

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

The single parameter 'question' has a description in the schema ('Your question or request in natural language'), and the description adds no further semantic detail. With 100% schema coverage, a score of 3 is appropriate.

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

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

The description clearly states the verb 'ask' and resource 'pipeworx', emphasizing plain English queries. The examples and phrase 'best available data source' differentiate it from sibling tools like discover_tools, which require browsing schemas.

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 use cases and examples, but does not explicitly state when to avoid using this tool or name specific alternatives. However, the overall context implies it is for natural language queries, which is sufficient.

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, indicating safe read operation. The description adds rich behavioral context: how it resolves the market (slug/URL/text), classifies the bet, and fans out to relevant data packs (e.g., crypto+fred+gdelt). This goes beyond annotations and provides valuable insight into the tool's internal logic.

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

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

The description is well-structured and front-loaded: first sentence states the core action, subsequent sentences detail inputs, internal logic, output, and use cases. It is slightly verbose but every sentence adds value. No redundancy with annotations or schema.

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 main aspects: input forms, internal processing (classification, fan-out), and output (evidence packet + comparison). It does not detail error handling or edge cases (e.g., invalid market), but is sufficient for an agent to understand the tool's capabilities.

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

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

Input schema has 100% description coverage for both parameters, but the description adds nuance: for 'depth' it clarifies enum values ('quick = 2-3 evidence sources, thorough = full fan-out') and states the default ('thorough'). For 'market', it reiterates allowed formats (slug, URL, question text). This enriches the schema's bare descriptions.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the input types (slug, URL, question text) and outlines the core functionality (resolve market, classify bet, fan-out to packs, return evidence packet and comparison). There is no sibling tool with overlapping purpose.

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

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

The description provides explicit use cases: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?".' It also positions the tool as the core demo product. However, it does not specify when not to use it or mention alternatives among siblings.

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

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

Without annotations, description discloses returns paired data and resource URIs. Does not mention side effects, permissions, or error handling. Adequate but lacks depth on behavior beyond output.

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

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

Two sentences with examples, no unnecessary words. Front-loaded with purpose and key details. Efficiently conveys all 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?

Covers purpose, usage, and data sources. No output schema, but mentions return format (paired data + URIs). For a comparison tool with moderate complexity, it is largely complete, though missing details on error states.

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 meaning by explaining enum values (company vs drug) and providing examples for values array (tickers, CIKs, drug names), 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?

Clearly states the tool compares 2-5 entities side by side, specifying data fields for company (revenue, net income, cash, long-term debt) and drug (adverse-event report count, FDA approval count, active trial count). Distinguishes from sequential calls by noting efficiency.

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: comparing multiple entities of type company or drug. Provides number of entities and data fields. Implicitly suggests alternatives (sequential calls) but does not explicitly say 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?

No annotations provided, so description carries full burden. It discloses that it searches a catalog and returns relevant tools, but lacks details on side effects, auth, or read-only nature. Adequate but minimal.

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 purpose then usage. Every word earns its place. No fluff or 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?

No output schema, but description specifies return type: tool names and descriptions. Lacks details on sorting or pagination, but sufficient for a discovery tool. Completes the context well.

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 clear descriptions for both query and limit. The tool description adds no additional parameter semantics, so baseline score of 3 is appropriate.

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

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

Clearly states the verb 'search' and the resource 'catalog', defines output as relevant tools with names and descriptions. Distinguishes from siblings like ask_pipeworx and compare_entities, which serve different purposes.

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

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

Explicitly says to call this FIRST when 500+ tools are available, providing clear context. Does not explicitly list when not to use or name alternatives, but the guidance is strong.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns pipeworx:// citation URIs and is efficient (replaces 10-15 calls). However, it does not explicitly state that the operation is read-only or mention any rate limits or potential side effects, though the context implies a safe query 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 concise, front-loaded with the main purpose, and uses bullet-point-like details. Every sentence adds value without redundancy. It is appropriately sized for the tool's complexity.

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

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

Given the tool's complexity (aggregating multiple data sources), the description covers inputs, outputs (citation URIs), and alternatives. Without an output schema, it could state more about the response structure or error conditions, but it is sufficiently complete for an agent to understand the tool's capabilities.

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

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

The input schema has 100% description coverage for the two parameters. The description adds meaning beyond the schema by explaining the type enum (only 'company' supported), clarifying that value can be a ticker or CIK, and noting that names are not supported (suggesting resolve_entity). This provides helpful context for the agent.

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

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

The description clearly states what the tool does: fetches a full entity profile across multiple Pipeworx packs. It lists specific data types (SEC filings, XBRL, patents, news, LEI) and distinguishes from sibling tools by noting it replaces numerous sequential calls and that for federal contracts, a different tool should be used.

