Arcgis Grandchute

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

Annotations indicate readOnly, openWorld, idempotent, and not destructive, which the description aligns with and expands upon by detailing the return structure (score, confidence, signals, raw_response) and noting the payment model for Anthropic. 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 a single paragraph that front-loads the main purpose and return type, then efficiently covers parameter details and use cases. Every sentence adds value without redundancy.

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

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

Given the tool's complexity (multiple models, optional key, scoring, no output schema), the description is comprehensive. It explains the scoring range, per-model and combined views, and the payment model for Anthropic, leaving no critical 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?

With 100% schema coverage, the description adds significant meaning: it explains the 'entity' parameter with examples, clarifies 'models' defaults to workers-ai, notes that '_apiKey' is only needed for anthropic, and describes 'context' as a disambiguator. This goes beyond the schema's basic 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 probes LLMs for knowledge about an entity and returns a visibility score per model. It distinguishes itself from sibling tools by specifying its niche in AI-marketing audits, brand checks, and competitive monitoring, which are not covered by other tools.

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

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

The description provides clear usage contexts (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to use the optional API key for Anthropic. It does not explicitly state when not to use the tool or list alternatives among siblings, but the context given is sufficient for most scenarios.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds beyond this: it explains the routing to 4,476 tools across 1,127 sources, that it fills arguments, returns structured answers with stable pipeworx:// citation URIs, and is a single fast call. No contradictions.

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

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

The description is long but well-structured, with key information front-loaded and every sentence adding value. While slightly verbose, it remains efficient for the depth of guidance provided.

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

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

Given the tool's complexity (4,476 tools, no output schema), the description thoroughly explains behavior: routing, citation URIs, speed, and coverage. It adequately compensates for the missing output schema by describing return values.

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

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

Schema coverage is 100% with all parameters described as aliases for 'question'. The description adds minimal parameter-level info beyond the schema (e.g., examples), but the baseline is 3 due to high coverage. The description does not deepen understanding of 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 answers factual questions using authoritative structured data with citations, providing specific verbs and resources. It explicitly distinguishes from siblings like ask_pipeworx_grounded and deep_research, making its unique purpose evident.

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

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

The description gives explicit when-to-use (for factual questions, prefer over web search), when-not-to-use (breaking news already handled), and alternatives (use ask_pipeworx_grounded for hallucination-resistant single answers, deep_research for multi-part questions). It also provides numerous examples.

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 and non-destructive hints; description adds valuable details about the extraction process, refusal reasons, and additional LLM cost 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?

Description is well-structured with key purpose upfront, but slightly verbose; each sentence provides useful information, though some trimming could improve conciseness.

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

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

Despite no output schema, description fully details return format (answer, evidence, confidence, etc.), refusal conditions, and use cases, making it self-sufficient for agent understanding.

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

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

Schema coverage is 100% with six parameters described as aliases; description clarifies that 'question' is the main input and accepts natural language, adding context beyond schema.

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

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

Clearly states it is a hallucination-resistant answer mode for high-stakes reads, describes the routing process, and distinguishes from sibling ask_pipeworx by noting the extra LLM call cost and extraction method.

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

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

Explicitly states when to use (high-stakes, quoted/cited answers, financial/legal/medical) and when not (prefer ask_pipeworx for casual lookups), providing clear alternatives.

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

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

The description goes well beyond the annotations (readOnly, idempotent, etc.) by detailing fan-out processes, safety mechanisms (low-confidence short-circuit, closed-market handling), resolution risk, and result structure. 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 well-structured with clear sections (usage, classifiers, fan-out examples, response shapes, safety). Every sentence adds value, and the key information is front-loaded.

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

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

Given the tool's complexity (multiple data sources, classifiers, edge cases), the description is comprehensive. It covers all relevant aspects including resolver contracts, parent events, news fallbacks, and resolution-rule risks, making it fully actionable.

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 tool description largely echoes the schema's parameter descriptions and does not add significant new semantics beyond examples already present in the schema.

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

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

The description clearly states the tool researches a Polymarket bet by pulling relevant Pipeworx data in one call. It lists specific use cases like 'should I bet on X' and distinguishes from sibling tools by focusing on in-depth single-bet 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 states when to use the tool with examples ('should I bet on X'). While it does not explicitly mention when not to use or compare to siblings, the provided use cases and detailed result interpretation guidance make usage clear.

