Data Kingcounty

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

Annotations already indicate safety (readOnlyHint, idempotentHint). Description adds valuable context: default model is free, Anthropic requires BYO key, and output format details. No contradictions. Could mention rate limits or error handling but not required given annotations.

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

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

Three sentences, each earning its place: first defines purpose, second adds key usage detail, third lists output and use cases. 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 fully covers input parameters, output structure, use cases, and default behavior. For a tool with 4 parameters, this is comprehensive and leaves no obvious 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%, but description adds meaning: explains default model, clarifies that _apiKey is only needed for Anthropic, and provides examples for 'entity' and 'context'. This goes 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 probes LLMs for knowledge about entities and scores visibility (0-100), which is a specific verb+resource+outcome. It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on general brands and topics.

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 use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Also explains when to provide an API key for Anthropic. No explicit 'when not to use' or alternatives, but context signals provide sibling names for comparison.

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

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

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) already indicate a safe, non-destructive, idempotent tool. The description adds context about routing to thousands of tools and returning stable citation URIs, which is useful but does not contradict annotations.

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

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

The description is long but well-structured: begins with a bold directive, then lists use cases, examples, and sibling guidance. It is front-loaded and every sentence adds value, though could be slightly more concise.

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 the tool (routing to thousands of sources) and the rich annotations (openWorldHint, idempotentHint) and full schema coverage, the description provides sufficient context, examples, and usage differentiation. No gaps identified.

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

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

Schema coverage is 100% (all 6 parameters fully documented). The description adds examples and clarifies that the question should be factual, but the schema already covers the parameter meaning. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: it answers factual questions about current or historical data by routing to a large set of verified sources. It distinguishes itself from web search and sibling tools like ask_pipeworx_grounded and deep_research.

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

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

Explicitly provides when to use (prefer over web search, start here) and when not to use (step up for grounded or deep_research). Includes examples and mentions it works on every tier. Very clear guidance.

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

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

The description extensively explains the tool's internal behavior: it routes similarly to ask_pipeworx, extracts answer only from tool result, and returns structured success/refusal with specific reasons. This builds upon annotations (readOnlyHint, openWorldHint, idempotentHint) by detailing the grounding mechanism and refusal scenarios, which is critical for high-stakes use.

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 concise. It starts with a clear purpose statement, then provides routing details, return format, and usage guidelines efficiently. Every sentence adds value, with no redundancy.

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

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

Given the tool's complexity (high-stakes, no output schema) and rich context signals, the description fully covers purpose, usage, behavior, and return format. It explains the structured response including evidence, confidence, and refusal reasons, which compensates for the absence of an output schema.

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

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

Schema coverage is 100% and each parameter has description including aliases. The description adds no additional semantics beyond what is already in the input schema (e.g., mentioning that 'question' accepts aliases), so it meets the baseline for high coverage without further value.

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

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

The description clearly states the tool's purpose as a 'Hallucination-resistant answer mode for high-stakes reads' and contrasts it with ask_pipeworx by detailing its extraction process and refusal behavior. It uses specific verbs and resources, distinguishing it from siblings listed.

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

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

Explicitly defines when to use this tool (for quotes, citations, high-stakes decisions) and when not to (casual lookups, where ask_pipeworx is preferred). It also mentions the cost trade-off of an extra LLM call, providing clear 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?

Extensive disclosure beyond annotations: resolver contract, parent event extractor, news fallback mechanisms, safety blocking for low-confidence matches, closed markets, spread warnings, and cancellation rule parsing. Annotations already indicate read-only, idempotent behavior; description adds critical behavioral nuances.

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 long but well-structured with labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, etc.). Front-loaded with purpose. Some redundancy (e.g., repeating classifier list), but justified given tool complexity.

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

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

Given no output schema, description fully explains response shapes (result.market, result.analysis, result.evidence) and edge cases. Covers all aspects: resolution, classification, fan-out, safety, cancellation rules. No gaps for an AI agent to invoke correctly.

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

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

Schema coverage 100% and description adds meaning: explains `market` accepts slug/URL/question, `depth` quick vs thorough, `include_raw` summarized vs full payload. Provides examples and clarifies default behavior.

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

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

Description clearly states 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call' and lists input formats. It distinguishes from siblings by its comprehensive evidence packet and market-vs-model comparison, which other tools like 'polymarket_edges' likely do not provide.

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

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

Explicit use cases given: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z"'. Includes examples of fan-out for different bet types. Lacks explicit alternatives for when not to use (e.g., simple price checks), but context is clear.

