Arcgis Cedarpark

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds context about API calls to Anthropic when a key is provided, the default free model, and that the user pays directly for Anthropic calls. This goes beyond annotations.

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

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

The description is concise with three sentences: first states main purpose, second explains default and optional key usage, third lists use cases. No fluff, front-loaded with key information.

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

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

Given 4 parameters, no output schema, and annotations already providing safety info, the description covers purpose, usage, parameter details, and return structure (per-model fields). It is complete for an agent to understand and invoke the tool correctly.

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

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

Schema description coverage is 100%, so schema already documents parameters. The description adds meaning by explaining the default model, the role of the API key, and the context parameter for disambiguation. This adds value beyond the schema.

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

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

The description clearly states it probes LLMs for knowledge about an entity and scores visibility per model. It specifies the default model and optional Anthropic probing. It differentiates from siblings like 'deep_research' and 'compare_entities' by focusing on AI visibility scoring.

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

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

The description lists use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not explicitly state when not to use, but the sibling list implies other tools for other purposes. The alternative of passing an API key for Anthropic is mentioned, providing some guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds useful behavioral context: it is a single fast call, works on all tiers, returns structured answers with citation URIs, and routes to the appropriate tool among thousands. This adds value beyond annotations without contradicting them.

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

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

The description is relatively long but each sentence earns its place by adding critical information: preference over web search, list of data sources, example queries, and differentiation from siblings. It is front-loaded with the key directive ('PREFER OVER WEB SEARCH'), making it efficient despite length.

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

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

Given the tool's complexity (routing to thousands of sources) and the absence of an output schema, the description comprehensively covers what the tool does, what kind of input it expects, what output format to expect (structured with citations), and how it fits into the ecosystem of sibling tools. It leaves no critical gaps for an AI agent to understand its use.

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

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

The input schema already provides 100% description coverage by explaining each parameter is an alias for the question. The description enhances understanding by providing usage examples and specifying the natural language format. This adds meaning beyond the schema, justifying a score above baseline.

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

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

The description clearly states the tool's purpose: it routes natural language questions to a vast set of authoritative sources for factual, structured data. It uses a specific verb ('ask') and resource ('Pipeworx'), and distinguishes itself from siblings like ask_pipeworx_grounded and deep_research by explaining when to use each.

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 'PREFER OVER WEB SEARCH' and provides detailed when-to-use guidance, including specific question types and examples. It also mentions when to step up to alternatives (grounded for verbatim evidence, deep_research for multi-part questions), making the usage context very clear.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds significant behavioral context: it explains the refusal reasons (not_in_source, no_tool_match, etc.), confirms it uses only tool results for answers, and mentions the extra LLM call cost.

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

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

The description is well-structured: it first defines purpose, then explains process, return format, refusal reasons, and usage guidance. It is slightly lengthy but every sentence adds value. Minor redundancy could be trimmed.

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

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

The description is highly complete given the complexity: it explains the tool's behavior, return format (including fields like evidence, confidence, source, fetched_at, refusal_reason), refusal scenarios, and usage context. No output schema exists, so the description fully compensates.

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 baseline is 3. The description does not add meaningful parameter semantics beyond what the schema already provides (all parameters are documented as aliases for 'question').

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

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

The description clearly defines the tool as 'Hallucination-resistant answer mode for high-stakes reads' and explains it extracts answers using only the tool result. It distinguishes from sibling 'ask_pipeworx' by emphasizing greater reliability at a cost of an extra LLM call.

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

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

The description explicitly states when to use this tool: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with specific examples like financial verdicts and medical lookups. It also advises preferring ask_pipeworx for casual lookups due to extra cost.

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

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

The description extensively details behaviors beyond annotations: resolution market match confidence, parent event extraction, news field fallbacks, safety mechanisms for low-confidence matches, spread liquidity checks, and resolution rule risk. Annotations only hint at idempotency and read-only nature, so this adds significant transparency.

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

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

The description is front-loaded with purpose and usage, then systematically covers classifiers, fan-out examples, response shapes, and safety. While lengthy, each section is necessary given the tool's complexity. Could be condensed slightly but is 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 (3 parameters, no output schema, multiple data sources and behaviors), the description is exceptionally complete. It covers resolution confidence, parent events, news fallbacks, safety, spread liquidity, and cancellation rules, leaving no significant gaps.

