Dst Dk

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds behavioral detail: it explains the return structure (per-model {score, confidence, signals, raw_response} + combined view), documents that Anthropic calls are billed separately via BYO key, and notes that the context parameter helps disambiguate. This goes beyond the annotations by describing output and API key handling.

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

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

The description is a single, well-structured paragraph with no wasted words. Each sentence contributes: main action, default model, API key requirement, return format, and use cases. It is front-loaded with the core purpose.

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

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

For a tool with 4 parameters (all documented) and no output schema, the description covers all essential aspects: purpose, parameters (including defaults and prerequisites), output structure, and typical use cases. Even though there is no output schema, the description specifies the return shape and fields. The annotations cover safety and idempotency further, making the description 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%, but the description adds extra meaning: it clarifies that 'workers-ai' is the free default, that '_apiKey' is only required when 'anthropic' is in models, and that context helps disambiguate common names. It also explains that the API key is passed straight through to Anthropic. This adds value 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 explicitly states the tool probes LLMs for AI visibility and scores them 0-100 per model. It specifies the default model and optional Anthropic integration, clearly distinguishing the tool's specific output (per-model score, confidence, signals, raw_response) from potential siblings like 'scan_competitor_ai_presence'. The verb 'probe' and resource 'LLMs' are concrete and unambiguous.

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

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

The description lists concrete use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to provide an API key for Anthropic models. It also mentions the free default model. However, it does not provide explicit exclusions or contrast with alternatives among the sibling tools, though the context implies the tool is for general visibility checks.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by explaining the routing to 4,862 tools, citation URIs, and one fast call. No contradiction; scores 4 for adding meaningful context beyond annotations.

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

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

The description is front-loaded with key info and each sentence serves a purpose. However, it is somewhat verbose, especially the domain list. Still well-structured, earning a 4.

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

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

Given the tool's complexity and lack of output schema, the description covers core functionality but does not detail edge cases, error handling, or return format specifics. Adequate but incomplete, scoring 3.

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

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

Schema has 100% description coverage for all parameters, so baseline is 3. The description reinforces the parameter purpose and provides examples, but does not add new semantic meaning beyond the schema. Score 3 is appropriate.

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

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

The description clearly states the tool is for asking questions about current or historical data from authoritative sources, listing many domains. It contrasts with siblings like ask_pipeworx_grounded and deep_research, fulfilling the criteria of specific verb+resource and sibling differentiation.

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

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

Explicitly says 'PREFER OVER WEB SEARCH' and 'START HERE for most questions', and provides when to step up to alternatives, along with many examples. This gives clear context and exclusions, earning a 5.

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

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

The description discloses behavioral traits beyond annotations: same routing as ask_pipeworx, then extracts answer using only tool result, returns specific response structure (answer, evidence, confidence, source, etc.) and explicit refusal reasons. This is a rich behavioral description that complements the readOnlyHint and idempotentHint 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 and front-loaded with purpose. Every sentence adds value, though it could be slightly more concise given the length. Overall good balance of detail and clarity.

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

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

Given the tool's complexity (1272 sources, 4862 tools), the description fully covers the return format (answer, evidence, confidence, source, fetched_at, refusal_reason) and refusal scenarios (not_in_source, no_tool_match, etc.). It also addresses cost and use cases, making it complete for an agent.

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

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

Schema description coverage is 100%, so baseline is 3. The description repeats parameter info already in schema (aliases for question). It adds no additional semantic meaning beyond what the schema provides, so no bonus.

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 'Hallucination-resistant answer mode for high-stakes reads' with specific verb+resource (ask_pipeworx_grounded). It distinguishes from sibling 'ask_pipeworx' by explaining the grounded extraction mechanism, making the purpose unique and clear.

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

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

The description explicitly states when to use this tool: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements).' It also provides an alternative: 'prefer ask_pipeworx for casual lookups' and notes the extra cost.

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

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

Annotations already indicate readOnly, idempotent, non-destructive. The description adds extensive behavioral detail: fan-out, response shapes, safety mechanisms (low-confidence short-circuit, closed/inactive market handling, wide-spread warnings), resolution rule risk, and more. 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 lengthy but front-loaded with core purpose and structure. It systematically covers all aspects (fan-out examples, response shapes, safety, edge cases). Could be slightly trimmed, but every section earns its place. A well-organized, detailed guide.

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 classifiers, fan-out patterns, response shapes, safety behaviors), the description is remarkably complete. It covers input handling, resolution confidence, parent events, news fallbacks, and resolution rule risks. No output schema exists, but response shapes are fully described.

