Arcgis Washingtoncomn

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

The description adds behavioral context beyond annotations: it explains scoring (0-100), per-model and combined returns, default model, and that Anthropic probing requires the user's own API key (incurring costs). This complements the annotations (readOnlyHint, idempotentHint) well. It does not mention rate limits or latency, but given annotation coverage, this is sufficient.

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 sentences, each earning its place: first defines the core action, second details models and returns, third lists use cases. No redundant or verbose phrasing. Front-loaded with the primary purpose.

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

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

Given the tool's complexity (probing multiple LLMs, returning per-model data) and the absence of an output schema, the description adequately covers return structure (per-model: score, confidence, signals, raw_response; plus combined view). It could be improved by briefly outlining the combined view format, but the essential information is present.

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

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

The input schema covers all 4 parameters with descriptions, so baseline is 3. The description adds significant value beyond the schema: it explains 'entity' is the thing to ask about, 'models' defaults to workers-ai, '_apiKey' is for Anthropic (BYO key), and 'context' disambiguates. It also clarifies the default model and costs, which the schema does not.

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

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

The description clearly states the tool probes LLMs for brand/product visibility and provides a score (0-100). It specifies the verb 'probe' and resource 'LLMs', and distinguishes from sibling 'scan_competitor_ai_presence' by focusing on what LLMs know rather than scanning presence. Use cases are explicitly listed.

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

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

The description provides clear usage contexts: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also explains the default model and the option to add Anthropic with an API key. However, it lacks explicit guidance on when not to use this tool or how it compares to similar siblings like 'scan_competitor_ai_presence'.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint as true, and destructiveHint false. The description adds valuable context: it routes to many tools across sources, returns structured answers with citation URIs, handles various factual queries. No contradictions. Slightly redundant on safety but adds behavioral detail.

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

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

The description is somewhat lengthy but well-structured: starts with a strong directive, provides context, examples, and clear differentiation. Every sentence adds value, though could be slightly trimmed without losing meaning. Front-loaded with key guidance.

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 (many parameters, no output schema), the description is complete: explains the core functionality, routing mechanism, citation format, and provides numerous examples. It covers what the agent needs to know for correct invocation.

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

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

Schema description coverage is 100%, with all 6 parameters (including aliases) documented. The description does not add new parameter-level details beyond what the schema provides, which is acceptable given the high coverage. Baseline 3 is appropriate.

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

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

The description clearly states the tool routes natural language questions to 4,552 tools across 1,160 verified sources, returning structured answers with citations. It distinguishes itself from siblings like ask_pipeworx_grounded and deep_research by specifying 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?

Explicitly advises to prefer this tool over web search for factual queries, provides example question types, and directs when to step up to alternatives (grounded, deep_research) for specific needs. Includes clear when-to-use and when-not-to-use guidance.

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

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

Adds significant behavioral context beyond annotations: describes the extraction process, return format with evidence and refusal reasons, and cost implication (one extra LLM call). No contradiction with annotations.

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

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

Reasonably concise and well-structured, front-loading purpose and usage guidelines. Every sentence adds value, though slightly verbose in listing refusal reasons.

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?

Comprehensively covers the tool's behavior, return format, failure modes, and usage context. No output schema exists, so the description compensates well.

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 six aliases for 'question'. The description simply notes 'Your question in natural language' without adding detail beyond the schema.

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

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

The description clearly states the tool is a 'hallucination-resistant answer mode for high-stakes reads' and distinguishes it from the sibling ask_pipeworx by emphasizing grounded extraction using only tool results.

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

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

Explicitly specifies when to use: 'whenever an answer will be quoted, cited, or acted on', and advises preferring ask_pipeworx for casual lookups. Provides 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 indicate read-only, idempotent, non-destructive. The description adds rich behavioral detail: resolver contract with confidence levels, parent event extraction, news fallback fields, safety mechanisms (low-confidence short-circuit, closed markets, wide-spread warnings), and resolution-rule risk. All this goes beyond annotations and helps the agent anticipate tool behavior.

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

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

The description is front-loaded with the purpose, but then becomes very detailed with lists of classifiers, fan-out examples, response shapes, and edge cases. While comprehensive, it could be more concise. Some redundancy exists (e.g., multiple paragraphs on blocking conditions). Ideal length would be shorter for quick consumption.

