Data Providence

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive nature. The description adds valuable behavioral details: default model, cost implications (free vs BYO key for Anthropic), and the structure of the return value (per-model score, confidence, signals, raw_response). No contradictions.

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

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

The description is a single concise paragraph with 4-5 sentences. It front-loads the main purpose and then provides essential details (default model, API key usage, return format) without redundancy or fluff.

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

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

Given the lack of output schema, the description clearly explains the return value (per-model object with score, confidence, signals, raw_response + combined view). It covers all aspects of the tool's behavior (parameters, optionality, output) and addresses typical use cases. For a tool with 4 parameters and moderate complexity, it is fully sufficient.

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

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

All 4 parameters have descriptions in the schema (100% coverage). The description adds meaning beyond the schema by explaining the default model, the effect of _apiKey (triggers Anthropic probing), and the purpose of the context parameter. This enriches understanding for the AI agent.

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 specifies the verb 'probe' and the resource 'LLMs for what they know about a business / brand / product / topic', and explains the output is a visibility score (0-100) per model. It clearly differentiates from siblings like 'scan_competitor_ai_presence' by focusing on scoring visibility across multiple models.

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

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

Explicitly states use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. While it doesn't explicitly state when not to use or list alternatives, the context of siblings and the focused description provides 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful context: returns structured answer with pipeworx:// citation URIs, routes to the right internal tool, works on every tier, one fast call. No contradictions, and the description enhances understanding beyond annotations.

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

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

The description is detailed but somewhat lengthy. It includes examples, usage guidelines, and contrast with siblings, which is valuable, but the opening sentence is verbose. It could be more concise without losing clarity.

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

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

Given the tool's complexity (routing to thousands of sources) and no output schema, the description is thorough. It explains the tool's role, when to use it, what it returns, and provides many examples. It also addresses edge cases by listing specific domains (SEC filings, FDA drug data, etc.).

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 parameters are aliases for the question field). Description adds natural language examples and clarifies that any of the aliases can be used. This provides additional context beyond the schema's 'Alias for question' 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: routing questions to authoritative structured data sources. It uses a specific verb ('routes') and resource ('questions to one of 4,396 tools across 1102 verified sources'). It distinguishes from siblings by naming alternatives (ask_pipeworx_grounded, deep_research) and contrasting use cases.

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

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

The description provides explicit guidance: 'PREFER OVER WEB SEARCH', use as default entry point, step up to alternatives when needed. It also lists example queries and when to use other tools. This gives clear context for selection.

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

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), description discloses the extra LLM call cost, the structured refusal reasons, and that extraction is limited to tool result content, which are key behavioral traits for agent decision-making.

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 detailed but well-structured, with the key functionality and use-case in the first sentence. Each subsequent sentence adds necessary context about behavior, return format, and cost. Could be slightly tighter but overall efficient.

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

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

Given no output schema, the description fully explains the return structure (answer, evidence, confidence, source, etc.) and possible refusal reasons, ensuring agents know what to expect. Also covers when to use and cost trade-off, leaving no gaps.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by listing common aliases (query, q, prompt, text, input) and clarifying that the input is natural language, which helps agents understand parameter flexibility.

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 distinguishes it from the sibling ask_pipeworx by detailing its behavior of extracting answers only from tool results and providing explicit refusal reasons.

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 (when answers will be quoted, cited, or acted on, and invention is unacceptable) and when to prefer the alternative ask_pipeworx (for casual lookups), providing clear guidance for selection.

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

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

The description extensively discloses behavioral traits beyond the annotations (readOnlyHint, idempotentHint, etc.), including fan-out logic, response shape, safety mechanisms (low-confidence short-circuit, closed market skip), and potential issues like GDELT 429s with fallback handling. It adds significant context about the resolver contract, parent event extractor, and resolution-rule risk. No contradiction with annotations.

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

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

While the description is thorough and well-structured with sections, it is very dense and lengthy (multiple paragraphs of detailed examples and edge cases). This could be overwhelming for an AI agent scanning quickly. The first sentence front-loads the purpose, but overall conciseness is sacrificed for completeness.

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

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

Given the tool's complexity and the lack of an output schema, the description is remarkably complete. It covers input parsing, classification, fan-out examples for various bet types, response shapes with specific fields, safety mechanisms, and resolution rule risks. An agent can fully understand the tool's behavior without external documentation.

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

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

With 100% schema description coverage, the description still adds substantial meaning: it explains that the 'market' parameter accepts slug, URL, or question text; clarifies 'depth' enumerations ('quick = 2-3 evidence sources, thorough = full fan-out'); and provides context for 'include_raw' (summarized vs. full payloads). This goes well beyond the schema definitions.

