Openchargemap

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

Annotations indicate safe, read-only, idempotent behavior. The description adds context about probing multiple models, cost implications for Anthropic API calls, and the structure of the response (score, confidence, signals, raw_response). No contradictions.

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

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

The description is concise at 3-4 sentences, front-loaded with the primary function. Every sentence adds unique value: purpose, default model, key handling, output structure, and use cases.

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

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

Despite no output schema, the description explains the return format (per-model object with score, confidence, signals, raw_response, plus combined view). It covers all parameters, optionality, and usage scenarios, making it fully informative for an AI agent.

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

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

Schema descriptions cover 100% of parameters. The description adds value by explaining the default model, the significance of _apiKey, and the purpose of 'context' for disambiguation, going beyond the schema.

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

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

The description explicitly states the tool probes LLMs for knowledge about an entity and returns a visibility score per model. It highlights the default model and optional Anthropic integration, clearly distinguishing from sibling tools like entity_profile or ask_pipeworx.

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

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

The description lists specific use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to provide _apiKey. It does not explicitly state when not to use this tool versus alternatives, but the purpose is clear enough for appropriate selection.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds unique behavioral context: routing to specific sources, filling arguments, returning citations with pipeworx:// URIs, and being a fast default entry point. 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 informative and front-loaded with key guidance ('PREFER OVER WEB SEARCH'), but it is somewhat lengthy. Each sentence serves a purpose, though minor trimming could improve conciseness.

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

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

For a tool with 6 parameters and complex routing logic, the description covers purpose, usage context, examples, and sibling differentiation. It explains return value (structured answer with citations) despite no output schema. No obvious gaps for an agent to misuse 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 description coverage is 100%, with clear descriptions for each parameter. The description adds no additional parameter-specific details beyond the schema, but the examples illustrate usage. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states the tool routes questions to the appropriate source among 5,008 tools across 1,318 verified sources, returning structured answers with citations. It provides specific examples like 'current US unemployment rate' and distinguishes itself from siblings like ask_pipeworx_grounded and deep_research.

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

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

Explicitly instructs to 'PREFER OVER WEB SEARCH' for factual questions, provides when-to-use and when-to-step-up to alternatives (e.g., deep_research for broad multi-part questions, ask_pipeworx_grounded for hallucination-resistant answers). Includes concrete examples of user queries.

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. Description adds beta context, source-preference ranking, and comparison to stable router. No contradictions.

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

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

Description is informative but slightly verbose with parenthetical details. Front-loaded with key purpose; every sentence adds value, though could be slightly trimmed.

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

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

No output schema, but description explains response shape is same as ask_pipeworx. Covers experimental nature, routing behavior, and comparison to stable. Complete for a complex router tool.

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

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

Schema coverage is 100% with all parameters documented as aliases for 'question'. Description reiterates 'same arguments' but adds no extra semantic detail beyond the schema. Baseline of 3 is appropriate.

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

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

Clearly states it's a beta version of ask_pipeworx, a universal router with 5,008 tools, and explains the experimental improvements (source-preference ranking). Distinguishes from sibling ask_pipeworx.

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

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

Explicitly says 'Use it exactly like ask_pipeworx when you want the newest routing' and compares to stable router. Mentions fallback behavior and that it's a full working router.

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

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

Discloses extra LLM call cost, structured refusal reasons, and evidence extraction. Annotations already cover readOnly, openWorld, idempotent, and non-destructive; description adds context beyond that.

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 thorough but each sentence serves a purpose. Front-loaded with key value proposition. Slightly verbose but justified by the need to explain refusal logic and tradeoffs.

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 explains return format, failure modes, cost tradeoff, and references sibling. Comprehensive for a tool with 6 parameters and 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% with descriptions for all 6 parameters. Description adds that the question is in natural language but does not significantly enhance beyond schema details. Baseline 3 is appropriate.

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

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

Description clearly states this is a hallucination-resistant answer mode for high-stakes reads, distinguishes from sibling ask_pipeworx by explaining the extraction mechanism 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?

Explicit when-to-use: 'Use whenever an answer will be quoted, cited, or acted on' and when-not: 'prefer ask_pipeworx for casual lookups' 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?

The description extensively discloses behavioral traits beyond what annotations provide, such as resolver confidence handling, market match alternatives, parent event extraction, news fallback, safety short-circuits, and resolution-rule risk. This is comprehensive and adds significant value for correct invocation and interpretation of results.