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

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

Discloses data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of fiscal years, and output behavior (sorted by primary metric, citation URIs). Annotations declare readOnly and idempotent, which are consistent with description.

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

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

Front-loaded with examples and clear preference. A few extra details (fiscal year handling) are valuable, but slightly longer than necessary. Still 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?

Covers purpose, usage, data sources, entity count, output description (paired data + citation URIs). No output schema needed given the description completeness.

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

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

Schema coverage is 100%, but description adds significant meaning: explains how to specify entities (tickers/CIKs for company, names for drug), the range (2-5), and the resulting sorting and citation URIs.

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

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

Description uses specific verbs and resources: 'side-by-side comparison of 2–5 companies or drugs'. It distinguishes from siblings by explicitly preferring over sequential lookups and naming the alternative (single-pack lookups).

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

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

Explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities'. Provides clear context for when to use (comparing entities) but does not mention when not to use it.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds context: accounts required, not open-web, parallel execution, returns findings with gaps, expected latency (15-60s), behavior with breaking news topics. 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 relatively long but front-loaded with critical account information. Every sentence adds value, covering purpose, usage, limitations, and output. Could be more structured (e.g., bullet points) but remains clear and readable.

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 (2 params, no output schema, many siblings), the description is remarkably complete. It explains the research process, output format (findings with evidence, confidence, gaps), and limitations (breaking news, account tiers). Without an output schema, the description compensates by detailing what the agent can expect.

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 descriptions for both parameters. Description adds value by explaining depth enumeration values ('quick=3, standard=5 (default), thorough=8 (paid plans)') and clarifying that the question should be natural language and can be broad/multi-part. This enriches rather than merely repeating schema info.

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: 'Grounded multi-source research across Pipeworx's 1127 STRUCTURED data sources' with specific verb ('research'), resource ('structured data sources'), and behavior ('decomposes your question into focused facets, routes each to the right one of 4,476 tools IN PARALLEL'). It distinguishes itself from siblings like ask_pipeworx by emphasizing it is not open-web search and handles broad/multi-part questions.

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

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

Provides explicit when-to-use guidelines: 'Best for broad/multi-part questions over structured data'. Also gives when-not-to-use: 'For a single lookup use ask_pipeworx' and 'For BREAKING or colloquial CURRENT-NEWS…prefer ask_pipeworx'. Additionally mentions account requirements and alternatives like ask_pipeworx for those not signed in.

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 valuable context: it returns top-N tools with names, descriptions, and full input schemas (with curated examples), and states each result is ready to call directly without a second lookup. This goes beyond the annotations to explain the output and immediate usability.

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 but includes a long list of example domains that could be streamlined. While every sentence adds value, the list makes it slightly verbose. For a discovery tool, brevity is acceptable, but it could be more concise 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 role as a discovery meta-tool, the description covers what it does, when to use it, and what it returns (names, descriptions, schemas). It lacks details on pagination or error handling, but these are not critical for a straightforward search tool. The context is sufficient for an agent to understand its purpose and 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 coverage is 100% with detailed descriptions for all 6 parameters, including aliases and default values. The description does not add significant meaning beyond what the schema already provides; it only reiterates that query accepts aliases and the default limit. With high schema coverage, 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 finds tools by describing the data or task, using specific verbs like browse, search, look up, discover. It lists example domains and explicitly contrasts itself with other tools by saying 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This effectively distinguishes it from siblings.

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

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

Provides explicit guidance: 'Use when you need to browse, search, look up, or discover what tools exist for...' and 'Call this FIRST when you have many tools available and want to see the option set.' This clearly indicates when to use this tool versus other more specific tools, with no 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 declare readOnly, openWorld, idempotent, non-destructive. The description adds that it fans out across multiple sources and discloses that USPTO PatentsView API may soft-fail due to sunset. This provides behavioral context beyond annotations.

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

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

The description is well-structured with front-loaded examples and a clear flow. Slightly verbose but every sentence adds value. Could be more concise, but it's effective for the 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?

Given no output schema, the description covers inputs, sources, return fields (cik, filings, fundamentals, patents, news, LEI), and limitations (USPTO sunset, name restriction). It is sufficiently complete for a profile tool.

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

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

Schema coverage is 100%, but the description reinforces that type must be 'company' and value must be ticker or CIK. It adds the crucial constraint that names are not supported, which is not obvious from schema alone.

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

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

The description explicitly states the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call'. It provides example queries and distinguishes from chaining single-pack lookups. Clear verb and resource with specific scope.