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?

100% schema coverage, and the description enriches each parameter: 'market' with three input formats and examples, 'depth' with enum meaning, 'include_raw' with default and use case. Every parameter's purpose is fully explained.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data, specifies input types (slug, URL, question), and explicitly contrasts with sibling tools like polymarket_arbitrage, polymarket_edges, etc. It uses specific verbs and scope.

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

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

Explicitly lists use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and provides numerous examples. While it doesn't explicitly mention when not to use or direct alternatives, the extensive context makes usage clear. A minor omission prevents a 5.

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

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

Adds significant context beyond annotations: data sources (EDGAR/XBRL, FAERS), fields returned, fiscal year handling, sorting by primary metric, and citation URIs. 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?

Front-loaded with trigger phrases and key advantage. Packs essential info into a coherent paragraph. Slightly long but every sentence adds value; minor room for tightening.

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

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

For a complex tool with no output schema, description covers return data, sorting, and citations. Lacks error/pagination details, but sufficient for correct 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 100%. Description adds meaning: tickers/CIKs for company, drug names, examples, and what data each type retrieves. Could detail return fields more, but adds value beyond schema.

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

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

The description clearly states the tool performs side-by-side comparisons of 2-5 companies or drugs in one parallel call, specifying the data pulled for each type. It distinguishes from sequential lookups and sibling tools like entity_profile.

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

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

Explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and provides trigger phrases. Could mention alternatives like entity_profile for single entity cases, but overall clear.

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

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

Beyond annotations (readOnly, openWorld, idempotent, non-destructive), description details decomposition into facets, parallel routing to 4,862 tools, output format (evidence, gaps[], hop, contradictions for thorough), behavior for unsupported topics (empty gaps), and timing (15-90s). No contradictions.

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

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

Description is informative but somewhat lengthy. However, it is well-structured: starts with essential requirement, then core functionality, comparative guidance, depth specifics, and performance. Could be slightly more terse but effective.

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

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

Given the tool's complexity (4,862 tools, multiple depths, structured data sources), the description covers prerequisites, behavior, output, limitations, and performance. No output schema but output is described sufficiently. Completes all contextual needs.

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 meaning by explaining depth options ('quick=3, standard=5, thorough=8 with gap recovery and contradictions') and the question parameter ('natural language, broad/multi-part fine'). Enhances understanding beyond schema.

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

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

The description clearly states the tool performs 'grounded multi-source research across Pipeworx's 1272 STRUCTURED data sources in ONE call', distinguishing it from open-web search and sibling tools like ask_pipeworx (single lookup). It uses specific verbs and resources.

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

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

Explicitly provides when to use (broad/multi-part questions over structured data) and when not to use (breaking news, current topics). Recommends alternative (ask_pipeworx) and mentions account requirements including paid plan for 'thorough' depth. Full guidance.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds that it returns top-N tools with names, descriptions, and schemas, and that results are ready to call directly. This covers the absence of an output schema and gives 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 two sentences, front-loaded with the core purpose and usage context, followed by output details and strategic advice. Every sentence adds value; no fluff.

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

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

Given no output schema, the description adequately explains the return value (top-N tools with details) and when to use this tool first. It could mention relevance sorting but is overall complete for its purpose.

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

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

Schema coverage is 100% with descriptions for all 6 parameters, so baseline is 3. The description adds value by listing aliases and providing example queries, enhancing understanding beyond the schema.

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

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

The description clearly states the tool's function: to find tools by describing data or task, listing many domains. It distinguishes itself from siblings by being the only tool-discovery tool, and it emphasizes returning ready-to-call tool definitions.

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

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

It explicitly says to use when needing to browse, search, or discover tools, and to call it FIRST when many tools are available. While it doesn't explicitly say when not to use, the context is clear: use when unsure which tool to use.

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

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

The description adds significant behavioral context beyond the annotations: it fans out across multiple sources, mentions the USPTO API sunset soft-fail, describes data freshness (LATEST 10-K), and outlines the exact return fields. No contradiction with annotations (readOnlyHint=true, 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?

The description is informative but somewhat verbose (multiple sentences, enumeration of return fields). It is front-loaded with user queries, which is good, but could be slightly more concise without losing critical details.

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

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

Given the absence of an output schema, the description fully explains the return structure (cik, filings with URIs, fundamentals sorted, patents, news, LEI). It also covers edge cases (optional USPTO soft-fail, naming constraints), making the tool's behavior predictable and complete.