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

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

The description provides explicit when-to-use (for company profiles across packs) and when-not-to-use (for federal contracts, use usa_recipient_profile). It also explains input constraints (type: company only, value: ticker or CIK, not names) and directs to resolve_entity for name resolution.

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?

No annotations provided, so description must fully convey behavioral traits. It only states 'delete', implying destructive action, but offers no details on permanence, authorization, or side effects.

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

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

Single sentence conveys purpose efficiently with no extraneous words. Perfectly concise for the tool's simplicity.

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

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

Tool is simple with one parameter and no output schema. The description covers the essential action. Minor lack of detail on expected behavior (e.g., return value, error handling) but adequate for a basic delete operation.

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

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

Schema covers 100% of the single parameter with clear description. The tool description does not add additional semantic value beyond the schema, so baseline score of 3 is appropriate.

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

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

Description uses strong verb 'Delete' and specifies resource 'stored memory by key'. Clearly distinguishes from sibling tools like 'remember' (store) and 'recall' (retrieve).

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?

No explicit guidance on when to use this tool vs alternatives. However, sibling names imply different operations, and the tool's purpose is straightforward. Lacks explicit when-not scenarios.

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

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

Annotations already indicate the tool is non-destructive, idempotent, and safe. The description adds meaningful behavioral context by describing the process: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This goes beyond annotations without contradiction.

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

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

The description is extremely concise with three sentences: purpose, process, and use cases. Every sentence adds value, and the structure is front-loaded with the core action. No fluff or 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 the return value ('a single text blob ready to drop at site-root/llms.txt'). The tool is simple with 2 parameters, and the description covers purpose, process, and applications completely.

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

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

The input schema has 100% description coverage, so parameters are well-defined. The description reiterates the URL usage in context ('Fetches the page') but does not add additional semantic detail beyond the schema. Thus, it meets the baseline expectation.

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: 'Generate a production-ready llms.txt file for any URL so AI crawlers can index the site cleanly.' It specifies the verb (generate), resource (llms.txt), and targets a specific audience. No sibling tool overlaps, so differentiation is inherent.

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 context on when to use the tool via explicit use cases (indexing client site, drafting for own project, auditing competitor). However, it does not explicitly state when not to use it or suggest alternative tools among siblings, leaving some ambiguity for edge cases.

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

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

Describes return data (noise, riot, classification, etc.) and implies read-only nature, but lacks details on rate limits or authentication requirements. Annotations are empty, so description carries the burden adequately.

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

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

Two sentences with no wasted words. Front-loaded with verb and resource, followed by return fields and usage hint.

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 lookup tool, description covers purpose, return fields, and usage context. Lacks output schema or error handling details, but is fairly complete.

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

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

Schema covers 100% of parameters with description 'IPv4 address'. The description adds no additional parameter detail beyond the schema, justifying a baseline score.

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

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

Clearly states verb 'Look up' and specific resource 'IPv4 against GreyNoise Community dataset'. Lists return fields, distinguishing it from sibling tools like compare_entities or resolve_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?

Indicates usefulness for 'noise-suppression in SOC alerts', providing context, but does not explicitly state when not to use 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?

No annotations provided, so description bears full burden. It discloses rate-limiting ('5 messages per identifier per day') and advises on content (describe in terms of Pipeworx tools/data). Does not explicitly state if feedback is read-only or has side effects, but this is implied.

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

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

Three sentences, no filler, front-loaded with purpose. Every sentence earns its place.

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

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

For a simple feedback tool without output schema, description covers purpose, usage, parameters, and behavioral constraints fully. No gaps.

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

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

Schema coverage is 100%, but main description adds value: explains message should be specific and max 2000 chars, and provides guidance on context. The enum meanings are already in schema, but overall adds helpful constraints.

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

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

Description clearly states 'Send feedback to the Pipeworx team' and enumerates the specific types (bug reports, feature requests, etc.). It distinguishes itself from sibling tools by being the only feedback channel.

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

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

Explicitly states when to use (bug reports, feature requests, missing data, praise) and provides guidance on what not to include ('do not include the end-user's prompt verbatim'). Also mentions rate limits.

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 aggregation from CF analytics, no PII, caching 5min-1h. No contradiction with annotations (readOnlyHint, openWorldHint, etc.), adding significant value beyond 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?

Well-structured: front-loaded with main purpose, then use cases, then technical details. Not overly long, though a sentence could be trimmed.

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

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

Complete for a simple tool with one optional enum parameter, no output schema. Covers return content, use cases, caching, and privacy. 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?

Only one parameter 'window' with enum, schema coverage 100%. Description adds context on shorter vs longer window purpose, 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?