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

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

Schema coverage is 100%, but the description adds substantial meaning: 'market' can be slug, URL, or question text; 'depth' quick/thorough is explained; 'include_raw' details the trade-off between summary and full payloads. This goes beyond schema definitions.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It provides example inputs (slug, URL, question text) and usage contexts (e.g., 'should I bet on X'). This distinguishes it from siblings like polymarket_edges and polymarket_arbitrage.

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

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

The description explicitly suggests use cases ('Use for...') and explains how the tool resolves, classifies, and fans out data. However, it does not explicitly state when NOT to use this tool or provide direct alternatives, though the context is clear.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds valuable behavioral detail: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), off-calendar fiscal year handling, sorted results by primary metric, and citation URIs. No contradictions.

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

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

The description is fairly long but all sentences add value, front-loading with common query phrases and the core purpose. Every element earns its place; no fluff. Could be slightly tighter but remains clear.

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

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

Given the tool's complexity (two entity types, two data sources) and lack of output schema, the description covers essential details: data sources, edge cases (off-calendar fiscal years), result order, and citation URIs. It also quantifies efficiency gains. Minor gaps on error handling, but overall complete.

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

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

Schema coverage is 100%, but the description enriches understanding: explains what data each type pulls (10-K fields vs adverse events), provides concrete examples for values (tickers/CIKs, drug names), and notes result sorting. Adds meaning beyond the schema.

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

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

The description explicitly states the tool's purpose: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It provides trigger phrases like 'compare X and Y' and distinguishes itself from sibling tools like entity_profile by emphasizing preference over sequential 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?

The description clearly states when to use: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It gives example queries. However, it does not explicitly state when not to use (e.g., for single entity details), which would strengthen 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?

Beyond annotations, description details parallel decomposition, 4396 tools, findings packet format (verbatim evidence, confidence, source, citation, gaps), and that it returns gaps for unanswered facets. Warns it is not open-web search and gives expected response time (15-60s). 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?

Long but highly informative. Information front-loaded with critical account requirement. Each sentence serves a purpose, from prerequisites to use cases to output behavior. Could be slightly condensed, but no waste.

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

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

No output schema, but description fully compensates: details output format (findings packet with evidence, confidence, source, citation, gaps), covers prerequisites, exclusions, timing. Complete for tool complexity and parameters.

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

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

Input schema covers 100%, so baseline 3. Description adds context: depth meanings (quick=3, standard=5, thorough=8 paid) and that question can be broad/multi-part. This enriches utility beyond schema alone.

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

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

The description clearly states the tool performs grounded multi-source research across structured data sources in one call, decomposing questions into facets. It distinguishes itself from siblings like ask_pipeworx (single lookup) and search_within by emphasizing parallel tool routing and returning finding packets with citations and gaps.

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

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

Explicitly explains when to use (broad/multi-part questions over structured data) and when not to (single lookup → ask_pipeworx; current news → ask_pipeworx). Provides account requirement and alternative tool names, with concise 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 indicate read-only, idempotent, non-destructive behavior. The description adds that results include full schemas with curated examples ready to call directly, which is useful behavioral context beyond annotations.

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

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

The description is front-loaded with the main purpose and domain list. While slightly lengthy, every sentence adds value (use case, return value, invocation order). Could be trimmed slightly but remains effective.

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

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

Given the tool's discovery nature and full schema descriptions, the description adequately covers what the tool does, what it returns (tool names, descriptions, schemas), and when to use it. No output schema, but output intent is clear.

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

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

Schema description coverage is 100% with clear descriptions for all 6 parameters. The description adds value by explaining that query accepts multiple aliases (task, q, search, description) and natural language input, and mentions default/max for limit.

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

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

The description explicitly states the tool's purpose: 'Find tools by describing the data or task.' It lists specific domains (SEC filings, FDA drugs, etc.) and distinguishes from sibling tools by positioning itself as the tool discovery mechanism, not a data analysis 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?

Clearly states when to use: 'Use when you need to browse, search, look up, or discover what tools exist for...' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This implies when not to use (for a single answer) and positions it as a preliminary step before other tools.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds that patents 'soft-fails until reactivated' and details fanning across sources. No contradiction. Adds useful context beyond annotations, though slightly verbose.

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 verbose with multiple example queries and details about soft-failing patents. While all sentences add value, it could be more concise without sacrificing 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?

With no output schema, the description provides a detailed list of return fields (cik, company_name, recent_filings with URIs, fundamentals, patents, news, LEI). This gives the agent a good understanding of expected output, though explicit structure could further improve completeness.