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

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

The description clearly states the tool's purpose: research a Polymarket bet by pulling relevant Pipeworx data in one call. It specifies input types (slug, URL, question text) and output (evidence packet + market-vs-model comparison). The 'use for' scenarios explicitly differentiate it from sibling tools like polymarket_edges or ask_pipeworx.

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

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

The description provides clear use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and includes detailed guidance on when to inspect specific response fields (e.g., market_match_confidence) and blocking conditions (low-confidence matches, closed markets). However, it does not explicitly mention when not to use this tool or suggest alternative tools, which would be beneficial.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds context: single parallel call, handles off-calendar fiscal years, sorts results by primary metric, returns citation URIs. No contradiction.

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

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

Description is front-loaded with example queries and uses clear sectioning. Slightly verbose but every sentence adds value. Minor deduction for length.

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

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

No output schema, but description mentions return format (paired data + citation URIs). Covers inputs, behavior, output style, and value proposition (replaces 8-15 lookups). Complete for a comparison tool.

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

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

Schema coverage is 100%, but description adds significant meaning by explaining what each 'type' value retrieves and providing examples for 'values'. This goes beyond the schema 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 it performs side-by-side comparison of 2-5 companies or drugs, with explicit examples of queries. It distinguishes from siblings like 'entity_profile' and 'deep_research' by specifying use case.

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 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear when-to-use guidance and implying alternatives.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds detail about the output fields and suggests a subsequent tool use, enhancing understanding beyond annotations.

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

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

Two sentences, front-loaded with the main action, and no superfluous text. The second sentence efficiently conveys output format and next steps.

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 specifies the returned fields and provides a hint for further action. For a simple search tool, this is fully sufficient.

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

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

Schema description coverage is 100%, so the schema already documents parameter meanings. The description does not add significant additional semantics beyond the obvious (e.g., 'query' for keyword search).

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

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

The description clearly states the verb 'Search', the resource 'Providence Open Data catalog of open datasets', and lists the returned fields. It distinguishes from sibling tools by specifying a unique data catalog.

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 the tool (keyword search in the catalog) and hints at follow-up usage ('pass the resource_id to query/metadata'). Does not explicitly exclude alternatives, but the purpose is unambiguous.

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 (readOnlyHint, openWorldHint), the description adds expected response time (15-60s), behavior of returning gaps[] when data is unavailable, that it never invents, and the parallel decomposition/routing to 4,396 tools. This provides rich 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 long but well-structured with clear sections (account requirements, what it does, use cases, comparisons, expected output). Every sentence adds value, though some redundancy could be trimmed.

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

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

Given the tool's complexity (2 parameters, no output schema), the description covers all necessary context: behavior, usage guidelines, constraints, output format (findings packet with evidence, confidence, source, gaps), and response time. It is fully self-contained for agent decision-making.

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

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

Schema coverage is 100%, so baseline is 3, but the description adds meaningful context: depth explains the number of facets (quick=3, standard=5, thorough=8) and question clarifies it accepts broad/multi-part natural language. This goes beyond the schema's minimal descriptions.

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

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

The description clearly states it performs grounded multi-source research across structured data sources, distinguishes itself from siblings like ask_pipeworx, and explicitly says it is NOT open-web search. It provides concrete use cases such as comparing regulatory and financial exposure.

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

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

The description explicitly states when to use this tool (broad/multi-part structured queries) and when to use alternatives (ask_pipeworx for single lookup or current news, and a requirement to use ask_pipeworx if not signed in). It also notes account requirements and depth plan limitations.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds value by explaining the output: 'Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed.' 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 efficient with three clear sections: verb+task, when to use, output. Could be slightly trimmed but achieves good front-loading of purpose and usage.

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 the output format and its utility. Combined with schema coverage and annotations, the description is complete for this metatool that helps discover other tools.

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

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

Schema coverage is 100%, baseline 3. Description adds context by providing examples for 'query' and listing aliases, but limit is not further explained. Some additional value beyond schema.

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

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

Description clearly states 'Find tools by describing the data or task' and lists specific domains, distinguishing from sibling tools like 'datasets' that return data. It is specific and actionable.

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 available' — provides clear when-to-use guidance with implied alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as true/true/true/false, and the description adds rich behavioral details: parallel fan-out across sources, specific returned fields (cik, company_name, filings, fundamentals, patents, news, LEI), soft-fails for patents due to API sunset, and GDELT→GNews fallback for news. No contradiction with annotations.

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

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

The description is relatively long but front-loaded with example user queries and a succinct statement of purpose. Every sentence provides value (e.g., fallback behavior, unsupported name format). Could be slightly trimmed, but overall well-structured for a complex tool.