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

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

Given the tool's complexity and lack of output schema, the description is remarkably complete. It covers all key behaviors: input types, resolution process, classification, fan-out, response structure, safety checks, and special edge cases (closed markets, wide spreads, cancellation rules). No gaps are apparent for an AI agent to use it effectively.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add significant per-parameter semantics beyond the schema's own descriptions (which are clear). However, the overall context about classification and fan-out indirectly enriches understanding of the 'market' parameter, but not enough to raise the score.

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 first sentence clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the resource (Polymarket bet, Pipeworx data) and provides usage examples ('should I bet on X'). This distinguishes it from sibling tools like polymarket_arbitrage or ask_pipeworx.

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

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

The description explicitly says 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also hints at when not to use it implicitly through context, though it does not explicitly name alternatives. The extensive detail on fan-out and response shapes helps the agent understand the tool's scope.

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 details data sources (SEC EDGAR for companies, FAERS for drugs), specific metrics retrieved, handling of fiscal year offsets, sorting by primary metric, and return format including citation URIs.

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

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

The description is front-loaded with example queries and contains all necessary details without being overly verbose. A minor trade-off for completeness.

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

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

Despite no output schema, the description explains the return data shape (paired data, citation URIs) and behavior (sorted results). It adequately covers what the agent needs to know for correct invocation.

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

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

Schema coverage is 100%, and the description adds context like exact data fields per type, handling of tickers/CIKs for companies, and drug names, plus examples. This enriches the schema's descriptions.

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

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

The description uses clear phrases like 'Compare X and Y' and explicitly states it provides side-by-side comparison of 2-5 companies or drugs in one call, distinguishing it from sibling tools like entity_profile.

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

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

Explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', giving clear when-to-use guidance and implicitly indicating when not to use it.

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

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

Describes output format (findings packet with citations, gaps), response time (15-60s), and limitations. Complements annotations (readOnlyHint, openWorldHint) without contradiction.

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

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

Well-structured with bullet points and front-loaded critical info. Slightly long but every sentence adds value—no redundancy.

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

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

Given the tool's complexity (multiple sources, parallel tools), the description covers inputs, behavior, outputs, limitations, and alternative scenarios comprehensively. No output schema but describes return values adequately.

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?

Adds meaning beyond schema: explains depth enum values (quick=3, standard=5, thorough=8) and reinforces question suitability. Schema coverage is 100% so baseline is 3, but description adds extra 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?

Clearly states it performs grounded multi-source research across Pipeworx's 1160 structured data sources, distinguishing it from siblings like ask_pipeworx. Specific verb+resource+scope.

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

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

Explicitly tells when to use this tool vs alternatives (ask_pipeworx for single lookups or news), and notes account requirement. Provides clear exclusions and context.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. Description adds that results include full input schemas with curated examples ready to call directly, explaining what happens beyond safety traits.

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 one focused paragraph, front-loaded with the primary action. Every sentence adds value: examples, return format, usage guidance. No superfluous text.

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

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

Given tool complexity (6 params, 1 required), 100% schema coverage, and no output schema, the description adequately explains what the tool returns (top-N tools with schemas) and when to use it. No gaps for agent understanding.

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 each parameter having a clear description (including aliases for query). The description adds examples of query values but does not significantly extend beyond the 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?

Description uses specific verb 'Find tools' and resource 'by describing the data or task', clearly distinguishing it from siblings that perform other functions (e.g., deep_research, validate_claim). Examples of use cases (SEC filings, FDA drugs, etc.) reinforce its purpose.

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

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

Explicitly states 'Call this FIRST when you have many tools available and want to see the option set', providing clear when-to-use and when-not-to-use guidance ('not just one answer'). Helps agent decide between this and sibling 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 readOnly=false (actually readOnlyHint=true), idempotent=true, openWorld=true. Description adds valuable behavioral context beyond annotations: notes that USPTO PatentsView API 'soft-fails until reactivated', describes parallel call nature, and lists all data sources. No contradiction with annotations.

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

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

Description is a single paragraph that efficiently packs much information (examples, sources, output shape, limitations). Could be improved with bullet points for readability, but it's not verbose given the tool's complexity.

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

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

Lacking an output schema, the description fully explains what the tool returns (cik, company_name, recent_filings with URIs, fundamentals, patents, news, LEI) and covers key details like sorting of fundamentals and failure mode for patents. Provides enough context for an agent to use the tool correctly.

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

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

Schema coverage is 100% and description adds significant meaning: explains that 'value' can be ticker or zero-padded CIK, that names are not supported, and that 'type' is currently limited to 'company'. This clarifies usage beyond the schema's enum and type hints.