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

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

The description advises 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' for holistic views. It notes that names are not supported and recommends using resolve_entity first. It lacks explicit when-not-to-use for other scenarios but provides clear context.

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

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

Annotations already provide destructiveHint=true and readOnlyHint=false. The description adds context about clearing sensitive data, which is useful but not critical beyond annotations.

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

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

Three sentences, no fluff. Action and purpose are front-loaded. 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?

With only one parameter, no output schema, and low complexity, the description provides complete guidance: action, when to use, and companion tools.

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

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

Schema coverage is 100% with the 'key' parameter described as 'Memory key to delete'. The description does not add extra detail 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 verb 'Delete', the resource 'previously stored memory', and the qualifier 'by key'. It also distinguishes from siblings by mentioning 'Pair with remember and recall'.

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

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

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

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, indicating safe, non-destructive behavior. The description adds valuable context: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' It explains the process without contradicting annotations.

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

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

The description is concise (4 sentences) and front-loaded: first sentence states purpose, second explains process, third output format, fourth use cases. Every sentence adds value with no repetition or fluff.

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

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

Given the tool's simplicity (2 parameters, no output schema, no nested objects) and thorough annotations, the description is complete. It covers input, process, output, and use cases, enabling an agent to invoke it correctly without additional information.

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

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

Schema coverage is 100% (both parameters have descriptions). The description adds practical details beyond the schema: for 'url', it clarifies 'Full URL of the site to summarize'; for 'max_links', it specifies 'default 25, max 50'. This helps agents set appropriate values.

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

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

The description clearly states the tool's purpose: 'Generate a production-ready llms.txt file for any URL so AI crawlers can index the site cleanly.' It specifies the output format and provides concrete use cases, distinguishing it from sibling tools like ai_visibility_check or scan_competitor_ai_presence.

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 enumerates explicit use cases ('getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor'), guiding when to use. It does not directly mention alternatives or when not to use, but the context is clear and the sibling list shows no overlapping 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, so the safety profile is clear. The description adds that it retrieves schema information, which is consistent with read-only behavior. No contradictions or additional behavioral context needed 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?

A single sentence that is front-loaded with the verb and resource, and it efficiently enumerates the returned data without extraneous words. Every part adds value.

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

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

For a simple tool with one parameter, no output schema, and rich annotations, the description fully covers what the tool does and what it returns (fields, geometry type, count, capabilities). It is complete for an agent to understand and invoke correctly.

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

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

Schema coverage is 100% with a clear parameter name and a detailed example. The description mentions the URL once, but does not add further semantics beyond the schema's description. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description uses a specific verb 'Get' and clearly identifies the resource as an ArcGIS Feature/Map Service layer's schema, listing the exact data returned (fields, geometry type, record count, capabilities). This clearly distinguishes it from siblings like 'query_layer', which would query actual features.

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 guidance is provided on when to use this tool versus alternatives such as 'query_layer' or 'search_within'. The description only states what it does, but does not exclude cases where an agent might mistakenly use it for querying data or other tasks.

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, and destructiveHint. The description adds the specific return fields, confirming the read-only nature and 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?

Two sentences, front-loaded with core purpose and return fields, then usage guidance. Every sentence adds value with no redundancy.

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

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

Given low complexity (one optional parameter, no output schema), the description covers purpose, return fields, and usage adequately. Minor omission: no mention of pagination, but likely not essential.

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 only parameter (include_inactive) with a description. The tool description does not add extra parameter semantics, but 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 'List the caller's active subscriptions' with a specific verb and resource. It also lists return fields, distinguishing it from siblings 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 says 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This provides clear when-to-use and implies when not needed, with direct reference to sibling tools (subscribe, unsubscribe).

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

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

Discloses rate limits (5 per identifier per day), that feedback is free and doesn't count towards quota, and that the team reads digests daily. Annotations do not contradict and are supplemented by these details.

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

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

Concise with no fluff. Each sentence adds value: purpose, use cases, guidelines, rate limits, and impact. Information is 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?

Covers purpose, when to use, behavioral constraints, parameter guidance, and expected output (digested). No output schema but description explains the outcome sufficiently.

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

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

Schema coverage is 100% with descriptions for all parameters. Description adds guidance to describe issues in terms of tools/packs and not paste prompts, but this does not significantly extend meaning beyond the schema.

