Hnb Hr

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds details on default model (Workers AI Llama), optional Anthropic with BYO key, and the return structure (per-model scores with confidence, signals, raw_response). No contradictions.

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

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

The description is four sentences, front-loaded with the primary function. Every sentence adds essential information: function, optional models, return format, use cases. No fluff.

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

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

For a tool with 4 parameters and no output schema, the description thoroughly explains the return structure (per-model object + combined view), default behavior, optional API key, and usage context. It covers all aspects an agent needs to decide and invoke correctly.

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

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

Schema description coverage is 100%, but the description adds context: e.g., entity examples ('Pipeworx', 'OpenInvoice'), and clarifies _apiKey usage. This adds value beyond the schema's descriptions.

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

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

The description clearly states the tool's purpose: probe LLMs for knowledge about an entity and score visibility (0-100). It specifies verb (probe), resource (LLMs knowledge), and output (score). No sibling tools duplicate this function.

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: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also explains when to use the API key for Anthropic. However, it does not explicitly contrast with sibling tools 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?

Annotations already mark it read-only and non-destructive. The description adds behavioral context: it routes to sub-tools, fills arguments, returns a structured answer with stable citation URIs, makes one fast call, and can route to live news. This goes beyond the annotations without contradicting them.

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

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

The description is long but well-structured, starting with a directive ('PREFER OVER WEB SEARCH'), then explaining functionality, giving examples, and listing alternatives. Every sentence contributes to usage guidance, though some repetition (e.g., examples and trigger phrases) could be trimmed. It is appropriately sized for a default router tool.

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

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

For a tool with no output schema, the description explains the output format (structured answer with citation URIs), covers a wide range of query types, gives usage examples, and clarifies when to use alternatives. It does not detail error handling or limits, but for a broad routing tool it is sufficiently 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?

All six parameters are aliases for the same 'question' parameter. Schema coverage is 100% and already documents that the parameter accepts natural language and includes alias names. The description adds no additional parameter semantics beyond what the schema provides, so the baseline of 3 applies.

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

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

The description states a specific function: routes questions to one of 5,439 tools across 1,412 sources, fills arguments, and returns structured answers with citation URIs. It clearly distinguishes itself from sibling tools by explicitly naming ask_pipeworx_grounded and deep_research as alternatives for different needs.

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

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

The description gives explicit guidance on when to use this tool ('PREFER OVER WEB SEARCH', 'START HERE for most questions'), trigger phrases to recognize, and when to step up to alternatives ('Step up only when needed...'). It also provides concrete examples and mentions the tool is a default entry point working on every tier.

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 beyond the readOnly/openWorld/idempotent annotations by disclosing the beta nature, live candidate routing improvements, and that no candidate is currently active. It also clarifies it falls back to nothing, being a full working router. Slightly more detail on potential behavioral differences could push it higher.

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 deliver the essential information about the beta status, current equivalence, and usage. Slightly verbose with the date and outcome detail, but each sentence contributes.

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 beta variant of a known tool, the description covers how it relates to ask_pipeworx, when to use it, and its current state. The lack of an output schema is mitigated by referring to the same response shape as ask_pipeworx.

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 description refers to 'same arguments' but adds no parameter-specific meaning. The input schema covers all six parameters with 100% coverage, including aliases, so the baseline of 3 applies.

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

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

The description clearly identifies this as a beta version of ask_pipeworx, a universal router with the same 5,439 tools, arguments, and response shape. It distinguishes itself from the stable ask_pipeworx by noting experimental routing improvements and current exact match.

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

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

Explicitly instructs to 'Use it exactly like ask_pipeworx when you want the newest routing' and notes results are compared against the stable router. This provides clear when-to-use guidance and contrasts with the stable sibling.

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 the readOnlyHint and idempotentHint annotations, the description discloses the refusal behavior with specific refusal_reason values, the evidence quote inclusion, and the extra LLM call cost. This is rich behavioral transparency that helps the agent predict outcomes and costs.

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 yet well-structured, opening with the core purpose, then method, output/refusal format, use cases, and cost tradeoff. Every sentence adds distinct value with no repetition, qualifying as genuinely concise despite length.

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

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

No output schema exists, but the description explicitly defines the return shape and refusal reasons, compensating fully. It also covers routing, evidence-grounded extraction, and cost side-effects, making the description self-sufficient for correct invocation and expectation setting.