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

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

The description clearly states the tool provides a full cross-source profile of a US public company. It uses specific verbs ('returns', 'fans out across'), lists multiple example queries, and differentiates from sibling tools like resolve_entity by specifying it handles tickers/CIKs not names.

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

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

Explicitly says 'ALWAYS PREFER over chaining single-pack...' for holistic views, and instructs to use resolve_entity first if only a name is available. Provides clear when-to-use and when-not-to-use guidance, including that names are not supported.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true. The description adds minimal extra behavioral context beyond the word 'Delete,' which is consistent. It could elaborate on permanence or side effects.

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

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

The description is two sentences long with no superfluous words. It is front-loaded with the purpose and immediately provides usage context.

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

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

For a simple tool with one parameter and no output schema, the description is complete. It explains what the tool does, when to use it, and how to use it via the key parameter.

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 'key' parameter in the schema is sufficient: 'Memory key to delete.' The tool description does not add additional 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 the action ('Delete') and the resource ('a previously stored memory by key'). It distinguishes itself from sibling tools like 'remember' and 'recall' by being the deletion counterpart.

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

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

Explicit usage guidance is provided: 'when context is stale, the task is done, or you want to clear sensitive data.' It also mentions pairing with 'remember and recall.' However, it does not explicitly mention when not to use it.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description confirms this by describing a read-only fetch and extraction process, adding context about the output format and crawling behavior without contradicting annotations.

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

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

The description is concise (three sentences) and well-structured, front-loading the core purpose and then adding detail and use cases. Every sentence adds value.

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

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

For a simple tool with two parameters and clear annotations, the description covers the functionality, output format, and usage context adequately. It does not discuss error handling or rate limits, but these are not critical for this tool's typical 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?

With 100% schema coverage, baseline is 3. The description adds value by clarifying the url parameter with an example and specifying default/max for max_links, enhancing understanding beyond schema descriptions.

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

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

The description clearly states the tool generates a production-ready llms.txt file for a given URL, specifying the actions (fetch, extract, emit) and the output format. It distinguishes the tool's purpose well from its 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?

The description explicitly lists three common use cases (client site indexing, drafting for own project, auditing competitor visibility), providing clear when-to-use guidance. It does not, however, compare with sibling tools or state when not to use it.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, confirming safety. The description adds behavioral context by listing the exact schema components returned, which goes beyond what annotations provide. 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?

Single sentence that efficiently conveys the tool's purpose and output. No redundant words.

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

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

For a simple tool with one parameter and no output schema, the description fully covers the return values (fields, geometry type, record count, capabilities). No gaps given the low 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?

The input schema has 100% coverage with a description for the 'url' parameter. The description adds no new parameter-level information beyond the schema, so a 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?

Description uses specific verb 'Get' and clearly identifies the resource as an ArcGIS Feature/Map Service layer's schema, listing specific return items (fields, geometry type, record count, capabilities). This distinguishes it from sibling tools like query_layer.

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 by specifying 'by url', but it does not explicitly state when to use this tool versus alternatives (e.g., query_layer) or provide exclusion criteria. Usage is inferred but not guided.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint, and openWorldHint. The description adds value by listing specific return fields (id, type, params, etc.) and explaining the include_inactive parameter effect, but does not add extensive behavioral disclosure beyond the annotations.

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

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

Two concise sentences with no wasted words. The core action is front-loaded, and the additional usage guidance is 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?

For a simple list tool with one optional parameter and comprehensive annotations, the description covers return fields, usage scenarios, and parameter behavior fully, making it complete without need for an output schema.

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

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

Schema description coverage is 100%, so the baseline is 3. The description reinforces the parameter's purpose and default behavior ('include cancelled subscriptions in the response (default false)'), but adds no new syntax or meaning beyond the schema.

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

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

The description clearly states the verb 'list' and resource 'subscriptions', specifies scope ('caller's active'), and lists return fields. It effectively distinguishes from sibling tools like 'subscribe' and 'unsubscribe'.

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

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

The description provides clear usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells when to use the tool, though it does not explicitly name alternative tools for other scenarios.

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

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

Annotations are present and consistent. The description adds that feedback is free, rate-limited to 5 per identifier per day, doesn't count against tool-call quota, and that team reads digests daily. No contradiction with annotations.

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

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

The description is concise yet comprehensive, front-loaded with purpose. Every sentence adds value, no redundancy. Well-structured with clear guidance.