Description clearly states it returns top tools, top packs, and total call volume over a recent window. Uses specific verbs and distinguishes from siblings by focusing on trending 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?

Provides three explicit use cases (discovering hot data sources, confirming canonical choice, seeing alignment) and notes window options. Lacks explicit when-not-to-use, but context is clear.

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

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

The description goes well beyond the annotations by detailing the internal process: walking child markets, extracting dates/thresholds, sorting, and reporting violations. Annotations already indicate read-only and non-destructive behavior, and the description adds rich 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?

The description is compact yet informative: four sentences covering purpose, mechanism, usage, and output. It is front-loaded with the core function, and every sentence adds value. No unnecessary words or repetition.

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

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

For a single-parameter tool with no output schema, the description is complete. It explains input format, logic, and output structure (list of entries with fields). Annotations cover safety aspects. The agent has all necessary information to invoke the tool correctly.

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

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

The input schema has 100% description coverage, meaning the schema already adequately describes the 'event' parameter. The tool description merely echoes the schema's description, adding no additional semantic value. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: finding arbitrage opportunities via monotonicity violations. It specifies the exact resource (Polymarket event) and the specific logic (checking price ordering by date/threshold). No sibling tool serves this function, so differentiation is inherent.

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 tells the agent when to use the tool: when a Polymarket event has multiple date/threshold markets and the agent wants to check for arbitrage. It explains the prerequisite logic (monotonicity rule) and provides a usage example (passing slug or URL). While it doesn't explicitly state when not to use or list alternatives, the context is sufficient for a specialized tool.

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

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

The description adds significant behavioral context beyond annotations: it explains the grouping by asset, one-time price fetch, model probability computation, ranking by edge magnitude, and return of top N with suggested trade direction. 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 structured with a clear lead sentence and subsequent details. It is somewhat verbose but each sentence adds value. Could be slightly more concise without losing essential information.

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

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

The description covers the main workflow (scan, group, fetch, compute, rank) and mentions return structure (top N ranked by edge magnitude with trade direction). Given no output schema, this is sufficient for the tool's complexity. It lacks details on error cases or limits, but overall complete.

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

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

Schema description coverage is 100%, so the baseline is 3. The description provides minimal additional semantic value beyond the schema descriptions, such as mentioning default values (already in schema) and the ranking context. No deeper parameter usage details are added.

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 high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price. It specifies the model (lognormal from FRED + coinpaprika) and the intended use case, distinguishing it 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 explicitly addresses the 'what should I bet on today' question, indicating when to use the tool. It mentions discovering opportunities without manual paging, but does not explicitly state when not to use or provide alternative tools, though context from sibling names suggests 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 declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: the rationale for the spread (different participant pools), the two modes, and the return structure (prices and spread). 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 with clear sections for modes and return format. It is somewhat lengthy but each sentence adds value. 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?

Despite no output schema, the description describes the return structure in detail (prices in 0-1, spread in percentage points). It fully explains the two modes and how parameters interact, making the tool self-contained.

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

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

Schema coverage is 100% (3 parameters described). The description adds meaning beyond the schema: explains that topic is a shortcut and explicit parameters override, and provides examples of values.

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

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

The description clearly defines the tool as computing a cross-venue spread between Kalshi and Polymarket for the same resolving question, with two distinct modes (topic and explicit). It differentiates from siblings like 'polymarket_arbitrage' by specifying the cross-venue nature.

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

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

The description explains when to use each mode: topic for pre-mapped macros and explicit for custom pairings. It provides clear guidance but does not explicitly mention when not to use it versus 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?

As no annotations are provided, the description should disclose behavioral traits. It indicates a read operation but lacks details on edge cases (e.g., missing key) or return format.

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

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

Two concise sentences, front-loaded with key info, no wasted words.

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

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

Given the simple one-parameter input and no output schema, the description adequately covers usage. Some details on return value format could improve completeness.

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 restates the schema info without adding significant new meaning, earning a score of 3.

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

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

The description clearly states the tool retrieves a memory by key or lists all memories, distinguishing it from sibling tools like remember and forget.

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

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

It specifies when to use: to retrieve context saved earlier. While it doesn't explicitly mention alternatives, the context of siblings implies not for storing or deleting.

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?

No annotations provided, so description carries full burden. It discloses parallel fan-out to SEC EDGAR, GDELT, USPTO for company type, and mentions return includes structured changes, total_changes count, and URIs. It does not discuss auth or rate limits, but for a read-only tool this is acceptable.

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

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

Two sentences with front-loaded purpose. Second sentence is dense but packs necessary details. Could be slightly more structured, but overall efficient for the information conveyed.

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 adequately covers return values (structured changes, count, URIs). Three parameters are well-described. Some details on exact structure of changes would be helpful but not critical for an agent to invoke correctly.

