Missouri License Offices

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

Annotations already indicate readOnly, idempotent, openWorld hints, and description adds value by explaining cost implications (free default vs BYO key for Anthropic) and return structure (per-model score, confidence, signals, raw_response). No contradictions with annotations, and behavioral traits beyond what structured fields provide are disclosed.

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

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

The description is concise (4-5 sentences) and front-loaded with the core action and outputs. Every sentence adds distinct value: action, default model, optional API key, return format, and use cases. No redundant or filler content.

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 4 parameters, no output schema, but rich annotations, the description covers purpose, usage, parameter specifics, and return format. It is sufficiently complete for an AI agent to invoke correctly, though it could mention error handling or rate limits for API calls.

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

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

All parameters have descriptions in schema (100% coverage), and the description elaborates on their roles, including default models, API key purpose, and disambiguation via context. For example, it specifies that 'models' can be 'workers-ai' (free) or 'anthropic' (requires key), adding clarity beyond the schema.

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

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

Description clearly states the tool probes LLMs for knowledge about an entity and scores visibility (0-100). It identifies the action ('probe'), resource ('LLMs'), and output format. While it doesn't explicitly distinguish from similar siblings like 'scan_competitor_ai_presence', the use cases (AI-marketing audits, brand checks) provide enough context for differentiation.

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

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

The description explains default model usage and when to provide an API key for Anthropic, giving basic usage guidance. However, it lacks explicit instructions on when not to use this tool or comparisons to alternatives like 'scan_competitor_ai_presence'. The mention of 'AI-marketing audits' implies a context but is not definitive.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that it routes to 5,305 tools across 1,391 sources and returns structured answers with citation URIs, providing context beyond annotations without contradiction.

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

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

The description is well-structured, starting with a strong directive, followed by scope, usage guidelines, examples, and caveats. Every sentence adds value, and the length is justified by the tool's complexity.

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

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

Given the tool's complexity (routing to many sources) and the absence of an output schema, the description sufficiently explains the output format (structured answer with citation URIs) and behavior, making it complete for an agent to understand and 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% with clear descriptions for each alias. The description adds examples of questions but does not provide additional parameter-level semantics beyond what the schema offers, so a baseline score of 3 is appropriate.

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

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

The description explicitly states that the tool routes questions to a vast array of verified sources for factual queries, and distinguishes itself from siblings by recommending ask_pipeworx_grounded for single answers with evidence and deep_research for broad searches.

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 when-to-use guidance (prefer over web search, for factual questions, examples of query types) and when-not-to-use (step up to grounded or deep research), with explicit naming of alternatives.

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

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

The description discloses several behavioral traits beyond annotations: it's a full working router (not a fallback), results are compared against the stable router for decision-making, and it has experimental routing improvements. Annotations already indicate read-only, idempotent, open-world, and non-destructive, but the description adds valuable context about its experimental nature and comparison logic.

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

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

The description is moderately concise, with about 6 sentences. It front-loads the core purpose and then provides necessary context about experimental status and current behavior. While it is slightly verbose, every sentence adds value, and the structure is logical.

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 universal router with 6 parameters (all aliases) and no output schema, the description is complete. It explains the beta nature, identical behavior to ask_pipeworx, experimental routing, and current state. It covers what the tool does, when to use it, and how it relates to stable version. No gaps.

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

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

Schema coverage is 100%, so the input schema already documents all 6 parameters as aliases for 'question'. The description only mentions 'same arguments as ask_pipeworx' without adding details. Given high schema coverage, the baseline is 3, and the description does not significantly enhance parameter understanding.

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

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

The description clearly states it's a beta version of ask_pipeworx, an identical universal router with the same tools and arguments, but with candidate routing improvements. It differentiates from the stable ask_pipeworx by highlighting its experimental edge and current identical behavior. This makes the purpose very specific and distinguishable.

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: 'when you want the newest routing'. It also provides context that no candidate is active now, so it matches ask_pipeworx exactly. While it doesn't explicitly state when not to use it, the differentiation from the stable sibling is clear, providing good usage guidance.

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

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

Annotations already declare readOnlyHint=true, etc., and the description adds valuable behavioral details: same routing as ask_pipeworx, refusal reasons (not_in_source, no_tool_match, etc.), and an extra LLM call cost. No contradictions with annotations.

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

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

The description is moderately long but front-loaded with key purpose and usage. Every sentence serves a purpose, though it could be slightly more concise without losing clarity.