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 covers what agents need: rate limits, quota impact, team responsiveness, and roadmap influence. It's complete for a feedback tool.

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

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

Schema coverage is 100%, but description adds value by explaining enum values in more detail, providing guidance on context fields, and specifying length limits for message (1-2 sentences, 2000 chars max).

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 the tool is for sending feedback to the Pipeworx team about bugs, missing features, data gaps, or praise. It specifies the exact actions and resource, clearly distinguishing it from other 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?

Explicit when-to-use scenarios are provided: bug (wrong/stale data), feature/data_gap (missing tool), praise (working well). Also gives guidance on what to include and not include, plus rate limit and quota info.

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

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

Beyond annotations (readOnly, idempotent, non-destructive), the description adds that data is self-aggregating from CF analytics engine, contains no PII, and is cached 5min-1h. This provides valuable behavioral context.

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

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

The description is concise (5 sentences), front-loads the core output, and every sentence adds value. No wasted words.

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

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

For a simple tool with 1 parameter and no output schema, the description covers purpose, usage, behavior, and parameter guidance completely.

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

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

Schema coverage is 100% with a descriptive enum for 'window'. The description adds nuance about short vs long windows, enhancing meaning beyond schema.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a selected window. It uses specific verbs and resources, and the distinct focus on trending data differentiates it from siblings like 'discover_tools'.

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

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

Three explicit use cases are provided (discovering hot data sources, confirming a popular tool, aligning with agent needs). It lacks explicit when-not-to-use guidance but effectively communicates context.

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

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

Annotations indicate read-only, idempotent, non-destructive behavior. The description adds valuable context: FILL CHECK that prices against live CLOB depth and warns not to trade if unrealizable, SEMANTIC ANCHOR for cross-event similarity, PARTITION FILTER for placeholder slugs. 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 detailed but lengthy, covering many procedural details and edge cases. While every sentence adds value, it could be more concise or structured to aid quick reading. Front-loads the main purpose but risks overwhelming the agent.

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

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

Despite no output schema, the description explains the response structure (opportunities[], partition_check, fill_check) with key fields. It covers all modes adequately. Minor gap: exact fields for trending_scan mode are not fully specified.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning beyond the schema by explaining accepted formats (slug or full URL for event, seed question for topic) and elaborating on how each parameter drives different analysis modes. This extra context justifies a 4.

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 three modes (no args, event, topic) and is specific about the resource and verb, setting it apart from sibling tools like polymarket_edges or polymarket_fill_risk.

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

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

Explicit guidance on when to use each mode: 'Call with NO args for a trending_scan... pass event... or topic...' and recommends event for specific markets. Mentions cross-event catches patterns single-event misses, and references polymarket_fill_risk for custom sizing. However, it does not fully cover when not to use this tool versus all alternatives.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds extensive behavioral details: three model families, edge calculation, Kelly fractions, slippage, tradeable-edge knobs, diagnostics, caching, and more. 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 very long with multiple paragraphs and detailed bullet points. While well-structured, it is not concise and could be significantly shortened without losing essential information.

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

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

Given the complexity (9 params, no output schema), the description is remarkably complete. It explains every segment, diagnostics, edge computation, and caching. No gaps for an agent to infer.

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 descriptive parameter descriptions. The description adds some context (e.g., min_partition_leg_kelly explanation) but mostly repeats schema info. Baseline 3 is appropriate.

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

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

The description clearly states it scans top Polymarket markets to find opportunities where Pipeworx data disagrees with market price, using a specific verb 'scan' and resource. It distinguishes from siblings like polymarket_arbitrage by focusing on edge detection.

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 'Built for what should I bet on today' and explains the purpose. It provides guidance on using filters like min_liquidity and max_spread_pp, but does not explicitly contrast with sibling tools like polymarket_edge_tracker.

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

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

Description adds behavioral details beyond read-only/idempotent annotations: it reads from daily snapshots, decay is computed on absolute edge_pp_net, and snapshots may have gaps. No contradictions with annotations.

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

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

Description is front-loaded with key purpose, then details response structure and limitations in a clear, logical order. Slightly long but 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?

Without an output schema, the description thoroughly explains the response structure (tracked[], expired[], snapshot_dates[]) and field meanings. Covers limitations and data gaps, making the tool fully understandable.

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?

Parameters are already well-described in schema (100% coverage), but the tool description adds context: default lookback of 14 days, max 30, and default window of '1wk' with expected values (24hr, 1wk, 1mo). Provides practical usage details.