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

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

The description clearly states the tool is for sending feedback to Pipeworx about bugs, missing features, or praise. It is distinct from sibling tools which are primarily for querying or information retrieval.

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

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

Explicitly describes when to use each feedback type (bug, feature, data_gap, praise) and instructs not to paste end-user prompts. Includes rate limit and quota information.

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

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

Annotations indicate readOnly, idempotent, and non-destructive behavior. The description adds valuable behavioral context: data source (CF analytics-engine), no PII, and caching policies (5min-1h). 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 (3 sentences plus bullet-like items) and front-loaded with the core action. Every sentence adds value without redundancy.

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

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

Despite lacking an output schema, the description adequately hints at return values (top tools, packs, total call volume). Combined with annotations and parameter clarity, the description is complete for this simple tool.

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

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

Schema coverage is 100% with a clear enum description. The description goes beyond by explaining the semantic difference between short and long windows, which aids parameter selection.

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 that the tool returns top tools, top packs, and total call volume over a specified window, and lists concrete use cases. It distinguishes itself from sibling tools by focusing on trending 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?

The description provides explicit scenarios for when to use the tool (discovering hot data sources, confirming popular tools, aligning use cases) and explains the implications of different window parameters ('hot right now' vs 'steady-state demand'). No need to contrast with siblings as no other trending tool 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?

The description details the inner workings: walks markets, checks monotonicity and partition sums, applies Jaccard similarity threshold, filters placeholder slugs, and performs fill checks against live order books. Annotations (readOnlyHint, openWorldHint, idempotentHint) are consistent and the description adds significant behavioral context.

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

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

The description is comprehensive but somewhat verbose. Important constraints (REQUIRES one of) are front-loaded, but some details like fill check explanation could be condensed. Still, the structure with sections and examples is 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?

No output schema exists, but the description fully covers return structure (opportunities[] with gap_pp, suggested_trade, reasoning, monotonicity context; partition_check fields; fill check details). It also explains edge cases like placeholder filtering and similarity threshold, making the tool self-contained.

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

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

Schema coverage is 100% with descriptions for both parameters, but the description adds substantial value: explains event slug format (including example slugs and URL acceptance), topic semantics with seed question examples, and how each parameter triggers distinct search/analysis behavior.

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

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

The description clearly states it finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks, with two distinct modes (event and topic). This specificity differentiates it from siblings like polymarket_edges and polymarket_fill_risk.

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

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

The description explicitly requires one of 'event' or 'topic', explains when to use each (event for specific markets, topic for cross-event scanning), and provides when-not-to-trade guidance (realizable_edge_pp ≤ 0). It references sibling 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 (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false) are consistent. The description adds extensive behavioral context: model families, edge calculation, Kelly fractions, slippage, filters, caching at KV level for 1 hour, and diagnostic output. 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 dense and informative, using numbered lists and bold keys for structure. It is front-loaded with purpose. While lengthy, every sentence adds value. Could be slightly more scannable, but acceptable given the complexity.

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

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

Despite no output schema, the description explains the full response structure: top-level by_segment with three segments, fed_candidates/fed_note, and _diagnostics. It details what each segment contains and even diagnostic funnel counters for debugging. Completely covers what an agent needs to use the tool effectively.

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

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

Schema coverage is 100% (all 9 parameters described in schema). The description adds significant actionable guidance beyond schema, e.g., 'min_edge_pp' explains edge is net of slippage, 'slippage_pp' notes Polymarket's zero fees but bid/ask spread, 'max_spread_pp' suggests setting to 2, 'min_liquidity' to 5000. This provides concrete usage advice.

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

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

The description clearly states the tool scans Polymarket markets for opportunities where Pipeworx data disagrees with market price. It explicitly positions the tool for 'what should I bet on today' discovery, distinguishing it from siblings like polymarket_arbitrage by its unique approach.

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

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

The description explains the tool is built for discovering betting opportunities without manual paging. It mentions tradeable-edge knobs and notes that Fed bets surface but are excluded from ranking. While it doesn't explicitly state when not to use it, the context clearly differentiates its use case from 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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds substantial behavioral context: response structure (tracked[], expired[], snapshot_dates[]), cache-miss gaps, decay from daily closes (not intraday), and 60-day snapshot TTL. No contradictions.

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

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

The description is fairly long but well-structured with sections for purpose, args, RESPONSE, and LIMITS. Each sentence adds necessary detail; however, minor redundancy (e.g., repeating 'edge_pp_net' context) could be trimmed.

