Predicthq

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

Annotations already indicate read-only, idempotent, open-world behavior. The description adds critical behavioral details: cost implications for Anthropic probing (BYO key, direct payment), default free model, and return structure (per-model score/confidence/signals/raw_response + combined view). No contradictions with annotations.

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

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

The description is a single, well-structured paragraph. It front-loads the primary action and outcome, tucks details in parentheses, and ends with three clear use cases. Every sentence adds 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 lacking an output schema, the description fully explains return values (per-model fields + combined view) and notes cost/free aspects. It covers all parameters' purpose and the tool's overall functionality for a non-trivial tool with 4 parameters.

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 beyond schema by clarifying the default model, that '_apiKey' is only needed for Anthropic, and that 'context' helps disambiguate. This additional context is valuable for effective usage.

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

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

The description uses specific verbs ('Probe', 'score') and clearly identifies the resource (LLMs) and the entity being probed. It distinguishes itself from sibling tools like 'compare_entities' and 'scan_competitor_ai_presence' by focusing on AI visibility scoring per model, which is a unique action.

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

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

The description explicitly states use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring'), providing clear context for when to employ the tool. However, it does not explicitly mention when not to use it or list alternatives, though the sibling set implies 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 declare readOnlyHint, openWorldHint, and idempotentHint. The description adds value by noting the tool routes to multiple sources and returns stable citation URIs, but does not fully describe the routing behavior or error cases.

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

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

The description is comprehensive but somewhat verbose. It is well-structured with clear sections and front-loaded key information, but could be more concise without losing essential details.

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

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

Given the tool's complexity (5,245 tools), the description covers purpose, usage, and examples adequately. However, it lacks details about the output format beyond citation URIs, which would be useful for an agent.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds aliases for the question parameter and provides usage examples, enhancing understanding of how to formulate queries.

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 routes questions to a vast set of tools and returns structured answers with citations. It distinguishes itself from siblings like ask_pipeworx_grounded and deep_research by specifying when each should be used.

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

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

Explicit guidance is given to prefer this over web search for factual questions, with examples of when to use. It also specifies conditions to step up to ask_pipeworx_grounded or deep_research, providing clear alternative selection criteria.

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 no destructiveness. The description adds context that it is a full working router with experimental edge and that no candidate is active, which informs behavior beyond annotations without contradiction.

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

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

The description is well-structured with the main idea upfront, followed by details on current status and usage. It contains no redundant sentences, though it is slightly longer than necessary.

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

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

Given the tool's complexity (beta version with candidate routing), the description explains the current state, how to use it, and the fallback behavior. The full schema coverage and annotations support completeness, though no output schema is provided.

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

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

Schema coverage is 100% with detailed parameter descriptions for all aliases of 'question'. The description only mentions 'same arguments' and does not add new semantic meaning, so the 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 it's the beta version of ask_pipeworx with identical functionality, distinguishing it from the stable sibling. It specifies the verb 'ask' and the resource 'Pipeworx Beta', and notes it is a universal router with the same tools and arguments.

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 recommends using it 'exactly like ask_pipeworx when you want the newest routing' and explains that results are compared to decide merges. It also clarifies the current inactive status, but does not explicitly state when not to use it.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context: the extra LLM cost, refusal reasons (not_in_source, no_tool_match, etc.), and success return structure. 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 front-loaded purpose. Every sentence adds value, but slightly long. Could be more concise while retaining all 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 and lack of output schema, the description comprehensively covers return format, refusal reasons, trade-off vs sibling, and use cases. No gaps.

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

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

Schema coverage is 100% with all parameters described. The description mentions query aliases but does not add meaning beyond what the schema already provides. Baseline 3 is appropriate.

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

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

Clearly states it's a hallucination-resistant answer mode for high-stakes reads. Describes the process: picks tool, fetches data, extracts answer only from result. Distinguishes from sibling ask_pipeworx by noting extra LLM call and use case.

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

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

Explicitly says 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with examples (financial verdicts, legal claims). Also says 'prefer ask_pipeworx for casual lookups', providing clear when-to-use and when-not.

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?

Extensively details behavior beyond annotations: resolution process, fan-out examples, response shapes, resolver contract, parent_event extractor, news fields, safety, wide spreads, resolution-rule risk. No contradictions.

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

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

Well-structured with sections but lengthy. Every sentence adds value; slightly verbose but 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?

Comprehensive coverage of edge cases, error states (low confidence, closed markets, wide spreads), and warnings (resolution-rule risk). No output schema, but description compensates fully.