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

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

The description is front-loaded with the core purpose and usage, followed by detailed explanations of classifiers, fan-out examples, response shapes, and safety considerations. While comprehensive, it is somewhat long but each section adds necessary information for proper use, so it earns its length.

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

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

The description is highly complete for a tool with no output schema, thoroughly explaining all response fields, edge cases (low-confidence matches, closed markets, wide spreads, resolution rule risk), and fallback behaviors. It covers everything an agent needs to correctly interpret results.

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

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

The description adds significant meaning beyond the input schema by explaining the three possible input formats (slug, URL, question text), clarifying the effect of depth (quick vs thorough fan-out), and detailing the include_raw parameter's impact on response size. This enhances parameter semantics despite high schema coverage.

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

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

The description clearly states the tool's function: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the verb and resource, but does not explicitly differentiate from sibling tools like polymarket_edges or polymarket_arbitrage.

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

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

The description provides explicit use cases ('Use for "should I bet on X"...') and examples, giving clear context for when to invoke the tool. However, it does not mention when not to use it or suggest alternatives among siblings.

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

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

Annotations already declare readOnlyHint and statelessness. The description adds behavioral details: for company type, it pulls specific financial data from SEC EDGAR/XBRL with proper fiscal year handling; for drug type, it pulls FAERS, FDA approval, and trial counts. Results sorted by primary metric. This provides valuable behavioral context beyond annotations.

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

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

The description is front-loaded with trigger phrases and core purpose, then details per type. It is informative but slightly long; every sentence adds value, though minor trimming could improve conciseness.

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

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

Given no output schema, the description states it returns 'paired data + pipeworx:// citation URIs per entity' and highlights efficiency ('replaces 8–15 sequential lookups'). It could specify the return format more precisely, but it is sufficient for an agent.

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

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

Schema coverage is 100% with descriptions for both parameters. The description reinforces the enum values for 'type' and provides concrete examples for 'values' (tickers/CIKs for companies, drug names), adding meaning beyond the schema.

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

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

The description clearly states the tool's purpose: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It uses specific trigger phrases and distinguishes itself from sequential single-pack lookups, making the purpose highly clear.

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 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing clear when-to-use guidance. It does not explicitly state when not to use, but the context implies it is for comparisons.

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

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

Adds substantial behavioral context beyond annotations: expected latency (15-60s, up to 90s), return of gaps[] when topics unavailable, semantic excerpting of large records, and inclusion of hop/citation_uri/contradictions 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?

The description is long but each sentence adds value. It front-loads critical context (account, alternative) and organizes details logically. Could be slightly reorganized for skimmability, but it earns its length.

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

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

With no output schema, the description fully explains the return format: findings packet with evidence, confidence, source, fetched_at, citation, gaps, hop, contradictions. It covers latency, data sources, and error cases, leaving no ambiguity about what the tool does and returns.

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?

Even though schema coverage is 100%, the description adds meaning by explaining each depth level's behavior (e.g., quick=3 facets, standard=5+gap recovery+contradictions) and that the question can be broad/multi-part. This goes well 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 it performs 'grounded multi-source research across Pipeworx's 1318 STRUCTURED data sources' and distinguishes from siblings like ask_pipeworx by noting it is for broad/multi-part questions, not single lookups or breaking news.

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

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

Explicitly specifies when to use deep_research (broad/multi-part questions) vs ask_pipeworx (single lookup, breaking news) and when not to (if not signed in). Also notes account requirements and that depth:'thorough' needs a paid plan.

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, destructiveHint. The description adds that it returns top-N relevant tools with full input schemas and curated examples, and each result is ready to call directly without a second schema lookup. This goes beyond annotations and clarifies behavior.

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

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

Description is two sentences, front-loaded with purpose. It could be slightly more concise (e.g., the list of domains is long), but it remains efficient and informative without redundancy.

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

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

Despite no output schema, the description explains what is returned (top-N tools with names, descriptions, schemas). It covers purpose, usage, behavior, and parameter aliases. The examples and 'ready to call' detail make it complete for a discovery 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 3. The description mentions the query parameter accepts natural language and lists aliases, but this information is already in the schema. The description adds examples, but they are in the schema's examples field. No significant added 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 the tool's purpose: finding tools by describing data or task. It lists specific domains (SEC filings, FDA drugs, etc.) and emphasizes it's for browsing/discovery. It distinguishes from siblings by saying 'Call this FIRST when you have many tools' and contrasts with getting a single answer.

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

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

Explicitly says 'Use when you need to browse, search, look up, or discover what tools exist' and provides examples like 'look up FDA drug approvals'. It advises calling this first to see the option set, which guides the agent on when to use this tool versus others.