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% (all aliases for 'question' are documented). The description adds no new parameter-level semantics beyond what the schema already provides, but it doesn't need to since the parameter is a simple natural-language question.

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

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

The description clearly states this is a 'hallucination-resistant answer mode for high-stakes reads' and explicitly contrasts with ask_pipeworx ('Same routing as ask_pipeworx') while detailing the unique extraction step. This differentiates it from the sibling tool while naming the specific resource (answer extraction grounded in tool results).

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

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

Provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on' and when-not: 'prefer ask_pipeworx for casual lookups' due to the extra LLM call cost. Names the alternative directly and explains the tradeoff.

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

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

Annotations already indicate safe, read-only, idempotent behavior. Beyond that, the description richly details resolution logic, classifier fan-out, response shapes, resolver contract with confidence levels, parent event extraction, news fallback handling, safety short-circuits, closed market handling, wide spread warnings, and cancellation rule parsing. This provides extensive behavioral insight beyond annotations.

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

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

The description is long and detailed, which is justified by the tool's complexity. It is front-loaded with purpose and structured logically, but contains verbose explanations that could be condensed. Every sentence adds value, but overall length reduces conciseness.

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

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

Given no output schema, the description comprehensively explains return values: result.market fields, analysis with edge and warning, evidence keyed by source, resolver contract with confidence and alternatives, parent_event extractor, news fields with fallback info, safety mechanisms, and cancellation rule parsing. Covers all necessary context for a complex tool.

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

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

Schema coverage is 100% (3 parameters fully described). The description adds examples and context for the 'market' parameter (slug, URL, text) and mentions 'depth' and 'include_raw' defaults, but does not significantly augment what the schema provides. Baseline 3 is appropriate.

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

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

The description explicitly states the tool researches a Polymarket bet by pulling relevant Pipeworx data. It specifies the verb 'research' and resource 'bet' with concrete input types (slug, URL, question text). The detailed classifier list and fan-out examples distinguish it from sibling tools like polymarket_edges.

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

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

The description provides clear use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z'). It explains when to use the tool but does not explicitly state when not to use or compare with related siblings, though the examples and classification system imply differentiation.

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

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive. Description adds substantial behavioral context: data sources (SEC EDGAR, FAERS), handling of off-calendar fiscal years, sorted results by primary metric, and inclusion of citation URIs. No contradictions.

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

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

Description is somewhat long but each sentence earns its place. It is front-loaded with query examples, making purpose instantly clear. A slight trim could improve conciseness, but current structure is effective.

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

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

Given the tool has 2 parameters (100% schema coverage) and no output schema, the description covers input semantics, data sources, and behavioral details. It mentions return format (paired data + citation URIs) but lacks detailed structure of output. Sufficient for effective use.

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

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

Schema coverage is 100%, but description adds meaning: for 'type' enum it explains the data pulled (financial vs adverse events), for 'values' it specifies tickers/CIKs for companies and names for drugs, and reinforces min/max constraints. Goes beyond schema.

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

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

The description clearly states the tool performs side-by-side comparisons of 2-5 companies or drugs, with specific verb phrases like 'Compare X and Y' and 'head to head'. It distinguishes from siblings such as entity_profile by emphasizing parallel lookup.

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 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing strong usage guidance. It also explains when each type (company vs drug) is appropriate, but does not explicitly state when not to use (e.g., for single entity lookups), though implied.

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 the findings packet format, gap/contradiction handling, excerpting behavior, latency, and depth-dependent heuristics beyond the annotations. It also clarifies it never invents answers. No contradiction with annotations.

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

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

The description is dense and informative, but front-loads an account warning before the core purpose, and repeats contradiction/depth details across paragraphs. Still, each sentence provides actionable information, so it's appropriately sized for a complex tool.

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

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

For a complex tool with no output schema, the description fully covers return format, latency, constraints, and usage contexts, making it self-sufficient for an agent to decide when and how to invoke it.

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

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

The description enriches both parameters: explains that 'question' can be broad/multi-part and that 'depth' controls parallel facets, hop recovery, and contradiction scan. It also specifies paid threshold for thorough, which is 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 identifies the tool as grounded multi-source research across Pipeworx's 1412 structured data sources in one call, and explicitly contrasts it with open-web search and sibling tools like ask_pipeworx. It specifies the resource and behavior with a concrete verb.

