Arcgis Charlotte

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds beyond this by specifying the default free model (Workers AI), the need for a BYO API key for Anthropic, and details about the output structure (per-model score, confidence, signals, raw_response). No contradictions.

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

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

The description is four well-structured sentences, front-loaded with the core function and score range. Every sentence adds value, with no redundancy or unnecessary detail. It efficiently conveys purpose, usage, and output format.

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

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

Given four parameters, no output schema, and moderate complexity, the description is complete. It explains all inputs, outlines the return structure (per-model data plus combined view), and provides examples of use cases. This fully equips an agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100%, so the baseline is 3. However, the description adds meaning beyond the schema: it explains the default model for 'models' parameter, the pricing implication for '_apiKey', and how 'context' aids disambiguation. This justifies a score of 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: it probes LLMs for knowledge about a business or entity and returns a visibility score from 0-100. The verb 'probe' and resource 'LLMs' are specific, and the description differentiates it from sibling tools like 'scan_competitor_ai_presence' by focusing on general AI visibility scoring rather than competitor-specific or deep research.

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

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

The description explicitly mentions use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains the default model vs. optional BYO key for Anthropic. It provides clear context for when to use the tool, though it does not explicitly exclude scenarios or mention alternatives among siblings.

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

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

Discloses routing to 4,552 tools, returns structured answers with citation URIs, and notes it's a fast, single call. All annotations (readOnlyHint, openWorldHint) are consistent.

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

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

Well-structured with bullet-like lists, front-loaded key directive, and each sentence adds value. Not overly verbose for its comprehensive scope.

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?

Very complete given no output schema: explains behavior, scope, and limitations. Covers all necessary context for an agent to use correctly.

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

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

Schema coverage is 100% with good aliases. The description adds context on the type of questions accepted and provides examples, 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?

The description clearly states it answers factual questions using structured data from thousands of sources, and distinguishes from sibling tools like ask_pipeworx_grounded and deep_research.

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

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

Explicitly provides when to use (e.g., factual questions, 'what is', 'look up'), when not to use (e.g., hallucination-resistant answers or broad/multi-part queries), and suggests 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, openWorldHint, idempotentHint, destructiveHint=false. The description adds significant behavioral context: the two-step process (routing then extraction), the exact response format with evidence and refusal reasons, and the cost implication. No contradiction with annotations.

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

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

The description is relatively long but well-structured: it front-loads the core purpose, then explains the process, use cases, and limitations. Every sentence adds value, though it could be slightly more concise 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 tool's complexity (routing among 4,552 tools across 1160 sources, two-step process, refusal modes, no output schema), the description covers all necessary aspects: behavior, output format, refusal reasons, cost trade-off, and appropriate contexts. It is sufficiently complete for an agent to use correctly.

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

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

Schema coverage is 100% as all six parameters are aliases for 'question' and each has a description. The description repeats that the tool accepts a natural language question but does not add meaning beyond the schema. Baseline score of 3 is appropriate.

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

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

The description clearly states it's a 'hallucination-resistant answer mode for high-stakes reads'. It specifies the verb 'answer' and the resource 'Pipeworx data'. It distinguishes itself from the sibling 'ask_pipeworx' for casual lookups, making the purpose 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?

Explicitly tells when to use: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts'. Also gives a clear alternative: 'prefer ask_pipeworx for casual lookups'. Mentions the cost trade-off (one extra LLM call), providing complete usage 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 goes far beyond annotations (readOnlyHint, idempotentHint) by detailing resolution methods, fan-out logic, response shapes, resolver contract, parent extractor, news fallbacks, safety mechanisms, and resolution-rule 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 lengthy but well-structured with clear sections and front-loaded purpose. Every sentence adds value, though it could be slightly more concise. The detail is justified by the tool's complexity.

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

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

Given no output schema, the description thoroughly explains response shapes, evidence structure, resolver contract, parent event extraction, news fallbacks, and safety/edge cases. It covers all needed context for correct invocation.

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

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

Schema coverage is 100%, but the description adds meaningful context: explains market parameter accepts slug/URL/question, depth enum values (quick vs thorough), and include_raw default behavior with size implications. This adds value beyond the schema.

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

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

The description clearly states 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the specific verb and resource, distinguishes from sibling tools like ask_pipeworx and polymarket_edges, and provides concrete use cases.

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

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

Usage guidelines are explicit: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also details when not to trust results (low-confidence matching, closed markets, wide spreads) and provides safety instructions.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. Description adds significant context: details data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/clinical trials for drugs), handling of off-calendar fiscal years with examples, sorting by primary metric, and output format (paired data + 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?