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

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

Beyond the annotations (readOnlyHint, etc.), the description adds substantial behavioral context: it fans out across multiple sources (SEC EDGAR, XBRL, USPTO, GDELT/GNews, GLEIF), returns specific fields, notes the patent API sunset May 2025 with soft-fail behavior, and explains the news fallback chain. 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 front-loaded with relatable example queries, then efficiently covers purpose, usage preference, return fields, and known limitations. Every sentence adds unique value without repetition, achieving high density of useful 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 (multi-source aggregation), lack of output schema, and many sibling tools, the description is remarkably complete. It enumerates all returned data elements (cik, company_name, recent_filings with URIs, fundamentals with specific fields, patents with caveats, news, LEI) and covers edge cases (name unsupported, patent sunset). 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?

The input schema covers 100% of parameters, but the description adds key meaning: it explains that type is currently limited to 'company,' that value accepts ticker or zero-padded CIK but not names, and provides examples. It also advises using resolve_entity if only a name is known, which the schema does not convey.

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

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

The description opens with numerous example user queries that directly map to the tool's purpose, then states 'full cross-source profile of a US public company in ONE parallel call.' It clearly specifies the verb (profile) and resource (US public company) and distinguishes itself from sibling tools like resolve_entity by noting it does not support names.

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 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' providing clear when-to-use guidance. It also states when not to use it (if only a name is available, use resolve_entity first), effectively excluding 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 and destructiveHint=false. The description adds valuable context about automatic geocoding and return fields (operator, status, connector types, speed, distance), enhancing transparency beyond annotations.

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

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

Description is front-loaded with purpose, followed by concise details. Four sentences with no wasted words.

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

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

Despite no output schema, the description sufficiently explains return fields and behavior. Given 9 parameters and no required fields, it provides complete context for an agent to invoke correctly.

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

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

Schema coverage is 100% with detailed parameter descriptions. The description summarizes the two location methods and filtering, but does not add significant meaning beyond the schema.

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

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

The description clearly states the tool finds EV charging stations near a place, specifying the resource and action. It distinguishes from sibling 'get_station' by implying it returns a list, not details of a single station.

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 usage instructions (location name or coordinates, optional filters) but does not explicitly state when to use this tool versus alternatives like 'get_station'.

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 destructiveHint=true and idempotentHint=true. The description adds context about clearing sensitive data, but no contradictions. Additional details about side effects (e.g., persistence) could be beneficial but are not required.

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 action. No unnecessary 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 the tool's simplicity (single parameter, no output schema, clear annotations), the description fully covers its purpose, usage context, and pairing with related tools.

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

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

Schema coverage is 100% and the schema description for 'key' is clear: 'Memory key to delete'. The tool description does not add extra meaning beyond the schema, which is acceptable but not exceeding baseline.

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

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

The description uses a specific verb 'Delete' and resource 'memory by key', making the action unambiguous. It distinguishes from sibling tools 'remember' and 'recall' by focusing on 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?

Explicitly states when to use the tool: 'when context is stale, the task is done, or you want to clear sensitive data'. Also suggests pairing with sibling tools 'remember and recall'.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: it fetches the page, extracts title/description/key links, emits standard llms.txt markdown, and outputs a single text blob ready for site-root. This goes beyond annotations without contradiction.

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

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

The description is concise with only three sentences, front-loading the purpose and then adding behavioral details and use cases. 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?

For a simple read-only tool with no output schema, the description covers purpose, behavior, output format, and usage scenarios. It is sufficiently complete to guide an agent without missing critical information.

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

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

Schema description coverage is 100% with clear parameter descriptions. The description adds no additional semantic detail beyond what the schema provides, meeting the baseline of 3. It does not elaborate on max_links semantics or special formatting requirements.

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, specifying the verb 'Generate' and resource 'llms.txt file'. It distinguishes itself from siblings by being the only tool focused on this specific output, and provides concrete use cases.

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

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

The description provides clear context on when to use the tool (e.g., getting a client's site indexed by AI, drafting llms.txt for own project, auditing competitor visibility). However, it does not explicitly mention when not to use it or suggest alternatives, missing some exclusion 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 declare readOnlyHint and nondestructive. The description adds useful context about returned data (connector types, speed, status, comments), which is beyond the annotations. 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 efficiently convey the tool's purpose and key details. Every word adds value, with the primary action front-loaded.