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: best for broad/multi-part questions over structured data, alternatives for single lookup (ask_pipeworx) and current news, plus account requirements. It even names alternatives and explains when each is appropriate.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, indicating a safe, read-only operation. The description adds valuable behavioral context: it returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples)' and states results are 'ready to call directly, no second schema lookup needed.'

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 efficiently structured: a clear one-sentence purpose, followed by examples, return value specifics, and usage guidance. Every sentence adds value, and the most important information is front-loaded.

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

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

Given the tool's complexity (6 parameters, 1 required, no output schema, no nested objects), the description is complete. It covers purpose, usage, behavior, and return format. No gaps are apparent.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description reinforces that 'query' is the main parameter and accepts aliases, but adds minimal new meaning beyond the schema. Baseline of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It provides specific examples of searchable domains (SEC filings, financials, FDA drugs, etc.) and distinguishes itself from sibling tools by being the discovery tool for browsing the toolset.

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

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

The description explicitly advises: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear usage context. While it doesn't explicitly list alternatives, the instruction to use it first implies when not to use it.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds significant value by detailing the parallel fan-out across multiple sources, specific returned fields, the USPTO API sunset and soft-fall, and limits like 'up to 5 filings'. 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?

Description is somewhat long but every sentence serves a purpose. It is front-loaded with query examples and immediately states the core function. Could be slightly more structured but remains clear and efficient.

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

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

Given the complexity (multiple data sources, varied return fields, fallback behavior, no output schema), the description provides enough detail for an agent to understand what to expect, including limits (5 filings) and deprecation notes (USPTO). Leaves no critical gaps.

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

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

Schema coverage is 100% with both parameters described. Description enhances with examples (e.g., 'AAPL', '0000320193'), clarifies type^enum is only 'company' currently, and explains that value does not accept names. Adds practical guidance 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 explicitly states 'full cross-source profile of a US public company' and provides concrete query examples (e.g., 'Tell me about X', 'brief me on Tesla'). It distinguishes from siblings by contrasting with chaining single-pack lookups and mentioning resolve_entity for name resolution.

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?

Clearly states when to prefer this tool ('ALWAYS PREFER... when the user asks for a holistic view') and when not to use it ('names not supported – use resolve_entity first'). Provides explicit alternatives and context.

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

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so the description does not add significant behavioral detail. It echoes the destructive nature but does not go beyond.

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?

Extremely concise: two sentences that cover purpose, usage, and pairing. No wasted words.

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

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

For a simple tool with one required parameter and no output schema, the description fully covers the action, triggers, and complementary 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% and already describes the key parameter. The description does not add new semantic information 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?

The description uses a specific verb 'delete' and clarifies the resource 'previously stored memory by key'. It also distinguishes from siblings by mentioning 'pair with 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'. Also suggests pairing with other tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, ensuring the agent knows it's a safe, idempotent read operation. The description adds behavioral context: fetches the page, extracts title/description/key links, and emits standard markdown format, which goes beyond annotations.

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

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

The description is two sentences plus a bullet list, front-loaded with the main purpose. Every sentence earns its place 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?

Despite no output schema, the description specifies output as a single text blob in standard markdown format. Combined with annotations for safety, the description fully covers what the tool does and produces.

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 marginal value with an example URL format and mentions the output format, but parameter details are already in the schema.

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

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

The description clearly states it generates a production-ready llms.txt file for any URL, specifying the verb (generate), resource (llms.txt file), and purpose for AI crawlers. It distinguishes from sibling tools like ai_visibility_check and scan_competitor_ai_presence by focusing on file generation rather than visibility scanning.

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

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

The description lists explicit use cases: getting a client's site indexed, drafting for own project, or auditing competitor. It provides clear context for when to use the tool, though it does not explicitly state when not to use it 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 provide readOnly/idempotent hints. Description adds return format details (string with comma decimal separator), field name 'srednji_tecaj', and date format, which go 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?

Single dense paragraph with clear structure: purpose, input format, three modes, data format. 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 no output schema, description explains return structure and key field. Covers all usage modes. Lacks error handling but adequate for a simple rate query tool.

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

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

Schema coverage is 100%, baseline 3. Description adds examples, clarifies relationship between date and start_date/end_date, and explains modes, providing value beyond schema.