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

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

Schema coverage is 100%. Description adds context for each parameter: explains 'market' can be slug/URL/question text, 'depth' enum values, 'include_raw' default and effect. Adds meaning 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 it researches Polymarket bets, specifies input types (slug, URL, question text), and differentiates from siblings like ask_pipeworx or deep_research by focusing on bet-specific research with fan-out logic.

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 use cases ('should I bet on X', etc.) and scenarios (fan-out examples), but lacks explicit when-not-to-use or alternatives. Still strong guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral context: results sorted by primary metric for 'largest'/'most', correct handling of off-calendar fiscal years for companies, and specific data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/trials for drugs). 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?

Single paragraph but well-structured: starts with trigger phrases, then core function, then details per type, then result sorting, then efficiency claim. 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?

Given two entity types and multiple data sources, description covers data pulled for each, result ordering, return format (paired data + citation URIs), and efficiency gain. No output schema but description sufficiently explains what to expect.

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

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

Schema coverage is 100%; both parameters have descriptions. Description adds meaning: for type, explains what data each enum pulls; for values, gives examples (tickers/CIKs for companies, drug names) and reinforces min/max constraints. Adds value beyond schema.

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

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

Description clearly states the tool does side-by-side comparison of 2–5 companies or drugs in a single call, with trigger phrases like 'compare X and Y' and 'X vs Y'. It distinguishes from siblings like entity_profile by specifying parallel comparison vs single lookups.

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

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

Explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear when-to-use guidance. Implies when-not-to (single entity) and differentiates from alternative approaches like sequential entity_profile calls.

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

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

Annotations indicate readOnly, openWorld, idempotent, and non-destructive. The description adds crucial context: account requirement, paid plan for thorough depth, data sources, parallel execution, return format (evidence, confidence, gaps, contradictions, hop, citation_uri), timing (15-60s, thorough up to 90s), and semantic excerpting. 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 lengthy but front-loaded with critical prerequisites (account, alternative). Every sentence provides specific detail; however, it could be slightly more concise without losing information.

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

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

Given no output schema, the description thoroughly explains the return findings packet. It covers prerequisites, usage, behavior, parameters, timing, and return format, making it complete for agent invocation.

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

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

Schema coverage is 100%. The description adds meaning beyond schema by explaining depth enum values (quick=3, standard=5, thorough=8) and their behaviors (gap recovery, contradictions scan), and clarifies that the question parameter accepts natural language and broad questions.

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

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

The description clearly defines the tool as 'Grounded multi-source research across Pipeworx's 1369 STRUCTURED data sources' and explains it decomposes questions into facets, routes to tools in parallel, and returns a findings packet. It also distinguishes from sibling tools like ask_pipeworx, which is for single lookups.

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

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

Explicitly states when to use: 'Best for broad/multi-part questions over structured data' and when not: 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx' and 'If you are not signed in, use ask_pipeworx instead.' It also mentions the alternative ask_pipeworx directly.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds that returned results include "full input schemas (with curated examples)" and are "ready to call directly," which is useful 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 concise, well-structured, and front-loaded with the main purpose. Every sentence adds value without unnecessary detail.

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

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

The description fully explains the tool's behavior, output, and use case. No output schema is needed as the tool's return format is described verbally.

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 parameters are well-documented. The description mentions the query parameter but doesn't add significant new meaning beyond what the schema provides.

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

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

The description clearly states the tool's purpose: "Find tools by describing the data or task." It explains it returns relevant tools with schemas, distinguishing it from sibling tools that perform specific tasks.

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

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

The description explicitly states when to use: "Use when you need to browse, search, look up, or discover what tools exist for ..." and "Call this FIRST when you have many tools available and want to see the option set." This gives clear usage context and implies alternatives.

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

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

Annotations indicate readOnly and idempotent. Description adds: 'Fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF', notes patent soft-fail, parallel call behavior, and specific return fields. No contradiction with annotations.

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

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

Front-loaded with examples, but description is somewhat long. Every sentence adds value, but could be slightly more structured. Still effective.

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

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

With 2 parameters and no output schema, description fully details what it returns (cik, filings, fundamentals, patents, news, LEI). No missing information for the tool's purpose.

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 context: 'type' is only 'company', 'value' is ticker or CIK, and explicitly states names not supported. This adds meaning beyond the schema.

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

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

The description explicitly states 'full cross-source profile of a US public company' with examples like 'Tell me about X' and 'research Acme'. It distinguishes from siblings by stating 'ALWAYS PREFER over chaining single-pack ... lookups'.