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

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

Given the tool's simplicity, annotations, and full schema coverage, the description sufficiently covers the return data and usage. No gaps remain for an agent to invoke it 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?

Both parameters have schema descriptions, giving 100% coverage. The description reiterates the purpose of the 'id' parameter but does not add significant new meaning beyond the schema.

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

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

The description clearly states the action ('get full detail'), specific resource ('single EV charging station'), and the method of identification ('by its Open Charge Map POI ID'). This distinguishes it from sibling tools like 'find_stations'.

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 when to use (when a specific POI ID is known) and hints at the limitation to a single station. It does not explicitly exclude other contexts or mention alternatives, but the sibling context provides differentiation.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds further context by listing the exact fields returned and explaining the tool's role in reviewing subscriptions, which supplements the annotations without contradicting them.

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

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

The description is exceptionally concise—three sentences that pack the core purpose, return fields, and usage guidance. It is front-loaded with the main action and has no unnecessary words.

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

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

Given the simple parameter set and lack of output schema, the description sufficiently covers the tool's behavior and return values. It could mention pagination or limits, but for a straightforward list tool, it is complete enough.

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 a description for the only parameter (include_inactive). The tool description does not add additional semantic meaning beyond what the schema already provides, so the score is at baseline 3.

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

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

The description clearly states the tool lists the caller's active subscriptions, specifies the returned fields (id, type, params, created_at, last_fired_at, fire_count), and contrasts with sibling tools like subscribe and unsubscribe by indicating its use for review before adding or obtaining an id for cancellation.

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

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

The description 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 for usage, though it does not explicitly list alternative tools or 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?

Discloses rate limit (5 per identifier per day), free usage, and that it doesn't count against tool-call quota. No contradiction with annotations (all false) and adds significant 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?

Concise single paragraph of 6 sentences, each sentence adds value. Front-loaded with main action, then specifics. No unnecessary words.

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

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

For a simple feedback tool, the description covers all necessary aspects: usage scenarios, constraints, expected input format. No output schema needed; completeness 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% with descriptions. Description adds extra guidance on how to write messages and clarifies the enum values, enhancing understanding beyond schema.

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

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

Description clearly states the tool's purpose: to provide feedback about broken, missing, or needed things. It specifies concrete categories (bug, feature, data_gap, praise) and differentiates from sibling tools which are query/action tools.

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

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

Explicitly tells when to use: for bugs, feature requests, data gaps, praise. Also states what not to do (don't paste end-user prompt) and provides context on usage limits and impact on roadmap.

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

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

Description adds valuable context beyond annotations: data source (CF analytics-engine), privacy (no PII), and caching behavior (5min-1h). No contradiction with annotations present.

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

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

Three concise sentences with clear structure: returns statement, use cases in bullet-like format, and technical note. No fluff.

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

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

For a simple 1-param tool with no output schema, the description sufficiently explains the return value (top tools, packs, volume) and provides enough context for correct invocation.

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

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

The only parameter 'window' is fully covered by schema with enum and description. The description enriches it with default and behavioral guidance (short windows for hot, long for steady-state).

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 returns trending data (top tools, packs, call volume) over a window. It distinguishes itself from sibling tools like discover_tools by focusing on popularity metrics rather than general discovery.

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 specific use cases (discovering hot data sources, confirming canonical tool, aligning use case with agent needs). However, it does not explicitly mention when not to use or alternatives, leaving room for improvement.

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, open-world, idempotent, and non-destructive. The description adds significant behavioral context: walking child markets, checking order constraints, performing partition checks, Jaccard similarity filtering, placeholder filtering, and fill checks against live order book depth. No contradictions with annotations.

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

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

The description is comprehensive but slightly lengthy. It is well-structured with clear sections for each mode and technical details. Every sentence adds value, though a small reduction in verbosity could improve conciseness.

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

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

Given the tool's complexity (multiple modes, internal checks, fallback references), the description is fully complete. It explains output format (opportunities array, partition_check object), conditions for non-trade, and mentions related tools. No output schema is needed as the description covers the return structure.

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

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

Both parameters are described in the schema with 100% coverage. The description adds further meaning, explaining the mode each parameter triggers, providing example inputs, and detailing internal logic such as event slug or topic seed question usage.

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

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

The description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes three modes (no args, event, topic) with specific explanations, and differs from siblings like polymarket_edges and 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?

The description provides explicit guidance: when to use each mode, what arguments to pass, and conditions to avoid trading (if realizable_edge_pp ≤ 0). It also references the alternative tool polymarket_fill_risk for custom sizing.

