Manifold

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds critical behavioral context: the default model is free, while probing Anthropic requires a BYO API key with direct payment to Anthropic. It also describes the return structure (per-model + combined view), which adds value 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 a single paragraph of four sentences, front-loading the main purpose and then detailing options and return values. Every sentence serves a purpose without redundancy.

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

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

Given 4 parameters, no output schema, full schema coverage, and annotations present, the description covers purpose, parameters, use cases, cost implications, and return structure (per-model + combined view). It is fully self-contained for an agent to decide and invoke the tool correctly.

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

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

Schema coverage is 100%, so parameters are well-documented. The description adds meaning by clarifying the default model for the 'models' parameter and explaining that '_apiKey' is only needed for Anthropic. It also notes that 'context' helps disambiguate, which supplements the schema description.

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

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

The description clearly states the tool probes one or more LLMs about a business/brand/product/topic and scores visibility (0-100). It specifies the default model and optional Anthropic probe, distinguishing it from siblings like 'scan_competitor_ai_presence' which may be more focused. The verb 'probe' and resource 'visibility' are specific.

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

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

The description explicitly mentions use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also clarifies when to use the optional `_apiKey` parameter (if probing Anthropic). However, it does not explicitly state when not to use this tool or compare directly to similar siblings.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. Description adds that it routes questions, returns structured answers with pipeworx:// citation URIs, and handles authoritative data. No contradictions.

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

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

Front-loaded with key recommendation, but the list of domains and examples is lengthy. Could be trimmed slightly while retaining clarity.

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

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

Given no output schema and broad applicability, the description effectively covers scope, behavior, and examples. Missing error handling or limitations, but sufficient for a wide-ranging 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%, but description adds value by providing examples and clarifying the single question parameter accepts natural language, including aliases. Enhances understanding beyond the schema.

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

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

The description clearly states the tool answers questions about current/historical data from 3,770 tools across 893 verified sources, with a directive to prefer over web search. It distinguishes from siblings like deep_research by specifying structured data and citations.

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 recommends over web search for factual questions, lists example queries and domains. Does not mention alternatives among siblings like deep_research, but the context is clear enough for 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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds valuable behavioral context: hallucination-resistance, explicit refusal reasons, returns evidence, and cost trade-off. 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?

Description is well-structured with front-loaded key phrase. Each sentence adds value: explains process, return format, refusal reasons, and usage guidance. Could be slightly shortened but overall concise for the information provided.

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 the tool (routing, extraction, refusal modes), the description is complete. It explains return format with example fields, all possible refusal reasons, and usage context. No output schema exists, but return values are described sufficiently.

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 being aliases for 'question'. Description mentions natural language question and lists aliases already present in schema. Adds no further semantic meaning beyond schema, so baseline 3 is appropriate.

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

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

The description clearly states it provides a hallucination-resistant answer mode for high-stakes reads. It specifies the process: picks the right tool, fills arguments, fetches data, and extracts answer using only tool result. It distinguishes from sibling ask_pipeworx by emphasizing groundedness. The return format and refusal reasons are explicitly listed.

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

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

Explicitly states when to use: when answer will be quoted, cited, or acted on, and agent must not invent facts (e.g., financial verdicts, legal claims). Also advises preferring ask_pipeworx for casual lookups due to extra cost. Clear alternatives and context provided.

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

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

Annotations indicate readOnly, idempotent, non-destructive. Description adds extensive behavioral context: resolver contract with match confidence, parent event extractor, news fallback fields, safety short-circuits on low confidence, closed market handling, wide-spread warnings, and resolution-rule risk. No contradictions.

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

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

The description is long but well-structured with section headers and examples. It is front-loaded with core purpose and each section adds value. While comprehensive, it could be slightly more concise.

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

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

Despite no output schema, the description thoroughly details response shapes, error states, edge cases, fallback mechanisms, and safety checks. Covers input, processing, output, and error handling for a complex tool.

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

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

Schema coverage is 100%. Description adds significant meaning: explains 'market' accepts slug/URL/question, 'depth' enum with default behavior, and 'include_raw' with usage guidance and size implications.

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 researches a Polymarket bet by pulling Pipeworx data. It specifies input types (slug, URL, question text) and distinguishes from siblings like 'search_markets' and 'get_market' through its specific focus on research with evidence packet and market-vs-model comparison.