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

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

Clear guidance: 'when the user asks for a holistic view' and 'ALWAYS PREFER over chaining'. Also specifies when not to use: 'names not supported (use resolve_entity first if you only have a name)'.

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 destructiveHint=true and idempotentHint=true. The description adds context about why to delete (stale context, sensitive data) but does not detail idempotency behavior or error handling. It does not contradict annotations.

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

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

Two sentences, front-loaded with purpose, no wasted words. Every sentence adds value.

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

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

Given a simple tool with one parameter, comprehensive schema coverage, annotations, and sibling context, the description is complete enough. No output schema is needed.

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

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

The input schema has 100% coverage with one parameter 'key' described as 'Memory key to delete.' The description adds no additional meaning beyond the schema, so baseline 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 states the tool's purpose: 'Delete a previously stored memory by key.' The verb 'delete' and resource 'memory' are specific, and the mention of siblings 'remember' and 'recall' helps distinguish that 'forget' is exclusively for deletion.

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

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

The description explicitly states when to use the tool: 'Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' It also mentions pairing with 'remember' and 'recall,' providing clear guidance on alternative tools.

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

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

The description explains the read-only, idempotent behavior (fetches page, extracts data, emits format) consistent with annotations (readOnlyHint, idempotentHint, not destructive). It adds behavioral context beyond annotations, such as emitting standard markdown and being a single text blob, with 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 (two main sentences plus a bullet list of use cases) and front-loaded with the primary purpose. Every sentence contributes 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 (2 parameters, no output schema, no nested objects), the description is complete: it explains input (URL), process (fetch and extract), output (markdown blob), and practical use cases. No critical information is missing.

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 fully described in the schema. The description does not add significant new semantic meaning beyond the schema, so it meets the baseline but does not exceed.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, explaining the process (fetches page, extracts info, emits markdown) and listing specific use cases, distinguishing it from sibling tools which cover AI visibility, research, etc.

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

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

The description explicitly lists three use cases (client site indexing, personal project drafting, competitor auditing), providing clear context for when to use it. However, it does not explicitly exclude any scenarios or differentiate from siblings, which is acceptable given the sibling diversity.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds context on returned fields and provides an example, which is valuable but not extensive.

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 an example. Concise and front-loaded with essential information.

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

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

No output schema exists, but the description lists the returned fields. It does not cover errors or rate limits, but for a simple get-by-id tool, it is fairly complete.

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

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

Schema coverage is 100% with clear descriptions for each parameter. The description adds an example but no additional semantic information beyond the schema.

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

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

The description clearly states it fetches a single event by id and lists the returned fields, distinguishing it from sibling tools like search_events.

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

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

The description implies usage when you have a specific event id but lacks explicit guidance on when not to use or comparisons to alternatives.

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

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

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by listing the specific return fields (id, type, params, etc.) and noting the default behavior (active subscriptions) and the optional include_inactive parameter. 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 with no wasted words. First sentence states purpose and return fields, second provides usage guidance. Front-loaded with key information.

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

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

Given the tool's simplicity (one optional parameter, no output schema) and strong annotations, the description covers purpose, usage context, and return format adequately. 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 description coverage is 100% with a clear description for the single optional parameter 'include_inactive'. The description does not mention the parameter, but since the schema already fully documents it, baseline score of 3 is appropriate.

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

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

The description clearly states 'List the caller's active subscriptions' with a specific verb and resource, and enumerates return fields. It distinguishes from sibling tools like 'subscribe' and 'unsubscribe' which are for creating or removing subscriptions.

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

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

Explicitly advises when to use the tool: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This provides clear context and alternatives.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and no destruction. The description adds behavioral details not in annotations, such as ranking by predicted impact and providing an example call. No contradiction with annotations.

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

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

The description is concise at three sentences plus an example. The main purpose is front-loaded, and each sentence adds meaningful 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?

With 8 parameters fully documented in the schema, annotations provided, and no output schema, the description offers a high-level overview and a concrete example. This is sufficient for an agent to understand and invoke the tool correctly.

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

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

Since schema description coverage is 100%, the baseline is 3. The description does not add new parameter meaning beyond the schema, but the example provides a practical context for parameter combinations, which is valuable.

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 action ('Find PredictHQ events within a radius of a geographic point') and distinguishes it from sibling tools by specifying geographic proximity. The inclusion of a concrete example reinforces the core purpose.