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

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

Given the tool's complexity (multiple data sources, fallbacks) and no output schema, the description fully explains what the tool returns (list of fields) and how it handles potential failures. It references sibling tool resolve_entity for name resolution, closing a common usage gap. The description is complete for an agent to understand behavior and dependencies.

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

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

Schema coverage is 100%, so baseline is 3. The description reinforces that value accepts ticker or zero-padded CIK and adds the critical context that names are not supported, directing to resolve_entity. This adds meaningful guidance beyond the schema's 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 that the tool provides a 'full cross-source profile of a US public company in ONE parallel call' and lists specific data sources (SEC EDGAR, XBRL, USPTO, news, GLEIF). It uses concrete examples like 'Tell me about X', making the purpose unambiguous. This distinguishes it from siblings like resolve_entity by specifying what it does uniquely.

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

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

Explicitly states 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view', providing a clear usage recommendation. Also advises using resolve_entity first if only a name is available, offering an alternative path. This goes beyond vague guidance into actionable when-to-use/when-not-to-use.

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

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

The description 'delete' aligns with annotations (destructiveHint=true, idempotentHint=true). It adds context about clearing sensitive data but does not introduce new behavioral traits beyond the annotations. No contradictions found.

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

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

The description is three sentences, each adding distinct value: action, usage context, and sibling pairing. No redundant or superfluous 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?

For a simple, one-parameter tool with no output schema and rich annotations, the description covers purpose, usage guidelines, and related tools comprehensively.

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

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

Schema coverage is 100% for the single parameter 'key', with a schema description already stating 'Memory key to delete'. The tool description repeats 'by key' but adds no meaningful extra detail 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 pair. It distinguishes itself from sibling tools like 'remember' and 'recall' by being the deletion operation.

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

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

The description explicitly states when to use the tool: 'when context is stale, the task is done, or you want to clear sensitive data'. It also suggests pairing with 'remember' and 'recall', 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, establishing the tool as safe and non-mutating. The description adds valuable behavioral context beyond annotations: it explains the tool fetches the page, extracts title/description/key links, and produces standard markdown. This process detail informs the agent about external network calls and output format without contradicting 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 a single paragraph that opens with the main action ('Generate a production-ready llms.txt file for any URL') and provides essential context in a front-loaded structure. It condenses purpose, process, and use cases into three sentences, with the last using a bullet-style list for clarity. No redundant phrases; every sentence serves a purpose.

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

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

Given the tool has 2 parameters, no output schema, and clear annotations, the description covers the output format (standard llms.txt markdown), the process (fetch, extract, emit), and the target audience. It omits details on error handling or invalid URLs, but these are minor gaps. The description provides sufficient context for the agent to understand the tool's capabilities and output without needing additional fields.

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

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

Schema coverage is 100%—both parameters (url, max_links) have descriptions in the schema. The tool description does not add new semantic meaning beyond what is already present in the JSON schema. However, it implicitly reinforces that 'url' is the site to summarize and 'max_links' controls the number of link entries, which is consistent with the schema. Baseline score of 3 is appropriate given high coverage and no additional disclosure.

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

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

The description uses specific verbs ('generate', 'fetches', 'extracts', 'emits') and explicitly names the resource ('production-ready llms.txt file'). It states the tool's purpose for AI crawlers (ChatGPT, Claude, Perplexity) and differentiates from sibling tools like 'scan_competitor_ai_presence' by focusing on file generation rather than scanning. The use of 'production-ready' and standard format indicates clear output expectations.

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

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

The description lists three concrete use cases: getting a client's site indexed, drafting your own llms.txt, or auditing competitor indexing. This provides clear when-to-use guidance. It does not explicitly exclude scenarios or name alternatives, but the context is sufficient for an agent to decide appropriately given the sibling set includes related but distinct tools like 'scan_competitor_ai_presence'.

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

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

Annotations already provide readOnlyHint, destructiveHint, idempotentHint, openWorldHint. Description adds return field details and purpose, consistent with annotations. No contradictions.

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

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

Two concise sentences: first describes purpose and return fields, second provides usage guidance. No filler, front-loaded with action.

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

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

Simple tool with one optional parameter. Description covers purpose, return fields, and usage scenario. No gaps given simplicity and annotation richness.

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

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

Schema coverage is 100% with clear parameter description. Description does not add parameter meaning beyond 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?

The description clearly states the tool lists active subscriptions and specifies return fields. It distinguishes from siblings like subscribe and unsubscribe by mentioning usage for review and finding id to cancel.

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: 'review what you're monitoring before adding more or to find an id to cancel.' Implies not for creating or modifying. Sibling tools provide context for alternatives, but no explicit 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 provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds no additional behavioral traits beyond summarizing the output. It does not contradict annotations.