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

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

Given no output schema, the description comprehensively explains return values (tracked[], expired[], snapshot_dates[]) and their fields, including trend computation, decay rate, and lifespan. It also covers limits and data gaps, making the tool fully understandable 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% with descriptions for both parameters. The description adds value by specifying default values (14 days, '1wk'), clamping range for days (2-30), and clarifying the meaning of 'window' as snapshot family. This enhances understanding beyond the schema.

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

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

The description clearly identifies the tool's purpose: edge persistence and decay telemetry. It explicitly answers 'how long has this edge existed and is it shrinking?' and contrasts fresh vs old wide edges, distinguishing it from the sibling 'polymarket_edges' which likely provides raw edge 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?

The description explains when to use the tool (to assess edge aging and decay) and includes a LIMITS section detailing history depth and decay computation. However, it does not explicitly name alternatives or state when not to use it, though the context from sibling names implies 'polymarket_edges' for raw current edges.

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 aligns with annotations (readOnlyHint, idempotentHint, destructiveHint=false) and adds behavioral context: it checks order book depth without modifying any state, returns a verdict (clean/degraded/cannot_fill), and warns about thin legs and forced directional risk. No contradiction with annotations.

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

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

The description is comprehensive but somewhat verbose, containing multiple sentences with details. It is well-structured with clear sections (single-market, basket, usage guidance) and front-loads the core purpose. However, it could be slightly more concise 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?

Despite lacking an output schema, the description thoroughly documents return values for both modes (e.g., top_of_book, vwap_fill_price, slippage_pp for single-market; theoretical_sum, realizable_sum, capture_ratio for basket). It also explains the significance of thin legs and forced directional risk, making 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 the description adds significant meaning: it explains that `market` is for single-market mode, `event` for basket mode, `side` defaults appropriately for each mode, and `size_usd` is interpreted differently for buys vs sells (max spend vs target proceeds) and as settlement notional for basket mode. This goes well beyond the schema descriptions.

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

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

The description clearly states it performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and explains both single-market and basket modes. It distinguishes itself from siblings like polymarket_arbitrage and polymarket_edges by explicitly stating its role as a pre-trade validation 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 explicitly advises 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500' and explains why: 'theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position'. This provides clear when-to-use and when-not-to-use guidance with specific alternatives.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds rich behavioral context: compatibility warnings for shape mismatches, temporal alignment checks, and explanation of skipped cross types. 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 comprehensive but slightly verbose. However, it is well-structured with clear sections (modes, response, safety fields) and front-loaded with the core purpose. Each sentence adds value, but could be 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 complexity and absence of an output schema, the description is remarkably complete. It covers both usage modes, all response components, edge cases (compatibility warnings, temporal alignment), and provides concrete examples in the input schema.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds meaning by listing the exact topic shortcuts, explaining the override behavior, and detailing the response fields 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 states exactly what the tool does: compute cross-venue spreads between Kalshi and Polymarket for the same resolving question. It distinguishes from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on venue comparison.

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

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

Explicitly describes two modes (topic shortcuts and explicit tickers) and provides detailed guidance on when spreads are meaningful via safety fields like compatibility_warning and temporal_alignment. It warns that pre-mapped topics are not automatically tradeable.

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. The description adds that it returns 'attribute rows (and geometry)', complementing annotations without contradiction.

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

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

The description is two sentences, front-loaded with purpose, and includes a concrete example. Every sentence adds value with no redundancy.

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

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

Given 6 parameters and no output schema, the description covers what the tool returns (rows and geometry) and gives a sampling hint. It could detail pagination or output format more, but is largely adequate.

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

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

Schema coverage is 100% and description adds context beyond schema, e.g., SQL-like syntax examples and the 'sample' usage pattern. This provides practical guidance for parameter use.

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 'Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets)', specifying verb, resource, and source tool. This effectively distinguishes it from siblings like search_datasets and 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?

The description gives explicit usage example ('Use where="1=1" + out_fields="*" to sample') and lists parameters, but does not provide when-not-to-use or compare directly with siblings beyond the reference to search_datasets.

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 adds context such as the ability to list all keys when omitting the argument, scoping to an identifier, and the pairing advice with 'remember' and 'forget', all of which enrich the agent's understanding beyond annotations.

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

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

The description is concise (three sentences) and front-loaded with the core action. Every sentence provides essential information without redundancy, making it efficient for an AI agent to parse.