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

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

The description provides clear usage context such as 'demand-forecasting queries' and lists filtering options (category, date window). Although it does not explicitly contrast with siblings like 'search_events', the context is sufficient for an agent to infer appropriate use cases.

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

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

Annotations are all false, consistent with a feedback tool. The description adds key behavioral details: rate-limited to 5 per identifier per day, free, doesn't count against tool-call quota, and team reads daily.

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

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

The description is concise and well-structured: purpose, usage instructions, formatting advice, team reading habits, and constraints. 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?

Despite no output schema and moderate complexity (3 params, 1 nested object), the description fully covers what the tool does, when to use it, how to use it, and its limitations. 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 all parameters with descriptions. The description adds valuable context on how to format the message (describe in terms of tools/packs, no end-user prompt). This enhances understanding beyond the schema.

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

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

The description clearly states the tool's purpose: sending feedback to the Pipeworx team about bugs, missing features, data gaps, or praise. It distinguishes itself from sibling tools by focusing solely on feedback.

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

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

The description provides explicit guidance on when to use the tool (bug, feature/data_gap, praise) and what to include (avoid pasting end-user prompts). It also mentions rate limits and quota, aiding appropriate use.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable context beyond annotations: self-aggregation from CF analytics-engine, no PII, and caching behavior (5min-1h). No contradictions.

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

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

The description is three well-structured sentences plus bullet-point use cases. It front-loads the primary function and uses efficient language with no redundancy. Every sentence serves a purpose.

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

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

With one optional parameter, no output schema, and rich annotations, the description covers all necessary context: data source, caching, privacy, and usage scenarios. It is complete for an agent to confidently invoke the tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining the window enum implications ('Shorter windows surface what's hot right now; longer windows show steady-state demand'), which exceeds the basic schema definition.

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 exactly what the tool does: it returns top tools, top packs, and total call volume over recent windows. It uses specific verbs and resources ('Returns the top tools, top packs, and total call volume') and clearly differentiates from sibling tools by uniquely addressing trending Pipeworx usage.

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

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

The description provides three explicit use cases (discovering hot data sources, confirming canonical tool, aligning use case) and explains window trade-offs. While it doesn't mention when not to use it or list alternatives, the guidance is clear and actionable.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive. The description adds critical behavioral details: fill check warnings against trading when edge exists only at last-trade, semantic anchor constraints, partition filter for placeholder slugs, and response structure. 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 with clear sections for modes, but longer than necessary. Some details like partition filter and semantic anchor could be summarized, though they add value. Front-loading of core functionality is good.

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

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

No output schema, but description fully explains the response: opportunities array with gap_pp, suggested_trade, fill check with realizable_edge_pp and thin_legs. Covers edge cases like placeholder slugs and low similarity. Complete for understanding tool behavior.

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

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

Schema has 100% coverage with descriptions. The description adds significant value: explains event/topic modes with examples (e.g., 'fed-decision-may-2026'), recommends when to use each, and clarifies how values affect scanning behavior.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket using monotonicity violations and partition-sum checks. It distinguishes three modes (no args, event, topic) with specific resource targets, differentiating from sibling tools like polymarket_edges or polymarket_fill_risk.

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

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

Explicit guidance on when to use each mode: no args for trending scan, event for per-market, topic for cross-event. Recommends event for specific markets and directs users to polymarket_fill_risk for custom sizing, providing clear alternatives.

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

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

Annotations already declare read-only, idempotent, non-destructive. Description adds extensive behavioral details: caching, diagnostics, Fed bet exclusion, edge calculation, slippage, and Kelly sizings. No contradictions.

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

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

Well-structured with clear sections, but overly verbose with exact model parameters and numerical thresholds that could be simplified. Front-loaded with purpose, but later details are dense.

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

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

Describes output structure (by_segment, diagnostics), explains caching, and covers edge cases (e.g., Fed bets filtered but noted). Sufficient for correct invocation despite no output schema.

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

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

Schema coverage is 100%, but description adds significant context for each parameter (e.g., slippage_pp rationale, min_partition_leg_kelly usage). Provides actionable guidance beyond schema descriptions.

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

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

Clearly states it scans top Polymarket markets for opportunities where Pipeworx data disagrees with market price, specifically for 'what should I bet on today' discovery. Distinguishes from siblings by focusing on edge detection without paging hundreds of markets.

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 for use (daily opportunity discovery) and explains tradeable-edge knobs. However, does not explicitly compare to alternatives like polymarket_arbitrage or polymarket_edge_tracker, leaving some room for ambiguity.