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

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

The description explicitly states 'Use for should I bet on X, what does the data say about Y, or is there edge in Z' and provides numerous examples. It implies appropriate contexts but does not explicitly exclude use cases or compare to alternative tools like 'validate_claim'.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, indicating safe, non-destructive, idempotent operation. The description adds that results are sorted by primary metric, includes citation URIs, and handles off-calendar fiscal years correctly. These details go beyond annotations and provide helpful behavioral context.

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

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

The description is relatively long but every sentence provides unique value. It is front-loaded with example user queries that help the agent recognize when to call this tool. It could be slightly more terse, but overall it is efficient and well-structured.

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

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

Given the tool's complexity (two entity types, multiple data sources, no output schema), the description covers what data is returned for each type, sorting behavior, and citation URIs. It does not address error handling or edge cases, but for a comparison tool with these parameters, it is sufficiently complete.

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

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

Schema coverage is 100% with both parameters described. The description enriches the meaning: for type, it explains the exact data fields pulled (e.g., 'LATEST 10-K revenue + net income + cash + long-term debt' for company; 'FAERS adverse-event counts, FDA approval counts, active trial counts' for drug). For values, it adds context about minItems/maxItems and examples. This adds value beyond the schema, so a 4 is appropriate.

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

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

The description starts with numerous typical user queries ('Compare X and Y', 'X vs Y', 'which is bigger', 'rank these companies') and explicitly states 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call'. It details what data is pulled for company (financial data from SEC EDGAR/XBRL) and drug (FAERS, FDA approvals, trials). This distinguishes it from sibling tools like entity_profile (single entity) and get_market (financial markets).

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

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

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and mentions it 'Replaces 8–15 sequential lookups', guiding the agent to use this tool for comparisons instead of multiple single lookups. It also clarifies when to use company vs drug type based on the entities being compared.

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 parallelism, grounding with verbatim evidence and stable citations, explicit gaps, and expected duration (15-60s). Adds context beyond annotations (e.g., no invention, decomposition process). Could mention potential latency or tool count more explicitly.

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 core value, efficient. Each sentence adds distinct information. Slightly dense but 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?

Despite no output schema, description adequately describes return format: structured findings packet with verbatim evidence, confidence, source, citation, gaps. Covers key behavioral aspects for a complex tool. Minor omissions like pagination or error handling.

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 meaningful detail: depth values correspond to facet counts (3,5,8) and links 'thorough' to paid plans, and clarifies that broad questions are acceptable. Useful additions beyond schema.

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

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

Clearly states it performs grounded multi-source research in one call, decomposing questions into sub-questions and routing to 3,770 tools across 893 sources in parallel. Distinctly differentiates from siblings like ask_pipeworx for single lookups.

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

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

Explicitly recommends for broad or multi-part questions and directs to use ask_pipeworx for single lookups. Mentions account requirements and paid plan for 'thorough' depth, giving clear when-to-use and when-not-to-use guidance.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that it returns top-N tools with schemas and examples, ready to call directly. No contradictions.

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

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

The description is informative and front-loaded with purpose, but slightly long. Every sentence adds value, but could be tightened slightly.

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

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

Given no output schema, the description adequately explains return content (top-N tools with names, descriptions, schemas). Covers query, limit, and usage. Seems complete for a search/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 description coverage is 100% with parameter descriptions. The description adds value by explaining query aliases, limit defaults, and examples, and that results include input schemas.

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 tools by describing data or task, lists many domains, and explains it returns top-N relevant tools with full schemas. This distinguishes it from siblings as a meta-tool for 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 explicitly says 'Call this FIRST when you have many tools available and want to see the option set', providing clear usage context. It lacks explicit when-not-to-use or alternatives, but the guidance is sufficient.

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

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

Annotations already declare safety (readOnly, idempotent, not destructive). Description adds detail on parallel fan-out across sources, specific return fields, and known limitation (patents soft-fail on May 2025). No contradiction.

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

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

Description is well-structured with examples and line breaks for readability. Slightly verbose with full source list but front-loaded with example phrases. Efficient for the complexity.

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

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

Despite no output schema, description thoroughly explains all returned data fields (CIK, filings, fundamentals, patents, news, LEI) and notes limitations (patents soft-fail, name requirement). Parameters are fully explained.

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

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

Schema has 100% coverage and description reinforces that type must be 'company', value as ticker/CIK, and repeats that names not supported. Provides context beyond schema by explaining conversion and alternatives.