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

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

The description covers purpose, usage, behavior, and sibling relationships well. However, since there is no output schema, the description could briefly mention the return format (e.g., returns a value or list of keys) for complete clarity.

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

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

The input schema already has 100% coverage for the single parameter 'key'. The description adds value by explaining the behavior when the parameter is omitted ('list all saved keys'), which is not captured in 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 verb ('Retrieve' or 'list') and resource ('value previously saved via remember' or 'saved keys'). It distinguishes from siblings by explicitly mentioning pairing with 'remember' and 'forget', making its unique role evident.

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 specific examples of when to use the tool (e.g., 'user's target ticker, an address, prior research notes') and explains the scoping to an identifier. While it doesn't explicitly state when not to use it, the contrast with 'remember' and 'forget' effectively guides the agent on 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 show readOnlyHint=true, idempotentHint=true, and destructiveHint=false, indicating safe read operations. The description adds value by explaining the mark_read behavior (which mutates state) and confirming polling is safe, going beyond annotation details.

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

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

The description is concise with no wasted words. It is front-loaded with the main purpose and each subsequent sentence adds relevant detail. Structure is effective and efficient.

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

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

Given no output schema, the description covers return fields (source, citation_uri, raw event payload) and usage patterns like filtering and marking read. It lacks details on pagination or rate limits, but for a simple read tool with all optional parameters, it is mostly 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%, so the description adds minimal extra meaning beyond the schema. It reinforces that type and since are filters and mentions mark_read, but does not introduce new semantic details.

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

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

The description clearly states the tool pulls fired events from a subscription feed and specifies what each alert carries (source, citation_uri, raw event payload). It distinguishes itself from sibling tools like subscribe or list_subscriptions by focusing on retrieval of recent alerts.

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 guidance on filtering by type and since, explains the mark_read functionality to manage read state, and notes that polling is fine. It also mentions an alternative endpoint for scripts, offering context. However, it does not explicitly contrast with siblings or state when not to use this tool.

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

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

Adds substantial context beyond annotations: mentions multiple sources with fallbacks, PatentsView sunset, `since` formats, and return structure. No contradiction with readOnlyHint etc.

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 but each sentence adds value. Could be structured with bullet points for readability, but no waste.

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

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

No output schema, but description explains return structure (`changes[]` grouped by source, `total_changes`, citation URIs). Covers all parameters and behavior comprehensively.

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

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

Schema coverage is 100%, so baseline 3. The description adds extra context such as relative shorthand examples and typical usage ('Use \"30d\" or \"1m\"), which justifies a score above baseline.

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

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

The description clearly states it returns a change feed for a company, covering SEC filings, news (GDELT/GNews), and patents. It uses specific verbs like 'fans out' and explicitly distinguishes from sibling tool 'entity_profile'.

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

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

Provides explicit examples of questions it answers, explains when to use 'entity_profile' instead, and details fallback behavior (GNews when rate-limited).

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

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

Adds important details beyond annotations: key-value scoping, persistence differences between authenticated (persistent) and anonymous (24-hour) sessions. 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?

Four sentences, front-loaded with purpose, no wasted words. Efficient and 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?

Covers purpose, usage, behavior, and pairing. For a simple two-param tool with no output schema, the description is fully complete.

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

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

Schema already covers both parameters with examples; description repeats examples but adds no new semantic information beyond the schema.

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

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

Clear verb ('save') and resource ('data') with explicit purpose: reuse later across conversations/sessions. Distinguishes from siblings recall and forget by mentioning 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?

Explicit when-to-use examples ('discover something worth carrying forward') and pairing with recall/forget. Lacks explicit when-not-to-use, but guidance is strong.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: internal cascading through multiple lookup endpoints, auto-disambiguation for company, and that it replaces 2-3 manual lookups. This goes beyond annotations, providing transparency about complexity and response structure.

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

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

The description is well-structured, starting with example queries to set context, then stating the primary use case, and finally detailing supported types. At about 150 words, it is slightly long but every sentence adds value. Minor redundancy in listing supported types twice could be trimmed, but overall effective.

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

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

Given the complexity of two entity types with multiple outputs and internal cascading, the description covers the key aspects: what it returns, how it works, and typical use. However, it does not address potential error handling (e.g., ambiguous or unmatched names) or rate limits, which could be important for an AI agent. Overall, it is very complete for a lookup tool with strong annotations.