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

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

Schema coverage is 100% with descriptions for both parameters. The description reinforces and expands on the schema by providing concrete examples ('AAPL', '0000320193'), stating that names are not supported, and noting the type enum is currently limited to 'company'.

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

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

The description starts with multiple user query examples and explicitly states it provides a 'full cross-source profile of a US public company in ONE parallel call'. It lists the data sources and specific return fields, clearly defining the tool's purpose. It also distinguishes from siblings like resolve_entity.

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

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

Explicitly states 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view', providing clear when-to-use guidance. Also specifies that names are not supported and directs to use resolve_entity first, offering actionable exclusion criteria.

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 and idempotentHint. Description adds context about clearing sensitive data, confirming irreversibility, but does not contradict annotations.

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

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

Two concise sentences: first states action, second provides usage guidance. No wasted words.

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

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

Given the tool's simplicity (one param, no output schema), the description fully covers purpose, usage, and relationships to siblings.

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 100% of parameters with description for 'key'. Description adds no further detail beyond the schema's 'Memory key to delete'.

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 deletes a memory by key, with specific verb and resource, distinguishing it from siblings like '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 describes when to use (stale context, task done, clearing sensitive data) and pairs with sibling tools.

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

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

The description states it 'fetches the page, extracts title/description/key links', which aligns with annotations (readOnlyHint, openWorldHint). It adds value beyond annotations by detailing the extraction process and output format, though it could mention network dependencies or error handling.

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

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

The description is three sentences front-loaded with purpose, followed by process and use cases. Every sentence is informative with no redundancy; it efficiently conveys all necessary information.

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

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

Given the tool's simplicity (2 params, no output schema, clear annotations), the description provides sufficient context for an agent to invoke correctly. It explains the output format and use cases; minor gaps exist around error handling or URL prerequisites, but these are acceptable for this tool.

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

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

Schema coverage is 100% with both parameters well-described. The description does not add significantly to the parameter meaning (e.g., how max_links affects results), so it meets the baseline but doesn't exceed schema documentation.

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

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

The description uses a specific verb 'Generate' and resource 'llms.txt file', clearly stating it produces a standard markdown file for AI crawlers. It differentiates from siblings like 'ai_visibility_check' and 'scan_competitor_ai_presence' by focusing on file generation rather than analysis or scanning.

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

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

The description explicitly lists three use cases (getting client site indexed, drafting for own project, auditing competitor), giving clear context for when to invoke. It does not explicitly mention when not to use or alternatives, but the provided use cases imply appropriate 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. The description adds behavioral details: omitted variables are aggregated to total, and format returns specific structures. No contradictions with annotations.

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

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

Description is a single paragraph that covers all key points without redundancy. It is front-loaded with the main action and then provides details. Could be slightly more structured (e.g., bullet points for parameters) but is concise 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?

Given no output schema, the description explains return formats (JSON-stat or CSV). It covers required inputs and behavior (aggregation of omitted variables). Might lack details on error handling or large dataset behavior, but sufficient for typical use.

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

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

Schema coverage is 100%, but the description enriches meaning: explains variables map structure (code: value(s)), allowed values (single, array, '*'), and aggregation of omitted variables. Also clarifies format options beyond schema descriptions.

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

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

The description clearly states 'Pull data from a table,' specifying the verb (pull) and resource (table). It distinguishes from siblings like list_tables and table_info, which handle metadata or listing, not data 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?

The description provides detailed usage instructions: required parameters (tableId, variables), value formats, and format options. It implies when to use this tool (when actual data values are needed) versus siblings, but does not explicitly exclude use cases.

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 tree navigation behavior (drill, recursive expand) beyond annotations, which is helpful. No contradictions.

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

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

Two concise sentences with no wasted words. The first sentence states the overall purpose, the second explains parameters. Perfectly front-loaded.

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

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

Given 3 parameters and no output schema, the description covers the main functionality. It could briefly mention return format (e.g., 'returns a list of subjects with IDs and names'), but overall it is fairly 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. The description adds meaning for 'subjects' (drill into children) and 'recursive' (expand whole subtree), beyond the schema. 'lang' is unchanged. This adds value.

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 browses the Statistics Denmark subject tree, with a specific verb 'Browse' and resource 'subject tree'. This distinguishes it from siblings like 'list_tables' or 'get_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 it (browsing tree, drilling into children, expanding subtree) but does not explicitly state when not to use it or mention alternatives. It provides clear context for usage.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds context on returned fields and active subscriptions. Consistent, no contradiction.

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

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

Two sentences, front-loaded with action and returns, then usage guidance. No wasted words.