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 'full cross-source profile of a US public company in ONE parallel call' and lists specific outputs, distinguishing from sibling tools that do single-source lookups.

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

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

Explicitly says when to use (holistic view) and when not (names not supported, must have ticker/CIK), and advises to prefer over chaining, with alternative 'resolve_entity' mentioned.

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 destructiveHint=true and idempotentHint=true. Description adds context about clearing sensitive data, but not much beyond annotations.

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

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

Two sentences: first states core purpose, second provides usage guidance and pairing. No redundant words, 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?

Simple tool with one parameter and clear annotations. Description covers purpose and usage adequately. Could mention outcome of deletion (e.g., memory permanently removed), but not necessary.

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

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

Only one parameter 'key' with schema description 'Memory key to delete'. Schema coverage is 100%, and the tool description adds no extra 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?

Clear verb 'Delete' and resource 'previously stored memory by key'. Distinguishes from siblings 'remember' and 'recall'.

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

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

Explicitly states when to use: when context is stale, task done, or to clear sensitive data. Also recommends pairing with remember and recall. No exclusions provided.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds that it fetches and extracts page content without side effects, reinforcing safe read-only 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?

Three sentences, front-loaded with purpose, then details, then use cases. No redundant 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?

Even without output schema, describes output as 'standard llms.txt markdown format' and 'single text blob'. Annotations cover safety. Complete for a simple read-only tool with only two params.

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

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

Schema coverage is 100% for both parameters. Description adds minimal extra: 'fetches the page, extracts...' for URL and restates default/max for max_links already in schema. No significant additional meaning.

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

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

Description clearly states the tool generates a production-ready llms.txt file for any URL, with specific verb and resource, and lists actions (fetch, extract, emit). It is distinct from sibling tools, which are about research, market queries, etc.

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

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

Explicitly mentions three use cases (client site indexing, own project drafting, competitor auditing). No explicit 'when not to use' 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 adds valuable context beyond annotations: it explains the return content (plain-text description, creator, volume, liquidity, resolution) and special behavior for multiple-choice markets (top 12 answers with probabilities). It also states 'Keyless', implying no authentication required, which is not in annotations.

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

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

The description is extremely concise, using two sentences to convey purpose, input constraints, and key features. It is front-loaded with the main action and resource.

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

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

For a read-only tool with no output schema, the description reasonably covers return fields and behavior. It lacks mention of error handling or field optionality, but given the tool's simplicity, it is adequate.

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

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

Schema description coverage is 100%, so the schema already documents both parameters. The description adds only the constraint 'exactly one', which is useful but minimal. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'Get' and the resource 'full detail for a single Manifold market', specifying the input method (URL slug or id). It distinguishes from sibling tools like 'search_markets' by focusing on a single market.

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

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

It explicitly states 'Provide exactly one of slug/id', giving a clear usage constraint. However, it does not name alternative tools or contexts where this tool should not be used.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description supplements by specifying the scope (caller's subscriptions) and the default of including only active ones. 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 efficient sentences with no extraneous information. The verb and resource are upfront, and 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?

The description lists all return fields, which compensates for the lack of output schema. It does not mention pagination or limits, but given the simple nature of the tool, it is largely complete.

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

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

Schema coverage is 100% as the only parameter has a description. The tool description does not add additional semantic detail beyond what the schema provides, so 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 lists the caller's active subscriptions and enumerates returned fields. The title 'List Subscriptions' and description align, and it is distinct from sibling tools like subscribe and unsubscribe.

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

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

Explicitly tells when to use: 'review what you're monitoring before adding more or to find an id to cancel'. This gives clear context and purpose., effectively guiding the agent.

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

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

Beyond annotations (which indicate non-read-only, non-idempotent), the description adds important behavioral traits: rate-limited to 5 per identifier per day, free, doesn't count against quota, and the team reads digests daily. This gives the agent full awareness of usage constraints.

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 then provides usage guidelines and behavioral notes. It is slightly long but each sentence serves a purpose. No wasted words.

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

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

For a feedback tool with 3 parameters, no output schema, the description covers all necessary aspects: what the tool does, when to use, how to format input, and behavioral constraints. The context of sibling tools is not needed for completeness.