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

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

Beyond the annotations (readOnly, openWorld, idempotent), the description adds rich behavioral detail: it makes one parallel call, replaces 8-15 sequential lookups, handles off-calendar fiscal years, and returns sorted results with citation URIs. No contradiction with annotations.

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

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

The description is front-loaded with example queries and uses bullet-like structure. Every sentence adds value, though it could be slightly more concise. Still well-organized and efficient for the information density.

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 (comparison, two entity types, multiple data sources) and lack of output schema, the description thoroughly explains input, process, output (paired data + citations), and edge cases (off-calendar fiscal years). The agent can confidently select and invoke it.

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

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

Schema coverage is 100%, and the description adds meaning: for type='company', it clarifies data source (SEC EDGAR/XBRL) and financial metrics; for type='drug', it specifies FAERS data. For values, it distinguishes tickers/CIKs for companies and names for drugs, enriching 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 clearly states the tool's purpose: side-by-side comparison of 2-5 companies or drugs. It provides example queries ('Compare X and Y', 'X vs Y') and specifies the data each entity type yields. It distinguishes itself from sequential lookups and sibling tools like entity_profile.

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

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

The description explicitly instructs 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing clear context for use. It does not explicitly state when not to use this tool, such as for non-comparison tasks, but the guidance is strong for its intended use case.

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

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

The description adds value beyond annotations by detailing the returned fields (resource_id, name, description, category, update date) and suggesting next steps. It aligns with annotations (readOnly, idempotent) and provides useful behavioral context.

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

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

The description is two sentences with no wasted words. The purpose is front-loaded, and every sentence adds value. Excellent conciseness.

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

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

Despite lacking an output schema, the description clearly states what the tool returns (five fields) and suggests next steps. It covers the main use case but does not address error conditions or limitations.

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

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

All parameters have descriptions in the input schema, so the description does not add extra meaning. The description's mention of 'keyword' implicitly relates to the query parameter, but no new semantic information is provided beyond the schema.

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

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

The description clearly states the action ('Search'), the resource ('King County Open Data catalog'), and the mechanism ('by keyword'). It specifies the returned fields, making the purpose distinct. However, it does not explicitly differentiate from sibling tools like 'query' or 'metadata'.

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

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

The description provides guidance on using the output ('pass the resource_id to query/metadata'), but it lacks explicit when-to-use or when-not-to-use advice compared to alternatives. The context is implied rather than stated as a guideline.

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

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

Adds context beyond annotations: describes decomposition into facets, parallel routing to 4,819 tools, return format (findings packet with citations and gaps), and expected 15-60s response time. No contradictions with annotations.

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

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

Front-loaded with critical account/depth info, then explains functionality and usage. While comprehensive, slightly verbose; could trim redundant phrases but overall well-organized.

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

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

Despite no output schema, the description sufficiently explains the return packet and response time. Given the complexity (2 params, 100% schema coverage, annotations present), the description covers all necessary aspects 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?

Adds meaning beyond the schema: explains that 'quick=3, standard=5, thorough=8' and that 'thorough' needs a paid plan. Also confirms that broad/multi-part questions are fine, which is not in the schema.

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

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

The description clearly states the tool performs grounded multi-source research across 1259 structured data sources in one call, and distinguishes it from sibling tools like ask_pipeworx by specifying it is for broad/multi-part questions over structured data.

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

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

Explicitly states when to use this tool (broad/multi-part questions over structured data) and when not to (single lookup use ask_pipeworx, breaking/current news prefer ask_pipeworx, and if not signed in use ask_pipeworx). Also notes that 'thorough' depth requires a paid plan.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds value by explaining return format: 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly'. 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 a single paragraph, front-loaded with purpose and key use cases. Each sentence adds value: purpose, scenarios, return format, guidance. No wasted words, though slightly longer than minimal.

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

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

Given no output schema, description fully explains what is returned (top-N tools with full schemas, examples). Covers parameter limits (max 50, default 20) and aliases. Context signals (6 params, 1 required, high coverage) are well addressed.

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

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

Schema coverage is 100% with descriptions for all 6 parameters. Description adds that 'query' accepts multiple aliases (task, q, description, search) and natural language examples. Baseline 3 for high coverage; description provides minimal extra meaning.

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

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

Description clearly states verb ('Find tools'), resource ('tools'), and action ('by describing the data or task'). Provides specific use cases (SEC filings, FDA drugs, etc.) and distinguishes from siblings by advising 'Call this FIRST when you have many tools'.