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

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

Schema coverage is 100%. Description adds meaning: type limited to 'company', value can be ticker or zero-padded CIK, and explicitly says names are not supported (with a suggested alternative). This adds value beyond the schema.

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

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

Description clearly states it returns a 'full cross-source profile of a US public company in ONE parallel call' and lists specific data points (cik, company_name, filings, fundamentals, patents, news, LEI). It distinguishes from chaining single-pack lookups, making purpose unmistakable.

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 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' for holistic views and instructs to use resolve_entity if only a name is available. Provides clear when-to-use and alternatives.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. The description adds value by explaining the purpose ('clear sensitive data') and the effect (delete memory by key), which goes beyond the annotations. No contradictions.

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

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

The description is concise with two sentences. It front-loads the primary action and follows with usage context, no wasted words.

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

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

Given one parameter, full annotations, and no output schema, the description provides complete context: what the tool does, when to use it, and how it relates to 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?

The input schema has one parameter 'key' with a brief description. Schema description coverage is 100%, so the description does not need to add much. It does not elaborate beyond the schema, so a baseline of 3 is appropriate.

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

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

The description clearly states the verb 'Delete', the resource 'previously stored memory', and the method 'by key'. It effectively distinguishes itself from sibling tools 'remember' and 'recall' by implying the inverse operation.

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

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

The description explicitly lists when to use this tool: when context is stale, task is done, or to clear sensitive data. It also suggests pairing with remember and recall, providing clear guidance on usage context.

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

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

Description explains fetching, extraction, and output format, adding detail beyond annotations. No contradictions; annotations already indicate read-only, idempotent 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?

Reasonably concise with two sentences plus a list of use cases. Every sentence adds value; could be slightly more compact but not wasteful.

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 input, process, output format, and use cases comprehensively. No output schema needed given simple text output. Annotations handle safety.

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 description adds context (default 25, max 50 for max_links) and process details, enhancing meaning beyond schema.

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

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

Clearly states the tool generates an llms.txt file for any URL, with specific verb and resource. Use cases listed but no explicit sibling 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?

Provides use contexts (client site, own project, competitor audit) but does not explicitly contrast with sibling tools or state 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, openWorldHint, idempotentHint, and destructiveHint. The description adds value by specifying exactly what the tool returns (fields, geometry type, count, capabilities) and that it takes a URL. No contradictions. However, it does not disclose potential errors or rate limits, but the annotations cover the safety profile.

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?

A single, front-loaded sentence that efficiently conveys the action, input, and output. No wasted words. Every part earns its place.

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

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

Given the tool's simplicity (1 parameter, no output schema, clear annotations), the description adequately covers purpose, input, and output summary. It could mention potential limitations or how to format the URL precisely, but it is largely complete for a straightforward schema retrieval 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 a clear example for the 'url' parameter. The description mentions 'by url' but does not add significant meaning beyond the schema's description. Baseline of 3 is appropriate since the schema does the heavy lifting.

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

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

The description uses a specific verb 'Get' and clearly states the resource: 'an ArcGIS Feature/Map Service layer's schema'. It lists the specific information returned: fields, geometry type, total record count, and capabilities. This clearly distinguishes it from siblings like 'query_layer' which queries actual data, not schema.

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 does not provide explicit guidance on when to use this tool versus alternatives. While the purpose is clear, there is no 'use this if...' or 'instead, use...' language to help the agent decide among siblings. The context is implied but not explicit.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds value by clarifying default return behavior (only active subscriptions) and listing returned fields, with no contradiction to 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 with front-loaded purpose. No redundant information; 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?

Despite no output schema, the description lists all essential return fields. For a simple list tool with one optional parameter, the description is fully 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 coverage is 100% and the description of the single parameter 'include_inactive' is adequately covered by the schema. The description adds context about 'active' vs 'cancelled' but does not provide new syntax 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 'List the caller's active subscriptions' and lists the fields returned. It distinguishes from sibling tools like 'subscribe' and 'unsubscribe' by specifying it lists subscriptions, not modifying them.

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

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

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

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

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

Annotations indicate the tool is not read-only, not idempotent, and not destructive. The description adds valuable behavioral context: rate-limited to 5 per identifier per day, free, and doesn't count against tool-call quota. No contradictions.

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

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

The description is four sentences, each packed with value: purpose, usage scenarios, content guidance, and behavioral notes. No fluff or repetition.

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

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

For a feedback tool with 3 parameters, the description covers all necessary context: when to use, what to include, rate limits, and quota. No output schema is needed for this 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?