Description is front-loaded with the core purpose and preference instruction. It is well-structured with separate details per entity type. However, the opening examples ('Compare X and Y', etc.) are a bit verbose; could be slightly trimmed without losing clarity.

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

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

Given the tool's complexity (two entity types, multiple data sources, sorting), the description thoroughly covers what it does, how to use it, what data is returned, and even handles edge cases (off-calendar fiscal years). No output schema exists, so the description compensates fully, enabling correct agent selection and invocation.

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

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

Schema coverage is 100%, so baseline is 3. Description adds meaning beyond schema: for type, explains what each enum value retrieves; for values, gives concrete formatting examples (tickers/CIKs for company, names for drug) and explains constraints (2-5 items). This provides useful context not present in minimal schema descriptions.

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

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

Description clearly states the tool performs side-by-side comparison of entities, specifies the resource (companies or drugs), and distinguishes from sequential lookups with 'ALWAYS PREFER over sequential single-pack lookups'. It gives example queries ('X vs Y', 'which is bigger', etc.) that match the tool's function.

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

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

Description explicitly advises to prefer this tool over sequential lookups when comparing entities. It outlines the specific data pulled for each type (company: financials; drug: FDA/clinical data) and provides example queries. It does not explicitly mention when not to use or name alternative tools, but the context is clear enough for selection.

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

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

The description adds significant behavioral context beyond annotations: account requirement, parallel decomposition, return format (findings packet with verbatim evidence, confidence, source, citation, gaps), and expected time (15-60s). Annotations already declare readOnlyHint, so 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 quite lengthy and could be more concise. While it front-loads the account requirement and key comparisons, it includes repetitive information (e.g., mention of structured data multiple times). Every sentence adds value, but the verbosity slightly reduces clarity.

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

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

Given the complexity (multi-source research across 1160 sources), 2 parameters, annotations present, and no output schema, the description fully covers what to expect: findings packet structure, gaps array, citation format, and failure modes (empty for non-structured topics). It addresses common edge cases and sets expectations for time.

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

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

Schema coverage is 100% with both parameters described. The description adds meaning by explaining that depth controls number of facets (quick=3, standard=5, thorough=8) and that question accepts natural language. This enriches the schema, though schema already covers basics.

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 purpose: 'Grounded multi-source research across Pipeworx's 1160 STRUCTURED data sources' and distinguishes it from open-web search. It also differentiates from sibling tools like ask_pipeworx by noting that deep_research is for broad/multi-part questions over structured data.

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 guidelines are provided: 'Best for broad/multi-part questions over structured data', and when not to use: 'For a single lookup use ask_pipeworx' and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'. It also recommends ask_pipeworx for those cases, giving clear alternatives.

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

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

Annotations already declare the tool as read-only and idempotent. The description adds behavioral context: it 'Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly.' This clarifies the output's structure and readiness, going beyond annotations.

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

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

The description is reasonably concise given its informative content. It is front-loaded with the core verb and resource, followed by concrete examples of domains and usage. Every sentence contributes meaning, though the list of domains could be slightly trimmed without losing clarity.

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

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

For a discovery tool with 6 parameters and no output schema, the description covers when to use, what it returns, and query examples. It does not detail exact output format, but the lack of output schema limits expectations. The description is sufficiently complete for the tool's role.

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

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

Schema coverage is 100% with detailed field descriptions and aliases. The description adds value by explaining the purpose of the query parameter ('Natural language description') and the effect of 'limit' (max 50). It also notes 'curated examples' in results, which enhances parameter 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 explicitly states 'Find tools by describing the data or task.' It specifies the verb ('discover'), the resource ('tools'), and the context ('browse, search, look up'). It also distinguishes from sibling tools by positioning itself as a first-step discovery tool, not a data-retrieval 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?

The description provides clear usage guidance: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set.' It implies when-not by contrasting with direct answers, but lacks an explicit 'do not use if you already know the tool' statement.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds significant context: it fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), describes return fields (CIK, filings, fundamentals, patents, news, LEI), and notes a sunset for the USPTO API with soft-fail behavior. No contradictions with annotations.

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

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

The description is dense but well-structured: example queries upfront, then clear statement of purpose, then detailed breakdown of sources and return fields. Every sentence adds value; it could be slightly shortened without loss, but it remains efficiently packed with necessary information.