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

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

Explicitly says 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools and want to see the option set'. Clearly defines when to use versus 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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavior beyond annotations: fans out across multiple sources, soft-fails for patents (USPTO API sunset May 2025), returns specific fields. 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 front-loaded with example queries, which is helpful. However, it is somewhat verbose with many details. Every sentence provides value, but could be slightly trimmed for 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?

Given complexity (multiple sources, soft-fail, return fields) and no output schema, the description is highly complete. It explains what is returned (cik, filings with URIs, fundamentals, patents, news, LEI) and covers edge cases like patent soft-fail and name resolution prerequisite.

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% (2 parameters described both in schema and description). Description adds meaning: explains 'type' is only 'company', 'value' is ticker or zero-padded CIK, explicitly says names not supported. This goes beyond schema.

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

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

The description clearly states the tool's purpose: 'full cross-source profile of a US public company'. It provides specific verbs ('profile', 'brief me on') and resource ('US public company'), and distinguishes from sibling tool 'resolve_entity' which handles name resolution.

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

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

Explicitly states when to use: 'ALWAYS PREFER over chaining single-pack...when the user asks for a holistic view'. Also tells when not to use: if only a name, use resolve_entity first. Provides clear alternatives and context.

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

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

Annotations already include destructiveHint=true and idempotentHint=true, so the description adds behavioral context about use cases (clearing stale or sensitive data). No contradiction.

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

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

Two sentences, no wasted words, front-loaded with action and purpose.

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

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

For a simple delete tool with one required parameter and no output schema, the description covers purpose, usage, and parameter fully.

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

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

Schema coverage is 100% with parameter description 'Memory key to delete'. The description reinforces this by saying 'by key', but adds minimal new information beyond the schema.

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

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

The description clearly states 'Delete a previously stored memory by key', which is a specific verb+resource. It differentiates from siblings by mentioning 'Pair with remember and recall'.

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

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

Explicitly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' Also provides pairing with siblings.

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

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

The description adds behavioral details beyond annotations: it fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. No contradictions.

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

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

The description is two sentences plus a concise list of use cases. It front-loads the purpose, then behavior, then usage. Every sentence earns its place with no redundancy.

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

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

Given the simple tool with 2 parameters, full schema coverage, and annotations, the description is complete. It explains what the tool does, how it works, and when to use it, leaving no gaps 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%, so the description adds little extra meaning. It mentions 'Full URL' and 'Maximum number of link entries (default 25, max 50),' which are already in the schema. Baseline 3 applies.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the verb (generate) and resource (llms.txt file). It distinguishes itself from sibling tools like ai_visibility_check or scan_competitor_ai_presence by focusing on file generation.

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 use cases: getting a client's site indexed, drafting llms.txt for projects, or auditing competitors. It does not explicitly state when not to use it, but the use cases give clear context. Missing exclusions keep it from a 5.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds minor context (default returns active subscriptions) but does not disclose significant behavioral traits beyond what annotations provide.

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

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

Three sentences with no extraneous information. The core action is front-loaded, and the structure is efficient.

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

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

For a simple list tool with one optional parameter and no output schema, the description is adequate. It specifies return fields and usage context. Lacks mention of error cases, but annotations compensate.

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% (1 parameter fully described). The description does not add any additional meaning about the parameter beyond the schema, so baseline score of 3 applies.

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

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

Description specifies verb 'List' and resource 'the caller's active subscriptions', clearly distinguishing it from siblings like 'subscribe' and 'unsubscribe'. It also enumerates the returned fields, leaving no ambiguity.

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

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

Explicitly advises using the tool to 'review what you're monitoring before adding more or to find an id to cancel.' While it doesn't mention when not to use it, the context is clear and sufficient for an AI agent.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety. The description adds the specific fields returned but does not disclose additional behaviors like rate limits or authentication needs.

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 sentence that is front-loaded with the purpose, containing no filler words. Every part earns its place.

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

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

Given the tool's simplicity (one parameter, no output schema), the description adequately lists what the tool returns. It does not mention any special cases but is complete for a metadata retrieval tool.

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

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

The single parameter 'resource_id' is fully described in the schema with an example. The description repeats the same example, adding no new semantics. With 100% schema coverage, baseline 3 is appropriate.

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

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

The description clearly states the verb 'Get' and the resource 'King County Open Data dataset's schema + metadata', listing specific fields (columns, types, row count, category, last-updated) and giving an example resource_id. This distinguishes it from sibling tools like 'datasets' which likely list datasets.