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

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

Tool clearly states it tracks 'edge persistence and decay' from daily snapshots, answering the specific question of how long an edge has existed and whether it is shrinking. It distinguishes itself from a basic edge viewer (e.g., polymarket_edges) by focusing on historical persistence.

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 context on when to use (e.g., distinguishing between fresh and aged edges) and notes limitations (60-day TTL, snapshot gaps). Could explicitly mention when not to use or alternatives, but the guidance is strong.

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

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive traits. The description adds behavioral details: it walks the ladder, returns verdicts (clean/degraded/cannot_fill), and for baskets identifies thin legs and forced directional risk. 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 fairly long but well-structured with clear headers (SINGLE-MARKET, BASKET) and bullet-like enumeration of return fields. Every sentence adds value; the length is justified given the tool's complexity and the lack of an output schema. Minor reduction could be possible, but overall effective.

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

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

With no output schema, the description thoroughly explains return values for both modes, including verdict, per-leg details, thin legs, and forced directional risk. It covers constraints (required one of market/event, defaults, clamp) and gives example usage context. Complete for the tool's complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant context: explains how size_usd is interpreted differently in single-market vs basket (max spend vs settlement notional), describes side defaults and auto-detection for basket, and clarifies the required parameter constraint (market or event). This goes well beyond the schema descriptions.

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

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

The description clearly states it performs a realizable-vs-theoretical edge check against live CLOB order-book depth. It distinguishes between single-market and basket modes, and specifies the outputs for each. This is a specific verb+resource that differentiates it from sibling tools like polymarket_arbitrage or polymarket_edges.

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

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

Explicitly advises using this tool before acting on polymarket_arbitrage signals or polymarket_edges trades above ~$500. It explains the consequences of not checking (unhedged directional positions) and when to use single-market vs basket mode, providing clear when-to-use and when-not-to-use guidance.

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

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

The description extensively discloses behavioral traits beyond annotations: compatibility_warning conditions, temporal_alignment, skipped_cross_type/subtype counters, and the rarity of real spreads. It aligns with annotations (readOnlyHint, openWorldHint, idempotentHint, non-destructive) and adds rich context.

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

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

The description is well-structured with clear sections (two modes, response, safety fields) and front-loaded with purpose. It is somewhat verbose but every sentence adds value, earning a 4 rather than a 5.

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 parameters, no output schema, and rich annotations, the description is highly complete. It explains response structure, safety fields, and conditions for meaningful spreads, covering all information an agent needs to decide when to use and interpret results.

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

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

With 100% schema description coverage, the baseline is 3. The description adds value by explaining how parameters interact (overrides) and providing the list of pre-mapped topics, which goes beyond the schema's inline description.

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

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

The description clearly states it calculates cross-venue spread between Kalshi and Polymarket for the same resolving question. It uses specific verbs and resources, and distinguishes from sibling tools like polymarket_arbitrage by focusing on cross-venue rather than intra-venue 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 covers two usage modes (topic and explicit), provides context for when each is appropriate, and warns that pre-mapped topics may not yield tradeable spreads. It does not explicitly list when not to use or reference sibling tools, but the guidance is clear and actionable.

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

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

Annotations provide readOnly, openWorld, idempotent, and non-destructive hints. The description adds that it returns attribute rows and geometry, and mentions pagination via limit/offset, but does not disclose external API dependency or potential error conditions.

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

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

Two sentences plus a usage example. Front-loaded with action and key details. Every sentence adds value, and the example reinforces usage.

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

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

Given no output schema, the description adequately states return of attribute rows and geometry. It does not cover error cases or rate limits, but the annotation-rich context reduces the need for extensive elaboration.

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

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

Schema covers all 6 parameters with descriptions. The description adds SQL-like context, clarifies comma-separated out_fields, and gives example values ('1=1', '*'), which enhances understanding beyond the schema.

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

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

The description clearly states the tool queries an ArcGIS Feature/Map Service layer by URL, and specifies SQL-like parameters. It distinguishes from siblings like 'layer_info' (metadata) and 'search_datasets' (discovery), and includes an example query.

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 from search_datasets and gives a sample query pattern, but does not explicitly state when not to use this tool or compare to alternatives like 'layer_info'.

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. Description adds scoping details (anonymous IP, BYO key hash, account ID) and behavior of listing all keys when key omitted, which goes beyond annotations.