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

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

The description discloses key behavioral traits beyond annotations: data source (daily snapshots), snapshot TTL (60-day), decay calculation (from daily closes, not intraday), response structure (tracked, expired, snapshot_dates), and potential gaps in snapshot dates. 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 relatively long but well-structured, front-loading the purpose and then detailing parameters and response. Each sentence adds information, though a slight trimming could improve conciseness without losing value.

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

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

With no output schema, the description thoroughly explains the response structure: tracked array with fields like edge_pp_net time-series, first_seen, trend, decay_pp_per_day; expired array with lifespan_days; snapshot_dates. It also covers limitations like TTL and snapshot start time. Comprehensive.

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 value by providing default values and constraints ('default 14, max 30' for days, default '1wk' for window), and clarifying the meaning of 'window' as a snapshot family. These details are not present in the schema alone.

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

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

The description clearly states the tool's purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers a specific question about how long an edge has existed and whether it is shrinking, distinguishing it from sibling tools like polymorphic_edges which provide raw snapshots.

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

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

The description provides context on when to use the tool (to understand edge history and decay) and contrasts fresh vs old edges. It does not explicitly list alternatives or state when not to use it, but the context from the sibling list and the tool's specificity makes it clear.

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

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

Annotations already mark readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds detail: walks the ladder, returns fill details/verdict, and for basket mode notes forced_directional_risk and that partial fills convert arb into unhedged directional position.

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 well-structured with clear sections (REQUIRES, SINGLE-MARKET, BASKET, USE THIS). Every sentence adds value, though a slight trim could tighten it.

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 4 parameters and no output schema, description lists return fields and explains behavioral context (e.g., thin books, directional risk). Fully covers what an agent needs to know.

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: defines side defaults, size_usd interpretation (max spend on buys, target proceeds on sells for single-market; settlement notional for basket), and clamp range 10–1,000,000.

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

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

The description clearly states it performs a realizable vs theoretical edge check against live order-book depth, and distinguishes between single-market and basket modes. This differentiates it from siblings like polymarket_arbitrage and 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?

Explicitly says 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500', and explains the reasoning (theoretical overround not capturable, partial basket fills create directional risk).

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

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

Beyond annotations (readOnly, idempotent), the description details compatibility_warning conditions, temporal_alignment, and skipped leg counters, giving full transparency on when spreads are meaningful versus when they are not.

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

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

While the description is lengthy, it is well-structured with clear sections (modes, response, safety fields). Each sentence serves a purpose, though a slight reduction in verbosity could improve scannability.

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 compensates by explaining the response structure, safety fields, and edge cases, making it complete for an agent to understand and 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%, and the description adds value by explaining the topic shortcuts, how explicit tickers override, and provides examples—all enhancing understanding beyond the schema.

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

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

The description clearly states the tool computes a cross-venue spread, explains the two modes (topic shortcuts and explicit tickers), and distinguishes it from sibling tools like polymarket_arbitrage.

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

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

Explicitly specifies when to use the pre-mapped topic mode vs. explicit pairings, and warns that most pre-mapped topics currently return compatibility warnings, guiding the agent to set expectations correctly.

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

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

Annotations already indicate readOnly, idempotent, and non-destructive. Description adds value by detailing behavior: retrieval by key or listing all keys, scoping to identifier, and pairing with remember/forget. 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 concise (3 sentences), well-structured, and front-loaded with the main action. 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 1 optional parameter, no output schema, and complete annotations, the description fully explains usage, scoping, and interactions with sibling tools. Complete for an agent.

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

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

Schema has 100% coverage with parameter description. Description adds meaning by clarifying that omitting the key lists all saved keys, which is not explicit in schema examples.

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

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

Description clearly states the tool retrieves a value saved via remember or lists keys if omitted. Identifies the resource ('memory') and distinguishes from siblings (remember, forget).

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

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

Description explains when to use (look up context stored earlier, avoid re-deriving) and mentions scoping. Does not explicitly state when not to use or provide alternative tools, 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?

Annotations already indicate read-only, idempotent, safe. Description adds key stateful behavior: mark_read flag affects next call. 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?

Three dense sentences, no fluff, front-loaded with purpose. Every sentence adds value.

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

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

No output schema, but description details return fields (source, citation_uri, payload). Covers parameters, behavior, and usage suggestions. Slightly missing pagination or limit details.