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

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

The description is a single, well-structured sentence (~20 words) that front-loads the action and resource, with no redundant information. Every word 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 the low complexity (1 parameter, no output schema), the description adequately specifies the tool's purpose, input format, and output content. Minor improvement would be to explicitly state the tool is read-only, but annotations cover that.

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% (resource_id is described as 'Dataset id, e.g. "vank-fyx9".'). The description does not add new parameter semantics beyond the schema.

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

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

The description uses the specific verb 'Get' and identifies the resource as a Providence Open Data dataset's schema + metadata, listing the fields returned (columns, types, row count, category, last-updated) and providing an example resource_id. This clearly distinguishes the tool from siblings like 'datasets' or 'query'.

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 for retrieving dataset schema and metadata, but does not explicitly state when to use this tool over alternatives like 'datasets' (which might list available datasets) or 'query' (for data retrieval). No exclusions or when-not-to-use guidance is provided.

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

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

Description adds behavioral context beyond annotations: rate limit of 5 per identifier per day, free usage, no impact on tool-call quota, and daily team review. No contradiction with annotations (readOnlyHint etc. are false, appropriate for a feedback submission).

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 yet comprehensive, front-loaded with the main purpose, followed by use cases and behavioral details. Every sentence adds value without redundancy.

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

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

Given no output schema, the description adequately covers all aspects: purpose, usage guidelines, parameter semantics, and behavioral traits. It is complete for a feedback submission 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%, and description adds meaning by explaining the 'type' enum with examples, detailing the 'context' object structure, and specifying message length limit (2000 chars). 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?

Description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It specifies distinct use cases (bug, feature/data_gap, praise) and distinguishes itself from sibling tools by being a feedback mechanism.

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 the tool: for bugs, missing features/data gaps, or praise. Includes guidance on what not to do ('don't paste the end-user's prompt') and mentions rate limits and quota, providing clear criteria for 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?

Discloses self-aggregating signal from CF analytics-engine, no PII, and caching behavior (5min-1h). Adds value beyond annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) with concrete context. 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?

Three sentences, no filler, front-loaded with primary output. Every sentence earns its place; efficient and well-structured.

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

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

Given no output schema, description adequately explains return format (top tools, top packs, total call volume). Simple tool with one optional param; complete and sufficient 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 100% with one optional enum parameter. Description adds context that shorter windows surface hot trends and longer windows show steady-state demand, enhancing the schema 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?

Description clearly states it returns top tools, top packs, and total call volume over a recent window, with specific verb 'returns' and resource. Distinguishes from sibling tools by focusing on trending/call volume rather than general query or search.

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

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

Explicitly lists three use cases (discovering hot data sources, confirming canonical tool, aligning use case). No explicit when-not-to-use or alternatives, but guidance is clear and helpful 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?

Description discloses required parameters, monotonicity checks, partition-sum logic, semantic anchor Jaccard similarity, partition filter, fill check with theoretical vs realizable edge, and response structure. No contradictions with annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint all consistent). Adds significant behavioral context beyond annotations.

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

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

Description is thorough but moderately long; each sentence provides necessary detail. Some redundancy (partition_check explained twice) slightly impacts conciseness. Front-loaded with requirement and key information.

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

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

No output schema, so description fully explains return values (opportunities[] and partition_check) and additional features like fill check and custom sizing. Covers edge cases: cross-event similarity threshold, partition filter placeholder slugs, fill risk metrics. Complete for a complex tool.

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

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

Schema coverage is 100% with descriptions for both parameters. Description adds examples of valid values (e.g., 'fed-decision-may-2026'), explains that full URLs are accepted for event, and clarifies that topic is a seed question for cross-event scanning. Provides practical usage context, enhancing 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 explicitly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks, with two distinct modes (single-event and cross-event). Clearly differentiates from sibling tools like polymarket_edges and polymarket_fill_risk by focusing on arbitrage detection and fill checking.

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

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

Provides explicit guidance on when to use each mode: `event` for specific markets, `topic` for cross-event scanning. Warns that calling with no args fails. Could mention when not to use alternatives but overall clear context.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds significant behavioral context, detailing how edges are computed, the model families, response segments, caching (1h KV level), and diagnostic outputs. This goes well beyond the annotations, though the description is very dense.

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 extremely long and dense, covering many technical details about model families, segments, and diagnostics. While thorough, it lacks conciseness and could overwhelm an AI agent. The first sentence effectively states the purpose, but the overall length is excessive for a tool description.

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

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

Despite having no output schema, the description thoroughly explains the response structure (by_segment, fed_candidates, diagnostics). It covers caching, parameter effects, and edge-case scenarios (e.g., why segments may be empty). This provides a comprehensive understanding for invocation, given the tool's complexity and 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% with well-described parameters. The description reiterates some parameter details (e.g., min_liquidity, max_spread_pp) and adds context for partition arb Kelly behavior. However, it does not significantly enhance meaning beyond the schema. Baseline score of 3 is appropriate given high 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?