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

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

Given low complexity (one optional param, no output schema), description adequately covers purpose, usage, and return fields. Could elaborate on 'params' field but sufficient.

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

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

Schema coverage is 100% with one well-described boolean parameter. Description does not add extra meaning beyond the schema.

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

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

Clearly states 'List the caller's active subscriptions' with specific verb and resource, and lists returned fields. Distinct from sibling tools like subscribe and unsubscribe.

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

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

Provides explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' Implies when not to use (when subscribing/unsubscribing) via sibling tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds the return structure (table ids, titles, etc.), providing additional transparency beyond the annotations.

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

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

Two concise sentences with no wasted words. The key purpose and parameters are 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?

With 5 parameters and no output schema, the description explains the return fields and hints at downstream use. It is reasonably complete but could mention that results are paginated or limited.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds context for 'subjects', 'search', and 'pastdays' but does not cover 'lang' and 'includeInactive', which are already described 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 'List/search available tables' and specifies filtering options and return fields, distinguishing it from related tools like table_info and get_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 mentions using results with table_info/get_data, providing usage context. However, it does not explicitly state when not to use this tool.

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

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

Annotations give no special hints, but the description adds valuable context: team reads digests daily, signal affects roadmap, rate-limited to 5 per identifier per day, and it's free with no quota usage. This goes beyond annotations.

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

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

Concise single paragraph (~100 words) with front-loaded purpose. Every sentence is informative, no redundancy. 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?

The description covers purpose, usage, constraints, and parameter details adequately. Lacks explicit return value info, but for a feedback tool it's typically a success/failure message, which is implied.

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 further clarifies message content (be specific, 1-2 sentences typical) and type enum meanings, adding value beyond the schema.

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

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

The description clearly states the tool's purpose: sending feedback about Pipeworx tools (bugs, features, data gaps, praise). It differentiates from sibling tools like get_data, discover_tools, etc., which are not feedback-related.

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

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

Explicitly tells when to use (bug, feature, data_gap, praise) and what to include (describe in terms of tools/packs, not end-user prompts). Provides guidance on message content and length, plus rate limits and quota implications.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds value by explaining data source (CF analytics-engine), no PII, and caching behavior (5min-1h). No contradiction.

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

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

Concise at 3 sentences plus numbered list. Core function stated upfront, followed by use cases, then technical details. Efficient and well-structured 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?

For a simple tool with one optional parameter and no output schema, the description covers what it returns (top tools, top packs, total call volume), caching, data source, and purpose. No gaps given the 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?

Only one parameter 'window' with enum and description in schema. Description adds semantic guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This goes beyond the enum 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?

Clearly states the tool returns top tools, top packs, and total call volume over a recent window. Uses specific verb 'returns' and resource 'trending data on Pipeworx'. Differentiates from sibling tools like 'ask_pipeworx' and 'discover_tools' by focusing on aggregated usage data from other agents.

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

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

Provides three explicit use cases (discovering hot data sources, confirming canonical choice, seeing alignment). Does not explicitly state when not to use or alternatives, but the context is clear enough for an agent to decide.

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

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

Annotations indicate readOnly, openWorld, idempotent, and not destructive, which is consistent with a scanning tool. The description goes far beyond by detailing the scanning algorithm (monotonicity, partition checks), semantic anchor (Jaccard similarity), partition filter (placeholder slugs), and fill check (CLOB depth). It honestly warns when not to trade, adding high transparency. No contradiction with annotations.

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

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

The description is relatively long (multiple paragraphs) but well-structured with sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). Every part adds value; however, it could be slightly more concise without losing clarity. Still, it's effective and well-organized.

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

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

Despite having no output schema, the description explains the response format clearly (opportunities[] with fields, partition_check in event mode, fill check details). It covers all behavioral aspects: scanning modes, filtering, and risk checks. The tool has only 2 simple parameters, so completeness is high.

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

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

Schema coverage is 100%, but the description adds substantial meaning: it explains that event accepts slugs or URLs, that topic is a seed question, and describes how each parameter influences the scan (event walks child markets, topic searches related events). This enriches the parameter semantics beyond the schema.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity and partition-sum checks. It distinguishes between trending_scan, event, and topic modes, each with specific purposes. The title and description together make the function unambiguous.

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

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

The description explicitly tells when to use each mode: no args for trending scan, event for a specific market, topic for cross-event scanning. It recommends event mode and explains cross-event catches patterns single-event misses. It also references polymarket_fill_risk for custom sizing, providing an alternative. In the fill check section, it advises not to trade when realizable edge is non-positive.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds rich behavioral context: caching behavior, output structure with diagnostics, model families, and tradeable-edge filters, all consistent with annotations.