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

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

Given 6 parameters (only 1 required) and no output schema, the description compensates by explaining return structure (answer, evidence, confidence, etc.) and refusal reasons. It also addresses cost and comparison with sibling, making it fully informative.

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% (all parameters well-defined). The description adds little beyond listing aliases already in the schema; it does not provide new semantic information about parameters.

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

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

The description clearly states the tool provides hallucination-resistant answers for high-stakes reads, specifying the verb 'answers' and the resource 'Pipeworx Grounded'. It distinguishes itself from the sibling 'ask_pipeworx' by noting it extracts answers only from tool results.

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

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

Explicitly states when to use: high-stakes reads where answers will be quoted, cited, or acted on, and the agent must not invent facts. Also advises preferring 'ask_pipeworx' for casual lookups, providing clear when-not-to-use guidance.

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

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

Annotations already indicate read-only and idempotent. The description adds extensive behavioral details: fan-out patterns, low-confidence short-circuit, closed market handling, wide-spread illiquidity, resolution-rule risk, and response structure. 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 thorough but verbose, with multiple paragraphs and repeated details. It could be more concise by omitting redundant classifier lists and examples. The structure is front-loaded with purpose, but overall length reduces conciseness.

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

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

Without an output schema, the description fully explains response shapes, including market analysis, evidence packet, resolver contract, parent events, news fields, and safety. It covers all necessary context for the tool's complexity.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds further context, such as the meaning of 'depth' (quick vs thorough) and 'include_raw' (when to set true), 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 it researches Polymarket bets by pulling Pipeworx data. It specifies inputs (slug, URL, or question text), process (resolve, classify, fan out), and outputs. While distinct from siblings, it could explicitly contrast with other Polymarket tools like polymarket_edges or 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?

Explicitly says 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' Provides context on when to use but does not specify when not to use or alternatives, though the use cases are clear.

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

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

Annotations declare read-only, idempotent, non-destructive. The description adds valuable context: off-calendar fiscal year handling, sorting by primary metric, parallel call efficiency, and citation URIs. No contradiction with annotations.

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

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

The description is well-structured, front-loaded with trigger phrases, and each sentence adds value. It is slightly verbose but appropriate for the tool's complexity.

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

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

Despite no output schema, the description mentions 'paired data + citation URIs' giving a general idea of return structure. For a 2-parameter tool, this is fairly complete, though more detail on output format could help.

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

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

Schema coverage is 100% with good descriptions. The description adds meaning beyond the schema: explains what data each entity type pulls and how results are sorted, enhancing parameter understanding.

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

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

The description uses explicit trigger phrases and clearly states it performs side-by-side comparison of 2–5 companies or drugs. It distinguishes between 'company' (SEC financials) and 'drug' (FAERS/fda counts) and highlights replacing sequential lookups, making it distinct from single-entity 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?

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing strong guidance on when to use. It also explains what each type pulls, but does not explicitly mention when NOT to use or list alternative tools.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds extensive behavioral details: parallel execution, gap recovery, contradictions scan, time expectations (15-90 seconds), excerpting large records, and citation structure. No contradictions with annotations.

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

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

Description is dense and front-loaded with critical info (account requirement, alternatives). Every sentence adds value, though slightly long. Could benefit from bullet points for depth levels, but otherwise well-structured.

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

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

Despite no output schema, description thoroughly explains return format (findings packet with evidence, confidence, source, citation, gaps, contradictions). All 2 parameters are well-documented. Complete for an AI to correctly invoke the tool.

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

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

Schema already covers both parameters with descriptions (100% coverage). Description adds practical context: depths function (quick=3, standard=5, thorough=8 facets), default behavior (standard default implied), and encourages natural language questions.

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

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

Description clearly states the tool performs grounded multi-source research across Pipeworx's structured data sources, decomposing questions into facets and returning a findings packet. It distinguishes itself from sibling tools like ask_pipeworx for single lookups or news queries.

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

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

Explicitly provides usage guidelines: when to use ask_pipeworx instead (single lookup, breaking news), account requirements (sign-in, paid plan for 'thorough'), and appropriate question types (broad/multi-part structured data queries).

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description does not contradict them. The description adds value by disclosing that results include full schemas with curated examples and are ready to call, which goes beyond annotations.

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

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

The description is concise with no fluff. Front-loaded with the primary action and key usage scenario, then lists specific domains and what results contain. Every sentence adds value.