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

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

Despite having no output schema, the description thoroughly explains return values: CIK, company name, recent filings with URIs, fundamentals with sorting, patents, news, and LEI. It also notes the USPTO API sunset. This provides sufficient context for an agent to understand what the tool returns and its limitations.

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

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

Schema coverage is 100% and includes descriptions for both parameters. The description adds meaning by explaining that 'value' accepts ticker or zero-padded CIK, explicitly stating names are not supported, and noting that 'type' only supports 'company' currently. This enhances understanding beyond the schema alone.

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

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

The description clearly states the tool's purpose: producing a full cross-source profile of a US public company in a single call. It provides example queries ('Tell me about X', 'research Acme') which directly illustrate the use case, and distinguishes from siblings like resolve_entity for name resolution.

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

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

Explicitly recommends preferring this tool over chaining individual data source lookups for holistic views. Also specifies that names are not supported and advises using resolve_entity first if only a name is available, giving clear when-to-use and when-not-to-use guidance.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true. The description adds context about clearing sensitive data and aligns with the destructive nature. It does not contradict annotations and provides additional behavioral context beyond structured fields.

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 concise sentences with no unnecessary words. It is front-loaded with the primary action and then provides usage guidance. 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, usage context, and behavioral traits. It is complete for an agent to correctly select and 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?

The input schema has 100% coverage, with a clear description of the 'key' parameter ('Memory key to delete'). The description simply says 'by key', adding no new meaning beyond what the schema already provides. Baseline 3 is appropriate.

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

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

The description clearly states the action: 'Delete a previously stored memory by key.' It specifies the resource (memory) and verb (delete), distinguishing it from siblings like 'remember' and 'recall' by mentioning pairing with them.

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

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

The description provides explicit usage scenarios: 'when context is stale, the task is done, or you want to clear sensitive data.' It also suggests pairing with remember and recall, offering guidance on when to use this tool. However, it does not explicitly state when not to use it or provide 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, openWorldHint, idempotentHint, destructiveHint, so the agent knows it's safe. Description adds process details (fetches page, extracts title/description/links, emits markdown) and output format, complementing the annotations without contradiction.

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

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

Description is two sentences plus a useful 'Useful for' list. It is front-loaded with the main action and efficient, though the list could be integrated more seamlessly. Still concise with minimal waste.

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

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

Given the simple tool (2 params, no output schema, no nested objects), the description fully explains what the tool does, how it works, and what to expect as output. No gaps remain.

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

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

Schema description coverage is 100% with clear descriptions for both url and max_links (including default 25, max 50). The main description adds no further parameter meaning beyond summarizing the tool's function, so baseline 3 applies.

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

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

Description uses specific verb 'generate' and resource 'production-ready llms.txt file' for a clear purpose. It distinguishes from siblings by its unique function; no other sibling tool generates llms.txt.

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

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

Provides explicit use cases (client indexing, own project, competitor audit), giving clear context for when to use. However, it does not mention when not to use or compare to alternatives 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?

Description adds context by listing returned data types, but no behavioral traits beyond annotations (readOnly, idempotent). Annotations already cover safety profile, so description adds moderate value.

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, no redundancy, clearly structured with key information front-loaded. Every word 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 simple tool with 1 parameter and annotations that cover safety, description explains return values sufficiently. No output schema, but description lists all key components. Missing minor context like error 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 has 100% coverage with description for the 'url' parameter. Description does not add extra semantic meaning beyond the schema's example, so baseline 3 is appropriate.

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

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

Description clearly states it retrieves schema of a Feature/Map Service layer, listing exact outputs (fields, geometry type, record count, capabilities). Distinguishes from sibling 'query_layer' which queries actual data.

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?

No explicit when-to-use or when-not-to-use guidance. Usage is implied but not contrasted with alternatives like 'query_layer'. Lacks exclusions or prerequisites.

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

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

Annotations already declare read-only, idempotent, non-destructive. Description adds response field details, but no extra behavioral context beyond that.

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

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

Two sentences, front-loaded with purpose and usage guidance, 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?

Covers purpose, usage, return fields, and parameter; no output schema needed since fields are listed.

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?

Single parameter 'include_inactive' is fully described in schema; description adds no extra meaning, meeting baseline for 100% coverage.

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

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

Clearly states 'List the caller's active subscriptions' and enumerates return fields, distinguishing it from sibling tools like subscribe and unsubscribe.

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

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

Explicitly tells when to use: 'review what you're monitoring before adding more or to find an id to cancel,' directly linking to subscribe/unsubscribe siblings.