The input schema has 100% coverage and already provides good descriptions. The tool description rephrases the enum options (bug, feature, data_gap, praise) and gives brief context usage, but adds minimal new meaning beyond the schema.

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

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

The description clearly states it sends feedback to the Pipeworx team about bugs, missing features, data gaps, or praise. It distinguishes from sibling tools by specifying its unique purpose; no other sibling serves a feedback function.

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

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

The description explicitly tells when to use: for bugs (wrong/stale data), feature requests, data gaps, or praise. It also mentions rate limits and that it doesn't count against quota. While it doesn't spell out when NOT to use, the positive guidance is clear and 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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds beyond this by disclosing data source ('derived from CF analytics-engine'), privacy ('no PII'), and caching behavior ('cached 5min-1h depending on window'). This is valuable 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?

Description is three well-structured sentences with no wasted words. Front-loaded with core purpose, followed by use cases and behavioral details. Every sentence earns its place.

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

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

For a simple read-only tool with one optional parameter and no output schema, the description covers what is returned, caching, data source, and privacy. Combined with full annotations, it is complete and leaves no 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% for the single parameter 'window'. The description adds behavioral guidance about window selection ('Shorter windows surface what's hot right now; longer windows show steady-state demand'), which adds meaning beyond the schema enum.

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

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

The description clearly states what the tool does: 'Returns the top tools, top packs, and total call volume over a recent window'. It uses specific verb 'returns' and resource, and the scope is well-defined. The purpose is distinct from siblings.

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

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

Three explicit use cases are provided (discovering hot data sources, confirming canonical tool, aligning with agent needs). While no direct alternatives to siblings are mentioned, the guidance is practical and context-rich, scoring 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 indicate readOnly, openWorld, idempotent, non-destructive. The description goes far beyond: it details internal checks (monotonicity, partition, semantic anchor, fill check), explains response structure, and warns when not to trade. No contradiction with annotations.

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

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

The description is lengthy but well-structured with sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). Every sentence adds value. Slightly verbose, but appropriate given the complexity of the tool.

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

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

No output schema exists, but the description fully explains the response (opportunities[], partition_check details). It covers edge cases (null arb signal for high placeholder fraction), fill check, and references sibling tools. 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?

Input schema covers both parameters with descriptions (100% coverage). The description adds substantial meaning: explains the two modes, provides example values, and distinguishes between recommended usage (event vs topic). It adds context not in the schema.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes between two modes (event and topic) and explains what each does. It references a sibling tool (polymarket_fill_risk) for custom sizing, aiding 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 provides explicit guidance on when to use each mode (event for specific market, topic for cross-event scanning) and notes that calling with no args fails. It mentions cross-event mode catches patterns single-event misses. However, it does not explicitly state when not to use this tool, only that custom sizing should go to polymarket_fill_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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds extensive behavioral details: caching logic, model families, segments, diagnostic output, and tradeable-edge knobs. 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 verbose (multiple paragraphs with technical details) and could be more concise. It front-loads purpose but dives deep into internals that might not be necessary for selection.

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

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

For a complex tool with 9 parameters and no output schema, the description thoroughly explains output structure (by_segment, diagnostics) and data fields (edge_pp_net, kelly_fraction). Complete.

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

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

Schema description coverage is 100%. The description adds value beyond schema by explaining the logic behind min_partition_leg_kelly and how parameters interact. Baseline 3, plus 1 for added 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 the tool scans top Polymarket markets for opportunities where Pipeworx data disagrees with market price, and it distinguishes from siblings by mentioning specific model families and segments. The verb 'scan' and resource 'Polymarket markets' are precise.

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 usage context: 'what should I bet on today' — agents discover opportunities without paging hundreds of markets. No explicit when-not-to-use or alternatives, but the context is clear.

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

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

Annotations already indicate readOnly, idempotent, non-destructive. Description adds valuable behavioral details: return structure (tracked, expired, snapshot_dates), limitations (60-day TTL, decay from daily closes), and computation basis. 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 structured logically (purpose, args, response, limits). Slightly verbose but every sentence adds value; minimal redundancy.

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

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

No output schema is provided, so the description fully covers return values (tracked, expired, snapshot_dates with key fields) and limitations. Complete for a complex time-series tool.

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

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

Schema coverage is 100% with descriptions that already include defaults and ranges. The description repeats this information without adding new semantics beyond the schema.