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

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

With 100% schema description coverage, the description still adds value by explaining the enum values (e.g., bug = something broke or returned wrong data) and reinforcing the message constraints (be specific, 1-2 sentences, 2000 chars max). Also warns against pasting end-user prompts.

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 is for sending feedback about broken, missing, or needed features. It distinguishes itself from sibling tools by being a feedback mechanism rather than a data retrieval or manipulation tool.

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

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

Explicitly specifies when to use (bug, feature, data_gap, praise) and provides a clear instruction to describe issues in terms of Pipeworx tools/packs, not paste end-user prompts. This helps the agent decide appropriately.

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

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

Annotations already mark it as read-only, open-world, idempotent, and non-destructive. The description adds that data is self-aggregating from CF analytics-engine, no PII, and caching details (5min-1h). No contradiction.

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

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

Two well-structured paragraphs: first states purpose and output, second lists use cases and extra details (caching, privacy). Every sentence adds value. 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 aggregation tool with one parameter, the description covers purpose, use cases, caching, and privacy. Missing detailed return format (e.g., ordering) but still relatively complete. Minor gap prevents a 5.

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 'window' already explains the different time windows. The description repeats the same info, adding no new meaning. Baseline 3 is appropriate.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a window, with specific use cases. It distinguishes from siblings like ask_pipeworx and discover_tools by focusing on trending usage from other agents.

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

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

Three explicit use cases are listed, covering discovery, canonical tool confirmation, and alignment checking. No explicit when-not or alternatives, but the context is clear. Sibling tools are listed separately for comparison.

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, not destructive. Description adds significant behavioral context: detection algorithm details, semantic anchor (Jaccard similarity), partition filter (placeholder slugs), fill check against live CLOB depth, and trading signals. However, slightly redundant 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 and dense with technical detail. It front-loads the required condition but then dives into algorithm specifics. Could be more structured with bullets or sections. Adequate but not concise.

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

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

Given no output schema, the description compensates by detailing response format (opportunities[], fields, partition_check, fill_check). Covers edge cases (no args fail, low similarity, placeholder filtering, fill check result). Provides complete context for an AI agent.

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

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

Schema coverage is 100%, but description adds extensive meaning beyond schema: explains event vs topic modes in detail, acceptable values (slugs, URLs, seed questions), behavior differences, and semantic conditions. Far exceeds 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 finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes two modes (event and topic) and differentiates from sibling tools like polymarket_fill_risk, polymarket_edges, etc.

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

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

Explicitly requires one of event or topic and warns that no args fails. Gives clear guidance on when to use each mode: event recommended for a specific market, topic for cross-event scanning. Mentions alternatives like 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds significant context: caching at KV level, detailed model family explanations, edge computation, and output structure, all consistent with annotations. No contradiction.

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

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

The description is long but appropriately densely packed with necessary details for a complex tool. It is well-structured, starting with purpose, then model families, response structure, and parameter guidance. Some redundancy could be trimmed, 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 the response top-level structure (by_segment, fed_candidates, _diagnostics), caching, filter skips, and edge cases. It is comprehensive for a tool of this 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 the schema already documents all parameters. The description adds extra value by explaining usage nuances (e.g., why min_partition_leg_kelly is needed for partition arbs) and providing default behaviors and recommendations.

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

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

The description clearly states the tool scans Polymarket markets and returns opportunities where Pipeworx data disagrees with market price. It uses specific verbs ('scan', 'return') and distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edge_tracker by focusing on edge opportunities with model families.

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 targets 'what should I bet on today' use case and provides extensive guidance on parameter knobs. However, it lacks explicit when-not-to-use instructions or direct mention of alternatives, though the context is clear for an AI agent.

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

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

Annotations (readOnlyHint, idempotentHint, destructiveHint) are consistent and the description adds behavior beyond them: snapshots are written on cache-miss, history depth bounded by 60-day TTL, decay from daily closes not intraday. No contradictions.

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

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

The description is front-loaded with purpose and detailed response structure. Slightly verbose (e.g., explaining tracked, expired, snapshot_dates arrays in full), but each sentence contributes value. Well-structured for quick parsing.

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 compensates by detailing the response structure: tracked[] with time-series and trend, expired[] with lifespan_days, snapshot_dates[]. Also notes limitations (history depth, decay source). Very complete.