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

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

Schema coverage is 100%, baseline 3. Description adds value by providing examples for 'since' (ISO date, relative like '7d') and explains 'value' as ticker or CIK, enhancing usability beyond schema.

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

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

The description clearly states the tool retrieves what's new about an entity since a given time, with specific details for company type. It distinguishes from siblings like entity_profile (static profile) and compare_entities (comparison) by focusing on change over time.

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

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

Explicitly suggests use cases: 'brief me on what happened with X' or change-monitoring workflows. Does not list when not to use or explicitly compare to siblings, but the use case is clear and appropriate.

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

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

Discloses persistence behavior: authenticated users get persistent memory, anonymous sessions last 24 hours. No annotations provided, so description carries full burden. Could mention overwrite behavior or limits, but sufficient.

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

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

Two sentences, front-loaded with the core action. Every sentence adds value 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?

The description covers the tool's purpose, usage scenarios, and behavioral persistence differences. No output schema needed, and all key aspects are addressed.

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

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

Schema coverage is 100% with descriptions for both parameters. Description adds example values and context (e.g., 'subject_property', 'target_ticker'), enhancing understanding beyond the schema.

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

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

The description clearly states the tool stores a key-value pair in session memory, with specific examples like saving findings, preferences, and context. It distinguishes from siblings like 'forget' (removal) and 'recall' (retrieval).

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

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

Explicitly tells when to use: to save intermediate findings, user preferences, and context across tool calls. Does not explicitly state when not to use or name alternatives, but the context of siblings implies proper 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'IDs and pipeworx:// resource URIs for stable citation'. It mentions supported entity types but lacks information on error handling, rate limits, or authorization requirements.

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

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

The description is two sentences long, with the first sentence stating the core function and the second adding details on types and output. It is front-loaded and contains 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 moderate-complexity tool with two parameters and no output schema, the description covers core functionality but lacks detail on the exact output structure (e.g., whether it returns a single object or list, or additional properties). It mentions 'IDs and resource URIs' but does not specify the format or required prerequisites.

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

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

Schema description coverage is 100% with descriptions for both parameters. The description adds value by providing concrete examples (e.g., 'ticker (AAPL), CIK (0000320193), or name'; 'brand or generic name (e.g., "ozempic", "metformin")') beyond the schema's basic definitions.

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

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

The description clearly states the verb 'Resolve an entity to canonical IDs' and specifies the resource 'across Pipeworx data sources'. It distinguishes between supported types ('company' and 'drug') and mentions that it replaces multiple lookup calls, differentiating it from siblings.

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 context: 'in a single call' and 'Replaces 2–3 lookup calls', indicating efficiency. It specifies input formats for each entity type (ticker/CIK/name for company; brand/generic for drug). However, it does not list explicit alternatives or when not to use.

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

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

Annotations already declare safe and predictable behavior. The description adds detail about probing each entity, ranking logic, and optional Anthropic API key usage. No contradictions. Could mention potential errors or rate limits.

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

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

The description is concise, front-loads the main purpose, and each 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?

The description explains the tool's function, inputs, and output (ranked list with score, confidence, signal density). Without an output schema, this provides adequate completeness, though error handling or limitations are not covered.

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 no additional parameter-level details beyond the schema, meeting the baseline for high 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 compares AI visibility across multiple entities, ranks them by score, and surfaces the most/least recognized. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (other attributes).

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

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

The description mentions competitive AI marketing audits and gives an example question, but does not explicitly state when not to use it or compare to alternatives. However, the use case is clear.

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

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

With no annotations provided, the description fully carries the burden. It discloses the type of output (verdict, actual value with citation, percent delta), the data sources (SEC EDGAR + XBRL), and that it replaces multiple calls. No destructive actions are implied.

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

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

The description is concise (two sentences) with front-loaded purpose. Every sentence adds value: purpose, supported scope, output details, and efficiency benefit. 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?

The description covers the tool's purpose, input, output, and limitations (v1, specific domain). While no output schema exists, it lists the output components. It is complete for a single-parameter tool, though could mention error handling or unsupported claims.

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

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

Schema coverage is 100% with a clear description for the 'claim' parameter. The tool description adds examples and specifies the expected format, enhancing the schema's brief description.

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

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

The description clearly states the tool fact-checks natural-language claims against authoritative sources, specifying the domain (company-financial claims for public US companies) and outputs. It distinguishes itself from siblings like 'entity_profile' and 'compare_entities' by being a specialized fact-checking tool.

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

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

The description explicitly states it replaces 4-6 sequential agent calls, indicating efficiency. It also notes the scope (v1, company-financial claims for US public companies), but does not provide explicit when-not-to-use or alternative tool references.

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