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

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

The description is verbose and includes technical model details (e.g., lognormal barrier, Kelly formulas) that could be condensed. It is structured in segments but could be more concise for efficient agent parsing.

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

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

For a complex tool with 9 parameters and no output schema, the description thoroughly explains the output structure (by_segment, diagnostics), filters, and edge cases (Fed bets, placeholder-slug filter), making it 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 coverage is 100% with detailed parameter descriptions. The tool description adds context beyond schema by explaining how parameters like min_partition_leg_kelly interact with segments and providing usage guidance.

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, with a specific verb+resource. It differentiates from siblings like polymarket_arbitrage by focusing on edge discovery across multiple model families.

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

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

The description provides clear context for when to use the tool ('what should I bet on today') and details knobs for filtering, but lacks explicit comparison to alternatives or 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, openWorldHint, idempotentHint, and destructiveHint false. The description adds significant behavioral context: explains the return structure (tracked, expired, snapshot_dates arrays), how trend and decay are computed, and that decay uses daily closes not intraday. No contradictions.

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

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

The description is dense but well-structured with a clear RESPONSE section and LIMITS. A few sentences could be trimmed, but overall it remains informative 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?

No output schema exists, so the description fully compensates by detailing the three return arrays and their fields (edge_pp_net time-series, first_seen, trend, decay_pp_per_day, expired lifespan_days, snapshot_dates). Also covers limitations thoroughly.

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% but only defines purpose and constraints. The description enriches both parameters: specifies default/max for days and lists exact window options (24hr, 1wk, 1mo) with default.

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

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

The description explicitly states the tool provides 'edge persistence and decay telemetry' and answers a specific question ('how long has this edge existed and is it shrinking?'). This clearly distinguishes it from sibling tools like 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 gives context on when to use the tool by contrasting fresh vs. old edges and mentioning limitations (60-day TTL, snapshot start). However, it does not explicitly state when not to use it or name alternative tools for different 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?

Beyond annotations (readOnly, idempotent), the description details behavioral traits: walking the ladder, returning specific fill metrics, and warning about partial basket fills converting arb into unhedged directional positions. This adds crucial context about risks and outcomes.

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

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

The description is lengthy but well-structured with clear sections for single-market and basket modes, and uses capitalization for essential keywords. Every sentence adds value, though some redundancy could be trimmed without loss.

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

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

Despite no output schema, the description enumerates all return fields (top_of_book, vwap_fill_price, slippage_pp, etc.) and explains edge cases like thin_legs and forced_directional_risk. It covers everything needed for an agent to understand the tool's full behavior.

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

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

Schema coverage is 100%, but the description adds significant meaning by explaining the dual-mode parameter logic (market vs event), side defaults for basket mode, and the different interpretation of size_usd for buys vs sells and single vs basket. It integrates parameters into a cohesive workflow.

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

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

The description clearly states it performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes. It uniquely identifies its purpose relative to sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

Explicitly instructs agents to 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500', providing clear context for when to invoke this tool and implying when it is less critical (e.g., smaller trades).

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 safe read operations. Description adds significant context: compatibility_warning conditions, temporal_alignment, skipped counters, and response structure. Full disclosure of edge cases and limitations.

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 purpose and modes, but somewhat dense with repeated safety field explanations. Could be slightly more concise while retaining all information.

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

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

Despite no output schema, the description thoroughly covers inputs, modes, output fields, edge cases (incompatibility, temporal alignment), and interpretation. Complete for an agent to use correctly.

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

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

Schema coverage is 100% with descriptions. Description adds value by explaining mode behavior, overriding logic, and output semantics, justifying a score above baseline.

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

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

Clearly states the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question. Describes two modes (topic shortcuts and explicit pairing) and distinguishes from sibling tools like polymarket_arbitrage.

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

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

Provides explicit when-to-use guidance: describes modes, explains when to use topic vs explicit, and warns when spreads are not tradeable (compatibility_warning). Also notes that real spreads are rarer than shortcut list suggests.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds scoping context (anonymous IP, BYO key hash, account ID) and pairing hints, which goes beyond the annotations without contradicting them.

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

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

The description is three sentences, front-loaded with the purpose, and every sentence adds value. It is concise and well-structured with 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?

For a simple retrieval tool with one optional parameter and no output schema, the description covers purpose, behavior, scoping, and pairing with siblings. It is fully complete for the agent to select and invoke the tool correctly.