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

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

Schema coverage is 100% for both parameters, and the description adds significant semantic context: default values (days=14, window='1wk'), clamping (days clamp 2-30), and the meaning of window as 'snapshot family'. This exceeds schema-only 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 provides 'edge persistence and decay telemetry' from daily snapshots, answering 'how long has this edge existed and is it shrinking?'. It distinguishes itself from sibling tools like polymarket_edges by focusing on time-series behavior rather than raw edges.

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

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

The description gives explicit context: 'a fresh wide edge and a 3-week-old wide edge are different trades', guiding when to use this tool. It doesn't explicitly list alternatives, but the context implies comparisons with polymarket_edges and others. Clear usage intent is provided.

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, so the agent knows it's a safe read. The description adds behavioral details like return fields (verdict, slippage, etc.) and warnings about thin books, but does not disclose rate limits or other behaviors. The description adds useful context beyond annotations.

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

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

The description is long but well-structured with sections for each mode and clear return fields. However, it is somewhat verbose; could be trimmed while retaining all necessary information.

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

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

Given the tool's complexity (two modes, no output schema), the description is highly complete: it explains required parameters, defaults, modes, return structure (including specific field names), and provides usage warnings. Without an output schema, the description fully conveys what the agent needs to know.

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

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

Schema coverage is 100%, so parameters are already described. The description adds extra context: e.g., for size_usd, 'max spend on buys, target proceeds on sells'; for side, 'default auto' for basket mode. This goes 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 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes two modes (single-market and basket) and contrasts with sibling tools like polymarket_arbitrage and polymarket_edges, specifying that it should be used before acting on their signals.

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

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

Explicit usage guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains why (thin books make theoretical overround uncapturable, partial fills create directional risk).

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds extensive behavioral context, including compatibility_warning conditions, temporal alignment, and skipped cross types, going beyond annotations.

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

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

The description is long but well-structured with clear sections for modes, response, and safety fields. Every sentence adds necessary information, though it could be slightly more concise.

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

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

Given the complexity of the tool (cross-venue, multiple modes, compatibility warnings, response structure), the description covers all aspects thoroughly. Without an output schema, it explains the response fields in detail.

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 3 parameters. The description adds value by explaining the two modes and the override behavior, but the schema already provides adequate 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 computes the spread between Kalshi and Polymarket for the same resolving question. It distinguishes itself from siblings like polymarket_arbitrage by focusing on cross-venue comparison.

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

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

Explicitly describes two modes (topic shortcuts and explicit tickers), when to use each, and warns that most pre-mapped topics yield compatibility warnings, making them not tradeable. Provides clear guidance on when not to use.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds scoping detail ('Scoped to your identifier') which is useful beyond 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?

Three sentences covering purpose, usage, and scoping. Each sentence adds value, though could be slightly more concise.

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

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

Given no output schema, the description explains return behavior (list vs single value) and scoping. With a single optional parameter, it is fully complete.

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

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

Schema coverage is 100% and the description clarifies that omitting the key lists all keys, which adds meaning beyond the schema definition.

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

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

The description states specific verbs ('retrieve' or 'list') and the resource ('previously saved via remember'). It distinguishes from sibling tools 'remember' and 'forget' by clearly defining its own role.

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

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

Explicitly says when to use ('look up context the agent stored earlier') and pairs with sibling tools ('remember' and 'forget'). Does not explicitly state when not to use, but context is clear.

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

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

The description adds behavioral context beyond the annotations: it explains that setting mark_read:true flags events as read so subsequent calls return only newer ones, and it lists the fields each event carries. This supplements the readOnlyHint and idempotentHint 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 (five sentences) and front-loaded: the first sentence states the overall purpose, the second details return fields, the third covers filtering, the fourth explains mark_read, and the fifth provides an alternative access method. Every sentence earns its place with no redundancy.

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

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

The description covers the essential aspects: what is returned, how to filter, the effect of mark_read, and an alternative access method. It lacks mention of error handling or edge cases like empty results, but for a read-only, idempotent tool with a simple interface, this is mostly 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?

With 100% schema description coverage, the schema already documents all five parameters. The description adds meaningful context for mark_read by explaining the consequence ('so the next call only shows newer ones'). This goes beyond the schema's description, justifying a score above the baseline of 3.

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

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

The description clearly states the tool pulls fired events from the subscription feed and returns the most recent alerts with specific fields (source, citation_uri, raw payload). It distinguishes itself from sibling tools like list_subscriptions or subscribe by focusing on recent alerts from the feed.