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

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

The description clearly states it provides 'edge persistence and decay telemetry' from daily snapshots, answering a specific question about edge duration. This distinguishes it from sibling tools like polymarket_edges (current edges) and others.

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 time-series edge analysis via 'how long has this edge existed' but lacks explicit when-to-use or when-not-to-use guidance. No alternatives are mentioned, but context is clear.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description aligns perfectly, adding behavioral details like walking the order-book ladder, returning verdicts (clean/degraded/cannot_fill), and explaining basket mode's forced_directional_risk. No contradictions; description supplements annotations well.

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 dense paragraph that covers both modes with significant detail. While it is relatively long, nearly all information is necessary given the tool's complexity. Front-loading the core purpose and requirement helps. Minor redundancy in slug/URL mentions, 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 enumerates all return fields for both modes (e.g., top_of_book, vwap_fill_price, slippage_pp, capture_ratio, thin_legs, forced_directional_risk) and explains their significance. It also addresses risks and edge cases (thin books, partial fills). The description is comprehensive and leaves no critical gaps for an agent to understand what the tool returns.

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

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

All four parameters have schema descriptions (100% coverage). The tool description adds further semantic value by explaining the dual interpretation of `size_usd` (max spend vs target proceeds in single-market; settlement notional in basket), the default and auto behavior for `side` in basket mode, and the mutual exclusivity of `market` and `event`. This is a meaningful enhancement over the schema.

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

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

The description clearly states it performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes two modes: single-market and basket. It references sibling tools like polymarket_arbitrage and polymarket_edges, positioning itself as a prerequisite risk check, which differentiates it well.

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 the requirement for one of `market` or `event`, and provides clear guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains the risks of partial fills and uncapturable theoretical overround, giving strong when-to-use and why context.

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

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

The description extensively covers behavior beyond annotations: explains the spread calculation, safety fields (compatibility_warning, temporal_alignment, skipped_cross counters), and how mismatches are detected. Annotations only indicate read-only/idempotent.

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: starts with purpose, then modes, then response fields, then edge cases. Some redundancy (e.g., repeating 'pre-mapped ≠ tradeable') but overall 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, the description fully explains the response structure (leg-by-leg prices, matched spreads, safety fields) and covers edge cases like temporal misalignment and incompatible bet shapes. Complete for a complex cross-venue 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?

With 100% schema coverage, the description adds significant meaning by explaining the two modes and the role of each parameter (topic as macro shortcut, tickers as overrides). This goes well beyond the schema descriptions.

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

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

The description clearly states the tool computes the cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes itself from sibling tools like polymarket_arbitrage by focusing on 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 usage modes (topic shortcuts and explicit tickers) and warns that most pre-mapped topics currently return compatibility warnings, guiding agents on when to expect valid spreads. Could be improved by explicitly naming when to use alternatives.

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

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

Annotations already indicate readOnlyHint=true, destructiveHint=false. The description adds that it returns attribute rows and geometry, and demonstrates pagination with limit/offset. This provides useful behavioral context beyond the annotations.

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

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

The description is two compact sentences, front-loaded with purpose, and contains zero wasted words. Every sentence adds value.

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

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

Given the tool has 6 parameters and no output schema, the description covers essential behavior: what it does, how to use it, and a sample usage. Lacks details on geometry return format but still sufficient.

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

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

Schema coverage is 100%, so baseline is 3. The description adds SQL-like semantics, examples for where clause, out_fields, and order_by. This adds 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 it queries an ArcGIS Feature/Map Service layer by URL, listing SQL-like parameters. This distinguishes it from sibling tools like search_datasets (which searches for datasets) and layer_info (which likely returns metadata).

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 an example of how to sample all data (where='1=1' + out_fields='*') and mentions that the URL comes from search_datasets. It provides clear usage context but does not explicitly state when not to use this tool or list alternatives.

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

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

Annotations already indicate readOnly, idempotent, non-destructive. The description adds valuable context about scoping ('anonymous IP, BYO key hash, or account ID'), which goes 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?

The description is three concise sentences, front-loaded with the primary action. Every sentence adds value, and there is no redundancy or filler.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description covers its usage context, scoping, and relationship to siblings. It is nearly complete; a note on return value format would be the only minor addition.

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 includes a description for the 'key' parameter. The description adds meaning by explaining the purpose ('value previously saved via remember') and scoping, which enhances the schema's 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 retrieves a value saved via remember or lists all keys when no argument is given. It uses specific verbs ('retrieve', 'list') and distinguishes itself from sibling tools 'remember' and 'forget'.