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

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

The description implies usage when one needs dataset metadata but does not explicitly state when to use or not use this tool, nor does it mention alternatives or prerequisites.

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

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

Annotations are neutral (no readOnly/destructive hints), so the description adds needed context: rate limit (5/identifier/day), free usage, and no quota impact. It covers behavioral traits beyond structured fields.

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

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

Concise and well-structured: first sentence states purpose, then usage guidelines, then important behavioral notes. Every sentence adds value without redundancy.

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

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

Despite no output schema, the description fully explains tool behavior, parameter roles, rate limit, and cost. Completely sufficient for an agent to decide when and how to invoke this tool.

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

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

Schema coverage is 100% with detailed descriptions. The description reinforces the enum meanings and context structure but doesn't add significant 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 clearly states the tool sends feedback to the Pipeworx team and enumerates specific feedback types (bug, feature, etc.). It distinguishes from siblings by focusing on anonymous feedback rather than querying or tool discovery.

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

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

Explicitly specifies when to use each feedback type, including a negative directive ('don't paste the end-user's prompt'). Provides contextual signals like rate limits and quota exemptions, fully guiding appropriate usage.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds important behavioral traits: self-aggregating from CF analytics-engine, no PII, cached 5min-1h depending on window. No contradiction with annotations.

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

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

Four sentences with bullet-point use cases, front-loaded with the key outputs. Every sentence adds meaningful information without fluff. Excellent structure for quick agent parsing.

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

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

Given the tool's simplicity, strong annotations, full schema coverage, and no output schema needed for a simple list, the description provides complete context: purpose, use cases, behavioral details (caching, aggregation, no PII). Nothing critical is missing.

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 single parameter 'window' has full schema coverage (100%) with enum and description. The description adds value by explaining the semantic difference between short and longer windows ('surface what's hot' vs 'steady-state demand'), going beyond the schema's basic description.

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

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

The description explicitly states the tool returns 'top tools, top packs, and total call volume' over a recent window, clearly distinguishing it from other tools that provide specific data or queries. The verb 'returns' and resource 'trending data' make the purpose unambiguous.

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

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

Three numbered use cases directly tell the agent when to use this tool, such as discovering hot data sources or confirming canonical tools. No explicit when-not-to-use, but the use cases implicitly guide appropriate contexts, and the tool is distinct from sibling tools that answer specific questions.

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

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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint symmetry. The description adds behavioral details beyond annotations, such as fill checks, realizable_edge_pp, and conditions for not trading. 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 verbose but well-structured with sections (modes, semantic anchor, partition filter, fill check). While informative, it could be more concise. However, the structure aids 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 of the tool and the absence of an output schema, the description thoroughly explains inputs, outputs (opportunities[], partition_check, fill check), and edge cases. It is complete enough for an agent to use effectively.

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

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

The input schema has 100% coverage with descriptions for both parameters. The description adds significant meaning by explaining that 'event' accepts slugs or full URLs, and 'topic' is a seed question. It also elaborates on how each parameter affects the analysis, which goes 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: finding arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes between three modes (no args, event, topic) with specific use cases, making it easy for an agent to understand what it does.

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

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

The description provides explicit guidance on when to use each mode: no args for trending scan, event for a specific market (recommended), topic for cross-event scanning. It also explains constraints like semantic anchor and partition filter. While it doesn't explicitly state when not to use the tool, 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?

Annotations are readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds extensive behavioral context: three model families, caching (1h KV-level), response structure, edge calculation details, and filter conditions. No contradiction.

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

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

The description is excessively long and dense with technical details, making it hard to parse quickly. Front-loads purpose but then overwhelms with specifics.

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 9 parameters and no output schema, the description fully covers response structure, caching, knobs, and edge cases like Fed bets and diagnostics.

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 valuable context beyond schema, e.g., min_liquidity explains execution impact. Baseline 3, extra value warrants 4.

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

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

The description explicitly states the tool scans top Polymarket markets for opportunities where Pipeworx data disagrees with market price, and distinguishes itself from siblings like polymarket_arbitrage by targeting daily betting decisions.

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

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

It clearly positions the tool for 'what should I bet on today' and explains tradeable-edge knobs, but lacks explicit when-not-to-use or 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, idempotent, safe operations. The description adds significant behavioral details: snapshots written on cache-miss, gaps meaning no scan, history bounded by 60-day TTL, decay computed from daily closes (not intraday), and response structure. No contradiction.

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

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

The description is information-dense and well-organized with response fields in uppercase. It is concise but could benefit from bullet points or separation of response sections. 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?