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

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

The description provides context for when to use the tool by mentioning polling works fine and that the same feed is available via a direct GET endpoint for scripts and dashboards. It doesn't explicitly state when not to use this tool versus alternatives, but the alternative is clear.

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

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

Annotations already indicate read-only and idempotent. The description adds significant behavioral context: it fans out to SEC EDGAR, GDELT→GNews fallback, and USPTO with a notice about PatentsView API sunset. It also describes the return structure with grouped changes and citation URIs.

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

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

The description is relatively long but each sentence serves a purpose. It front-loads the most common use cases and then details mechanics. Could be slightly more concise, but it is well-structured and not verbose.

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

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

Given no output schema, the description adequately explains the return format (structured changes grouped by source, total_changes count, citation URIs). It also covers the fallback behavior and API status, making the tool fully usable without additional context.

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

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

Schema coverage is 100%, but the description adds value with concrete examples (e.g., '7d', '30d', '3m', '1y' for 'since', and ticker or CIK for 'value'). It also explains the purpose of each parameter in the context of the tool.

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 provides a change feed for a company over a time window, with specific example queries like 'What's new with X'. It distinguishes itself from the sibling 'entity_profile' tool by explicitly saying to use that for static profiles.

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

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

Explicitly provides when to use (queries about recent changes, updates, news) and when not to (static profile). Also details the fan-out to multiple data sources and fallback mechanisms like GDELT→GNews.

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

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

Annotations already indicate write (readOnlyHint=false) and safe (destructiveHint=false, idempotentHint=true). Description adds persistence details (authenticated vs anonymous, 24-hour retention) and scoping by identifier. 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?

Four efficient sentences with no redundancy. Purpose first, then guidance, then technical details, then sibling pairing. 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?

For a simple write tool with two plain string params and no output schema, the description fully covers purpose, usage context, persistence, and how to integrate with siblings. No gaps.

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

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

Schema covers both parameters fully (100%). Description provides example key formats and notes value is any text, but adds little beyond what the schema already states. 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?

Clear verb+resource: 'Save data the agent will need to reuse later'. Specifies scope (across sessions) and distinguishes from siblings (recall, forget). Highly specific.

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

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

Explicitly says when to use: 'when you discover something worth carrying forward' with concrete examples. Mentions authentication and session duration. Lacks explicit when-not-to-use, but positive guidance is strong.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral context: 'Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.' This provides insight into internal complexity without contradicting annotations.

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

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

The description is extremely concise yet packed with actionable information. It starts with concrete examples, then states the primary use case, followed by detailed type specifications. Every sentence serves a clear purpose, and the structure is front-loaded for quick understanding.

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

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

Despite no output schema, the description fully specifies return values for each type (ticker, CIK, company name for company; RxCUI, ingredient, brand for drug) and even provides citation URIs. Given the tool's simplicity (2 parameters, no nested objects), this is complete and eliminates ambiguity.

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

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

Schema coverage is 100%, but the description adds meaningful examples (e.g., 'AAPL', '0000320193', 'ozempic') and clarifies what each parameter accepts and returns. It also explains auto-disambiguation for company type. This adds significant semantic value beyond the schema alone.

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

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

The description clearly defines the tool's purpose: resolving user-spoken names to canonical identifiers. It provides specific example queries ('What's the ticker for...', 'find the CIK for...', etc.) and explicitly distinguishes it from sibling tools by stating 'Use FIRST whenever you have a name but need an ID.' This makes the purpose highly specific and unambiguous.

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

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

The description gives clear guidance on when to use the tool ('Use FIRST whenever you have a name but need an ID') and details the supported entity types and their inputs. However, it does not explicitly mention when not to use it or compare against specific alternatives among the sibling tools, though the context is strong enough for an agent to infer appropriate usage.

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

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

Annotations indicate readOnly, idempotent, non-destructive. Description adds that it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, signal density. Discloses model parameter requirements. 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?

Three sentences: purpose, mechanism, use case. No wasted words. Front-loaded with key action. Well structured.

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

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

No output schema, but description specifies return type (ranked list with metrics). All parameters covered. Complexity (multi-entity with optional models) well addressed. Slight gap: does not mention that models are optional or default behavior beyond 'worker-ai'.

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

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

Schema coverage is 100%, baseline 3. Description adds meaning: entities first entry is the 'subject'. Models explains default and apiKey requirement. Context explained as shared probe context.