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

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

The description explicitly tells when to use the tool ('to look up context the agent stored earlier') and pairs it with remember/forget. It implicitly excludes other use cases but does not provide explicit 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?

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description reveals that alerts are from the evaluator's persisted feed, includes source and citation_uri details, and explains mark_read behavior. No contradictions with annotations.

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

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

The description is a single paragraph of about 5 sentences, front-loaded with the main purpose. It is well-structured but slightly verbose; could be trimmed without losing information.

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

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

Despite lacking an output schema, the description details the event fields (source, citation_uri, raw payload) and covers all parameters. It also addresses polling suitability and provides an alternative endpoint, ensuring completeness.

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

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

Schema coverage is 100%, and the description adds concrete meaning: example for type ('sec_8k'), ISO timestamp for since, and the effect of mark_read. This enriches the schema documentation.

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

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

The description clearly states 'Pull fired events from your subscription feed. Returns the most recent alerts', using a specific verb and resource. It differentiates from sibling tools like list_subscriptions, subscribe, and unsubscribe by focusing on alert retrieval rather than subscription management.

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

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

Explicitly describes filtering options (type, since), the mark_read parameter for marking events read, and that polling is appropriate. It also provides an alternative method via HTTP endpoint for scripts/dashboards, giving clear usage context.

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

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

The description discloses extensive behavioral details beyond annotations: fan-out to multiple sources (SEC EDGAR, GDELT→GNews, USPTO), fallback logic (GDELT preferred, GNews when rate-limited), USPTO soft-fail, return structure (changes[] grouped by source + total_changes + citation URIs), and parameter behavior for 'since' (ISO date or relative shorthand). Annotations already mark read-only, idempotent, and non-destructive, which are consistent.

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 thorough but slightly verbose due to listing multiple example queries. However, it is well-organized: starts with common use cases, then details sources, parameter behaviors, return structure, and ends with a comparison to a sibling. Almost every sentence contributes unique information, so it earns its length despite minor 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 complexity (multi-source fan-out, fallback, soft-fail) and absence of an output schema, the description covers the main behavioral aspects and return structure ('structured changes[] grouped by source...'). It addresses edge cases like USPTO soft-fail and provides enough context for an agent to use it correctly. Minor omission: what happens when no results are found, but overall 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?

Input schema coverage is 100%, so baseline is 3. The description adds value by explaining the 'since' parameter format with examples ('ISO date or relative shorthand ("7d", "30d", "3m", "1y")') and suggesting typical usage ('Use "30d" or "1m" for typical monitoring'). It also clarifies the 'value' parameter accepts ticker or CIK, and the 'type' parameter is limited to 'company'. This goes beyond schema descriptions.

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

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

The description explicitly states the tool provides a 'change feed for a company in the last N days/weeks/months in ONE parallel call' and lists specific use cases. It distinguishes from the sibling 'entity_profile' which covers static profiles, making the purpose clear and unique.

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 explicit alternative: 'Use entity_profile instead when you want the static profile...'. It also provides guidance on when to use this tool (e.g., 'latest on Y', 'updates on Acme') and typical parameter values like '30d or 1m for typical monitoring'.

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

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

Adds context beyond annotations: memory is scoped by user identifier, persistent for authenticated users, 24-hour retention for anonymous. 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 and front-loaded with purpose, but slightly lengthy. Every sentence is informative, 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?

Complete coverage for a simple key-value store tool. Includes purpose, when to use, persistence behavior, and pairing with siblings. No output schema needed.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value with examples of key formats and types of values, slightly enhancing understanding.

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

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

The description clearly states the tool saves data for later reuse across conversations or sessions. It provides specific examples (resolved ticker, target address, user preference) and distinguishes itself from sibling tools recall and forget.

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

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

Explicitly says when to use: 'Use when you discover something worth carrying forward.' Also mentions pairing with recall and forget, providing clear alternatives.

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

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

Annotations already declare readOnly, openWorld, idempotent. Description adds that each call cascades through several lookup endpoints internally, providing 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?

Description is somewhat long but well-structured, starting with examples, then usage, then type details. Every sentence adds value, though minor trimming could improve conciseness.

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

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

No output schema, but description fully explains returns for each type. Covers both complexity and parameter details, making it 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% (both parameters described). Description adds rich detail for each type (e.g., company returns ticker + CIK + name + citation URI), significantly enhancing understanding.