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

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

The description adds behavioral context beyond annotations: rate-limited to 5 per identifier per day, free, doesn't count against tool-call quota. Annotations only show readOnlyHint, destructiveHint, etc., which are false. No contradictions.

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

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

The description is concise but includes some redundancy (e.g., 'Free; doesn't count against your tool-call quota' could be combined). It is 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 three parameters, no output schema, and a nested object, the description covers use cases, constraints, rate limits, and formatting hints. It is complete enough for the tool's simplicity.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds extra nuance: 'Don't paste the end-user's prompt' for the message field. It also explains the type enum values more contextually.

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

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

The description clearly states the tool's purpose: sending feedback to Pipeworx about bugs, features, data gaps, or praise. It uses specific verbs and distinguishes from sibling tools like ask_pipeworx by focusing on feedback about tools/packs.

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

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

The description provides explicit guidance on when to use each feedback type (bug, feature, data_gap, praise) and what not to do (don't paste end-user prompt). It also notes rate limits and quota.

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. Description adds valuable context: data source (CF analytics-engine), privacy (no PII), caching (5min-1h). This goes beyond what annotations provide.

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

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

Three concise sentences plus a bulleted list of use cases. No redundancy or filler. Information is front-loaded with the primary purpose immediately clear.

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

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

For a simple tool with no output schema, the description covers return values (top tools, packs, call volume), caching, data sourcing, and privacy. No gaps given the tool's simplicity.

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

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

Only one parameter 'window' with full schema coverage (100%). Schema description explains enum values and usage rationale. Tool description only mentions window in passing, adding no new 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 returns trending tools and packs, with specific verb 'returns' and resource 'top tools, top packs, total call volume'. It distinguishes from siblings like 'search_datasets' or 'ask_pipeworx' by focusing on aggregate usage statistics rather than individual queries.

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 lists three concrete use cases (discovering hot data sources, confirming canonical tools, aligning use case with agent needs). Does not mention when not to use or alternatives, but the use cases are sufficiently directive.

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, so safety is clear. The description adds extensive behavioral details: monotonicity violations, partition-sum checks, fill check against live CLOB depth, and semantic anchoring (Jaccard similarity). No contradictions.

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

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

The description is well-structured, front-loading the core purpose and then systematically explaining modes, checks, and constraints. Every sentence provides necessary information without redundancy, achieving high information density for a complex tool.

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

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

Despite no output schema, the description fully describes the response structure (opportunities[], partition_check, fill check) and key fields (gap_pp, suggested_trade, reasoning). It covers edge cases like placeholders and thin legs, making it complete.

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

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

Schema coverage is 100% with both parameters described. The description adds value beyond schema by providing examples ('fed-decision-may-2026'), explaining mode implications, and noting that full URLs are accepted.

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 'find arbitrage opportunities' and the resource 'Polymarket'. It distinguishes three modes (trending_scan, event, topic) and explains when to use each, differentiating from sibling tools like 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 no args, 'event', or 'topic'. It recommends event mode for a specific market and explains cross-event use cases. It also advises not to trade when realizable_edge_pp <= 0 and directs to polymarket_fill_risk for custom sizing.

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

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

Annotations (readOnly, idempotent) are fully supported. Description adds caching details, edge calculation methodology, and diagnostic output - all consistent 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?

Dense but well-structured with numbered segments and clear headings. Front-loaded with purpose and overview. Some redundancy could be trimmed, but complexity justifies length.

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

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

Thorough explanation of response structure, caching, filter knobs, and diagnostics. No output schema needed - description covers return values comprehensively.

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

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

With 100% schema coverage, description adds significant value beyond schema definitions - explains edge calculation, slippage reasoning, filter interactions (e.g., min_kelly vs min_partition_leg_kelly).

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 Polymarket markets and returns opportunities where Pipeworx data disagrees with market price. It explicitly names three model families and segments, distinguishing it from siblings like polymarket_arbitrage.

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

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

Provides explicit use case ('what should I bet on today'), caching behavior, and parameter knobs for tradeability. Lacks direct comparison to sibling tools, but offers enough context to guide appropriate use.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds rich behavioral context: data from daily snapshots, bounded by 60-day TTL, decay from daily closes not intraday, and detailed response structure including tracked, expired, and snapshot_dates arrays. No contradictions.

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

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

The description is front-loaded with the core purpose and response structure. It is fairly concise given the complexity, with no redundant sentences. The technical jargon is necessary but could be slightly streamlined.

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 return values (tracked, expired, snapshot_dates) and their fields, along with limitations (TTL, non-intraday data). Complete for a read-only tool with two simple parameters.