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

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

The description clearly states 'Compare AI visibility across multiple entities side-by-side', specifying the verb and resource. It distinguishes itself from siblings like ai_visibility_check by focusing on multi-entity comparison and ranking.

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 gives a use case: 'competitive AI-marketing audits'. Implies not for single entity checks (use ai_visibility_check). Does not explicitly list when-not-to-use but context is clear.

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

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

Adds behavioral context beyond annotations: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, sources_failed listed on timeout. No contradiction with annotations.

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

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

Well-structured with front-loaded purpose, but slightly long. Every sentence adds value; could be trimmed slightly without loss.

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?

Compensates for missing output schema by listing all returned fields. Covers ecosystem constraints, performance notes, and error handling. 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% but description adds that 'package' accepts scoped packages and 'version' defaults to latest. Also details output fields, aiding understanding of parameter effects despite no output schema.

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

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

The description clearly states it's a composite check for deciding whether to add an npm package, covering deps.dev and bundlephobia. It distinguishes from siblings by being the only dependency scanning tool.

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

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

Explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also notes ecosystem limits: NPM only in v1, with alternatives for other ecosystems.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds return field details and 'Keyless' info, but no additional behavioral traits like rate limits or authentication beyond 'Keyless'.

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

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

Two concise sentences with no wasted words; front-loaded with purpose followed by useful context about returns and platform.

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 5 parameters with full schema coverage and no output schema, description covers return fields and platform context. Missing details like sorting behavior but overall 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 description coverage is 100%, so baseline 3. Description does not add significant extra meaning beyond the schema's parameter descriptions.

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

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

The description clearly states the tool searches prediction markets by topic or question text, using specific verb 'search' and distinguishing from siblings like 'get_market' and 'top_markets'.

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

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

The description implies usage for searching specific topics but does not explicitly state when to use this tool over siblings like 'top_markets' or provide when-not-to-use conditions.

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 signal safety (readOnly, idempotent, not destructive), and the description adds valuable details: BGE-base-en embeddings, cosine similarity over 500-char windows, 200K char cap with truncation flagging, and character offsets for verification.

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

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

Five compact sentences front-load the core purpose, provide usage guidance, disclose behavioral details, and note sibling pairing. Every sentence earns its place 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?

Covers purpose, usage, behavior (embeddings, windows, cap), return format (passages, offsets, scores), and pairing. Missing mention of error handling or edge cases, but adequate 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% with good descriptions for all parameters, so baseline is 3. The description adds usage context but no additional parameter-specific 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 verb 'search inside' and resource 'record', specifies the semantic nature, and distinguishes from siblings by emphasizing it operates on already-fetched text rather than external sources.

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 names the alternative 'ask_pipeworx_grounded' for grounding over passages, providing clear when-to-use and pairing 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?

The description adds extensive behavioral context beyond annotations: account type requirement, supported types with examples, delivery channels with rate limits and verification steps, webhook signing details, and auto-disabling after failures. Annotations provide idempotentHint=true but the description doesn't contradict; it complements with rich safety and usage info.

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

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

The description is a single paragraph that is front-loaded with the primary purpose and then details. It is moderately long but every sentence adds value, balancing detail and conciseness. Minor improvement could be a more structured format.

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, multiple types) and lack of output schema, the description is thorough. It covers account prerequisites, type-specific params, optional delivery with constraints, and return value. No missing critical information for an AI agent to use 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?

Schema coverage is 100%, but the description adds significant meaning with concrete examples for each subscription type's params field and clarifies delivery schema with constraints like phone verification and rate caps. This greatly aids an AI agent in constructing valid parameter values.

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

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

The description clearly states the tool creates a proactive monitoring subscription and returns a subscription id. It identifies the resource and action well, but does not explicitly differentiate from sibling tools like 'list_subscriptions' or 'unsubscribe'. However, the context from sibling names helps, making it a 4.

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 guidance on when to use the tool (to create a subscription for live-data events), requirements (Pipeworx OAuth account), and limitations (anonymous/BYO cannot persist). It does not explicitly state when not to use or suggest alternatives, but the context is sufficient for a 4.

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 that it returns category-bucketed example questions with exact tool+argument shapes, drawn from the live tool catalog. 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?

One dense paragraph front-loaded with common queries. It efficiently conveys purpose, usage, and return format. Slightly long but each sentence adds value.