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

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

Although there is no output schema, the description adequately explains what the tool returns (top-N tools with names, descriptions, full schemas). It mentions curated examples and that results are ready to call. Given the tool's complexity and lack of output schema, this is 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 description coverage is 100%, with all 6 parameters documented. The description provides no additional meaning beyond repeating that query is a natural language description and noting aliases, which are already in the schema. Baseline of 3 is appropriate since schema does the heavy lifting.

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

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

The description uses a specific verb ('Find tools by describing') and clearly identifies the resource (tools). It distinguishes itself from siblings by focusing on discovery of available tools, while siblings like 'search_within' or 'deep_research' handle data within tools. The extensive domain list further clarifies 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 states 'Call this FIRST when you have many tools' which provides clear when-to-use guidance. It implies using this before other tools to see options. However, it does not explicitly mention when not to use or compare with alternatives, though context is sufficient.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. The description adds valuable behavioral context: fans across multiple sources, returns specific fields, has a soft-fail for patents sunset, includes fallback mechanisms, and notes input constraints. 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 dense but front-loaded with examples and use-case. Every sentence adds value, though it is somewhat lengthy. Earns a 4 for being informative without unnecessary verbosity.

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

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

Given no output schema, the description thoroughly explains all return fields (cik, company_name, recent_filings with URIs, fundamentals, patents, news, LEI). It also mentions the patent sunset and fallback, making it complete for an agent to understand the tool's capabilities.

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 examples (ticker 'AAPL', zero-padded CIK) and clarifies that names are not supported, which enhances understanding beyond the schema alone. Slightly above baseline.

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

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

The description clearly states the tool provides a full cross-source profile of a US public company, lists example queries ('Tell me about X', 'research Acme'), and specifies the resources aggregated. It distinguishes itself from chaining individual lookups, earning a 5.

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

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

The description explicitly says to use this tool for holistic views, prefers it over chaining single-pack lookups, and warns that names are not supported (use resolve_entity instead). It also mentions ALWAYS PREFER over alternatives, providing clear when-to-use guidance.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. Description adds context about why deletion is appropriate (stale data, task completion, sensitive data removal), but does not mention idempotency or other behavioral traits.

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

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

Two concise sentences, no wasted words. First sentence states action, second provides usage context and sibling references.

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, clear annotations, and no output schema, the description fully covers when and why to use the tool. No gaps.

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

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

Schema coverage is 100% with a clear description for the 'key' parameter. The description adds no additional semantic information beyond the schema, resulting in baseline 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?

Description clearly states it deletes a previously stored memory by key. The verb 'Delete' and resource 'memory by key' are specific and unambiguous.

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

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

Explicitly lists use cases: when context is stale, task is done, or clearing sensitive data. Also mentions pairing with 'remember' and 'recall', providing guidance on alternatives.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. Description adds that it fetches page and extracts info, which is consistent but not deeply beyond annotations.

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

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

Three sentences, front-loaded with purpose, no unnecessary words. Highly efficient.

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

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

No output schema, but description explains output format and deployment location. Sufficient given low complexity (2 params, no enums).

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 example URL format and numeric limits for max_links, providing value beyond basic 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?

States specific verb ('Generate'), resource ('llms.txt file'), and context ('for any URL'). Distinguishes from siblings by focusing on AI crawler indexing.

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?

Lists clear use cases (client site, own project, competitor audit) but lacks explicit when-not-to-use or alternative tool references.

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, idempotentHint, and non-destructive. The description adds valuable behavioral information such as the returned fields (id, type, params, etc.) and the default of active subscriptions, enhancing agent understanding beyond structured data.

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

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

Two sentences, no redundancy, front-loaded with the core action. Every sentence adds value.

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

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

Despite lacking an output schema, the description lists the returned fields. Combined with usage guidance and annotations, the tool is fully understandable for an AI 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 coverage is 100% with a single boolean parameter fully described. The description implicitly references it ('active subscriptions') but adds no new semantics beyond the schema. Baseline of 3 is appropriate.

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

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

The description clearly states 'List the caller's active subscriptions', specifying the verb (list) and resource (subscriptions). This distinguishes it from sibling tools like 'subscribe' and 'unsubscribe'.

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

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

Explicitly advises when to use the tool: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This provides clear context and exclusions relative to siblings.

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

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

Annotations declare readOnlyHint, idempotentHint, destructiveHint=false. Description adds value by noting daily data refresh and specifying exact return fields, exceeding what annotations provide. 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?