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

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

The description adds significant behavioral context beyond what annotations (readOnlyHint, idempotentHint, openWorldHint) provide. It explains internal model families, response segmentation (by_segment), tradeable-edge knobs, and caching behavior. No contradiction with annotations; all disclosed behaviors are consistent with a read-only, idempotent operation.

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

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

The description is comprehensive but verbose, diving deep into model details (e.g., 'lognormal barrier from 90d FRED log-returns') that may not be necessary for tool usage. While front-loaded with purpose, it could be trimmed by ~30% without losing essential guidance. Structure is present but not lean.

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

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

Given the tool's complexity (9 parameters, no output schema), the description fully covers return format (by_segment, diagnostics, edge fields), parameter effects, and edge cases (e.g., Fed candidates excluded, filter skips explained). It also mentions caching and slippage assumptions. No gaps identified.

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

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

Input schema has 100% description coverage, so baseline is 3. The description adds value by grouping parameters under 'TRADEABLE-EDGE KNOBS' and explaining their interplay (e.g., how min_liquidity and max_spread_pp drop opportunities, how min_partition_leg_kelly applies to per-leg Kelly). This holistic context improves decision-making beyond individual 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: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It specifies the verb (scan/return) and resource (Polymarket edges opportunities), and distinguishes from sibling tools like polymarket_arbitrage by focusing on Pipeworx disagreement.

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

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

The description provides clear context for when to use the tool: 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets.' It implies daily scanning use case but does not explicitly exclude or compare with sibling tools like polymarket_arbitrage. No when-not instructions, 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 declare read-only, idempotent, non-destructive behavior. The description adds key behavioral details: snapshot-based, 60-day TTL, decay computed from daily closes (not intraday), and snapshot date gaps. No contradiction.

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

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

The description is dense and includes all relevant info, but is a single long paragraph that could be better structured for readability. It is front-loaded with 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?

Without an output schema, the description fully explains return fields ('tracked', 'expired', 'snapshot_dates') and their structure, plus limits and caveats. Complete for a read-only tool.

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

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

Schema coverage is 100% with descriptions, and the description adds context: default and max values for 'days', default for 'window', and explanation of snapshot families, enhancing 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 the tool's purpose with a specific verb ('track') and resource ('edges'), and distinguishes it from siblings like 'polymarket_edges' by focusing on persistence and decay over time.

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

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

The description implicitly indicates when to use this tool (when needing edge longevity and trend data) but lacks explicit contrast with alternatives like 'polymarket_edges' for raw snapshots.

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

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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds behavioral details beyond these: it explains that the tool walks the ladder, returns verdicts (clean|degraded|cannot_fill), and warns about thin books and forced directional risk. It does not contradict the annotations.

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

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

The description is well-structured with clear sections for SINGLE-MARKET and BASKET modes. It uses an efficient style with bold headings and concise parameter descriptions. While slightly long, every sentence adds necessary detail, and there is no redundancy.

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

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

Given the complexity of two operation modes and lack of output schema, the description is remarkably complete. It covers all return fields (top_of_book, vwap_fill_price, slippage_pp, etc.), explains the logic for verdicts, and warns about risks. The agent has all information needed to understand the tool's behavior and outputs.

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 detailed parameter descriptions. The description adds value by explaining how `size_usd` is interpreted differently in single-market vs basket mode, and how `side` defaults are determined in basket mode. This enriches the schema information.

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: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It explicitly distinguishes two modes (single-market and basket) and references sibling tools polymarket_arbitrage and polymarket_edges, differentiating its role as a pre-action risk check.

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 usage guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains why (theoretical overround not capturable, partial fills lead to directional risk) and states the requirement for either `market` or `event`.

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

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

Discloses key behavioral traits beyond annotations: two modes, response structure (leg prices, matched spread, safety fields), and detailed explanation of compatibility_warning triggers (non-equivalent bet shapes, semantically unrelated events). Annotations (readOnlyHint=true) are consistent; 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 (TWO MODES, RESPONSE, SAFETY FIELDS) and front-loaded purpose. Slightly verbose with parenthetical details, but every sentence adds value. Could be more concise, but effective overall.

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

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

Despite no output schema, the description thoroughly explains the response (leg-by-leg prices, spread[], safety fields like compatibility_warning, temporal_alignment, skipped counters). Covers edge cases and behavior for mismatches, providing complete contextual information for a tool with 3 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% with clear descriptions, but the description adds value by explaining the topic list (10 shortcuts), explicit ticker format with examples, and override behavior. This extra context elevates understanding beyond the schema alone.