No output schema exists, so the description fully explains the return structure (tracked, expired, snapshot_dates) and limitations (TTL, no intraday). This is complete for a query tool with rich 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%, so baseline 3. The description repeats parameter default and clamp ranges (already in schema) but adds context about 'snapshot family' and the meaning of days/window. However, this doesn't significantly extend beyond schema descriptions.

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

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

The description clearly states the tool's purpose: providing edge persistence and decay telemetry from daily polymarket_edges snapshots. It answers a specific question about edge age and shrinkage, distinguishing it from snapshot-only tools like polymarket_edges.

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

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

The description explains when to use this tool (to understand edge age and decay) and contrasts with fresh vs. old edges. However, it does not explicitly list alternatives or state when not to use it, but the context is clear given sibling tools.

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

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

The description discloses behavioral traits beyond annotations (readOnlyHint, idempotentHint): e.g., walks the ladder, returns fill metrics, clamps size_usd 10–1,000,000, and explains basket behavior. No contradiction with annotations.

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

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

The description is a single paragraph but well-structured: purpose first, then requirements, followed by mode details, output fields, and usage advice. Every sentence contributes meaningful information; no fluff.

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

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

Despite no output schema, the description lists all returned fields (top_of_book, vwap_fill_price, slippage_pp, etc.) and explains the behavioral implications of thin books and partial fills. This is fully sufficient for an agent to understand the tool's capabilities and risks.

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

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

Schema coverage is 100%, but the description adds significant meaning: explains the interpretation of size_usd as 'settlement notional S' for baskets, defaults for side, and the requirement for market or event. This enhances understanding beyond the schema alone.

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

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

The description clearly states the purpose: '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 advising to use this before acting on those signals.

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

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

Explicitly says when to use: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also warns about dangers of partial basket fills converting arb into unhedged positions, providing clear context for when not to proceed.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds context: cross-venue spread calculation logic, compatibility checks (bet shape equivalence), temporal alignment, skipped leg comparisons, and real-world limitations. This adds substantial value 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 dense but well-structured: starts with main purpose, then modes, response details, safety fields. Every sentence adds value, though it is somewhat verbose for the complexity. Still, 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?

Despite lacking an output schema, the description thoroughly explains response fields: leg-by-leg prices, matched spread, compatibility_warning, temporal_alignment, skipped counters. Covers all important aspects for a complex cross-venue spread tool.

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

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

Schema has 100% coverage with descriptions for all 3 parameters. Description adds meaning: explains two modes (topic overrides vs explicit), provides the full list of topic values, describes override behavior, and gives example formats for explicit parameters. This goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes two modes (topic shortcuts and explicit pairings) and differentiates from siblings like polymarket_arbitrage by focusing on cross-venue rather than intra-venue arbitrage.

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

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

Provides extensive guidance: when to use topic mode vs explicit, what to expect from compatibility warnings, limitations (most pre-mapped topics return warning), and when spreads are meaningless (temporal misalignment). Explicitly states that real spreads are rarer than shortcuts suggest.

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, openWorldHint=true, idempotentHint=true, and destructiveHint=false. Description adds that it returns JSON, which is helpful but not critical. It does not disclose additional hidden behaviors like rate limits or default pagination, so it adds moderate value 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?

Two concise sentences. First sentence states action and resource with an example resource_id. Second sentence lists query options and output format. No wasted words.

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

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

Given the rich annotations and detailed schema, the description covers the essential behavioral aspects: it is a read-only query, returns JSON, and uses SoQL. It does not mention prerequisites like obtaining a resource_id from a 'datasets' call, but that is reasonable for domain knowledge.

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

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

Schema coverage is 100% with detailed parameter descriptions. The description adds value by clarifying SoQL syntax (no leading $) and grouping parameters (where/select/group/order plus limit/offset), which is not present in the schema.

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

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

Description clearly states it runs a Socrata SoQL query against a King County Open Data dataset by resource_id, with specific examples. It distinguishes from sibling tools like 'datasets' and 'metadata' by focusing on querying data rather than listing metadata.

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

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

Description explains how to use parameters (e.g., SoQL clauses without leading $, limit/offset), but does not explicitly state when to use this tool over alternatives or when not to use it. Usage guidance is implicit rather than explicit.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds useful context like omitting key lists all, scoping to identifier, and pairing with remember/forget. No contradictions.

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

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

Two sentences with no wasted words. Front-loaded main action, then examples and context. Every sentence adds value.

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

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

Given no output schema, description covers retrieval and listing behaviors. Low complexity with one optional param, and all needed info for correct usage is present.