Single paragraph, no wasted words. Starts with purpose, lists returned fields, gives concrete examples, and ends with scope. Efficient and front-loaded.

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

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

Describes all return details (since no output schema) and data freshness. Does not explicitly state default behavior when no parameters given (returns first 50 offices?), but limit param covers that. Adequate for a lookup tool.

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

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

Schema covers all four parameters with descriptions. The description adds context with examples like 'Columbia' for city and '63101' for ZIP, and explains usage patterns (substring matching, prefix). This enhances understanding beyond schema alone.

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

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

Description clearly states 'Find a Missouri license office' and enumerates returned fields (address, phone, hours, coordinates, etc.). Provides example queries that differentiate from sibling tools (e.g., 'DMV office in Springfield MO' vs. unrelated tools like deep_research).

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

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

Examples of use cases (closing dates, office hours, location queries) implicitly guide when to use. No explicit exclusions or alternatives, but context of sibling tools makes it clear this is the DMV office 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?

Discloses rate limiting (5 per identifier per day), free cost, and that it doesn't count against tool-call quota. Annotations only indicate non-read-only and non-destructive; the description adds critical constraints.

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?

Extremely concise: three sentences covering purpose, usage, and behavioral constraints. No wasted words, front-loaded with core action.

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

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

Covers all essential aspects for a feedback tool: what to submit, how to format, constraints, and outcome (roadmap impact). No output schema needed given simplicity.

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

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

Input schema already has 100% coverage with detailed descriptions for type and context. The description reinforces the schema but doesn't add much new; a 4 is appropriate as schema does the heavy lifting.

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

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

The description clearly states the tool is for sending feedback to the Pipeworx team about bugs, features, data gaps, or praise. It distinguishes itself from sibling tools like 'ask_pipeworx' which are for queries, not feedback.

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

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

Provides explicit guidance on when to use each feedback type (bug, feature, data_gap, praise) and what not to include (end-user prompt). Also mentions rate limits and free 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 readOnly, openWorld, idempotent, and non-destructive. The description adds critical behavioral details: data source (CF analytics-engine), no PII, caching behavior (5min-1h), and that it's self-aggregating. These go beyond annotations.

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

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

The description is concise: 4 sentences starting with a clear purpose, followed by bullet-point use cases, then technical details. Every sentence adds value, and the structure is front-loaded with the most important 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 explains what is returned (top tools, top packs, total call volume), the data source, caching, and privacy. It is complete for a read-only, single-parameter tool with comprehensive annotations.

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

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

Schema coverage is 100% (one parameter with enum). The description adds semantic value by explaining that shorter windows surface 'what's hot right now' and longer windows show 'steady-state demand', which aids parameter selection.

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

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

The description clearly states it returns top tools, packs, and call volume over a time window. It uses a specific verb ('Returns') and resource ('top tools, top packs, total call volume'), distinguishing it from siblings like discover_tools or ask_pipeworx.

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

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

The description lists three concrete use cases (discovering hot data sources, confirming canonical tool, aligning use case) and provides guidance on window selection. It lacks explicit 'when not to use' but makes context clear.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive. Description adds detailed behavioral context: algorithmic steps (Jaccard similarity threshold, placeholder filter, fill check), failure modes (low similarity, placeholder majority), and reliance on external tool (polymarket_fill_risk). 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 detailed and informative but slightly verbose. It front-loads the core purpose and then breaks down modes, but could be more concise while retaining 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?

Despite no output schema, description thoroughly explains response structure (opportunities array, partition_check fields, fill check results). Covers all modes, edge cases (low similarity, placeholder filtering), and references to companion tools. Complete for a complex multi-mode tool.

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

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

Schema has 100% description coverage for both parameters. Description adds significant meaning beyond schema: explains event slugs vs topics, how each mode works (walking child markets, cross-event Jaccard similarity, partition check details).

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

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

The description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes from sibling tools like polymarket_edges and polymarket_fill_risk by specifying its unique scanning modes (trending_scan, event, topic).

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

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

Explicitly explains when to use each mode: no args for trending scan, event for specific market, topic for cross-event scanning. Provides example inputs and warns when not to trade (realizable_edge_pp ≤ 0).

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds rich behavioral details: caching at 1h, Fed bet exclusion reasoning, three model family mechanics, response structure with diagnostics, and knob effects. No contradictions with annotations.