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

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

Schema description coverage is 100%, so the schema already documents both parameters (days and window) with defaults and constraints. The description adds no new semantic information beyond what the schema provides, hence baseline 3.

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

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

The description clearly states the tool provides 'edge persistence and decay telemetry' from daily snapshots and answers a specific question ('how long has this edge existed and is it shrinking?'). It names the resource (polymarket_edges snapshots) and distinguishes its purpose from sibling tools like polymarket_edges (which likely provides current edges) by focusing on historical tracking.

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 the tool is for historical edge persistence analysis but does not explicitly state when to use it versus alternatives like polymarket_edges or polymarket_arbitrage. It provides context but no exclusions or when-not-to-use guidance.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds substantial behavioral context: how it walks the order book, returns specific fields like top_of_book, vwap_fill_price, slippage_pp, thin_legs, forced_directional_risk, and warns about partial fills converting arbs to directional positions. This is far beyond what annotations provide.

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

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

The description is lengthy but well-structured with sections, bolded keywords, and clear mode separation. Every sentence adds value given the tool's complexity. Minor redundancy possible but not detrimental. Front-loaded with the core 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 (two modes, multiple return fields) and lack of output schema, the description fully explains all return fields: top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict for single-market; theoretical_sum, realizable_sum, capture_ratio, profit_usd, per-leg fill detail, thin_legs, max_clean_notional_usd, forced_directional_risk for basket. Also covers defaults and clamping.

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

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

Schema coverage is 100%, but the description adds significant meaning: for 'size_usd' it explains the different interpretation for single-market vs basket, and for 'side' it explains the default auto behavior in basket mode based on partition sum. These enrich 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?

The description clearly states the tool does a 'realizable-vs-theoretical edge check against live CLOB order-book depth', distinguishes between single-market and basket modes, and explains what each returns. It differentiates from siblings like polymarket_arbitrage and polymarket_edges by being a prerequisite risk check.

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

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

Explicitly states that one of 'market' or 'event' is required, and gives detailed guidance on when to use: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Explains why partial basket fills are risky, providing clear when-to-use 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 indicate read-only and idempotent behavior. The description goes beyond by detailing safety fields (compatibility_warning, temporal_alignment, skipped counters) and explaining conditions when spreads are not meaningful. No contradictions with annotations.

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

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

The description is appropriately sized for the tool's complexity, front-loaded with the core purpose, and well-structured into modes, response, and safety fields. Every sentence adds value.

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

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

Given no output schema, the description fully covers the response structure (leg-by-leg prices, spread array, safety fields) and explains when warnings appear. It also addresses edge cases like non-equivalent bet shapes and temporal misalignment.

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

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

Schema coverage is 100%, so baseline 3. The description adds value by explaining how the parameters interact (explicit overrides topic), listing valid topic values, and providing usage examples, which is helpful but not exhaustive.

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

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

The description clearly states the tool computes cross-venue spreads between Kalshi and Polymarket for the same resolving question. It specifies two modes (topic shortcuts and explicit parameters) and differentiates itself from sibling tools like polymarket_arbitrage by its cross-venue focus.

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 each mode and warns that most pre-mapped topics currently return compatibility warnings, advising against blind reliance. However, it does not explicitly contrast with intra-venue tools like polymarket_arbitrage, leaving some ambiguity.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds that it returns attribute rows and geometry, and explains parameter behavior (SQL-like where, defaults). This adds value beyond annotations without contradiction.

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

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

Two concise sentences: first defines purpose and parameters, second gives an example. No wasted words, front-loaded with key information.

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

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

Given no output schema, the description states it returns attribute rows and geometry, which is sufficient for a query tool. It covers parameters well with examples. Minor gap: does not specify geometry format (e.g., GeoJSON).

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

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

Schema descriptions cover 100% of parameters, so baseline is 3. The description adds practical examples (e.g., 'STATE = 'CA'', 'POP DESC') and usage tips, enhancing understanding beyond the schema.

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

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

The description clearly states the tool queries an ArcGIS Feature Service/Map Service layer by URL, links to sibling search_datasets, and lists key parameters. It differentiates itself by specifying the resource type and which sibling provides the URL.

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 an explicit usage tip ('Use where="1=1" + out_fields="*" to sample.'), which guides the agent on common operations. It does not explicitly state when not to use it, but the purpose is clear and no obvious conflict with siblings exists.

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 readOnly, idempotent, non-destructive hints. Description adds valuable context: two modes (retrieve/list), scoping, and pairing, 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?