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

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

Schema coverage is 100% with description for 'key'. Description adds meaning: 'omit to list all keys' and explains retrieval purpose, enhancing the schema's brief description.

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

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

Clearly states verb 'Retrieve' and resource 'a value previously saved via remember' or 'list all saved keys'. Distinguishes from sibling tools remember and forget with specific examples.

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

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

Explicitly says when to use: 'look up context the agent stored earlier'. Provides pairing and scoping info. Lacks explicit when-not-to-use or alternatives beyond remember/forget.

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

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

The description states that setting mark_read:true will 'flag returned events read so the next call only shows newer ones', implying a state change. However, annotations include readOnlyHint:true, which indicates the tool does not modify state. This contradiction could mislead an agent about the tool's side effects.

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

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

The description is a single paragraph of 4 sentences, front-loaded with the main purpose. It efficiently includes filtering options and a side note about an alternative endpoint. It is concise but could be slightly more structured with bullet points for 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?

Despite lacking an output schema, the description explains return fields (source, citation_uri, raw payload) and covers all parameters' behaviors. It also mentions poll compatibility and an alternative access method, providing a fairly complete picture for an agent without overwhelming detail.

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

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

The input schema has 100% coverage with descriptions, but the tool description adds valuable context beyond the schema: it explains the effect of mark_read (flags events read), the purpose of type (filter by subscription type like 'sec_8k'), and that since is an ISO timestamp. This enhances 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 the tool pulls fired events from subscription feed, returns recent alerts with specific fields (source, citation_uri, raw payload). It distinguishes itself among many sibling tools by its specific function of retrieving recent alerts with filtering and marking read.

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

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

The description provides clear usage context: filtering by type and since, setting mark_read to manage read status, and notes polling works fine. It also points to an alternative endpoint for scripts/dashboards. However, it does not explicitly state when to use this tool vs. its siblings, though the specific functionality makes it 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 fan-out to multiple sources (SEC, GDELT/GNews, USPTO), fallback behavior (GDELT preferred, GNews on rate limit/5xx, USPTO soft-fails), and return structure (grouped changes, total count, URIs). Complements readOnly/other hints without contradiction.

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

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

Well-structured: starts with example queries, states purpose, details sources and fallbacks, parameter hints, return format, and alternative tool. 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?

Covers all aspects: purpose, usage guidelines, parameter semantics, behavioral nuances (fallbacks, soft-fail), and return structure. No output schema, but description adequately explains output.

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

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

Schema coverage is 100%, and the description adds value by explaining 'since' accepts ISO dates or relative shorthand ('7d', '30d', '3m', '1y'), recommends '30d'/'1m' for monitoring, and clarifies 'value' can be ticker or CIK.

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

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

The description clearly states it provides a 'change feed for a company in the last N days/weeks/months' via example queries and explicitly distinguishes from sibling tool 'entity_profile' for static profiles.

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

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

Explicitly says when to use: for recent changes, and when not: 'Use entity_profile instead when you want the static profile' regardless of window.

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

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

Annotations already provide idempotentHint=true and destructiveHint=false. Description adds key behavioral details: scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous sessions. This goes beyond annotations.

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

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

Description is concise with no filler. Front-loaded with purpose, followed by usage context and behavioral notes. Every sentence adds value.

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

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

For a simple write tool with no output schema, the description covers purpose, usage, behavioral traits, and retention policy. No gaps identified.

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

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

Schema already describes both 'key' and 'value' with examples. Description adds context about key-value pairs and scoping but does not significantly extend the schema's meaning. Baseline 3 is appropriate given 100% schema coverage.

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

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

Description uses specific verbs 'Save data' and clearly states the resource (key-value memory). Distinguishes from sibling tools recall and forget, which are mentioned explicitly.

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

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

Provides clear context for when to use ('when you discover something worth carrying forward') and mentions companion tools (recall, forget). Lacks explicit when-not-to-use or alternative tool differentiation, but context signals show many siblings, and the description helps narrow usage.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds significant behavioral context: it cascades through several endpoints internally, returns different fields per type (ticker+CIK+company_name+citation for company, RxCUI+ingredient+brand for drug), and auto-disambiguates for company. This goes well beyond the annotations.

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

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

The description is reasonably concise but contains some redundancy (e.g., listing supported types twice). It is front-loaded with example queries and a clear use instruction. Each sentence adds value, but minor tightening could improve conciseness.