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

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

The description is lengthy (~500 words) but dense and well-structured. It front-loads purpose, then details segments, response, and knobs. Every sentence adds necessary information for a complex tool; no redundancy. The length is justified by the tool's complexity.

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

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

Given the complexity (3 model families, 9 parameters, no output schema), the description covers response structure, model formulas, tradeable-edge knobs, caching, and limitations. It thoroughly explains return values and diagnostics. Some explicit field listing for each segment would be ideal, but overall it's highly complete.

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

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

With 100% schema coverage, the schema already describes all 9 parameters. The description adds value by explaining parameter interactions (e.g., min_kelly vs min_partition_leg_kelly), providing usage examples (e.g., 'Set to 5000...'), and contextualizing default values. This exceeds the baseline of 3.

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

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

The description explicitly states the tool scans Polymarket markets for discrepancies with Pipeworx data, and contrasts it with the use case 'what should I bet on today'. It clearly distinguishes from sibling tools like polymarket_arbitrage by focusing on Pipeworx disagreement and specific 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 use (discovering betting opportunities) and details three model families with their rationales. It implies when each segment might be relevant but lacks explicit 'when not to use' comparisons with siblings. Overall, guidance is strong but not exhaustive.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds significant detail: response structure (tracked, expired, snapshot_dates), decay computation, edge signed by trade direction, and snapshot gaps. No contradictions.

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

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

The description is well-structured with sections for response and limits, but it is relatively long. It could be slightly more concise while retaining 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 no output schema, the description thoroughly explains the return fields (tracked, expired, snapshot_dates) and their sub-fields (trend, decay_pp_per_day, lifespan_days, etc.). It also covers edge cases like snapshot gaps and the 60-day TTL, making it 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 both parameters with descriptions. The description adds defaults (days=14, window='1wk'), clamps (days 2-30), and explains that window corresponds to snapshot families. This adds value beyond the schema.

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

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

The description clearly states the tool's purpose: answering questions about edge persistence and decay using daily snapshots. It distinguishes itself from the sibling 'polymarket_edges' by focusing on telemetry and time-series 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?

The description provides context on when to use the tool (to differentiate fresh vs aged edges) and mentions limits (60-day TTL, snapshot gaps). However, it does not explicitly contrast with sibling tools like 'polymarket_arbitrage' or 'polymarket_fill_risk'.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds extensive behavioral detail: walks the ladder, returns specific fields like top_of_book, vwap_fill_price, slippage_pp, verdict, and in basket mode explains thin_legs and forced_directional_risk. No contradictions with annotations.

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

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

The description is moderately long but well-structured with clear sections for single-market and basket modes. Every sentence provides useful information, though slight verbosity in explaining forced_directional_risk could be tightened. Overall, it earns its length.

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

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

Given the complexity (no output schema, 4 params, no nested objects), the description is remarkably complete. It explains return values for both modes, use cases, risks (thin books, partial fills), and provides explicit guidance. No gaps remain.

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

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

Schema coverage is 100% (all 4 parameters described in the input schema), so baseline is 3. The description adds significant meaning beyond the schema: it explains the auto default for side in basket mode, clarifies size_usd as settlement notional in basket, and details return fields not in the schema. This enrichment justifies a score above baseline.

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

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

The description clearly states the tool performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth'. It distinguishes two modes (single-market and basket) and explicitly contrasts with sibling tools like polymarket_arbitrage and polymarket_edges by providing context on when to use this tool before those.

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 directs: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It explains why—partial basket fills convert arbs into unhedged directional positions, a common loss mode. This provides clear when-to-use and when-not-to-use guidance.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral details: compatibility warnings for non-equivalent bet shapes or unmatched events, temporal alignment checks, skipped cross-type/subtype counters, and the rarity of real spreads. This goes well beyond what annotations provide.

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

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

The description is fairly long but well-structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS). Every sentence adds value. Could be slightly more terse, but the detail justifies the length.

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

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

Given the complexity (cross-venue, multiple modes, no output schema), the description covers: input modes, output format (leg-by-leg prices, spread, compatibility warnings), edge cases (temporal mismatch, skipped comparisons), and practical usage notes. It is fully self-contained.

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

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

Schema coverage is 100% and the description enriches each parameter: topic with an inline enum list, and the two override tickers with examples and behavior. It clarifies how they interact (overrides the topic-mapped side). No ambiguity remains.