Four concise sentences: action, use case, scoping, pairing. Every sentence provides essential information without redundancy.

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

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

Given the tool's simplicity (one optional param, no output schema), the description covers purpose, usage, parameters, and sibling relationships comprehensively.

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

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

Schema covers the parameter fully (100%). Description enhances with concrete examples (ticker, address, notes) and reinforces the two modes, aiding 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 it retrieves a saved value or lists keys, gives concrete examples (ticker, address, notes), and names sibling tools (remember, forget). It leaves no ambiguity about function or relation.

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

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

Explicitly states 'use to look up context ... without re-deriving' and pairs with remember/forget, implying when to use alternatives. Also clarifies scoping by identifier, essential for correct usage.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds behavioral detail: the mark_read parameter can flag events as read, which is a non-destructive side effect. Also describes output fields (source, citation_uri, raw payload) beyond schema. No contradiction.

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

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

Four sentences, front-loaded with purpose and key output. Each sentence adds value: purpose, output fields, filtering parameters, mark_read behavior, and alternative access. 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 list tool with 5 optional parameters and no output schema, the description covers: purpose, output structure, filtering options, side-effect of marking read, and alternative access method. It addresses common concerns (polling, direct API) and leaves no major gaps.

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

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

Schema coverage is 100%, so baseline is 3. Description adds an example for type ('sec_8k') and clarifies that 'since' expects an ISO timestamp and 'mark_read' defaults to false. This provides some extra meaning but is not extensive.

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 action ('Pull fired events') and resource ('your subscription feed'). Distinguishes from siblings like 'list_subscriptions' (which lists subscriptions) and 'recent_changes' (which likely tracks entity changes) by focusing on alerts from a persisted feed.

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

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

Provides explicit guidance on when to use the tool ('Polls work fine') and mentions the same feed is accessible via a direct API endpoint. Implicitly differentiates from query tools like 'ask_pipeworx'. Could be more explicit about alternatives among siblings, but the direct API mention is valuable.

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

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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint. The description adds context: fan-out to multiple sources, fallback logic, USPTO soft-fail due to API sunset, and return format with citations. Exceeds annotation info.

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

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

Single paragraph, every sentence adds value. Starts with example queries, explains fan-out, parameter details, ends with alternative tool. No wasted words despite length.

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

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

No output schema, but description explains return format (changes grouped by source, total_changes, citations). Describes all parameters, fallback behavior, and soft-fail. Covers moderate complexity 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%, baseline 3. Description adds value: explains 'since' format with examples (ISO date or relative shorthand), 'value' can be ticker or CIK, 'type' only supports 'company'. Provides typical usage recommendation for 'since'.

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

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

The description clearly states it provides a change feed for a company, fanning out to multiple sources (SEC EDGAR, GDELT/GNews, USPTO). It distinguishes from sibling tool 'entity_profile' by noting when to use that instead.

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

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

The description gives many example queries and explicitly says when to use 'entity_profile' instead. It also explains fallback behavior (GDELT preferred, GNews on rate limit), providing clear 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?

Description adds context beyond annotations: key-value scoping by identifier, persistent vs. anonymous sessions, 24-hour retention. No contradictions. Could mention overwrite behavior but idempotentHint covers it.

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, front-loaded with purpose, no wasted words. Effective structure.

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

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

Covers all essential aspects for a simple key-value store: purpose, usage, scoping, persistence, and pairing with sibling tools. No gaps.

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

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

Schema coverage is high (100%), so baseline 3. Description gives examples but does not add new meaning 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?

Description states 'Save data the agent will need to reuse later' with specific verb and resource. It distinguishes from sibling tools like recall and forget by clearly explaining the storing role.

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

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

Explicitly says 'Use when you discover something worth carrying forward' and describes pairing with recall and forget. Provides clear context and alternatives.

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

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) already declare safety profile. Description adds that it cascades through multiple endpoints internally, which is useful but not essential. 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 concise, front-loaded with examples and purpose, and every sentence adds value. It avoids redundancy while covering all necessary aspects.

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

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

Despite no output schema, the description details return fields for each entity type (ticker, CIK, company name, citation URI for company; RxCUI, ingredient, brand, citation for drug). This makes the tool's behavior 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?

Schema coverage is 100%, but description adds critical detail: for company, it clarifies input can be ticker, CIK, or name with auto-disambiguation; for drug, it specifies brand or generic name. This enriches parameter understanding.