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

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

Description clearly states the tool resolves names to canonical identifiers, with specific examples ('What's the ticker for…') and explicit supported types (company, drug). It distinguishes the tool's purpose from siblings.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID,' which is clear when-to-use guidance. It doesn't explicitly mention when not to use or list alternatives, but the strong directive 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 readOnly, idempotent, and non-destructive traits. The description adds valuable context: it probes each entity with ai_visibility_check, optionally across multiple models, and returns a ranked list with score, confidence, and signal density. This enriches the behavioral understanding 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?

Three concise sentences with clear front-loading of purpose. Every sentence adds value without repetition or fluff.

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

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

Given the four parameters, 100% schema coverage, no output schema, and detailed annotations, the description fully explains tool behavior, input semantics, and output format. Example usage further aids understanding. No gaps remain.

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

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

Schema coverage is 100%, so baseline is 3. Description adds extra meaning: clarifies that the first entity in the array is treated as 'subject' and others as competitors, and explains the models parameter including default and API key requirement. This adds value beyond schema.

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

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

The description specifically states it compares AI visibility across multiple entities side-by-side, using ai_visibility_check, and ranks results. This clearly distinguishes it from siblings like ai_visibility_check which handles single entities.

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 usefulness for competitive AI-marketing audits and gives an example question. While it doesn't list explicit exclusions, the context implies when to use versus alternatives like ai_visibility_check.

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

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

Annotations already mark it as read-only, idempotent, non-destructive. Description adds crucial behavioral detail: fans out across external APIs (deps.dev, bundlephobia), mentions partial failure grace – 'bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns'. No contradiction with annotations.

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

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

The description is a single long but well-structured sentence. It front-loads the purpose and then details behavior, limitations, and fallbacks. Could be broken into sentences for improved readability, but it is not verbose and each part earns its place. Slight deduction for lack of paragraph breaks.

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 enumerates return fields in detail (summary block, per-advisory detail, links, alternative versions). Covers edge cases: non-npm ecosystems, partial failures, timeouts. For a composite tool with 2 parameters and moderate complexity, this is 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 has 100% description coverage. Description goes beyond schema by explaining that scoped packages are accepted, version defaults to latest, and hints at the output structure (summary block fields like is_latest, license, advisory_count, etc.). This adds real value for the agent.

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

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

The description starts with a clear verb+resource: 'Composite should I add this npm package to my project check in ONE call'. It lists specific checks (license, advisories, version history, bundle size, etc.) and distinguishes itself from sibling tools, none of which seem to perform dependency analysis.

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

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

Explicit guidance: 'Use whenever an agent asks is X safe / popular / small or what does adding lodash cost me'. Also specifies ecosystem boundaries: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly' – telling the agent when NOT to use this tool.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context by specifying the exact return fields and the recommended follow-up tools, which aligns with and supplements the annotations.

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

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

The description is two sentences, concise, and front-loaded with the main purpose. Every sentence adds value without redundancy.

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

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

For a search tool, the description covers what it searches, the return fields, and how to proceed. Although there is no output schema, the description explains the output. Minor omissions like pagination or empty results don't significantly detract.

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 three parameters. The description does not add significant new parameter semantics beyond providing examples for the query parameter (e.g., 'parcels', 'crime'), which is helpful but not essential.

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 City of Cedar Park GIS datasets by keyword and details the returned fields. It distinguishes from sibling tools like query_layer and layer_info by mentioning the URL output, but does not explicitly differentiate from generic search tools like search_within or deep_research.

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

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

The description explains the output fields and suggests subsequent use of query_layer or layer_info with the URL, providing clear usage context. However, it lacks explicit guidance on when not to use this tool or alternatives.

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

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

Beyond annotations (readOnlyHint, idempotentHint), description adds technical details: embedding model (BGE-base-en), cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, and character offsets for verification. No contradictions.

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

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

Two concise paragraphs with front-loaded purpose. Every sentence adds value: use case, technical details, pairing advice. No redundancy or filler.

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

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

Given 3 params, no output schema, and rich annotations, the description fully explains input semantics, return format (passages with offsets and scores), technical constraints, and integration with sibling tools. Agent has all needed info.

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. Description adds context like examples for 'query' and clarifies that 'text' is the document to search, but doesn't repeat default for 'limit'. Adds value over schema.

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

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

Description clearly states 'Semantic search INSIDE a fetched record' with specific verb and resource. It distinguishes from sibling tools like ask_pipeworx (general QA) and search_datasets (external search) by emphasizing searching within a provided text.