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

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

The sole parameter 'key' is described with 'omit to list all keys', which adds meaning beyond the schema's description. Schema coverage is 100%, so baseline is 3, and the description provides extra context about behavior when omitted.

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

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

The description clearly states the tool retrieves a value saved via 'remember' or lists all keys when no key is provided. It specifies the verb 'Retrieve' and the resource 'a value', and distinguishes from sibling tools like 'remember' and 'forget'.

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

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

The description explains when to use the tool (look up context stored earlier) and mentions pairing with 'remember' and 'forget'. However, it does not explicitly state when not to use it or provide alternatives beyond sibling tools.

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

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

The description adds value by detailing returned fields and mark_read behavior beyond annotations. However, there is a contradiction: readOnlyHint annotation suggests no state modification, yet description reveals mark_read mutates read status. This inconsistency reduces transparency.

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

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

Three concise sentences front-load purpose, then detail return fields and parameters. No redundant information; every sentence earns its place.

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

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

Despite no output schema, description adequately explains return structure (source, citation_uri, payload). It covers all parameters implicitly and provides additional context on polling and external access, making the tool usage 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 covers 100% of parameters with descriptions; description adds examples for 'type' and 'since', and explains the effect of 'mark_read'. This enriches parameter understanding beyond the schema alone.

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

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

The description clearly states the tool pulls fired events from the subscription feed, specifying the verb 'Pull fired events' and resource. It distinguishes from siblings by focusing on reading recent alerts, as opposed to subscribing or listing 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?

Provides context on polling and an alternative HTTP endpoint, but does not explicitly compare against sibling tools like 'search_within' or 'get_data'. However, the filtering options and mark_read behavior are explained, giving clear usage guidance for the tool itself.

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

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

Annotations already indicate readOnly, idempotent, non-destructive. Description adds significant context: fan-out to three sources, fallback logic (GDELT→GNews), soft-fail for USPTO, and return structure (changes[], total_changes, citation URIs). No contradiction.

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

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

The description is a single, well-structured paragraph. It is front-loaded with example queries, and every sentence adds information. Slightly verbose but not wasteful.

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

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

Given the tool's complexity (multiple data sources, parameter details, fallback behavior), the description is complete. It explains return structure, sources, and provides comparison to a sibling tool. No output schema, so the description adequately covers 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%, baseline 3. Description adds value beyond schema: for 'since', it gives format examples and recommends '30d' or '1m'; for 'value', specifies ticker or CIK; for 'type', notes only 'company' supported.

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

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

The description clearly states the tool provides a 'change feed for a company in the last N days/weeks/months' fanning out to multiple sources (SEC EDGAR, GDELT→GNews, USPTO). It distinguishes from sibling 'entity_profile' by specifying when to use each.

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

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

Explicitly contrasts with 'entity_profile' ('Use entity_profile instead when you want the static profile...'). Provides example queries and recommended 'since' values ('30d', '1m'), and explains fallback behavior for rate limits.

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 key behaviors beyond annotations: scoped by identifier, persistence differences between authenticated (permanent) and anonymous (24-hour) sessions. Annotations (idempotentHint=true, destructiveHint=false) are consistent and supported by 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?

Three sentences front-loaded with purpose, followed by usage guidance and behavioral details. Every sentence adds value, but could be slightly more concise without losing information.

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

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

For a simple key-value store with no output schema, the description covers what it stores, when to use it, persistence behavior, and lifecycle (pair with recall/forget). No gaps identified.

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

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

Schema already provides 100% coverage with descriptions for both parameters. The description adds value by suggesting key naming conventions (e.g., 'subject_property') and clarifying that value can store any text (findings, addresses, preferences).

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 'Save data the agent will need to reuse later' with a clear verb (save) and resource (data). It distinguishes between this tool and siblings like recall and forget, making the purpose unmistakable.

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

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

Provides clear usage guidance: 'Use when you discover something worth carrying forward' with concrete examples (ticker, address, preference). Mentions pairing with recall and forget but lacks explicit when-not-to-use or alternative tool comparisons.

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 behavioral context by noting internal cascading through lookup endpoints and that it replaces multiple manual lookups. This goes beyond the annotations without contradiction.

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

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

The description is concise yet thorough, with front-loaded purpose and structured sections for supported types. Every sentence adds value, including explicit use-case examples and efficiency claims.

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 moderate complexity and no output schema, the description covers the return format for both entity types, including citation URIs. Annotations handle safety and idempotency. The description is complete enough for an agent to use the tool correctly.