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

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

Three sentences, each essential: first states core function, second gives use case, third covers scoping and relationships. No filler, perfectly front-loaded.

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

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

Given simple input schema and annotations covering safety, description explains behavior, scoping, and relationship to siblings. Could mention output format, but without output schema, this is adequate.

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

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

Schema has 100% coverage of the single parameter. Description adds meaning: omitting key lists all keys, examples of key values, and pairing with related tools. Fully informs parameter usage.

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

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

Description clearly states the tool retrieves a value saved via 'remember' or lists all keys if omitted. It gives concrete examples of stored context (ticker, address, research notes) and distinguishes from siblings '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?

Explicitly says to use for looking up stored context without re-deriving. Mentions pairing with remember and forget. Scoping to identifier is noted. Slight lack of explicit when-not-to-use, but context is clear.

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

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

The description contradicts the readOnlyHint annotation by stating mark_read:true flags events as read, which is a side effect modifying state.

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 covering purpose, return fields, filtering, and polling behavior, with no wasted words.

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

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

For a read-only tool with high schema coverage and annotations, the description fully explains return fields, filtering, and poll behavior, and provides an alternative access method.

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?

Adds practical meaning beyond the schema by providing an example filter value and explaining the effect of mark_read, even though 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 it pulls fired events from the subscription feed, distinguishing it from sibling tools like list_subscriptions which list subscriptions, not events.

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

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

Provides clear context for usage, including filtering options and polling suitability, and mentions an alternative endpoint for scripts/dashboards, but does not explicitly exclude alternatives.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint. The description adds rich behavioral detail: fans out to multiple sources (SEC, GDELT, GNews, USPTO), fallback logic (GDELT→GNews on rate limit), USPTO soft-failure, return structure (changes[], total_changes, citation URIs). 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 dense paragraph that efficiently conveys purpose, usage, sources, and return structure. Every sentence adds useful information, though the first line packs many example queries into a comma-separated list. Could be slightly more structured but is well front-loaded.

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

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

Given the tool's complexity (3 required params, no output schema), the description fully covers the input semantics, behavior across multiple data sources, fallbacks, time window handling, and return shape. No gaps remain for an agent to safely invoke the tool.

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

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

Schema coverage is 100%, yet the description adds significant value: explains `since` formats (ISO date or relative shorthand like '7d', '30d', '3m', '1y'), recommends defaults, and clarifies that `value` accepts ticker or CIK. This exceeds 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 starts with concrete example queries ('What's new with X', 'latest on Y'), explicitly states it returns a change feed for a company in a time window, lists data sources, and contrasts with sibling tool entity_profile.

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

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

The description explains when to use ('What's new', 'updates on') and provides a clear alternative: 'Use entity_profile instead when you want the static profile'. It also includes parameter recommendations like 'Use "30d" or "1m" for typical monitoring'.

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

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

The description adds significant context beyond annotations: scoping by identifier, persistence differences for authenticated vs anonymous users (24-hour retention), and key-value storage. Annotations already indicate idempotent and non-destructive, but description enriches with practical details.

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 (4 sentences) and front-loaded with purpose. Every sentence adds value: purpose, when to use, scoping, persistence details, and pairing with siblings. No wasted words.

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

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

For a simple tool with 2 parameters and no output schema, the description is complete. It covers purpose, usage context, persistence behavior, and relationships with sibling tools (recall, forget). No gaps identified.

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

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

The input schema already has 100% coverage with descriptions for 'key' and 'value'. The description adds little beyond the schema, mainly reinforcing the key-value pair concept. Baseline of 3 is appropriate as 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 clearly states the tool's purpose: 'Save data the agent will need to reuse later'. It specifies the resource as key-value pairs and distinguishes it from siblings by mentioning pairing with '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?

The description provides explicit when-to-use guidance: 'Use when you discover something worth carrying forward' with concrete examples (resolved ticker, target address, user preference). It also mentions pairing with recall and forget, but does not explicitly state 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 declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds significant behavioral details: internal cascading lookups, replacement of 2-3 manual steps, and specific return fields (ticker+CIK+company_name or RxCUI+ingredient+brand) with citation URIs. 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 longer but well-structured: starting with user-facing query examples, then the core purpose and usage hint, followed by detailed type-specific output. Every sentence adds value, though it could be slightly more terse 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?

The description covers input variations, output structure for each entity type, and citation URIs. It mentions auto-disambiguation for company. Minor gap: it does not explicitly address what happens when the entity is not found, but the examples and detail are sufficient for most use cases.