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 gives examples (SEC 10-K, article, long tool result). Mentions pairing with ask_pipeworx_grounded. Lacks explicit 'do not use' scenarios but is otherwise 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?

Beyond annotations, the description adds significant behavioral context: OAuth requirement, type-specific behaviors, delivery channel constraints (SMS cap, webhook auto-disable, secret returned once). No contradiction with annotations.

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

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

The description is relatively long but well-structured: purpose first, then type details, then delivery mechanisms. It front-loads key info. Some redundancy with schema, but overall 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?

Given the tool complexity (3 parameters, nested objects, no output schema), the description is thorough: covers all types and delivery options, return value, consumption methods, and constraints. Missing idempotency detail, but that is covered by annotations.

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?

Despite 100% schema coverage, the description adds substantial value: explains each type's parameters with concrete examples, clarifies nested delivery fields, and notes constraints (e.g., phone verification, webhook HMAC signing). This goes well 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 'Create' and the resource 'proactive monitoring subscription'. It specifies that it returns the subscription ID, distinguishing it from listing or unsubscribing siblings. The purpose is 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 explains when to use (for proactive monitoring) and gives detailed examples by type. It implicitly contrasts with pull-based tools like recent_alerts by mentioning they are for retrieval. However, it lacks explicit 'use this tool when...' and does not enumerate 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 readOnly, openWorld, idempotent, non-destructive. Description adds behavioral context: returns question examples with tool+argument shapes drawn from live catalog, and mentions meta-tools. No contradictions.

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

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

Description is front-loaded with common user queries and structured output explanation. While longer than minimal, every sentence serves a purpose for onboarding. Could be slightly more concise but overall efficient.

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

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

Given the tool's simplicity (1 optional param, no output schema), the description is fully complete. It covers purpose, usage, parameter behavior, and output content. No gaps remain for an agent to select or invoke incorrectly.

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 one optional parameter well-described. Description adds meaning: explains that omitting topic gives full spread, passing a topic focuses results, listing valid focus areas. This goes beyond the schema's generic 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 identifies the tool as the onboarding entry point that returns category-bucketed example questions with exact tool+argument shapes. It uses specific verbs ('returns', 'call') and distinguishes from siblings like 'discover_tools' or 'ask_pipeworx' by its focus on suggesting questions.

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

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

Explicitly states when to use: 'Use this FIRST when you do not yet know what Pipeworx can do for you' and describes how to call with topic or without. Lacks explicit 'when not to use' but context covers alternatives implicitly.

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

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

The description discloses that the row is deactivated, not deleted, and historical events remain available via recent_alerts. This adds behavioral context beyond the annotations, which indicate non-destructive and idempotent behavior. No contradiction with annotations.

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

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

The description is two sentences, concise and front-loaded. Every sentence adds value without unnecessary words.

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

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

The tool is simple with one parameter and no output schema. The description covers purpose, ownership constraint, and behavioral nuance (deactivation vs deletion). Given the complexity, 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?

The schema has 100% coverage for the single parameter 'id' with a clear description. The tool description does not add further parameter details, so baseline score of 3 is appropriate.

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

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

The description states 'Cancel a subscription by id.' This is a specific verb (cancel) and resource (subscription), clearly distinguishing it from sibling tools like 'subscribe' and 'list_subscriptions'.

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

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

The description explains ownership enforcement: 'you can only cancel your own subscriptions.' This provides clear context for when to use the tool. It does not explicitly mention alternatives but the constraint is informative 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?

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false. The description adds details on what it returns (verdict, structured form, actual value, citation, delta) and that it replaces multiple sequential calls, providing full transparency beyond annotations.

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

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

The description is relatively long but well-structured, starting with example queries, then usage, scope, output, and efficiency. Each sentence provides distinct value, though it could be slightly trimmed without losing meaning.

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

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

Given a single parameter, no output schema, and comprehensive annotations, the description is fairly complete. It explains what, when, and what to expect. Minor missing details like error handling or limitations, 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?

The single parameter 'claim' is fully described in the schema with examples. The description reinforces the natural-language format and expected domain, adding no new information but consistent with the schema. Schema coverage is 100%.

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

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

The description clearly states the tool verifies natural-language claims against authoritative sources, specifically company-financial claims. It lists example queries and distinguishes its niche scope, making it distinct from sibling tools.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and specifies the supported domain (company-financial claims for public US companies). It does not explicitly mention when not to use or alternatives, but the specificity guides usage effectively.

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