The description clearly states the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It also specifies its use case as 'what should I bet on today,' distinguishing it from sibling tools like polymarket_arbitrage or polymarket_edge_tracker.

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

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

The description provides clear context for when to use the tool: discovering opportunities without paging hundreds of markets. While it does not explicitly list alternatives or when not to use, the context is sufficient for an AI agent to select this tool for daily betting discovery. Slight deduction for lack of exclusionary guidance.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds extensive behavioral context: response structure (tracked, expired, snapshot_dates), how decay is computed, limits (60-day TTL, snapshot gaps), and the meaning of 'expired' opportunities. 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 detailed and front-loaded with key purpose, but it is somewhat verbose. However, every sentence contributes value, and it is well-structured. Slightly more concise would improve it.

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

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

Given no output schema, the description fully explains the return fields and limits. It covers response structure, edge cases (gaps, expiration), and constraints. Absent are error handling or empty result behavior, but overall adequate for the complexity.

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

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

Schema coverage is 100%, so parameters are documented. The description adds minimal additional semantics beyond the schema (e.g., 'snapshot family' label for 'window' and minor default clarification). Hence 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 provides edge persistence and decay telemetry, answering 'how long has this edge existed and is it shrinking?' It uses specific verbs and resources, and distinguishes from sibling 'polymarket_edges' by focusing on temporal analysis.

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

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

The description explains the use case by contrasting fresh and old edges, implying when to use this tool for historical context. However, it does not explicitly name alternatives or provide exclusion criteria, slightly reducing clarity.

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 and destructiveHint=false. The description adds behavioral details like walking the ladder, returning specific fields (top_of_book, vwap_fill_price, etc.), and warning about forced directional risk. This goes beyond annotations, though the core safety profile is already covered.

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

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

The description is front-loaded with the core purpose, then uses bold headings (REQUIRES, SINGLE-MARKET, BASKET) to structure a complex topic. Every sentence adds information; there is no redundancy or 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 fully documents all return fields for both modes, explains the risk of partial fills, and covers edge cases (thin_legs, forced_directional_risk). It is sufficient for an agent to understand tool behavior and interpret results.

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%, providing basic parameter descriptions. The description adds significant value: it explains single-market vs basket mode, how size_usd is interpreted differently (spend vs notional), and the auto-detection of side for basket. This enriches 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 opening line 'Realizable-vs-theoretical edge check against live CLOB order-book depth' clearly defines the tool's function. It distinguishes from siblings like polymarket_arbitrage by explicitly stating 'USE THIS before acting on any polymarket_arbitrage...', showing it serves as a prerequisite risk check.

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

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

The description provides explicit when-to-use guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It also explains why (theoretical overround not capturable, partial fills cause directional risk), effectively setting usage boundaries.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds significant context: compatibility_warning cases, temporal alignment, skipped_cross_type counters, and real-world caveats about pre-mapped topics.

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 longer but well-structured: purpose, modes, response fields, safety fields. Every section adds necessary detail for a complex tool; could be slightly tighter but remains clear.

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

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

Despite no output schema, the description fully covers return fields (prices, top_spreads_pp, compatibility_warning, temporal_alignment, skipped counters) and explains their interpretation. Complete for the tool's complexity.

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

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

Schema coverage is 100% with good descriptions. The description adds context by listing the ten topic shortcuts and explaining that explicit parameters override the topic-mapped side, but this largely reiterates schema info.

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

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

The description clearly states the tool computes cross-venue spreads between Kalshi and Polymarket for the same resolving question, distinguishing it from siblings like polymarket_arbitrage.

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

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

Explicitly describes two modes (topic shortcuts and explicit tickers) and provides guidance on when spreads are meaningful via compatibility_warning and temporal_alignment fields. Warns that pre-mapped topics often yield non-tradeable spreads.

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. The description adds that it returns matching rows as JSON, which is useful beyond annotations. 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 concise, two sentences front-loaded with the core purpose, followed by details on clauses and output. Every word adds value.

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

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

With 7 parameters, all fully described in schema, and no output schema, the description adequately covers the tool's behavior and output format (JSON). Could mention pagination or rate limits, but overall complete given annotations.

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

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

Schema coverage is 100% with each parameter having a description. The description adds context that SoQL clauses should be without leading '$' and mentions a default limit of 1000, providing extra value beyond the schema.

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

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

Description clearly specifies the tool runs a Socrata SoQL query against a dataset identified by resource_id, listing filter clauses like where, select, group, order. It distinguishes from siblings such as 'datasets' (which list datasets) and 'search_within' (which is likely different).