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

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

Schema covers all 5 parameters with descriptions. Description adds examples (e.g., 'sec_8k' for type, ISO timestamp for since) and clarifies mark_read effect. Adds marginal value.

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

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

Clearly states 'Pull fired events from your subscription feed' with specific verb and resource. Distinguishes from siblings like search_events but not explicitly; still clear purpose.

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

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

Mentions polling use and provides an alternative endpoint for scripts/dashboards. However, does not explicitly state when to use this tool vs other alert 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. The description adds critical behavioral context: parallel fan-out to multiple sources, GDELT→GNews fallback on rate limits/5xx, USPTO soft-fail due to API sunset, and relative shorthand parsing for 'since'. These go beyond what annotations provide.

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

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

The description is a single paragraph but well-organized: it opens with example queries, lists sources and their behaviors, then covers parameter meanings, then mentions output format, and closes with a sibling distinction. Every sentence adds value, but it could be slightly tighter (e.g., 'What's new with X' phrasing is redundant after the examples).

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

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

The tool is complex (multiple sources, fallback, window formats, no output schema). The description covers return structure (changes[] grouped by source, total_changes count, citation URIs), source-specific behaviors (GDELT priority, USPTO soft-fail), and parameter formats with examples. Nothing critical is missing for correct 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 description coverage is 100% (all three parameters have descriptions). The description adds meaning beyond the schema: it gives examples for 'since' (e.g., '30d', '1y'), explains that 'value' accepts ticker or zero-padded CIK, and clarifies that 'type' is currently limited to 'company'. This extra context aids correct invocation.

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 starts with concrete example queries showing exactly what the tool answers. It clearly states it provides a 'change feed for a company in the last N days/weeks/months in ONE parallel call' and lists specific sources (SEC EDGAR, GDELT→GNews, USPTO). Distinguishes from sibling tool entity_profile at the end.

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

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

The description gives explicit guidance: use this for recent changes in a time window, and use entity_profile instead for static profile. It explains fallback behavior for news sources and admits USPTO may soft-fail. While it does not enumerate all negative cases, the contrast with entity_profile is clear and actionable.

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

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

The description adds significant behavioral context beyond annotations: it mentions scoping by identifier, persistence differences between authenticated users and anonymous sessions (24-hour retention), and the key-value storage model. There is no contradiction with annotations (e.g., idempotentHint=true aligns with storing data).

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 highly concise: three sentences, each adding distinct value (purpose, usage, behavioral details). Front-loaded with the core action, it avoids fluff while covering key aspects.

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

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

Given the tool's simplicity (2 simple parameters, no output schema), the description covers all essential aspects: what it does, when to use it, how it behaves with respect to persistence and authentication, and how it relates to sibling tools. No gaps remain.

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

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

Schema coverage is 100% with clear descriptions for both parameters. The description adds example keys and values but does not provide additional syntactic or formatting constraints. Baseline score of 3 is appropriate since the schema already carries the burden.

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

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

The description clearly states the tool's purpose ('Save data the agent will need to reuse later') and provides concrete examples (e.g., 'a resolved ticker, a target address'). It distinguishes the tool from siblings like 'recall' and 'forget', which are explicitly mentioned as complements.

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

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

The description explicitly tells when to use the tool ('when you discover something worth carrying forward') and how to pair it with 'recall' and 'forget'. However, it does not explicitly state when not to use it, though the context is clear.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds behavioral context by noting that each call 'cascades through several lookup endpoints internally' and that using it replaces 2-3 manual lookups. This provides useful insight into the tool's internal complexity without contradicting annotations.

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

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

The description is concise yet comprehensive, using a clear structure with examples and bullet points. Every sentence adds value: purpose, usage guidance, supported types, input/output details. It is front-loaded with the core function and remains efficient 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 has no output schema, the description fully explains the return values for each entity type, including citation URIs. It covers all necessary aspects: purpose, input format, internal behavior, and output details. For a lookup tool, this is complete and leaves 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?

Input schema has 100% coverage and clearly describes both parameters with enums and examples. The description goes beyond the schema by specifying the return values for each entity type (e.g., 'returns ticker + 10-digit CIK + company_name' for company). This adds significant meaning and helps the agent understand what to expect.

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

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

Description clearly states the tool's purpose: resolving a user-spoken name to a canonical/official identifier. It provides specific examples (ticker, CIK, RxCUI) and distinguishes itself from siblings by explicitly saying 'Use FIRST whenever you have a name but need an ID.' This differentiates it from other tools that may take IDs directly.