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

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

The description uses specific examples ('ticker for', 'CIK for', 'RxCUI for') and clearly states it resolves names to canonical identifiers. It distinguishes from siblings like compare_entities by emphasizing it is the first step for obtaining IDs.

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 'Use FIRST whenever you have a name but need an ID,' providing clear context. It lists supported types and what each returns, 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, openWorldHint, idempotentHint, destructiveHint. The description adds value by explaining that it internally uses ai_visibility_check and returns a ranked list with score, confidence, signal density per entity, which is behavioral context beyond the annotations.

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

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

The description is four sentences, each serving a purpose: stating the action, explaining the method, giving a use case, and describing the output. It is front-loaded and efficient, though slightly longer than strictly necessary.

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

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

Given no output schema, the description mentions the return format (ranked list with score, confidence, signal density) and explains the internal probe process. With 100% schema coverage and good annotations, it is sufficiently complete for effective usage.

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 by explaining that the first entity is treated as the 'subject' for narrative and mentioning model-specific notes (e.g., 'Omit for just workers-ai').

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

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

The description uses specific verbs ('Compare AI visibility', 'Probes each entity', 'ranks by score') and clearly distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic) by focusing on AI presence and competitive audits.

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

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

The description explicitly states the use case ('competitive AI-marketing audits') and provides an example question ('does Claude know about us as well as our competitors?'), but does not explicitly exclude cases where it shouldn't be used or list alternatives.

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

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

Annotations already indicate read-only, idempotent, non-destructive, and open-world behavior. The description adds transparency about partial failures: bundlephobia's first measurement can take 5-30 seconds, and sources_failed lists it if timed out. No contradictions.

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

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

The description is well-structured, front-loading the core purpose and then detailing behavior. Every sentence adds value, though it is slightly long. Could be slightly more concise but remains clear.

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

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

Despite no output schema, the description thoroughly details the return structure: summary block with specific fields, per-advisory details, links, and alternative versions. It also explains error handling and partial failure behavior, making it highly complete for a complex tool.

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

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

Schema covers both parameters fully (100% coverage). The description adds extra meaning: scoped packages (e.g., '@types/node') are accepted, and version defaults to latest. This provides useful context beyond 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 clearly states the tool's composite purpose: a single call to check if a npm package should be added to a project, covering license, advisories, version history, and bundle size. It distinguishes from siblings by specifying 'NPM ecosystem only in v1' and directing other ecosystems to a different 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 tells when to use: for questions about a package's safety, popularity, or size. Also notes when not to use: for non-npm packages, and provides alternatives (deps.dev:version directly). Includes context on potential timeouts and graceful degradation.

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, etc. Description adds useful context about return fields and the next-step usage of the URL, but does not reveal additional behavioral traits beyond what annotations and the output description provide.

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

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

Two sentences, immediate action and resource, no unnecessary words. Perfectly concise 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 clearly explains the return structure and how to use the result. For a simple search tool, this is complete.

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

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

Schema description coverage is 100% and adequately documents each parameter. The tool description does not add additional meaning beyond the schema, so baseline score of 3 is appropriate.

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

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

The description clearly states the tool searches City of Charlotte GIS datasets and lists the types of datasets (parcels, zoning, etc.) and the returned fields. It distinguishes from siblings by mentioning the URL can be passed to query_layer/layer_info.

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 to use for keyword searching and directs to pass the returned URL to query_layer or layer_info for further operations, providing clear guidance on when to use this tool versus siblings.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds crucial details: BGE-base-en embeddings, 500-char overlapping windows, 200K char limit with truncation flagging, and output includes character offsets and similarity scores. This goes well beyond annotations.

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

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

The description is a single, well-structured paragraph. It front-loads the core purpose, then provides usage context, then technical details. Every sentence is meaningful and there is no redundancy.

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

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

For a tool with no output schema, the description adequately describes the return format (passages with offsets and scores), limitations (200K cap, truncation), and pairing with a sibling. It covers all necessary context for correct invocation.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by giving real-world query examples and clarifying 'top-N passages', which slightly enhances understanding beyond the schema alone.

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

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

The description clearly states the tool performs semantic search inside a fetched record, with a specific verb ('search inside') and resource ('fetched record'). It distinguishes itself from siblings by contrasting with prompt cramming and explicitly pairing with ask_pipeworx_grounded.