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

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

Schema coverage is 100%, but the description adds substantial meaning: it explains each parameter's input format (ticker, CIK, name for company; brand/generic for drug) and what the output includes for each type (ticker+CIK+company_name for company, RxCUI+ingredient+brand for drug). This goes far beyond the bare schema.

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

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

The description explicitly states that the tool resolves user-spoken names to canonical/official identifiers needed by other tools. It provides clear examples like 'What's the ticker for...' and lists supported entity types (company, drug), making the purpose highly specific and distinguishable from sibling tools.

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

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

The description advises 'Use FIRST whenever you have a name but need an ID,' providing clear context for when to invoke the tool. It also implies efficiency by noting it replaces 2-3 manual lookups. However, it does not explicitly mention when not to use it or list alternatives like entity_profile for post-resolution context.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds behavioral context: it probes each entity with ai_visibility_check, returns ranked list with score, confidence, signal density, 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?

The description is four sentences, front-loaded with main action. Each sentence adds value: purpose, mechanism, use case, output. No wasted words.

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

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

Despite no output schema, the description specifies output format (ranked list with score, confidence, signal density). Annotations cover safety and idempotency. All parameters explained. Complete for agent usage.

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

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

Schema description coverage is 100%, but the description adds significant meaning: explains models options (workers-ai vs anthropic), _apiKey necessity, context disambiguation function, and entities count and ordering. This goes well beyond schema.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, with specific verbs (probes, ranks, surfaces) and resource ('AI visibility'). It distinguishes from sibling tools like ai_visibility_check by emphasizing multiple 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?

The description explicitly states it is useful for competitive AI-marketing audits and gives an example question. It implies when to use this tool vs. single-entity checks, but does not explicitly mention alternatives or when not to use.

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

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

Annotations already convey safe, idempotent, non-destructive behavior. The description adds critical behavioral detail: bundlephobia first measurement can take 5-30s, partial failures degrade gracefully, and sources_failed lists timeouts. This 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?

Description is well-structured with clear sections: purpose, usage, details. It front-loads the key value proposition. While a bit long, each sentence contributes meaning. Could be slightly 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 a composite check from two external services, the description covers: ecosystem limitation, return format details (summary block, per-advisory, links, recent alternatives), and graceful degradation. No output schema, but description sufficiently informs the agent of what to 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 covers both parameters with descriptions. The tool description adds extra meaning: scoped packages are accepted, version defaults to latest. This enriches the schema's baseline semantic value.

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

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

The description clearly identifies the tool as a composite check for evaluating npm packages, combining license, advisory, version history, and bundle size analysis. It uses specific verbs like 'check' and 'fans out', and distinguishes from siblings by focusing on npm dependency analysis.

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

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

Description explicitly states when to use: when an agent asks 'is X safe/popular/small' or 'what does adding lodash cost me'. It also provides a clear exclusion: 'NPM ecosystem only in v1; PyPI/Maven/Cargo/Go fall under deps.dev:version directly.' This guides the agent to alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds detailed behavioral traits: uses BGE-base-en embeddings, cosine similarity, 500-char windows, 200K char cap, and return format (offsets, scores), going well beyond annotations.

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

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

The description is compact (few sentences), front-loaded with the core action, and every sentence adds value—no filler or repetition.

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

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

Despite no output schema, the description fully covers return values (passages with offsets and scores), technical details (model, window size), constraints (200K char cap with truncation flag), and use case context, making it self-sufficient.

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

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

Schema coverage is 100%, so parameters are fully described. The description adds context like 'pass the text you already pulled' for text, 'natural-language query' with examples for query, and 'top-N passages' for limit, enhancing meaning over the schema alone.

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

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

The description clearly states the tool does semantic search inside a fetched record, using specific verbs and resources. It distinguishes itself by explaining it works on already-pulled text and returns passages with offsets, differing from other tools like ask_pipeworx_grounded.

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

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

The description explicitly says when to use the tool: 'Use when the record is too big to cram into the prompt' and pairs it with ask_pipeworx_grounded for an alternative workflow, providing clear usage context.

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

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

Annotations indicate readOnlyHint=false, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds value beyond annotations by detailing the OAuth requirement, supported event types, delivery channels, constraints (phone verification, SMS cap), and that it returns a subscription id. It does not contradict annotations.

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

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

The description is relatively long but well-structured: it starts with the main purpose, then lists types with examples, and ends with delivery channels. Each sentence adds necessary detail; no obvious redundancy. Could be slightly more concise, but front-loads key info.

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 types, nested params, optional delivery), the description covers all essential aspects: prerequisites, type-specific parameters, delivery options with constraints, and return value (subscription id). It also mentions the one-time webhook secret. No output schema exists, so the description adequately addresses return expectations.