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

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

The description states when to use the tool (to query a dataset) and lists the supported SoQL clauses. It does not explicitly mention when not to use or provide alternatives, but the context signals and sibling list make the usage context clear.

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

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

Annotations already declare readOnlyHint and idempotentHint; description adds scoping to identifier and pairing context, but doesn't 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?

Three sentences, no wasted words, well-organized: function, examples, scoping/relation to siblings.

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?

Does not describe return format for single value retrieval or listing keys, nor error behavior (missing key). With no output schema, this gap reduces completeness.

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

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

Schema covers the single parameter well (100% coverage). Description adds usage examples and clarifies optionality, but doesn't add substantial new param info.

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

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

Clearly states it retrieves saved values or lists keys, provides examples (ticker, address, notes), and distinguishes from siblings (remember, forget).

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

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

Explains when to omit key (list all) and notes scoping and pairing with remember/forget, but lacks explicit when-not-to-use or alternatives beyond 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 states that setting mark_read:true flags events as read, modifying state, but annotations include readOnlyHint=true indicating no side effects. This is a clear contradiction. The description adds useful context about mark_read behavior, but the contradiction forces a score of 1.

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

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

Four sentences, each adding distinct value: purpose, return fields, filtering, and polling/alternatives. Front-loaded with the core action, no fluff or repetition.

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

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

For a tool with 5 optional parameters, no output schema, and strong annotations, the description covers purpose, return data, filtering, side effects, and alternative access. It is adequately complete for agent 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?

Despite 100% schema coverage, the description adds significant value by providing examples ('sec_8k'), explaining the consequence of mark_read, and clarifying ISO timestamp usage. This goes well beyond the baseline of 3.

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

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

The description clearly states the verb ('Pull fired events') and the resource ('your subscription feed'), specifying what is returned (source, citation_uri, raw payload). It distinguishes from siblings like 'list_subscriptions' by focusing on event retrieval with filtering options.

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

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

The description mentions that polling works fine and provides an alternative HTTP endpoint, implying usage context. However, it does not explicitly state when to use this tool versus other sibling tools, though no direct competitor exists.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and nondestructive. The description adds substantial behavioral context: fan-out to multiple sources, GDELT→GNews fallback, USPTO soft-fail, accepted date formats, return structure (changes[] grouped by source, total_changes count, 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 fairly long but every sentence adds value, covering purpose, usage, sources, fallback, return format, and sibling differentiation. It could be slightly more concise, but given the tool's complexity, the length is justified. The front-loading with example queries helps quick scanning.

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

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

No output schema exists, but the description adequately covers return values: 'Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs.' It also explains all data sources, fallback, and date handling, making the tool fully understandable without needing additional context.

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 extra meaning: explains that 'since' accepts ISO dates or relative shorthand with examples ('7d', '30d', '3m', '1y'), clarifies that 'type' is only 'company', and explains 'value' can be ticker or CIK. This goes well beyond the schema descriptions.

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

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

The description opens with concrete example queries ("What's new with X", "latest on Y") and explicitly states it provides a 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It distinguishes from the sibling tool entity_profile by saying 'Use entity_profile instead when you want the static profile.' This clearly defines the tool's purpose and differentiates it.

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

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

The description gives explicit when-to-use guidance through example queries and states the tool's scope (fans out to SEC, GDELT/GNews, USPTO). It provides a clear when-not-to-use directive by recommending entity_profile for static profiles. This helps the agent decide between tools.

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

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

Annotations indicate idempotent and non-destructive. Description adds key-value scoping by identifier, persistent for authenticated vs 24-hour anonymous, which goes beyond annotations without contradiction.

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

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

Description is informative but slightly verbose. Could be trimmed without losing meaning, but still efficient overall.

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

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

For a simple storage tool, description covers purpose, usage, scoping, persistence, and related tools. No output schema needed; completeness is high.

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

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

Schema has 100% coverage with good descriptions. Description adds context like key format conventions and value flexibility, but schema already suffices.

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 saves data for later reuse, with specific examples like resolved ticker and user preference. It distinguishes from sibling tools recall and forget, making purpose unambiguous.

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

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

Explicitly says when to use: 'when you discover something worth carrying forward' and pairs with recall and forget. Also explains scoping and persistence differences, 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=true and idempotentHint=true, indicating a safe, non-destructive operation. The description adds that the tool cascades through several lookup endpoints internally and replaces multiple manual lookups. It also details the return format for each entity type (e.g., company returns ticker, CIK, company_name, citation URI). This goes beyond annotations to explain internal behavior and output structure.

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

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

The description is front-loaded with example queries and a clear purpose statement. It then details supported types and their outputs. While it is relatively long, every sentence adds value. The structure is logical but could be slightly more concise by grouping some details. Overall, it is well-organized and efficient.