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%, providing baseline 3. The description adds meaningful context: explains that value can be ticker, CIK, or name for company (with auto-disambiguation), and brand or generic name for drug, with examples. This enriches the parameter understanding beyond the schema's enum and 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 starts with common query patterns ('What's the ticker for…') and explicitly states the tool resolves a name to a canonical identifier. It says 'Use FIRST whenever you have a name but need an ID,' clearly distinguishing it from sibling tools that handle other entity-related tasks.

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

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

The description provides explicit when-to-use guidance ('Use FIRST whenever you have a name but need an ID') and includes example query formats. While it does not enumerate alternatives, the context signals it as the entry point for entity resolution, and the sibling list suggests other tools have different purposes.

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, non-destructive. Description adds behavioral detail: probes each entity, ranks by score, surfaces most/least recognized. 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?

Three sentences, no fluff. Each sentence adds clear value: purpose, method, use case, and output structure. Front-loaded with key action 'Compare AI visibility'.

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

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

No output schema, but description clearly describes return value (ranked list with score, confidence, signal density). Missing error states or constraints (e.g., 2-8 entities limit), but these are in schema. Overall sufficient for a read-only comparison tool.

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

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

Schema coverage is 100% so baseline is 3. Description adds value by explaining that 'entities' first entry is treated as subject for narrative, and context disambiguates common names. These insights go 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?

Clear verb+resource: 'Compare AI visibility'. Explicitly states it uses ai_visibility_check and returns ranked results. Distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison) by specifying the multi-entity AI audit context.

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

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

Provides explicit use case: 'competitive AI-marketing audits' and example question. Does not explicitly state when not to use or suggest alternatives like ai_visibility_check for single entities, but context is clear enough for an agent to infer.

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 significant behavioral detail: it fans out across services, handles partial failures gracefully (bundlephobia first measurement takes 5-30s, sources_failed lists timeouts), and specifies what the summary block and additional outputs contain. No annotation 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 a single dense paragraph that front-loads the purpose. Every sentence adds value, but it could be slightly more structured (e.g., bullet points for return fields). No wasted words.

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

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

Despite no output schema, the description thoroughly explains the return structure (summary block with specific fields, per-advisory detail, links, alternative versions) and error handling behavior. For a complex multi-service tool, this provides sufficient 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% with descriptions for both parameters. The description adds value by mentioning that scoped packages (e.g., '@types/node') are accepted, which is a nuance not in the schema. However, it does not add new meaning beyond the schema for the default version behavior.

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

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

The description clearly defines the tool as a composite check for evaluating npm packages, listing specific data sources (deps.dev, bundlephobia) and what it returns. It distinguishes itself by being a one-call solution for safety, popularity, and size queries, unlike sibling tools such as 'discover_tools' 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?

Explicit usage guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also states limitations: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly,' providing clear alternatives and 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, destructiveHint false. The description adds context about the return structure (name, summary, record_count, owner/org, url) and the intended downstream flow, providing value beyond the annotations.

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

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

Two sentences with no redundancy. The first sentence states the core action and scope. The second tells what is returned and how to use the result. Very 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 no output schema, the description covers the return fields and next steps. It lacks details on error handling and result structure when no matches, but overall it is sufficient for the tool's purpose.

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

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

All parameters have schema descriptions that cover their purpose. The description adds real-world examples for the query parameter and explains the output, enhancing understanding of how to use the parameters.

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

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

The description clearly states it searches Washington County GIS open geospatial datasets by keyword, listing example dataset types and what is returned. It distinguishes from sibling tools by mentioning downstream tools like query_layer/layer_info, which are 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?

The description explains to search by keyword and that the returned url should be passed to query_layer/layer_info. However, it does not explicitly state when not to use this tool versus siblings like search_within.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds beyond: 'BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars.' It also mentions output format (passages with offsets and similarity scores). No contradiction with annotations.

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

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

Description is front-loaded with main purpose, then explains use case, output, pairing, and technical details. Each sentence has a clear purpose. Slightly long but all content is relevant and well-structured.

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

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

Despite no output schema, the description fully explains output (top-N passages with character offsets and similarity scores). It also covers technical details like embedding model and character cap, giving a complete picture of tool behavior.

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

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

Schema coverage is 100%, so baseline is 3. Description adds concrete examples for query (e.g., 'supply-chain risk'), default and range for limit (1-20, default 5), and char limit for text (max ~200K chars). These enrich parameter meaning.