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

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

Schema coverage is 100% with descriptions for both parameters. The description enriches this by explaining the 'type' enum values with concrete return examples (ticker+CIK for company, RxCUI for drug) and clarifying that 'value' accepts various formats with auto-disambiguation. This adds significant meaning beyond the schema.

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

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

The description clearly states the tool resolves names to canonical identifiers, with specific examples ('What's the ticker for...') and explicit use case ('Use FIRST whenever you have a name but need an ID'). It distinguishes itself from sibling tools like compare_entities and entity_profile by focusing on identifier lookup. The supported types (company, drug) are detailed with return values, showing clear 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 explicitly says 'Use FIRST whenever you have a name but need an ID', providing strong usage guidance. It also mentions that other tools require the ID as input, implying when to use it. However, it lacks explicit 'when not to use' or exclusion criteria, which would make it a 5. The context signals show many sibling tools, but the description sufficiently guides 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. Description adds valuable behavioral context: probes each entity with ai_visibility_check, ranks by score, surfaces most/least recognized, and treats first entity as subject for narrative.

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

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

Three sentences with no redundancy. Front-loaded with the core action and key differentiator. Every sentence serves a 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?

Despite no output schema, description explicitly states return format ('ranked list with score, confidence, signal density per entity'). Covers purpose, usage, and expected outcome fully.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds minor value by explaining the entities array structure (first entry is subject) and context disambiguation, but does not significantly extend schema docs for models or _apiKey.

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 verb+resource ('Compare AI visibility across multiple entities') and distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison).

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

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

Clearly states use case ('competitive AI-marketing audits') with an example question. Does not explicitly list when not to use, but the context makes alternative uses (e.g., individual probes via ai_visibility_check) implicit.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds behavioral details: fan-out to two services, bundlephobia's first measurement can take 5-30s, sources_failed list, and graceful degradation. No contradiction with annotations.

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

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

The description is a single dense paragraph but front-loaded with the core action. Every sentence adds value, though it is slightly long. Structure is logical: purpose, data sources, usage, limitations, return values.

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, so the description compensates by listing all return fields: summary block, advisory details, links, alternative versions. Also covers failure modes and timing. Complete for the tool's complexity.

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

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

Schema coverage is 100% with clear descriptions. The description adds value: scoped packages accepted for 'package', and 'version' defaults to latest. No enums or nested objects.

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: a composite check for adding an npm package, combining data from deps.dev and bundlephobia. It specifies the verb 'scan' and resource 'dependency', and distinguishes from siblings by focusing on npm packages.

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

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

Explicit guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' Also notes ecosystem limitation ('NPM ecosystem only in v1') and directs other ecosystems to another tool. Partial failure handling is described.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the agent knows it's safe. The description adds value by detailing the output fields (name, summary, record_count, owner/org, url) and the downstream usage, which is 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?

Two concise sentences. First sentence states the purpose and scope. Second sentence details the return values and actionable guidance. No wasted words, front-loaded.

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

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

Given that there is no output schema, the description fully covers what the tool returns (name, summary, record_count, owner/org, url) and how to use the result. With 3 well-described optional parameters and clear annotations, 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%, so all three parameters (limit, query, org_id) have descriptions. The tool description does not add extra meaning beyond 'by keyword'. 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 'Search' and the resource 'Town of Grand Chute GIS open geospatial datasets', listing example categories (parcels, zoning, etc.). It also distinguishes from siblings by specifying that the returned URL can be used with 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?

The description explicitly says to search 'by keyword' and directs the agent to pass the returned url to query_layer or layer_info. However, it doesn't provide explicit when-not-to-use or compare with other siblings like layer_info or query_layer directly.

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

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

Annotations already indicate read-only and idempotent. Description adds implementation details (BGE-base-en embeddings, 500-char overlapping windows, 200K char cap with truncation flag) and emphasizes offsets for verification, going beyond annotations. Lacks mention of caching or potential variability, but still strong.

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

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

Concise at ~150 words, front-loaded with purpose. Every sentence adds value: purpose, usage, pairing workflow, technical details. Slightly dense but appropriate for 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?

No output schema, but description fully explains return format (passages with offsets and similarity scores) and mentions the truncation behavior. Addresses key agent concerns (when to use, expected output, limits). Minor gap: could mention that passages are overlapping windows, but still highly 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%, so baseline is 3. Description repeats schema details (char limit, defaults) and adds example queries for the query parameter, but no deep new semantics. Meets minimum but doesn't elevate.