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%, but the description enriches parameter understanding with examples for each type, explains the params object structure, and details delivery options with specific constraints (e.g., phone verification, HMAC signing for webhooks). This adds meaning beyond the schema's brief 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 verb 'Create a proactive monitoring subscription' and specifies the resource 'live-data event stream'. It distinguishes from sibling tools like 'unsubscribe' and 'list_subscriptions' by its focus on creation. The title 'Subscribe to Alerts' reinforces the purpose.

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

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

The description explains when to use the tool (proactive monitoring), the requirement for a Pipeworx OAuth account, and supported subscription types. It implicitly contrasts with anonymous/BYO accounts. While not explicitly stating when not to use, it provides sufficient context for appropriate selection.

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

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

The description adds significant behavioral context beyond annotations: it explains that the tool returns category-bucketed example questions with exact tool+argument shapes drawn from the live catalog, and that calling with no arguments gives a full spread while passing topic focuses the results. This enriches understanding of the tool's behavior and output.

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

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

The description is well-structured, front-loading alternative phrasings and the core purpose. While every sentence adds value, it is slightly longer than necessary. However, it remains efficient and easy 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?

Given the tool has a single optional parameter and no output schema, the description is complete. It explains what the tool returns (category-bucketed examples with tool shapes), how to call it (with or without topic), and when to use it (onboarding). There is no missing critical 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?

Although the input schema already describes the topic parameter with its possible values, the description adds meaning by explaining the effect of omitting the argument (full cross-category spread) and by providing example values. It also ties the parameter to the overall purpose of focusing the output.

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 the onboarding entry point for an agent to learn what questions to ask Pipeworx. It uses specific verbs ('returns category-bucketed example questions') and distinguishes from sibling tools like ask_pipeworx by positioning itself as the first tool to use when unfamiliar with the system.

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

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

The description explicitly tells when to use this tool: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also implies when not to use it (after you have learned), and references alternatives like ask_pipeworx, entity_profile, etc.

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: describes the specific metadata returned (variables, codes) and its role as input preparation for get_data. No contradiction with annotations (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?

Two concise sentences, front-loaded with purpose, no extraneous information. 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?

Given no output schema, the description adequately explains all return values (title, unit, last updated, variables list with codes and value ids). Ties into workflow with get_data. Complete for a simple info tool.

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

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

Schema coverage is 100%, so parameters are fully described in schema. The description does not add new parameter semantics but provides an example of variable codes, which is more about output than input parameters.

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

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

Clearly states it provides metadata for a table: title, unit, last updated, and full list of variables with codes and value ids. Distinguishes from sibling tools like get_data and list_tables by explicitly noting it is a prerequisite.

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 instructs to read this before get_data and explains why (to learn variable codes and value ids). Provides concrete examples like 'OMRÅDE' and 'Tid'. Does not exclude alternatives but context is clear.

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

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

Adds detail beyond annotations: deactivation (not deletion) ensures historical events remain available. Consistent with destructiveHint=false. No contradictions.

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

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

Two efficient sentences, front-loaded with main action, followed by key constraints and effects. No extraneous information.

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

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

Given single parameter, no output schema, and thorough annotations, description fully covers ownership enforcement, deactivation behavior, and historical event availability.

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; description adds value by specifying that id is the subscription id returned by subscribe, linking tools.

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 action (cancel) and resource (subscription by id). 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?

Provides explicit constraint: ownership enforced (can only cancel own subscriptions). Implicitly guides when to use (when user wants to cancel their own subscription). Not fully explicit about alternatives, but 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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by detailing the return structure (verdict, structured form, actual value with citation, percent delta) and explaining that it consolidates 4-6 sequential calls. No contradictions found.

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 with information and front-loaded with keywords like 'Is it true that…' and examples. It is slightly verbose but each sentence contributes to understanding. Well structured overall.

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 financial claim verification, the description is comprehensive. It covers domain, sources, what the tool replaces, and the return structure. Annotations provide safety context, and there is no output schema needed due to thorough description.

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

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

The input schema has 100% description coverage for the single 'claim' parameter, including examples. The description does not add significant semantic nuance beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: natural-language claim verification against authoritative sources. It specifies the domain (company-financial claims for US public companies) and provides example queries. It distinguishes itself by explaining it replaces multiple sequential calls, setting it apart 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?

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also constrains to company-financial claims via SEC EDGAR + XBRL. While it doesn't name alternative tools for non-financial claims, the domain context is clear enough for an AI agent to decide.

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