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

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

The description provides explicit guidance: 'Use when the record is too big to cram into the prompt' and recommends pairing with ask_pipeworx_grounded. This is excellent context for when and how to use the 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 indicate mutability (readOnlyHint=false) and no destruction (destructiveHint=false). The description adds crucial behavioral details: authentication requirement, subscription creation, return of subscription ID, delivery channel limitations (SMS 10/day cap, webhook auto-disable after 10 failures), and HMAC signing. It does not address idempotency explicitly despite the idempotentHint=true annotation.

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 for the amount of information conveyed, with the core purpose in the first sentence. Every sentence adds value, covering auth, types, params, and delivery. No filler or repetition.

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

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

Given the complexity (3 params, nested objects, no output schema, annotations), the description covers essential aspects: purpose, auth, types, params, delivery behaviors, and webhook signing. It omits pagination, rate limits (except SMS), and explicit idempotency handling, but is still thorough for a subscription creation tool.

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

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

With 100% schema coverage, the baseline is 3. The description enriches the schema by providing concrete examples for type-specific params (e.g., sec_8k items, polymarket_edge topic, fred_series series_id) and delivery channel details. This adds significant meaning beyond the schema's structural 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 'Create a proactive monitoring subscription to a live-data event stream.' It uses a specific verb (create) and resource (subscription), and distinguishes from sibling tools like list_subscriptions and unsubscribe. The title 'Subscribe to Alerts' reinforces the purpose.

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

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

The description specifies when to use (need a Pipeworx OAuth account, anonymous/BYO cannot persist subscriptions) and provides detailed guidance on supported types and delivery options. However, it does not explicitly contrast with alternatives like list_subscriptions or unsubscribe, which are 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 already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds that it draws from a live catalog of thousands of tools and returns example questions, enhancing transparency without contradiction.

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

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

The description is dense and packed with information, but it is a single paragraph that could be slightly more structured. However, it is front-loaded with the key purpose and efficiently conveys all necessary details.

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

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

Given no output schema, the description fully explains the return type (category-bucketed example questions with tool+argument shapes). It covers the single parameter, use case, and behavior, making it 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?

With 100% schema coverage, the description adds extra meaning: explains that omitting topic gives full spread, passing a topic focuses on specific areas. This goes beyond the schema's enum list.

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 it is the onboarding entry point, returns category-bucketed example questions, and is the tool to use when the agent doesn't know what Pipeworx can do. This clearly distinguishes it from siblings like ask_pipeworx or 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?

The description says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and explains when to pass optional topic. It also mentions learning how to call meta-tools, providing explicit context for 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?

The description adds value beyond annotations by clarifying ownership enforcement and the deactivation (not deletion) behavior, which aligns with the destructiveHint false and idempotentHint true. 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?

Two sentences: the first clearly states the primary action, the second adds essential behavioral details. No redundant words, front-loaded with the core 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 simplicity (1 param, no output schema, annotations present), the description covers the key aspects: action, ownership, and data lifecycle. It could mention the return value or error conditions, but the lack of output schema makes this acceptable.

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

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

The schema already fully documents the single parameter 'id' (UUID from subscribe). The description does not add new parameter-level information but provides context about ownership and cancellation effect. With 100% schema 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 verb 'Cancel' and the resource 'subscription by id', and distinguishes from siblings like 'subscribe' (which creates) and 'list_subscriptions' (which lists). The ownership enforcement and deactivation behavior further specify the tool's unique purpose.

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

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

The description implies when to use (to cancel your own subscription) and provides post-cancellation context (deactivation preserves history). However, it does not explicitly state when not to use or offer alternative tools, though the context is clear enough for the agent.

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

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

Annotations already provide readOnlyHint, idempotentHint, etc. The description adds significant behavioral context: it returns a verdict with five categories, includes citation and percent delta, uses SEC EDGAR + XBRL, and notes that v1 supports only financial claims. 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 moderately concise and well-structured: it starts with example queries, states the purpose, details the domain, and lists return values. It could be slightly trimmed but remains clear and 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?

Despite lacking an output schema, the description fully explains the return format (verdict categories, structured form, actual value, citation, delta) and the tool's limitations (v1 financial claims). With only one parameter and no nested objects, the description is complete.

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

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

Schema description coverage is 100% (the 'claim' parameter is well-described). The description adds context beyond the schema by providing example claims and specifying the domain, which helps with parameter semantics.

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: natural-language claim verification against authoritative sources. It uses specific verbs ('validate', 'fact check', 'verify') and distinguishes from siblings by focusing on company-financial claims via SEC EDGAR + XBRL. It also notes that it replaces a multi-step sequential process.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and provides example inputs. It specifies the supported domain (company-financial claims) but does not explicitly state when not to use it or list alternative tools for other claim types.

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