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

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

Description clearly states: 'Semantic search INSIDE a fetched record.' It specifies the action (search inside), the resource (fetched record), and distinguishes from sibling tool ask_pipeworx_grounded by mentioning it as a companion tool.

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

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

Explicitly says: 'Use when the record is too big to cram into the prompt — search_within saves context.' It also pairs with ask_pipeworx_grounded, providing an alternative workflow. The description gives clear context for when to use and how it fits with other tools.

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

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

Discloses OAuth requirement, always-on feed, delivery channel constraints (SMS cap, webhook auto-disable), and type-specific param details, adding value beyond annotations that only provide readOnlyHint and idempotentHint.

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

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

Well-structured with key info front-loaded, but slightly verbose due to extensive examples; each sentence adds value.

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

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

Handles complex subscription logic with multiple types and delivery options comprehensively, covering account requirements, constraints, and examples. No output schema but return id is mentioned.

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

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

Schema covers all parameters, but description enriches with per-type examples and explains delivery object structure in detail, going 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 verb 'Create' and resource 'proactive monitoring subscription to a live-data event stream', returning id. Distinguishes from sibling tools like list_subscriptions and unsubscribe.

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

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

Specifies account requirement (Pipeworx OAuth) and lists supported types with examples. Does not explicitly contrast with alternatives but context implies when 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, destructiveHint. Description adds valuable context: returns category-bucketed example questions with tool+argument shape, drawn from live catalog. 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?

Reasonably concise given the amount of information. Front-loads purpose with common queries. Could be slightly shorter, but well-structured and scannable.

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

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

Given no output schema, description compensates by explaining return format (category-bucketed with tool+argument shape). Sibling tools context shows this is unique. Complete for an onboarding tool.

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

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

Schema has 100% coverage with description for topic parameter. Description adds meaning: lists example topic values and explains behavior when omitted ('full spread'). Adds context beyond schema.

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

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

Description clearly states it's the onboarding entry point, returns category-bucketed example questions with exact tool+argument shapes. Distinguishes from siblings like ask_pipeworx, compare_entities, etc.

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

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

Explicitly says to use 'FIRST when you do not yet know what Pipeworx can do' and provides guidance on optional topic filtering. Also mentions when not to use it (after familiarity).

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

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

The description adds significant behavioral context beyond annotations: ownership enforcement and deactivation vs deletion ('The row is deactivated (not deleted) so its historical events stay available'). This aligns with annotations (destructiveHint=false, idempotentHint=true) and enhances understanding.

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, no redundancy. The primary action is front-loaded, with additional behavioral details following logically. 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?

Given the tool's simplicity (1 parameter, no output schema), the description fully covers purpose, constraints, and behavior. No missing information for an AI agent to invoke correctly.

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

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

Schema coverage is 100%, and the description adds context by specifying 'by id' and clarifying that the id was 'returned by subscribe', linking to sibling tool. This provides more meaning than 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 explicitly states the action ('cancel a subscription') and resource ('by id'), distinguishing it from sibling tools like 'subscribe' and 'list_subscriptions'. The verb 'cancel' is specific and unambiguous.

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

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

The description provides clear usage context: ownership enforcement ('you can only cancel your own subscriptions'). While it doesn't explicitly state when not to use, the constraint is clearly communicated, and no alternative sibling tool exists for unsubscribing.

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, etc. Description adds value by listing return values (verdict types, percent delta, citation) and explaining that it consolidates multiple steps. No contradiction with annotations.

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

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

Description is front-loaded with user query examples, making purpose immediately clear. Each sentence adds value, though the last sentence about replacing 4-6 calls is slightly redundant but still informative.

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 single-parameter schema and rich annotations, the description covers the use case well, including output types. It honestly states 'v1 supports company-financial claims', implying limitations. No output schema, but the description explains return values sufficiently.

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

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

Schema description for 'claim' is 100% covered, and the description adds concrete examples like 'Apple's FY2024 revenue was $400 billion', which helps the agent understand expected input format 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?

Description uses specific verbs and examples ('check whether... factually correct', 'natural-language claim verification') and distinguishes from siblings by stating it replaces 4-6 sequential calls. Clearly states the domain (company-financial claims).

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

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

Explicitly states when to use: 'whenever the agent needs to check whether something a user said is factually correct.' Scope is defined (company-financial claims via SEC EDGAR + XBRL). No explicit when-not or alternative tools, but context is clear.

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