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 computes the cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes two modes (topic shortcuts and explicit tickers) and explains the output. The purpose is specific and distinct from siblings.

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

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

The description explains when to use each mode (topic for pre-mapped macros, explicit for custom pairs) and warns that most pre-mapped topics return compatibility warnings. It does not explicitly compare with sibling tools like polymarket_arbitrage but provides enough context for appropriate use.

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

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

Annotations already declare the tool as read-only and idempotent. The description adds value by explaining the dual behavior (retrieve vs list) and scoping, without contradicting annotations.

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

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

The description is concise (60 words), front-loaded with the main action, and every sentence provides useful information without redundancy.

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

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

Given the simple parameter schema and the presence of annotations, the description covers all necessary behavioral and contextual information for correct tool 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% and the parameter description is already clear. The description does not add new meaning beyond the schema.

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

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

The description uses specific verbs ('Retrieve', 'list') and identifies the resource ('value previously saved via remember', 'all saved keys'). It clearly distinguishes from sibling tools 'remember' and 'forget'.

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

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

The description states when to use this tool (to look up context stored earlier) and provides concrete examples (target ticker, address, research notes). It mentions scoping but does not explicitly list alternatives.

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

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds context about the feed persistence, the effect of `mark_read` on subsequent calls, and the data returned (source, citation_uri, raw event). This goes beyond what annotations provide.

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

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

The description is three sentences, each earning its place: purpose, return details, filtering and mark_read usage, and polling guidance. No redundancy, clear front-loading.

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

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

Despite lacking an output schema, the description explains what the tool returns (source, citation_uri, raw payload). All parameters are covered, usage guidance is given, and behavioral aspects (feed persistence, mark_read effect) are disclosed. The tool is simple, and the description is complete.

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

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

With 100% schema coverage, the baseline is 3. The description adds practical meaning: it gives an example for `type` ('sec_8k'), explains `since` as ISO timestamp, and clarifies that `mark_read` flags events as read so next calls show only newer events. This is useful beyond the schema's minimal descriptions.

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

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

The description starts with a specific verb-resource pair ('Pull fired events from your subscription feed') and clearly distinguishes the tool from siblings like 'list_subscriptions' and 'subscribe'. It states exactly what the tool returns (source, citation_uri, raw payload).

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 notes that polling works fine and provides an alternative access method via a GET endpoint, which guides when to use this tool vs. manual fetching. However, it doesn't explicitly mention when not to use it, though the read-only nature is clear from annotations.

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 not destructive. The description adds rich behavioral details: fans out to SEC EDGAR, GDELT→GNews fallback, and USPTO (soft-fail until reactivated), with structured return format including citation URIs. No contradiction with annotations.

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

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

The description is front-loaded with example queries, then explains the multi-source logic, parameters, and return format in a structured, non-repetitive way. Every sentence adds value, and the length is appropriate for the complexity.

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

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

Despite lacking an output schema, the description thoroughly explains the return structure (changes grouped by source, total_changes count, citation URIs). It also covers limitations (USPTO soft-fail) and the fallback behavior. The tool is fully described 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?

Input schema has 100% description coverage. The description adds value by explaining the `since` parameter accepts ISO dates or relative shorthand (with examples like '7d', '3m'), and that `value` accepts ticker or CIK. It also provides guidance on typical usage for `since`.

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

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

The description clearly states the tool provides a change feed for a company in a recent time window, with example queries like "What's new with X" and "latest on Y." It explicitly distinguishes itself from the sibling tool entity_profile, which handles static profiles regardless of time window.

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

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

The description gives explicit guidance on when to use (for time-window change queries) and when not to (for static profiles, pointing to entity_profile). It also advises on the `since` parameter with examples like '30d' or '1m' for typical monitoring.

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

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

The description adds critical behavioral context beyond annotations: 'scoped by your identifier,' 'Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours.' No contradictions with annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true).

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

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

Four sentences, each serving a distinct purpose: purpose, usage guide, behavioral details, and tool pairings. No fluff or repetition. Front-loaded with the core action.

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

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

Given the simple parameter set (2 required strings) and no output schema, the description covers all necessary aspects: purpose, when to use, persistence behavior, scoping, and sibling tool relationships. No gaps remain.

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

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

The input schema already provides descriptions for both parameters (100% coverage). The description adds value by suggesting example keys ('subject_property', 'target_ticker') and the nature of values ('findings, addresses, preferences, notes'), clarifying usage beyond the schema.