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

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

Description clearly states tool fetches HNB exchange rate for ONE foreign currency vs EUR, with three modes (latest, single date, date range). Distinguishes from potential sibling 'hnb_exchange_rates' by emphasizing single currency.

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 three usage modes and date format. Does not explicitly exclude alternatives or provide when-not-to-use, but context is clear for agent 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety. Description adds meaningful context: rates quoted against EUR, return fields (valuta, rates, apply date), and comma decimal separator. No contradictions.

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

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

Two sentences, front-loaded with purpose and key details. Every sentence earns its place: first introduces the tool, second details quotation and return format. No filler.

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

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

Given single optional parameter, no output schema, and full annotations, the description provides all necessary context: what the tool returns, how the data is expressed, and how to use the parameter. Complete for its complexity.

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

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

Schema coverage is 100% with clear schema description for the optional 'date' parameter. Description adds value by stating 'Omit for the latest applicable rates' and specifying the format 'YYYY-MM-DD' (already in schema but reinforced). Exceeds baseline 3.

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

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

The description clearly states 'official exchange rates for ALL foreign currencies on a given date', differentiating from the sibling 'hnb_currency_rate' (likely for a single currency). It specifies the resource (HNB exchange rates) and the scope (all currencies, date-driven).

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 explains when to use: provides a date for a specific day or omit for latest rates. Implicitly contrasts with sibling tool 'hnb_currency_rate' which presumably handles single currencies. No ambiguity about usage context.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. Description adds useful context by listing returned fields and the default active-only filter, enhancing transparency.

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

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

Two short sentences, zero wasted words. Information is front-loaded with the primary action and followed by usage hints.

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 one optional parameter and no output schema, the description sufficiently covers purpose, return fields, and usage context. 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 the single parameter with full description. The description adds no additional detail beyond implying the default behavior, so it meets the baseline for 100% 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?

Clearly states the tool lists the caller's active subscriptions, distinguishing it from related tools like subscribe and unsubscribe. The description is specific and action-oriented.

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: to review monitored subscriptions before adding more or to find an id to cancel. This helps the agent decide appropriately.

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 are all false, providing no safety hints, so the description carries the burden. It discloses significant behavioral traits: 'Rate-limited to 5 per identifier per day' and 'Free; doesn't count against your tool-call quota.' It also explains the impact ('signal directly affects roadmap') and scope ('ONLY for tools served by this Pipeworx connection'), offering transparency beyond structured 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 lengthy but information-dense; every sentence contributes purpose, usage, exclusions, or behavioral constraints. It is front-loaded with the core purpose and use cases, though it could be improved with bullet points or shorter paragraphs for scannability. It earns its length given the tool's nuance.

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 with no output schema, the description is exceptionally complete. It covers when to use, when not to use, content expectations, rate limits, quota impact, and the audience/review process. No critical context is missing, making the tool fully self-explanatory.

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 value by instructing users to 'Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt,' which clarifies how to fill the message parameter. This is a meaningful enhancement over the schema's field descriptions.

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

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

The description clearly states the tool's verb and resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It enumerates specific feedback types (bug, feature/data_gap, praise) and distinguishes itself from sibling tools like ask_pipeworx by focusing on reporting issues rather than asking questions.

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 conditions: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' It also gives a clear exclusion and alternative: if the tool is from a different MCP server, 'file it with that server instead.' This is exemplary guidance.

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

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

Description adds significant context beyond annotations: caching behavior (5min-1h), data source (CF analytics), privacy (no PII), and derived nature. Annotations already indicate readOnly, openWorld, idempotent, non-destructive.

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

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

Description is well-structured: a concise one-liner followed by bullet-point use cases and technical details. 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 the tool's simplicity (one optional parameter, no output schema), the description fully covers what the tool returns, its source, 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?

Schema covers window parameter with enum and description. The description adds semantic value by explaining the trade-off between hotness and steady-state demand for different window lengths.

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 that the tool returns trending tools, packs, and call volume from other agents. It distinguishes from siblings like 'discover_tools' by focusing on real-time popularity.

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

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

Three explicit use cases are listed, providing clear context for when to use the tool. However, it does not mention when not to use it or suggest 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 indicate safe, idempotent read. Description adds deep behavioral context: internal algorithms, response structure, fill check logic, semantic anchor, partition filter, and when not to trade. 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?

Front-loaded with main purpose, logically structured by mode. While lengthy, every sentence adds necessary detail for a complex tool. Slight wordiness but very 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?