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

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

Given the absence of an output schema, the description does a good job explaining return values for each type. It covers inputs, outputs, and internal behavior. However, it lacks information on error handling (e.g., what happens if the value is not found) and does not mention any rate limits or additional boundary conditions.

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% (both parameters have descriptions). The description adds much more: it explains the mapping of input to output for each type (e.g., for company: accepts ticker, CIK, or name and returns ticker+CIK+company_name+citation URI), and describes internal cascading behavior. This adds substantial meaning beyond the schema.

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

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

The description clearly states the tool resolves user-spoken names to canonical/official identifiers needed by other tools. It provides specific example queries (ticker, CIK, RxCUI) and lists supported types (company, drug). It distinguishes itself from siblings by focusing on identifier resolution, which is unique among the listed tools.

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

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

The description explicitly says 'Use FIRST whenever you have a name but need an ID,' providing a clear usage directive. It also mentions that it replaces 2-3 manual lookups, which helps agents understand its efficiency. However, it does not explicitly state when not to use it or mention alternatives, which could be improved.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds behavioral context: it probes each entity, ranks them, treats the first as the 'subject' for narrative, and returns a ranked list with score, confidence, and signal density. 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 two sentences long. The first sentence states the primary action and result, and the second provides a concrete use case and details about the return structure. No wasted words; everything serves a purpose.

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

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

Given the tool's simplicity (4 params, no output schema, no nested objects), the description is complete. It covers purpose, mechanism, use case, and return value structure (ranked list with score, confidence, signal density) adequately.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the mechanism (probing with ai_visibility_check), the narrative role of the first entity, and the ranking behavior. This provides meaning beyond the raw parameter 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 compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It provides a specific use case ('does Claude know about us as well as our competitors?'), making the purpose unambiguous and distinct from sibling tools.

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

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

The description explicitly mentions usefulness for competitive AI-marketing audits and implies that for single-entity checks, one should use ai_visibility_check instead. However, it lacks explicit when-not-to-use guidance or a direct comparison to alternatives, though the context is clear enough.

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

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds crucial behavior: partial failures with bundlephobia timing (5-30s), graceful degradation with 'sources_failed' listing. 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 dense paragraph that front-loads the main purpose. Every sentence adds value: sources, usage, return, limitations, failure behavior. Could be slightly more concise but overall well-structured with no redundant 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's moderate complexity (2 params, no output schema), the description provides a detailed return format (summary block fields, per-advisory detail, links, alternatives) and failure handling. Annotations cover safety. Complete for an analysis 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 description coverage is 100%, so the schema fully documents parameters. Description adds minor context (scoped packages like @types/node, version defaults). Beyond schema, it provides usage examples (lodash) but no major additional semantics.

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

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

The description clearly states the tool is a composite check for adding npm packages, covering license, advisories, version history, and bundle size. It differentiates from siblings by being specific to npm analysis, while siblings like 'scan_competitor_ai_presence' are unrelated.

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 whenever an agent asks is X safe / popular / small or what does adding lodash cost me.' Also clearly scopes to NPM ecosystem v1, noting alternatives for other ecosystems via 'deps.dev:version directly.' Provides usage context and exclusions.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds significant behavioral details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flagging. No contradictions.

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

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

The description is concise (~4 sentences), front-loaded with purpose, and every sentence adds value (usage, pairing, algorithmic detail, limits). No wasted words.

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

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

Despite no output schema, the description covers return format (passages with offsets and scores), input constraints, algorithm, and pairing with a sibling. Complete for a semantic search 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%, baseline 3. Description adds extra meaning for 'text' (source, char cap) and 'query' (examples), though 'limit' is only implicitly mentioned as 'top-N'. The added context justifies a 4.

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

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

The description clearly states the tool does semantic search inside a fetched record, with specific examples like SEC 10-K, article, long tool result. It pairs with a sibling but does not explicitly distinguish from all 40+ siblings, missing the top-tier 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 provides explicit when-to-use guidance: 'Use when the record is too big to cram into the prompt.' It also mentions pairing with ask_pipeworx_grounded. However, it lacks when-not-to-use or alternative tools, so it scores a 4.

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

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

The description reveals behavioral details beyond annotations: it notes the requirement for a Pipeworx OAuth account, delivery channel behaviors (SMS cap of 10/day, webhook auto-disable after 10 consecutive failures), and the return of a subscription id. No contradictions with annotations.

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

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

The description is well-structured with the core purpose first, followed by type and delivery details. It is slightly lengthy but every sentence earns its place; no redundancy.