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

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

The description clearly states the verb 'Save data' and the resource 'key-value pair', and distinguishes from siblings recall and forget. It specifies the tool is for storing reusable data across sessions, which is specific and unambiguous.

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

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

The description explicitly advises when to use: 'when you discover something worth carrying forward.' It also mentions complementary tools: 'Pair with recall to retrieve later, forget to delete.' This provides excellent guidance on tool 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?

Annotations declare readOnlyHint, idempotentHint, and destructiveHint. The description adds that each call cascades through several lookup endpoints internally, replacing 2-3 manual lookups, providing valuable behavioral context beyond annotations.

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

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

The description is well-structured with examples first, then supported types, then return information. Every sentence adds value, and it is concise despite the detail.

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 no output schema but returns structured data for two entity types, the description comprehensively documents inputs, outputs, internal cascading, and usage context. It covers all needed information for correct invocation.

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

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

Schema coverage is 100% and parameters are described. The description adds significant value by specifying exactly what each entity type returns (ticker, CIK, RxCUI, etc.) and the citation URIs, going beyond the schema's enum and string 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 resolves user-spoken names to canonical IDs, with concrete examples ('What's the ticker for…') and lists supported types (company, drug). This clearly distinguishes it from sibling tools like entity_profile or compare_entities.

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

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

The description says 'Use FIRST whenever you have a name but need an ID,' providing a clear usage directive. It also implicitly tells when not to use it (when you already have an ID) and lists alternative tools indirectly through the sibling list.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, etc., indicating safe, non-destructive behavior. The description adds value by detailing the process: 'Probes each entity... with ai_visibility_check, ranks by score' and output fields (score, confidence, signal density), which are not in annotations.

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

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

The description is concise (two sentences) and well-structured: first sentence states action and method, second provides use case and output. Every part adds value with no redundancy.

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

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

For a tool with 4 parameters, no output schema, and rich annotations, the description fully explains functionality, process, output fields, and use case. It covers all necessary context for an AI agent to select and 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 meaning: specifies entities range '2-8', clarifies that the first entity is 'treated as the subject', gives examples for context, and notes that models can be omitted for default. This provides context beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: 'Compare AI visibility across multiple entities side-by-side' with specific actions (probes, ranks, surfaces). It differentiates from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison) by detailing its method and output.

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 a clear use case: 'competitive AI-marketing audits' with an example question. It implies when to use it (comparing multiple entities) but does not explicitly state when not to use or list alternatives, though the context of sibling tools is available.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: it fans out across two external services (deps.dev and bundlephobia), mentions that bundlephobia's first measurement can take 5-30s, and explains that partial failures degrade gracefully with a sources_failed field. No contradictions.

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

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

The description is a single paragraph but packs a lot of information efficiently. It is front-loaded with the purpose and key details. Minor improvement could be using bullet points for the return summary, but it remains 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 comprehensively explains the return value: a summary block with 9 fields, per-advisory detail, links, and a list of alternative versions. It also covers partial failure behavior and ecosystem limitations. This is more than adequate for an agent to understand the tool's capabilities.

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

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

Input schema has 100% description coverage, so baseline is 3. The description adds minor context (scoped packages accepted, version defaults to latest), but these are already inferrable from the schema descriptions. No extra semantics beyond what the schema provides.

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

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

The description clearly states the tool's purpose: a composite check for npm packages covering license, advisories, version history, and bundle size. It uses specific verbs like 'fans out across' and explicitly answers questions like 'is X safe / popular / small' or 'what does adding lodash cost me'. This distinguishes it from sibling tools which are unrelated to dependency 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 provides explicit usage guidance: when to use (for npm packages when evaluating safety/popularity/size), when not to use (other ecosystems like PyPI, Maven, Cargo, Go, which should use deps.dev:version directly), and the behavior for partial failures (graceful degradation with sources_failed field). This is exemplary.

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

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

Annotations (readOnlyHint, idempotentHint, destructiveHint) indicate safe, non-destructive behavior. The description adds significant behavioral context: embedding model (BGE-base-en), similarity metric (cosine), chunking strategy (500-char overlapping windows), input limit (200K chars with truncation flag), and output details (character offsets, similarity scores). This far exceeds what annotations provide.

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

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

The description is a single, well-structured paragraph with no wasted words. The first sentence states the purpose, followed by usage context, then technical details. Each sentence adds essential information without redundancy.