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

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

The description clearly states 'Cross-venue spread between Kalshi and Polymarket for the same resolving question,' specifying the verb ('spread') and resources (Kalshi, Polymarket). It distinguishes from siblings like polymarket_arbitrage (intra-Polymarket) and provides a unique value proposition.

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?

Describes two usage modes (topic shortcuts vs explicit tickers) and explicitly warns that most pre-mapped topics return compatibility warnings, advising caution. Implicitly differentiates from sibling tools by focusing on cross-venue spreads, and explains when spreads are meaningless (temporal misalignment).

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds scoping info (anonymous IP, key hash, account ID) and behavior when key omitted (lists all). 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?

Description is 4 sentences, front-loaded with action. Every sentence adds value: purpose, usage examples, scoping, and pairing with siblings. No fluff.

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

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

Given one optional parameter and no output schema, description covers purpose, usage, scoping, and relationship to siblings. Lacks explicit mention of return format (value or list), but inferred from context.

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

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

Schema coverage is 100% with a single parameter, well-described. Description adds 'omit the key argument' for listing all keys, which reinforces schema. Marginal value beyond schema.

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

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

The description clearly states the tool retrieves values saved via 'remember' or lists all keys. It specifies the verb (retrieve/list) and resource (saved values/keys), and distinguishes from sibling tools like 'remember' and 'forget'.

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

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

Provides clear context for when to use: looking up stored context like ticker, address, notes. Mentions scoping and pairing with remember/forget. However, lacks explicit 'when not to use' or direct alternatives beyond implicit pairing.

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, and non-destructive. Description adds value by explaining the mark_read parameter's state-changing behavior (flags events as read for subsequent calls) and notes feed persistence, 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?

Four sentences front-loaded with purpose, then parameter details, and a note on alternative access. No redundancy or fluff; every sentence adds useful 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, so description must explain return values—it does by listing fields (source, citation_uri, raw payload). Covers parameter behaviors (mark_read) and notes feed ordering. Missing pagination details, but limits parameter implies control. Sufficient for the tool's complexity.

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

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

Schema coverage is 100%, so baseline is 3. Description adds meaning for type, since, and mark_read (e.g., 'Flag the returned events read in the same call'), going beyond schema descriptions. Limit and unread_only are already clear in schema, so description adds no extra for them but overall adds 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 the tool pulls fired events from a subscription feed, specifies returned fields (source, citation_uri, raw event payload), and distinguishes from sibling tools like list_subscriptions and subscribe by focusing on event retrieval rather than subscription management.

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: polling works, same feed available via direct URL. Implicitly guides when to use (retrieving alerts) and offers filtering by type and since. Lacks explicit when-not-to-use or alternatives, but context is sufficient.

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

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

Discloses fan-out to multiple sources, fallback logic (GDELT→GNews), USPTO soft-fail. Annotations already show safe read, but description adds rich behavioral details beyond annotations.

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

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

Single paragraph well-structured with front-loaded examples. 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?

Tool has 3 required params, no output schema, but description explains return structure (changes grouped by source, total_changes, citation URIs). Completes context 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 covers 100% parameters with descriptions. Description adds examples, relative shorthand explanation, and CIK format, enhancing understanding beyond schema.

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

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

Description explicitly states it returns change feed for a company with SEC filings, news, patents. Provides example queries and distinguishes from sibling tool entity_profile.

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

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

Gives clear when-to-use guidance with examples and differentiates from entity_profile. Provides parameter recommendations (use '30d' or '1m' for typical monitoring). Could include when not to use.

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

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

Beyond annotations, describes scoping by identifier, persistence duration (24h for anonymous, permanent for authenticated), and no side effects (destructiveHint=false).

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

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

Two sentences, front-loaded purpose, no redundant 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?

Complete for a simple key-value store: covers persistence, scoping, pairing with siblings; no output schema 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?

Schema covers both parameters (100%), but description adds concrete examples for key and explains value as arbitrary text, exceeding baseline.

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

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

Clear verb+resource ('Save data for reuse'), explicitly distinguishes from sibling tools recall and forget, and specifies cross-session context.

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

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

States when to use ('when you discover something worth carrying forward'), provides alternative tools (recall, forget), and explains pairing.

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, etc. The description adds behavioral context: each call cascades through several lookup endpoints internally, and it auto-disambiguates company names. No contradictions, and the additional context is valuable beyond annotations.