No output schema provided, but description thoroughly explains response fields, including opportunities, partition_check, and fill check details. Covers all modes, edge cases (semantic anchor, partition filter), and actionable guidance.

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

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

Schema descriptions cover both parameters 100%. Description adds practical context: accepted formats (full URLs for event), explains differences between event and topic modes, and how they affect scanning scope.

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 specific algorithms (monotonicity violations, partition-sum checks). Distinguishes from siblings like polymarket_edges and polymarket_fill_risk by describing unique approach.

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: call with no args for trending scan, use 'event' for specific market, use 'topic' for cross-event scan. Recommends 'event' mode and explains when to avoid trading via fill check.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds immense value beyond annotations, detailing caching (1h KV-level), response structure with three segments, edge computation models, Kelly sizing, slippage assumptions, and diagnostics for empty segments.

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 with clear sections for each model family, knobs, and diagnostics. Every sentence adds value, though it could be slightly more concise without losing critical details.

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

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

Despite lacking an output schema, the description thoroughly explains the response structure (by_segment, fed_candidates, diagnostics) and edge computation details. It covers caching, knob behaviors, and reasons for empty segments, making it highly complete for a complex tool.

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

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

Schema coverage is 100%, so the schema already documents all parameters. The description does not add significant new meaning beyond what the parameter descriptions in the schema provide (e.g., min_kelly, slippage_pp). Baseline 3 is appropriate.

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

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

Description clearly states it scans Polymarket markets for opportunities where Pipeworx data disagrees with market price. It distinguishes itself from siblings like polymarket_arbitrage and polymarket_edge_tracker by focusing on cross-data disagreement and providing a comprehensive edge 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?

Description explicitly targets agents discovering opportunities without paging hundreds of markets, and outlines tradeable-edge knobs and diagnostics for empty segments. It lacks explicit exclusions or alternatives but is clear in its use case.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds significant behavioral context: returns three arrays (tracked, expired, snapshot_dates), explains time-series computation, decay calculation from daily closes, note about data gaps, and limits like 60-day TTL.

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

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

The description is long but every sentence adds value. It is front-loaded with core purpose. Could be slightly more concise, but structure is clear with sections for args, response, and limits.

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

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

Given no output schema, the description thoroughly explains the three output arrays and their fields (tracked with time-series, first_seen, trend; expired with lifespan_days; snapshot_dates). Also mentions limitations. Complete for the tool's complexity.

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

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

Schema coverage is 100% for both parameters. The description adds meaning beyond schema by stating default values (14 days, '1wk'), max days (30), and explaining that window corresponds to a snapshot family. This adds useful context.

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 provides 'edge persistence and decay telemetry' and answers a specific question about edge duration and shrinkage. It distinguishes from siblings like polymarket_edges and polymarket_arbitrage by focusing on time-series 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?

The description explains when to use it (to assess edge persistence) and implicitly when not (since it's about historical trend, not current edge). It details parameters like days and window but does not explicitly compare to sibling tools, though context is clear.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, confirming a safe check. The description adds significant behavioral context: requires one of 'market' or 'event', explains verdict options ('clean|degraded|cannot_fill'), and warns about forced directional risk from thin legs. 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 sections ('SINGLE-MARKET', 'BASKET') and bullet points, front-loading the purpose. It is verbose but every sentence provides necessary detail; slight redundancy 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?

With no output schema, the description fully explains return values for both modes (e.g., top_of_book, vwap_fill_price, verdict for single; theoretical_sum, capture_ratio, profit_usd, thin_legs for basket). It covers edge cases like partial fills and directional risk, making it complete for informed tool 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 description coverage is 100%, baseline is 3. The description adds meaning beyond schema: explains side behavior for basket (auto-detect from partition sum), interprets size_usd differently for basket (settlement notional), and clarifies defaults and clamps. This adds value but the schema already has good 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 performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes. It explicitly ties its usage to specific sibling tools like polymarket_arbitrage and polymarket_edges, differentiating itself as a pre-trade validation step.

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 tells when to use the tool ('before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500') and explains the rationale (theoretical overround not capturable, partial fills create directional risk). It lacks explicit exclusion criteria or alternative tools for other scenarios.

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

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

Beyond annotations (readOnlyHint, etc.), the description adds rich behavioral details: compatibility_warning conditions, temporal alignment, skipped cross types, and the fact that most shortcuts return warnings. 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?