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

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

No output schema, but description clearly explains what the tool returns (category-bucketed questions, exact tool+argument shapes). It covers when to use (first), how to use (no args vs topic), and what to expect. Fully adequate for an onboarding 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% for the single optional 'topic' parameter. Description adds meaningful context: omitting gives full spread, passing topic focuses results. It also lists example values (e.g., finance, pharma) that are not constrained by enums.

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's an onboarding tool that returns example questions categorized by topic. It specifies the verb (suggest) and resource (questions/tool usage examples). It distinguishes itself from siblings by being the first entry point for new users.

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

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

Explicitly states 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains when to omit or pass the 'topic' argument for full spread vs focused results.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows it's safe. The description adds behavioral traits: 'Keyless' indicates no authentication required, and 'live' suggests real-time data. This adds value beyond annotations, though the return format is not fully detailed.

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

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

The description is extremely concise with two sentences and a key word. Every sentence adds value: first explains what it is and its purpose, second adds optional filtering and return format, third states authentication. No wasted words, well 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?

For a simple tool with two optional parameters and no output schema, the description covers the basic purpose and optional narrowing. However, it lacks details about the return record structure (e.g., fields like title, probability, volume) which could help an agent interpret results. More completeness would improve usability.

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

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

Schema coverage is 100%, so the baseline is 3. The description only mentions the 'term' parameter as optional narrowing, which adds no new meaning beyond the schema description. The 'limit' parameter is not mentioned in the description, but the schema already documents it. Thus, the description provides minimal additional value for parameters.

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 returns the most-active open Manifold markets, acting as a live attention/demand signal. The verb is implied but the resource and scope are specific. However, it does not explicitly distinguish from sibling tools like search_markets, so the purpose is clear but lacks differentiation.

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

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

The description implies usage for getting trending markets and mentions optional narrowing by topic, but it provides no explicit guidance on when to use this tool versus alternatives (e.g., search_markets or get_market). Without exclusion criteria or alternative naming, the usage context is only inferred.

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

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

The description adds significant value beyond annotations by detailing ownership enforcement, the deactivation (not deletion) behavior, and linking to 'recent_alerts' for historical events. This fully discloses the mutation and non-destructive nature, aligning 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 two sentences long, front-loaded with the core purpose and constraint, followed by a concise explanation of the effect. Every sentence adds value with no redundancy.

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

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

Given the tool's simplicity (single parameter, no output schema), the description covers all necessary aspects: purpose, ownership constraint, behavioral outcome, and relation to historical data. It is fully sufficient 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?

With 100% schema description coverage for the single parameter 'id', the baseline is 3. The description mentions 'by id' but does not add new meaning beyond the schema's description 'Subscription id (uuid) returned by subscribe.'

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 'Cancel a subscription by id' and specifies the resource 'subscription'. It effectively distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' by explicitly indicating it is 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 includes an important usage constraint: 'Ownership is enforced — you can only cancel your own subscriptions.' This guides the agent on when the tool is applicable. It also explains the behavioral outcome, but lacks explicit mention of when not to use it or reference to alternative tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral details: returns verdict, extracted form, actual value with citation, and percent delta. It explains it replaces 4-6 sequential calls, providing insight into efficiency. 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 comprehensive paragraph, front-loaded with common trigger phrases and examples. Every sentence adds information without redundancy. Minor improvement: could be slightly more structured (e.g., bullet points for returned fields), but still efficient.

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

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

Given one parameter, no output schema, and rich annotations, the description fully conveys purpose, usage, behavioral traits, and return format. It specifies domain limitations (company-financial, public US companies) and explains how it replaces multiple calls. Complete for agent selection and invocation.

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

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

Schema covers the single parameter 'claim' with description. Baseline 3; description adds value by giving natural-language examples and clarifying the domain (company-financial claims). Compensates for lack of enum or additional schema details.

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: natural-language claim verification against authoritative sources, specifically for company-financial claims. It uses explicit verb phrases like 'check', 'verify', 'confirm or refute' and distinguishes from siblings by noting it replaces multiple sequential calls.

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

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

Description explicitly says when to use: 'whenever the agent needs to check whether something a user said is factually correct.' It defines scope (company-financial claims for public US companies). While it doesn't list explicit alternatives, the context and sibling list imply the tool's niche.

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