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

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

The description clearly states it performs semantic search inside a fetched record, with specific verb and resource. It distinguishes from siblings by noting it works on already-fetched text and pairs with ask_pipeworx_grounded, making its unique role evident.

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

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

Explicitly says 'Use when the record is too big to cram into the prompt' and provides a complementary tool (ask_pipeworx_grounded) with a clear workflow. No ambiguous conditions.

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

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

The description reveals that the tool requires authentication, creates a persistent subscription, returns a subscription ID, and includes constraints like phone verification and SMS cap. This adds value beyond the annotations, which only provide basic hints. The idempotentHint annotation is not addressed, but the description does not contradict 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?

The description is fairly long but well-organized, with clear separation of purpose, types, and delivery. Every sentence adds useful information. It could be slightly more concise, but it remains clear and scannable.

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

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

Given the tool's complexity (3 parameters, nested objects) and no output schema, the description covers the return value, required authentication, type-specific details, and delivery constraints. However, it does not clarify whether repeated subscriptions with the same parameters are idempotent, which is a minor gap.

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?

While the input schema covers 100% of parameters, the description adds meaningful examples and details for each parameter, such as the structure of params for each type and delivery verification requirements. 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 title and description clearly state the tool creates a subscription to a live-data event stream. It specifies the verb 'Create' and the resource 'proactive monitoring subscription'. It is distinct from sibling tools like list_subscriptions and unsubscribe.

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

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

The description explains the requirement for a Pipeworx OAuth account, lists supported types with examples, and describes delivery channels. It provides good context for when to use the tool, but does not explicitly state when not to use it or mention alternatives for listing subscriptions.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds value by explaining that it returns example questions with tool shapes from a live catalog, 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?

Description is concise, front-loaded with user intents, and every sentence adds value. It efficiently conveys purpose, usage, and return format without unnecessary 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 simplicity of the tool (1 optional param, no output schema), the description covers purpose, when to use, parameter meaning, and nature of return (category-bucketed examples with tool shapes). Complete for the task.

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 the topic parameter fully described. The description reiterates the same information and adds contextual advice (e.g., omit for full spread). No new semantic detail beyond schema.

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

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

Description clearly states the tool returns category-bucketed example questions to help users understand what Pipeworx can do, and it distinguishes from siblings by being the onboarding entry point. It uses specific verb 'returns' and lists example user intents.

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

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

Explicitly says to use this tool when you do not yet know what Pipeworx can do or to learn how to call meta-tools. It provides guidance on omitting or specifying the topic parameter. Lacks explicit 'when not to use' but is comprehensive for its 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?

Adds context beyond annotations: ownership enforcement, non-destructive deactivation preserving historical events. 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, no wasted words, front-loaded key action. Highly efficient.

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

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

Given simple schema and no output schema, description fully covers purpose, constraints, and side effects.

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 description for the single parameter. Description adds no new param info beyond schema.

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

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

Clearly states 'Cancel a subscription by id' with specific verb and resource. Distinguishes from sibling tools like subscribe and list_subscriptions.

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

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

Explicitly states ownership enforcement ('you can only cancel your own subscriptions') and explains the behavioral effect (deactivation, not deletion). Provides clear usage boundary.

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 safety and idempotency. The description adds rich behavioral details: it returns a verdict with specific categories, extracted structured form, actual value with citation, and percent delta. It also explains the data source (SEC EDGAR + XBRL), going well beyond the annotation hints.

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 sized and well-organized, starting with illustrative query patterns, then stating the purpose, followed by scope and return details. While efficient, a few sentences could be tightened, but no part is extraneous.

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

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

Despite no output schema, the description fully explains the return format (verdict types, structured form, citation, delta). It covers input expectations and source methodology, providing sufficient context for an agent to invoke and interpret results correctly.

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

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

Schema coverage is 100%, so the parameter's description in the schema is adequate. The tool description adds general context about claim format but doesn't provide additional detail beyond what the schema already documents. 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 opens with relatable query patterns and explicitly defines the tool as a natural-language claim verifier against authoritative sources. It clearly distinguishes itself by noting it replaces multiple sequential calls, setting it apart from sibling tools like deep_research or compare_entities.

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

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

States 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear usage context. It also scopes the supported claim type (company-financial) and mentions version limitations. While it doesn't explicitly list when not to use it, the scope serves as implicit guidance.

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