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

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

The tool has no output schema, so the description must explain return values. It states passages with 'character offsets and similarity scores' and mentions truncation behavior. However, it could be more precise about the output format (e.g., JSON structure) and handling of empty results. Still, it provides enough for an agent to invoke and interpret the tool.

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

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

Schema coverage is 100% with basic descriptions for all three parameters. The description enriches understanding by providing concrete query examples and clarifying the text parameter's max length and the limit parameter's default (5) and range (1-20). It also explains how parameters fit into the overall search logic (e.g., overlapping windows).

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

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

The description clearly identifies the tool's core action: semantic search inside a specific record. It uses a strong verb ('Search Within') and specifies the resource type ('fetched record'). It differentiates from siblings by explicitly stating when to use it ('record too big to cram into prompt') and mentions pairing with ask_pipeworx_grounded, distinguishing it from other search tools in the sibling list.

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

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

The description provides explicit usage guidance: 'Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter.' It also suggests a complementary tool (ask_pipeworx_grounded). However, it does not explicitly state when not to use it or list alternatives for small records.

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 implies each call creates a new subscription (returns new id), but annotations set idempotentHint=true, suggesting the same subscription would be returned for identical parameters. This is a contradiction; description does not address idempotency.

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 each sentence provides necessary detail. It is front-loaded with the main purpose. Slightly more structured formatting (e.g., bullet points) would improve readability.

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 (multiple types, nested parameters, delivery channels, no output schema), the description is highly complete. It covers account prerequisites, type-specific filters, delivery methods with limitations, and webhook security.

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 beyond the schema: it explains type-specific parameters (e.g., items codes for sec_8k), delivery options with constraints (phone verification, daily cap, webhook signing), and examples.

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

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

The description clearly states 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' It uses specific verbs and resources, distinguishing from sibling tools like list_subscriptions and unsubscribe.

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

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

The description provides explicit context: 'Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions).' It lists supported types and delivery channels, though it does not explicitly state when not to use or alternative tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by explaining it returns category-bucketed example questions with exact tool + argument shapes drawn from live catalog, which is 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 well-structured and front-loaded with common queries. It is slightly long but every sentence adds value; no wasted words.

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

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

Given good annotations and a single simple parameter, the description is complete. It covers the return type (category-bucketed examples with tool+argument shapes) and usage scenarios. No output schema is needed.

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

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

Schema description coverage is 100% for the one parameter. The description adds meaning by listing example values (finance, pharma, etc.) and explaining the effect of omitting the topic.

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: returning category-bucketed example questions for onboarding. It uses specific verbs like 'returns' and distinguishes from siblings by positioning it as the first tool to use when unsure what Pipeworx can do.

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 instructs to 'Use this FIRST when you do not yet know what Pipeworx can do for you' and explains when to use the optional topic parameter. It does not explicitly list alternatives, but the context is clear.

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

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

Discloses key behaviors beyond annotations: ownership validation, deactivation instead of deletion, and idempotency hint is aligned. No contradiction with annotations.

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

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

Two sentences with clear action and important constraints, 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?

With minimal parameters and clear annotations, the description provides sufficient context for the tool's operation and side effects.

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

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

Schema covers the single parameter completely; description adds slight value by linking 'id' to subscribe's return 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?

Description clearly states the verb 'cancel' and resource 'subscription by id', distinguishing it from sibling tools like subscribe and list_subscriptions.

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

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

Explicitly mentions ownership enforcement (only cancel own subscriptions) and that deactivation preserves historical data, guiding proper use without listing alternatives.

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

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

Annotations indicate read-only, open-world, idempotent. Description adds behavioral details: dual pipeline (SEC EDGAR+XBRL vs. grounded), verdict types, evidence citation, and efficiency gains. 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 relatively long but well-structured: examples first, then usage guidelines, then internal logic, then return values. Some redundancy possible, but each sentence adds value.

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

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

Given no output schema, the description explains return values (verdict types, actual value with citation, reasoning). Also covers routing logic for different claim types. Adequate for 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%, so baseline is 3. Description adds context for 'tolerance_pct' (overriding default, use for hallucination detection), and clarifies 'claim' parameter with examples. Extra 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?

Description starts with natural-language examples and clearly states 'claim verification against authoritative sources'. It distinguishes from siblings by noting it replaces 4-6 sequential calls.

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

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

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and differentiates between financial and other claims. No explicit when-not-to-use, but context is clear.

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