Well-structured with clear sections (modes, response, safety fields), but somewhat verbose. Every sentence adds value, but 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?

For a complex cross-venue comparison tool, the description thoroughly covers edge cases, safety fields, and response format. Given no output schema, it sufficiently explains return values and conditions.

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 extra meaning: topic shortcuts are listed, explicit parameters explained with override behavior and examples. Adds value beyond schema definitions.

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question, with specific verb 'spread' and resource. It distinguishes from siblings like polymarket_arbitrage by focusing on cross-venue comparison.

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

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

Provides explicit when-to-use context: for comparing prices across venues, and warns that many pre-mapped topics return compatibility warnings. Lacks explicit alternatives or when-not-to-use, but context is clear enough for an agent.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds scoping context and listing behavior, 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?

Two sentences, no fluff, front-loaded with action and resource. Efficient and to the point.

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 (1 optional param, no output schema), description is complete: explains behavior, scoping, and pairing with related 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 has 100% coverage with parameter description 'Memory key to retrieve (omit to list all keys)'. Description reinforces this and adds no new info but confirms 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?

Description clearly states it retrieves a previously saved value or lists all keys. Verb 'retrieve' and 'list' are specific, and it distinguishes from sibling tools 'remember' and 'forget' by naming them.

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

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

Explicitly says to use for looking up context stored earlier, avoiding re-derivation. Mentions scoping and pairing with remember/forget. Does not explicitly state 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 annotations already indicate read-only, idempotent, non-destructive behavior. The description adds value by detailing the return fields (source, citation_uri, raw event payload) and the effect of the mark_read parameter on subsequent calls. No contradictions with annotations.

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

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

The description is concise at four sentences. Each sentence serves a purpose: stating the function, describing output, explaining filtering and mark_read, and noting polling and alternative access. No redundant or excessive text.

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

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

Given no output schema, the description adequately covers return structure, filtering options, and the mark_read side effect. It also mentions an alternative endpoint. While it does not discuss error handling or rate limits, the annotations cover safety aspects. The description is reasonably complete for this read-only tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context for the 'type' parameter with an example ('sec_8k'), explains 'since' as ISO timestamp, and clarifies 'mark_read' behavior. However, it does not elaborate on 'limit' or 'unread_only', so it only marginally improves over 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 'Pull fired events from your subscription feed', with a specific verb and resource. It details what the tool returns (most recent alerts with source, citation_uri, raw payload). While it distinguishes itself from the HTTP endpoint, it does not explicitly differentiate from sibling tools like 'list_subscriptions', which might also return alert-related 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?

The description mentions that polling works fine and provides an alternative HTTP endpoint for scripts/dashboards, offering some usage context. However, it does not specify when to use this tool versus other tools (e.g., 'list_subscriptions') or what prerequisites exist. No explicit when-not or alternatives are given.

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

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

Adds significant behavioral context beyond annotations: fans out to multiple sources in parallel, fallback logic, USPTO soft-fail, return structure (changes[] grouped by source + total_changes + citation URIs). 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?

Single dense paragraph front-loaded with query examples, followed by source details, parameter explanation, return structure, and sibling differentiation. 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?

Highly complete given no output schema: covers all sources, fallback, return format, parameter formats, and when to use alternatives. No missing crucial information.

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

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

Schema coverage is 100%, but description adds valuable meaning: examples for 'since' (ISO date and relative shorthand with recommendations), explanation of 'value' (ticker or CIK), and clarification that 'type' only supports 'company'. Exceeds 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 with specific verbs and resources: 'change feed for a company', fans out to SEC EDGAR, GDELT→GNews, USPTO. It provides query examples ('What's new with X', 'latest on Y') and distinguishes from sibling 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?

Explicit usage guidance: 'Use entity_profile instead when you want the static profile... regardless of window.' Explains fallback behavior (GDELT→GNews) and parameter recommendations ('Use "30d" or "1m" for typical monitoring').

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

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

Annotations provide idempotentHint=true and destructiveHint=false; description adds key-value scoping by identifier, persistence details (authenticated vs. 24-hour anonymous), and pairing with other tools. No contradiction.

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

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

Two sentences plus a short phrase, front-loaded with purpose, no wasted words. Every sentence adds essential information.