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

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

Description is detailed but well-structured with examples and bullet-like formatting. Every sentence adds value, though slightly verbose. Could be trimmed without losing clarity, but overall efficient.

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

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

Despite no output schema, the description fully explains return value structure for each supported type, including citation URIs. It covers input variations and internal cascading logic, making the tool's behavior fully understandable for an agent.

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

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

Schema coverage is 100%, but description adds rich semantics: for 'company' returns ticker+10-digit CIK+company_name+citation URI; for 'drug' returns RxCUI+ingredient+brand. It also clarifies acceptable input formats (ticker, CIK, name) and mentions auto-disambiguation, going well beyond schema.

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

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

The description clearly states the tool resolves user-spoken names to canonical identifiers, using strong verb+resource ('resolve entity'). It distinguishes from sibling tools by explicitly advising 'Use FIRST whenever you have a name but need an ID.' Example queries reinforce 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?

Explicitly states when to use: 'whenever you have a name but need an ID'. Provides example queries and notes that it replaces 2-3 manual lookups, giving clear context for tool selection.

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

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

The description adds valuable behavioral details beyond annotations (e.g., probing each entity, ranking, output fields like score/confidence/signal density), with no contradictions to the annotations.

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

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

Four well-structured sentences front-load the purpose, then mechanism, use case, and output. Every sentence is essential and efficiently phrased.

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

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

Despite no output schema, the description explains the return format. Parameter complexity is handled with both schema and additional context. The tool is well-positioned among 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 the description adds meaningful context: first entity as subject, default model, and example for context, 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 compares AI visibility across multiple entities side-by-side, specifying the process (probes with ai_visibility_check, ranks by score) and distinguishing it from single-entity checks.

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

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

The description explicitly frames the tool for competitive AI-marketing audits and indicates the first entity is treated as subject, but does not explicitly exclude single-entity use or mention sibling tools like ai_visibility_check as alternatives.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. Description adds valuable behavioral context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list if timeout. No contradictions.

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

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

Description is a single dense paragraph that front-loads the main purpose. Every sentence is informative, but could be more structured (e.g., bullet points). Minimal fluff.

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

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

Despite no output schema, description fully specifies return fields (summary block, per-advisory detail, links, recent versions) and behavior (partial failures, timeout handling). Covers ecosystem scope and version defaults comprehensively.

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 both parameters well-documented. Description does not add significant meaning beyond the schema, as it repeats the same info. 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?

Description clearly states it is a composite check for npm packages across deps.dev and bundlephobia, listing exact checks (license, advisories, version history, bundle size, etc.). It distinguishes from siblings by specifying npm-only scope and mentioning alternatives for other ecosystems.

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

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

Explicitly states when to use: 'whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me''. Also specifies when not to use: for PyPI/Maven/Cargo/Go, use deps.dev:version 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?

Description adds significant behavioral details beyond annotations: uses BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, 200K char cap with truncation flag. Annotations (readOnlyHint, idempotentHint) are supported and not contradicted.

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 front-loaded sentences covering purpose, use case, and technical details. Efficient but slightly wordy in technical specifics; no wasted sentences.

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

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

No output schema, but description explains return structure (passages with offsets and similarity scores). Covers embedding model, windowing, truncation, pairing with sibling tool. Complete for the tool's complexity.

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

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

Schema coverage is 100%, so baseline is 3. Description reinforces parameter purpose with examples (e.g., query examples like 'supply-chain risk') and clarifies limits (text max chars). 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 'Semantic search INSIDE a fetched record' with specific verb and resource. It distinguishes from sibling tool 'ask_pipeworx_grounded' by noting that search_within returns passages and offsets for verification, while the sibling grounds over those passages.

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

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

Explicitly says 'Use when the record is too big to cram into the prompt' and pairs with ask_pipeworx_grounded for grounding. Provides examples of queries. Lacks explicit when-not-to-use or alternatives, but context is clear enough.

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 behaviors beyond annotations: returns a new subscription id, delivery channel behaviors (feed always on, SMS cap, webhook signing secret returned once), and auto-disabling of webhook after 10 consecutive failures. It also specifies that the webhook response includes a one-time signing secret. These details are not in the annotations and provide essential operational 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 front-loaded with the purpose and is well-structured, but it is fairly long (three sentences with dense information). While every sentence adds value, the length might be slightly reduced without losing clarity. The organization is logical: purpose, then types with examples, then delivery channels. Minor penalty for length, but overall concise given the tool's complexity.