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 to use this tool first when needing an ID from a name. It lists supported entity types and their input formats. While it doesn't explicitly state when not to use it, the 'Use FIRST' directive provides clear context. No alternatives are mentioned, but the context is sufficient for an AI agent to decide.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds useful behavioral details: it probes each entity with ai_visibility_check, ranks by score, returns score, confidence, signal density per entity.

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

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

Two sentences, front-loaded with the main action. Every sentence adds value with no fluff. Efficient and clear.

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

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

Despite no output schema, the description covers return format (ranked list with score, confidence, signal density). All 4 parameters are documented in schema with additional context from description. Sufficient 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 the description adds meaning: 'First entry treated as the subject for narrative; rest are competitors,' and explains the context parameter. This goes beyond the schema descriptions.

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

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

The description clearly states the tool compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (more general).

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 'useful for competitive AI-marketing audits' and gives an example question. Implies when to use, though does not explicitly state when not to or list alternatives beyond being a batch version of ai_visibility_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 readOnly, openWorld, idempotent, non-destructive. Description adds beyond: bundlephobia first measurement can take 5-30s, sources_failed list on timeout, and describes the return structure (summary block, per-advisory detail, links, alternative versions). 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 fairly long but every sentence is informative. Front-loaded with purpose and use case, followed by details and edge cases. Could be slightly tighter but 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 fully compensates by listing all return fields (is_latest, license, etc.), explains partial failures, and mentions alternative versions. Covers input, behavior, failure modes, and output shape completely for a composite tool.

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

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

Schema coverage is 100%, so baseline is 3. Description adds minimal value: restates package accepts scoped packages and version defaults to latest. This reinforces but does not significantly extend schema info.

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 'check' and resource 'npm package' with clear scope: license, advisories, version history from deps.dev and bundle size from bundlephobia. It distinguishes from siblings by stating NPM ecosystem only in v1 and directing other ecosystems elsewhere.

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 agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. Mentions partial failures and graceful degradation. Does not explicitly state when-not, but implies exclusivity to NPM.

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, establishing safety. Description adds value by detailing that results are ranked by predicted impact and includes rank, predicted attendance, dates, and location. 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 concise (~100 words), front-loaded with purpose, and includes a specific example. Every sentence adds value – no fluff or redundancy.

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

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

With 7 parameters (0 required) and no output schema, description covers key aspects: filters, ranking, what is returned. Missing mention of the `limit` parameter, though schema describes it. For a search tool with rich filtering, this is sufficient.

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

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

Schema coverage is 100%, so baseline is 3. Description adds context by grouping filters (query, category, country, date window) and providing a concrete example, which helps agents understand parameter combinations. It does not simply repeat schema descriptions.

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

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

Description clearly states verb 'Search', resource 'PredictHQ Events API', and purpose 'for demand forecasting'. Lists specific event types (concerts, sports, etc.) and what is returned (rank, attendance, dates, location), distinguishing it from siblings like get_event or nearby_events.

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

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

Description specifies filters (query, category, country, date window) and ranking by impact, and provides an example. However, it does not explicitly state when to use this tool versus siblings such as get_event (single event lookup) or nearby_events (location-based), nor gives 'when-not-to-use' guidance.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and not destructive. Description adds details: 200K char cap with truncation flag, embedding model (BGE-base-en), window size (500-char overlapping), cosine similarity. This goes beyond annotations and provides useful behavioral context.

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

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

The description is well-structured, starting with the core functionality, then usage rationale, then technical specifics. At 3 sentences, it is concise but not overly terse; it efficiently conveys all necessary 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?

Despite no output schema, description covers return structure (top-N passages, offsets, scores). It addresses complexity (truncation, embedding details) and provides sufficient context for correct tool selection among many siblings.

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: explains 'text' as fetched record, gives natural-language query examples (e.g., 'supply-chain risk'), clarifies character limit, and describes purpose of 'limit'. Enhances practical understanding beyond schema descriptions.

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

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

The description clearly states 'Semantic search INSIDE a fetched record', specifying the action (search), resource (fetched record/text), and mechanism (semantic). It distinguishes from siblings by contrasting with whole-document use and mentioning pairing with ask_pipeworx_grounded.

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

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

Provides explicit guidance: use when the record is too large for the prompt, saves context, pairs with ask_pipeworx_grounded, and offsets enable verification. However, no explicit when-not-to-use or alternatives 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?