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

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

Given no output schema, description sufficiently explains storage behavior, scoping, and tool pairing, making it complete for a simple key-value store.

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

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

Schema coverage is 100%, but description adds value by listing example keys (subject_property, target_ticker) and explaining value as any text, enhancing semantic 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?

The description clearly states the tool saves data for later reuse, with concrete examples (resolved ticker, target address, user preference) and distinguishes from siblings recall and forget by mentioning pairing.

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 directs to recall and forget as alternatives for retrieval and deletion.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond these hints, such as explaining that each call cascades through several lookup endpoints internally, replacing multiple manual lookups, and detailing the exact return structure for each type.

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 example queries and core purpose, then detailing supported types. It is slightly long but every sentence adds value. No wasted words, though it could be slightly more concise without losing clarity.

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

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

Despite lacking an output schema, the description fully compensates by detailing what is returned for each type (ticker, CIK, company name, citation URI for company; RxCUI, ingredient, brand for drug). It also explains internal cascading behavior. All parameters are fully described, making the tool useable without external documentation.

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

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

Schema descriptions cover both parameters (100% coverage). The description adds significant value with examples for each type (e.g., 'AAPL', '0000320193', 'ozempic') and clarifies auto-disambiguation for company names. It goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: resolving a user-spoken name to a canonical identifier. It provides concrete example queries and distinguishes itself by explicitly saying 'Use FIRST' when you have a name but need an ID, which separates it from sibling tools like compare_entities or entity_profile.

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

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

The description gives clear guidance on when to use ('Use FIRST whenever you have a name but need an ID') and implicitly when not to (if you already have an ID). It does not explicitly state alternatives or when to avoid using it, but 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.

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds value by explaining that it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with specific fields (score, confidence, signal density). 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 two sentences, front-loaded with the main purpose, and every sentence provides essential information 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?

Despite no output schema, the description clearly states the return format (ranked list with score, confidence, signal density per entity). It also explains the internal mechanism (calling ai_visibility_check). This is sufficient for an agent to understand the tool's behavior and output.

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 additional meaning by explaining that the first entity is treated as the 'subject' for narrative and the rest as competitors, which goes beyond the schema's 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 it compares AI visibility across multiple entities, distinguishing it from single-entity tool ai_visibility_check and general compare_entities. It specifies the action (probe, rank, surface) and the output (ranked list).

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

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

The description explicitly mentions use for competitive AI-marketing audits and provides an example question. While it doesn't explicitly list when not to use it, the context of sibling tools and the description itself imply that it is for multi-entity comparison, not single-entity checks.

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 (read-only, idempotent, not destructive), the description adds critical behavioral details: fan-out architecture, partial failure handling, potential 5-30s delay on new versions, and the sources_failed field in response. 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?

Description is dense but well-structured: purpose first, then usage, behavior, return details, and ecosystem scope. Every sentence adds value; slightly verbose but not excessively.

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 comprehensively details the return structure (summary fields, advisory details, links, alternatives) and error behavior (graceful degradation, timeouts). Covers all needed context for an AI agent.

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

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

Schema covers 100% of parameters with descriptions. The description adds that version defaults to latest, but otherwise does not significantly augment the schema-provided semantics. Baseline 3 is appropriate.

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

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

The description clearly states the tool is a composite check for evaluating npm packages, listing specific data sources (deps.dev, bundlephobia) and return elements. It distinguishes itself from siblings by focusing on npm ecosystem and mentioning alternatives for other package managers.

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 an agent asks about safety, popularity, size, or cost of adding an npm package. Also specifies not to use for non-npm ecosystems, directing users to another tool (deps.dev:version) for PyPI, Maven, Cargo, Go.

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 details about the embedding model (BGE-base-en), windowing (500-char overlapping), character limit (200K chars with truncation flag), and that results include offsets for verification. 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 concise (about 6 lines) and well-structured. Each sentence serves a purpose: stating the action, usage context, technical details, and limitations. No redundant information.

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

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

Given the tool's complexity (3 parameters, no output schema), the description covers all necessary aspects: purpose, usage, behavioral details, parameter semantics, and return format (passages with offsets and scores). It is complete for an agent to select and invoke correctly.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds value beyond schema by providing example queries for the 'query' parameter, clarifying the max characters for 'text', and explaining the cap behavior. However, it could further detail the 'limit' parameter's default and range, which is already implied.