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

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

Given the tool's complexity (3 parameters, nested objects, 5 subscription types, multiple delivery options), the description covers the essentials: purpose, parameter semantics with examples, prerequisites (OAuth account), behavior (returns id), and limitations (SMS cap, webhook auto-disable). It lacks explicit mention of idempotency or output schema (though it states returns id). The sibling context is not needed in the description. It is complete enough 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?

The description adds substantial meaning beyond the input schema. It provides concrete JSON examples for each subscription type (e.g., sec_8k params: {ticker, items}, polymarket_edge: {topic, min_spread_bps}). For the delivery parameter, it explains email/SMS/webhook sub-fields with constraints (SMS must match verified phone, webhook HTTPS only, HMAC signature verification). Schema coverage is 100%, but the description's examples and behavioral notes significantly enhance understanding.

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: 'Create a proactive monitoring subscription to a live-data event stream.' It specifies the resource (subscription) and action (create), and provides explicit examples of supported types (sec_8k, polymarket_edge, etc.), distinguishing it from related tools like list_subscriptions, unsubscribe, and recent_alerts by focusing on creation.

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

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

The description provides explicit when-to-use guidance, including the requirement for a Pipeworx OAuth account and that anonymous/BYO accounts cannot persist subscriptions. It details delivery channels (feed always on, optional email/sms/webhook) with constraints (SMS 10/day cap, webhook auto-disable after 10 failures, phone verification needed). This helps the agent decide when the tool is applicable and what prerequisites exist.

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. Description adds context about returning example questions with tool+argument shapes, which goes beyond annotations by describing the output's nature and structure.

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 earns its place. It starts with purpose, then lists categories, then explains usage. Could be slightly more structured, but overall efficient for the amount of information conveyed.

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

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

Given no output schema, the description thoroughly explains what is returned: category-bucketed example questions with exact tool+argument shapes. This is complete for an onboarding tool, covering both what and how it helps the 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%, and the schema already describes the topic parameter. Description adds value by explaining what happens when omitted ('full spread') and when a topic is provided ('focus'), plus listing example values.

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

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

Description clearly states the tool is an onboarding entry point that returns category-bucketed example questions. It uses specific verbs ('returns') and resource ('example questions'), and distinguishes from siblings like ask_pipeworx and discover_tools.

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

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

Explicitly says to use it FIRST when you don't know what Pipeworx can do. Also explains when to omit or use the topic parameter, and mentions that it teaches how to call meta-tools.

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

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

Discloses ownership enforcement and clarifies that the row is deactivated (not deleted) to preserve historical events. Adds value beyond annotations by explaining the mutation and data lifecycle.

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 waste. First sentence captures the core action, second adds crucial constraints and effects.

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

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

For a simple one-parameter tool with strong annotations, the description covers purpose, ownership, and data change behavior comprehensively.

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%, missing only the id parameter. The description mentions 'by id' but does not add further detail beyond the schema's existing description.

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

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

Clearly states the action 'Cancel a subscription by id.' Distinguishes from siblings like 'subscribe' and 'list_subscriptions' by specifying the cancellation 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?

Provides ownership enforcement ('you can only cancel your own subscriptions') and explains the effect (deactivation not deletion). Does not explicitly contrast with alternatives but offers clear context.

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

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

Despite annotations already providing readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, the description adds substantial behavioral details: it describes the two processing paths (SEC EDGAR + XBRL vs grounded), the verdict types ('confirmed', 'approximately_correct', 'refuted', 'inconclusive', 'unsupported'), and that it returns citations. 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 somewhat long but well-structured with front-loaded intent and subsequent details. Each sentence conveys important information without redundancy. Could be slightly more concise, but overall efficient.

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

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

Despite no output schema, the description adequately explains the return value (verdict, actual value with citation, reasoning). For a tool of this complexity with rich annotations and schema, the description is comprehensive and leaves no ambiguity about what the tool does and returns.

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

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

Schema description coverage is 100%, but the description adds meaning beyond the schema: it explains that 'tolerance_pct' overrides the implied tolerance, suggests a range (0.5–50), and provides use-case guidance (set 1–2 for hallucination detection). This adds significant value for parameter usage.

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

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

The description clearly states the tool verifies natural-language factual claims against authoritative sources, with examples like 'Is it true that...' and 'fact check'. It distinguishes from siblings by explaining it replaces 4-6 sequential calls and covers both structured SEC data and other factual claims.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains when to use the structured path vs the grounded pipeline, providing clear context. However, it does not explicitly state when not to use the tool, which would be the only improvement.

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