Annotations provide some safety cues (non-read-only, non-destructive, idempotent, open-world). The description adds critical behavioral details: OAuth requirement, delivery channel specifics (SMS cap of 10/day, webhook auto-disable after 10 failures, webhook secret returned once). 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 organized into sections (types, delivery) but is verbose, repeating some schema info. It is front-loaded with the main purpose, but could be more concise.

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

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

Given the complexity of multiple types and delivery options, the description covers key constraints, return value (subscription ID), and failure conditions. No output schema, so the brief mention of return is acceptable.

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

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

Schema covers all parameters with descriptions. The description enhances understanding with concrete examples (e.g., sec_8k items, validation requirements for phone) and behavioral constraints (webhook auto-disable), 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 creates a proactive monitoring subscription to a live-data event stream and returns a subscription ID. It distinguishes itself from sibling tools like list_subscriptions and unsubscribe via its focus on creation.

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

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

The description explicitly states when to use the tool (to subscribe to specific alert types) and lists required authentication. It doesn't explicitly state when not to use it, but the context is clear as creation is a unique action.

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, openWorldHint=true, and destructiveHint=false. The description adds behavioral details beyond these: it explains the return format (category-bucketed example questions with tool+argument shape) and that the content is drawn from a live catalog of thousands of tools. No contradictions.

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

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

The description is front-loaded with common query phrases and is well-structured. It is slightly verbose with multiple examples, but every sentence adds information. Could be trimmed slightly without losing clarity, but overall 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's role as an onboarding entry point and the absence of an output schema, the description adequately explains what the tool returns (example questions with tool usage) and how to use it (with or without topic). It also mentions the live catalog context. This is complete for the intended use case.

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

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

Schema coverage is 100% with a description for the topic parameter. The description adds further value by explaining how to use it: 'pass `topic` (e.g. "finance", "pharma", "betting") to focus' and that omitting gives a full cross-category spread, which clarifies the parameter's semantics 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 uses specific verb 'returns category-bucketed example questions' and identifies the resource 'What can I ask Pipeworx?' as the onboarding entry point. It clearly distinguishes from sibling tools like ask_pipeworx by focusing on providing example questions and tool usage guidance.

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

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

The description explicitly states 'Use this FIRST when you do not yet know what Pipeworx can do' and provides examples of common queries that trigger this tool. It mentions optional topic filtering but does not explicitly exclude alternatives, though the context is clear.

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

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

Annotations indicate destructiveHint=false and idempotentHint=true. The description adds value by explaining that the row is deactivated (not deleted) and historical events remain available, and it mentions ownership enforcement, providing context beyond annotations.

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

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

Two sentences, no fluff. The first sentence states the core action and ownership constraint; the second clarifies the behavioral nuance. Information is front-loaded and every sentence is valuable.

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 a single parameter, good annotations, and no output schema, the description fully explains the tool's effect, ownership, and historical data retention. An agent has enough information to use the tool correctly.

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

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

Schema coverage is 100% and the description does not add extra meaning to the 'id' parameter beyond what the schema already provides ('Subscription id (uuid) returned by subscribe'). Baseline score 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 states 'Cancel a subscription by id,' clearly identifying the action (cancel) and resource (subscription) with a specific input (id). It distinguishes from siblings like 'subscribe' and 'list_subscriptions'.

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

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

The description explains ownership enforcement ('you can only cancel your own subscriptions') and notes the deactivation behavior instead of deletion. While it does not explicitly list when not to use it, the context is sufficient for an agent to understand appropriate usage.

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

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds significant context: internal routing logic (SEC EDGAR vs grounded pipeline), return structure (verdict, value, citation), and efficiency gains (replaces 4-6 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 moderately long but front-loaded with trigger phrases and structured logically. Every sentence adds value, though some repetition could be trimmed. Overall, it balances completeness with readability.

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

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

Given the tool's complexity (two verification pathways, return structure, and no output schema), the description thoroughly covers all necessary information: inputs, internal logic, outputs, and use cases. The agent can fully understand the tool's behavior without additional context.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by explaining the tolerance_pct default behavior (implied by wording, capped at 5) and its use case (hallucination detection). Also provides example values for the claim parameter, enhancing understanding beyond schema descriptions.

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

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

The description clearly states the tool's purpose: validate natural-language factual claims against authoritative sources. It provides trigger examples and distinguishes between financial and other claims, differentiating it from sibling tools like deep_research or ask_pipeworx_grounded.

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

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

Explicitly states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' While it doesn't list exclusions or alternatives, the scope is clearly defined with examples, making it easy for an agent to select correctly.

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