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

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

Given the tool's complexity (3 parameters, nested objects, multiple types), the description thoroughly covers prerequisites, type-specific filters, delivery options, and return value. No output schema exists, but the description sufficiently explains what is returned.

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

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

Although schema coverage is 100%, the description adds concrete examples for each subscription type (e.g., sec_8k with items codes) and details on delivery parameters such as webhook signing and phone verification, greatly enhancing understanding beyond the schema.

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

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

The description clearly states the action is to create a proactive monitoring subscription to a live-data event stream and that it returns a new subscription id. It fully distinguishes this 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 explicitly states the prerequisite of a Pipeworx OAuth account and lists supported subscription types and delivery channels, providing clear guidance for when to use. It lacks explicit mention of when not to use or direct alternatives, but the information is sufficient for typical use cases.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false, indicating safety and side-effect-free behavior. The description adds value by explaining the tool draws from the 'live catalog' and returns 'exact tool + argument shape', providing behavioral clarity 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 fairly concise given the amount of information conveyed. It is front-loaded with common user questions and then explains the tool's behavior. A few extra phrases could be trimmed, but nearly every sentence adds value.

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

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

For an onboarding tool with no output schema and rich annotations, the description fully covers purpose, usage, parameter behavior, and return format. It even suggests when to use the tool first. The description is complete for an agent to understand and invoke it correctly.

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

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

Schema coverage is 100% with a single optional 'topic' parameter. The description adds detailed context: lists the allowable values (finance, pharma, etc.), explains the effect of omitting it (cross-category spread), and ties it to the tool's purpose. This goes well beyond the schema's description.

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

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

The description clearly states the tool returns category-bucketed example questions for onboarding. It uses specific verbs ('suggest', 'returns') and resources ('questions'), and distinguishes from siblings like 'ask_pipeworx' and 'discover_tools' by being the 'onboarding entry point'.

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

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

The description explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and mentions calling with or without a topic argument. However, it does not provide explicit when-not-to-use scenarios or alternatives, though 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?

Beyond annotations (idempotent, not destructive), the description adds ownership enforcement and explains the deactivation (not deletion) behavior, preserving historical events via 'recent_alerts'. This provides valuable behavioral context that annotations alone do not convey.

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: the first states the core action and parameter, the second adds critical behavioral details. Every word earns its place, with no redundancy or filler.

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 one-parameter mutation tool with no output schema, the description covers the main action, parameter, ownership constraint, and side effect (deactivation vs. deletion). It is complete enough for an agent to use correctly, though the return value is not described.

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

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

The sole parameter 'id' is described in the schema as 'Subscription id (uuid) returned by subscribe.' The description adds meaning by implying the id must belong to the current user (due to ownership enforcement), going beyond the schema's type information.

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

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

The description clearly states the action ('Cancel a subscription by id.') and the resource (subscription). It distinguishes effectively from sibling tools like 'subscribe' (creates) and 'list_subscriptions' (lists), making the tool's purpose unambiguous.

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

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

The description provides clear context for use by noting ownership enforcement and the deactivation behavior (history remains). While it does not explicitly list when not to use or compare to alternatives, the context is sufficient for an agent to decide when this tool is appropriate.

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 show readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: returns a verdict (with specific categories like confirmed, refuted), structured form, actual value with citation, and percent delta. Also explains it replaces 4-6 sequential calls, highlighting efficiency. No contradiction with annotations.

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

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

Description is front-loaded with example user intents, then states purpose, then domain, then output details. Every sentence contributes useful information. While slightly long, it remains efficiently structured without redundancy.

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

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

Given the tool has no output schema, the description adequately explains return types (verdict categories, structured form, citation, delta). It mentions the data source (SEC EDGAR + XBRL) and domain limitation (company-financial, v1). Could be more explicit about error handling or unsupported claim types, but overall complete for the tool's complexity.

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

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

The input schema has one parameter 'claim' with 100% description coverage (examples and natural-language hints). The description reinforces the expected format with concrete examples like 'Apple's FY2024 revenue was $400 billion.' This adds meaning beyond the schema's brief description.

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

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

The description explicitly states the tool's purpose: verifying natural-language claims against authoritative sources, focusing on company-financial claims. Uses clear action verbs ('validate,' 'verify') and provides examples of user intents like 'fact check' or 'confirm or refute.' Distinct from sibling tools that handle queries, research, or data retrieval without direct claim-verification focus.

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

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

The description specifies when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also delimits the supported domain (company-financial claims for public US companies). However, it does not explicitly state when not to use or mention alternative tools for out-of-scope claims, just implies limitation via 'v1 supports.'

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