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, specifying the verb (search) and resource (inside a record). It distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded and emphasizing context-saving for large records.

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 the record is too big to cram into the prompt' and provides an alternative strategy: pairing with ask_pipeworx_grounded. This gives clear guidance on tool selection.

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

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

Adds significant behavioral context beyond annotations: authentication requirements, parameter constraints (SMS verification, 10/day cap), webhook HMAC signing and auto-disable details. 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?

Well-structured and front-loaded with core purpose. Somewhat verbose but every sentence adds value. Could be slightly more concise but remains readable.

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

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

Comprehensive for complexity: covers types, parameters, delivery options, return values. No output schema, but return value is stated. Provides enough context for an agent to use correctly.

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

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

Schema coverage is 100%, but description adds substantial value with real-world examples, constraints (e.g., phone must be verified, webhook limitations), and clarifies optional fields. Exceeds baseline of 3.

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

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

Clearly states 'Create a proactive monitoring subscription to a live-data event stream.' Specifies verb, resource, and return value ('Returns the new subscription id.'). Distinguishes from siblings like list_subscriptions 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?

Provides clear context: requires Pipeworx OAuth account, not for anonymous/BYO. Lists supported types and delivery channels with examples. Does not explicitly state when not to use or name alternatives, but 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.

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

Annotations already cover readOnly, openWorld, idempotent, and non-destructive hints. Description adds behavioral context: it draws from a live catalog, returns example questions with tool calls, and can be focused by topic. No contradictions.

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

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

Description is somewhat long but front-loaded with common queries and structured as bullet-like categories. Every sentence adds value, though it could be slightly more concise without losing clarity.

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

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

For a tool with one optional parameter, no output schema, and comprehensive annotations, the description is complete. It explains the output concept, usage patterns, and the purpose of the tool in the broader system.

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 the topic parameter described. Description adds value by explaining the topic values (finance, pharma, etc.) and the effect of omitting the argument (full spread). Enhances schema meaning.

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

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

Description clearly states it returns category-bucketed example questions with exact tool+argument shapes. It distinguishes itself as the onboarding entry point, separate from sibling tools like ask_pipeworx or discover_tools.

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' Provides clear context for when to use and implies when not to (after onboarding).

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 value beyond annotations by explaining the behavioral trait: the row is deactivated, not deleted, preserving historical events via recent_alerts. It also reinforces the ownership check. Annotations only indicate readOnly=false, destructive=false, but the description fills in the specific mutation behavior.

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

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

The description consists of two concise, front-loaded sentences. Every sentence provides essential information: the action, ownership constraint, and the effect on the row. No extraneous words.

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

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

For a simple tool with one parameter and no output schema, the description is complete. It covers purpose, usage constraint, and behavioral outcome. No additional context is necessary.

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 describes the 'id' parameter as a UUID returned by subscribe, with 100% coverage. The description adds no further parameter details beyond mentioning 'by id.' With full schema coverage, the baseline score is 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 'Cancel a subscription by id,' specifying the verb (cancel) and the resource (subscription). It distinguishes itself from sibling tools like 'subscribe' and 'list_subscriptions' by focusing on cancellation.

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

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

The description provides clear context for when to use the tool by noting ownership enforcement: 'you can only cancel your own subscriptions.' It also explains the consequence of deactivation versus deletion. However, it does not explicitly list conditions where the tool should not be used or suggest alternatives.

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

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

Annotations already provide readOnlyHint, idempotentHint, and no destruction. The description adds context on the return types (verdicts), internal routing, and performance gain (replacing 4-6 calls). No contradictions.

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

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

The description is effective but slightly verbose, with multiple clauses and examples. It is front-loaded with example queries, but could be tightened. Every sentence adds value, but not extremely 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 adequately explains the return values: verdict, actual value with citation, and reasoning. It covers the main aspects needed for the agent to use the tool appropriately.

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

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

Schema coverage is 100% with both parameters described. The description adds that tolerance_pct overrides default and provides guidance on setting it for hallucination detection, adding 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 performs natural-language claim verification, with specific examples like 'Is it true that…' and 'fact check'. It distinguishes itself from sibling tools by highlighting its two specialized pathways for company-financial vs other claims.

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 provides guidance on when the fast path (SEC EDGAR) vs grounded pipeline applies, but does not explicitly mention when not to use or list alternatives.

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