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

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

Given that there is no output schema, the description fully explains the return values for each entity type, including citation URIs. It covers input formats, supported types, and internal behavior (cascading lookups). For a tool with two parameters and no nested objects, this description is comprehensive and leaves no important gaps.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant semantic value: it explains that the 'value' parameter can be a ticker, CIK, or name for companies, and brand or generic name for drugs. It also describes what each 'type' returns, which is not in the schema. This additional context helps the agent understand parameter usage beyond the schema's brief descriptions.

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

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

The description clearly states the tool resolves names to identifiers, with examples like 'What's the ticker for...' and 'find the CIK for...'. It explicitly says 'Use FIRST whenever you have a name but need an ID,' distinguishing it from sibling tools that may require IDs. The verb 'resolve' and specific resources ('ticker', 'CIK', 'RxCUI') make the purpose precise.

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

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

The description provides explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It lists supported entity types and input formats, and explains that it replaces 2-3 manual lookups. While it doesn't explicitly say when not to use it (e.g., if you already have an ID), the implication is clear. No alternative tools are named, but the sibling list suggests tools like 'entity_profile' might be alternatives.

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

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

The description discloses it probes each entity with ai_visibility_check, ranks results, and returns a ranked list with score, confidence, and signal density. This adds behavioral context beyond the annotations (readOnly, idempotent, destructive false), and does not contradict them.

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

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

Two sentences plus a purpose statement. Every sentence adds value: main action, internal mechanism, output, and use case. No fluff, well-structured and front-loaded.

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

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

Despite no output schema, the description explains the output format (ranked list with score, confidence, signal density). Given 4 parameters and annotations, the description provides sufficient context for correct tool invocation and understanding.

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

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

With 100% schema coverage, baseline is 3. The description adds significant value: explains entities[0] is treated as subject, models default workers-ai is free, anthropic requires apiKey, _apiKey passed to api.anthropic.com, context disambiguates names. This greatly aids correct usage.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, using ai_visibility_check, ranking by score, and identifying most/least recognized. It distinguishes itself from sibling tool ai_visibility_check by focusing on multi-entity comparison.

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

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

The description provides a concrete use case: competitive AI-marketing audits, asking 'does Claude know about us as well as our competitors?'. It implies this tool is for comparison vs. single-entity check, but does not explicitly state when not to use.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds critical behavioral context: partial failure handling, bundlephobia's first-measure delay (5-30s), the 'sources_failed' field in the response. 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?

Front-loaded with the core purpose and usage in the first sentence. Every subsequent sentence adds meaningful detail (timing, fallback, scope limitations). No redundancy, appropriate length for the complexity.

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

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

With only two parameters, 100% schema coverage, and rich annotations, the description fully covers expected behavior, edge cases (partial failures, timing), scope, and output structure (summary block, per-advisory, links). No output schema exists, but the description compensates adequately by listing returned fields.

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

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

Schema coverage is 100% with both parameters described. The description adds minor but useful details: scoped packages (e.g. '@types/node') are accepted, and version defaults to latest. This adds value beyond the strict schema definition.

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

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

The description uses a specific verb ('composite check') and resource ('npm package'), and clearly states the tool's scope ('NPM ecosystem only'). It distinguishes from siblings by being a multi-source scan combining deps.dev and bundlephobia, unlike other tools that focus on single entities or general 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?

Explicit guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Also specifies what it is NOT for: 'PyPI / Maven / Cargo / Go fall under deps.dev:version directly', providing clear alternatives.

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

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

Discloses embedding model (BGE-base-en), similarity method (cosine), window size (500-char overlapping), and cap (200K chars with truncation flag). Annotations provide readOnly, idempotent, and openWorld hints, so no contradiction.

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

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

Single, focused paragraph with logical flow: purpose, use case, technical details. Dense but not verbose; every sentence adds value. Could be split into sections for easier scanning.

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

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

No output schema, but description specifies return values: top-N passages with character offsets and similarity scores. Covers parameters, behavior, and use case comprehensively. No missing information for agent invocation.

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

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

Schema coverage is 100%, but description adds value by restating limits (max ~200K chars for text, 1-20 for limit, default 5) and providing query examples. Reinforces schema without being redundant.

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 'Semantic search INSIDE a fetched record' with specific verb+resource and examples (SEC 10-K body, article). Distinguishes from sibling ask_pipeworx_grounded by explaining pairing intent.

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 ('too big to cram into the prompt') and explains benefits (saves context, returns passages with offsets). Pairs with ask_pipeworx_grounded for grounding, but no explicit 'do not use' conditions.

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

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

Annotations indicate a write operation (readOnlyHint false) with idempotentHint true and openWorldHint true. The description adds value by disclosing persistence requirements, delivery channel details, rate limits (10/day for SMS), webhook signing, and auto-disable condition. No contradiction with annotations.

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

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

The description is relatively long but well-structured, starting with the main purpose, then listing types, then delivery channels. Every sentence adds useful information. Minor conciseness loss due to detailed examples, but overall efficient for the complexity.

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

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

The description covers all key aspects: purpose, requirements, parameters, delivery options, and constraints. The lack of an output schema is partially mitigated by mentioning the return value (subscription id). No other gaps identified given the tool's complexity.

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

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

Schema coverage is 100%, but the description significantly enriches by providing concrete examples for each subscription type (e.g., sec_8k items, polymarket topics, fred_series) and delivery channel usage notes (e.g., phone verification, webhook secret return). This goes beyond the schema's minimal descriptions.

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

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

The description clearly states the action (create), the resource (proactive monitoring subscription to live-data event stream), and the outcome (returns new subscription id). It distinguishes from sibling tools like list_subscriptions and unsubscribe by specifying the creation aspect and the requirement for an OAuth account.

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

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

The description provides detailed context on when to use: to create subscriptions, with explicit requirements (Pipeworx OAuth account), supported types and their parameters, and delivery channel options. It does not explicitly state when not to use or directly compare to alternatives, but the sibling list and tool names imply usage boundaries.

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

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds that it returns live catalog examples with exact tool+argument shape, which is useful behavioral context. 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 the purpose and usage instructions. It is slightly verbose with multiple example phrases but packs necessary detail efficiently. Well-structured.

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

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

The description completely covers the tool's role as an onboarding entry point, explains what it returns, and how to use it. No output schema needed; the description accounts for return format sufficiently.

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

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

Schema coverage is 100% with a clear parameter description. The description adds context by explaining how to use the parameter (omit for full spread, pass topic for focus) and lists example values, but the schema already provides adequate 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?

The description clearly states the tool returns category-bucketed example questions and is the onboarding entry point. It distinguishes from siblings like ask_pipeworx and entity_profile by positioning itself as the first call for new users.

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

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

Explicitly states 'Use this FIRST when you do not yet know what Pipeworx can do for you' and mentions when to omit or pass the topic parameter. Provides alternatives (ask_pipeworx, entity_profile, etc.) for deeper queries.

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

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

The description adds critical behavioral context beyond annotations: ownership enforcement (security), and deactivation vs deletion (data persistence). Annotations already indicate idempotent and non-destructive, but the description explains these behaviors concretely.

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

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

Two sentences, front-loaded with main action, and every sentence adds value. Extremely concise and well-structured.

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

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

For a simple one-parameter tool without an output schema, the description covers all necessary behavioral aspects: action, ownership, side effects, and relation to sibling tool 'recent_alerts'. Complete.

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

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

Schema coverage is 100%, so baseline is 3. The tool description does not add any parameter-specific information beyond what the schema already provides. No improvement needed.

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

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

The description clearly states the verb 'Cancel' and resource 'subscription by id'. It distinguishes from siblings like 'subscribe' (opposite action) and 'recent_alerts' (related historical data). No tautology.

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

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

The description implies when to use: to cancel your own subscriptions. It mentions ownership enforcement but does not explicitly state when NOT to use or alternative tools. Nonetheless, 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 already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint as false. Description adds that it uses SEC EDGAR + XBRL, returns a verdict with specific categories (confirmed, refuted, etc.), and replaces multiple sequential calls. No contradictions with annotations. Does not disclose limitations (e.g., only US companies) but domain is mentioned.

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 moderately long but efficiently structured: begins with usage triggers, then states purpose, domain, output details, and value proposition. Some redundancy (e.g., lists verdict categories twice) but overall each sentence serves a purpose.

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

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

For a simple tool with 1 parameter and no output schema, the description covers the input domain, output format (verdict categories, actual value with citation), and replaces a multi-step process. It omits potential edge cases or error handling, but is complete enough for typical use.

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

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

Only one parameter 'claim' with 100% schema coverage via its description. The tool description adds value by providing natural-language examples and explaining the expected input format more richly than the schema alone, which already has an example.

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

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

Description starts with natural-language triggers ('Is it true that…'), defines tool as claim verification against authoritative sources, specifies domain (company-financial claims for US public companies) and output structure (verdict with categories). Clearly distinguishes from siblings like 'ask_pipeworx_grounded' or 'deep_research' by focusing on factual verification with a specific data source.

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: 'Use whenever the agent needs to check whether something a user said is factually correct.' Specifies the supported domain (company-financial claims). Does not explicitly state when not to use or mention alternatives, but the purpose is clear enough given the sibling list.

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