Data Ua

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

Adds useful detail beyond annotations: default model, fee structure, return format. No contradictions with readOnlyHint etc.

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

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

Single paragraph, front-loaded with main action, efficient but could be slightly more concise.

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

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

Covers purpose, usage, parameters, and return format (per-model data + combined view) despite no output schema. Fully adequate for a 4-param 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 value by explaining default model, _apiKey requirement for Anthropic, and context usage for disambiguation.

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

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

The description clearly states the tool probes LLMs for knowledge about an entity and scores visibility (0-100), distinguishing it from sibling tools like ask_pipeworx or deep_research.

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

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

Provides specific use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and notes default vs paid model. Lacks explicit when-not-to-use or alternative tools.

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

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

Annotations already indicate safe read operation. The description adds key behaviors: routing to one of 4,774 tools, filling arguments automatically, returning structured answers with stable citation URIs, working on all tiers, and being a fast call. No contradictions.

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

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

The description is front-loaded with the key recommendation, then covers domains, examples, and comparisons. It is structured but slightly dense; every sentence is valuable but could be streamlined slightly without losing meaning.

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, the description explains what it does, when to use, when not to use, provides examples, and mentions output format (citations). No significant gaps identified.

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

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

Schema covers 100% of parameters with a good description of the 'question' parameter and aliases. The description enriches this by giving concrete examples of what questions to ask and emphasizing natural language, adding value beyond the schema.

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

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

The description clearly states the tool routes questions to verified sources and returns structured answers with citations. It explicitly prefers over web search for factual queries. It distinguishes from siblings like ask_pipeworx_grounded and deep_research by specifying when to use each.

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

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

Provides explicit guidance to prefer over web search for many question types, lists example queries, and tells when to step up to other tools (hallucination-resistant answers, broad questions, breaking news). This is a comprehensive usage guide.

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

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

Annotations already provide readOnlyHint, idempotentHint, etc. The description adds behavioral details: costs one extra LLM call, returns refusal reasons, and explains the extraction process. No contradictions with annotations.

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

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

Description is front-loaded with purpose, then explains process, returns format, and usage. Every sentence adds value, though slightly verbose. Efficient for the amount of information.

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

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

Given the complexity (many aliases, no output schema), the description covers return format, refusal reasons, extra cost, and usage context. It is complete enough for an agent to understand tool behavior without 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 has 6 parameters but all are aliases for 'question'; schema descriptions are provided. The description clarifies that these are aliases, adding slight value. With 100% schema coverage, baseline is 3, and the description marginally improves it.

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, distinguishing it from sibling ask_pipeworx by noting the extra LLM call cost and its use case for quoted/cited answers.

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

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

Explicitly states when to use (high-stakes reads where facts must not be invented) and when not to use (prefer ask_pipeworx for casual lookups), providing clear guidance and alternatives.

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

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

The description goes far beyond the annotations (readOnlyHint, etc.) by detailing behavioral traits: fan-out logic, confidence levels, parent event extraction, news fallbacks, safety mechanisms for low-confidence matches, closed market handling, wide spread warnings, and cancellation rule parsing. These are all disclosed clearly, and no contradictions with annotations exist.

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 labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, etc.) and front-loads the core purpose. Every sentence adds value given the tool's complexity, though a minor trimming of repeated details (e.g., multiple cancellation rule examples) could improve conciseness 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 (3 params, no output schema), the description is exceptionally complete. It covers input handling, fan-out per category, response structure (market, analysis, evidence), resolver contract, parent event extraction, news fallback handling, safety checks, spread warnings, and cancellation rules. It fully compensates for the lack of output schema.

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

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

Schema description coverage is 100%, and the description adds substantial meaning: input types (slug, URL, question text), depth enumeration with exact behavior ('quick = 2-3 evidence sources, thorough = full fan-out'), and include_raw with default and size implications. Examples in schema further clarify 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's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It provides specific examples of inputs (slug, URL, question text) and outputs (evidence packet + comparison), and distinguishes from siblings through its unique focus on single-bet research with fan-out to category-specific data packs.

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 use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It does not explicitly mention alternatives or when not to use, but the examples of fan-out per category provide enough context to infer appropriate usage compared to sibling tools.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds important context: data sources, handling of off-calendar fiscal years, sorting by primary metric, and citation URIs. No contradictions.

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

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

The description is a single paragraph but packs essential information efficiently. It front-loads examples and key guidance. While clear, it could benefit from slight restructuring (e.g., bullet points) for even faster 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?

Given no output schema, the description explains return data (paired data + citation URIs). It covers purpose, input, behavior, and output comprehensively for a tool with only 2 parameters.

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 described), so baseline is 3. The description adds significant value with examples, constraints (2–5 items), and context about tickers vs drug names, justifying a 4.

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

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

The description clearly states the tool performs side-by-side comparison of 2–5 companies or drugs, with specific data sources (SEC EDGAR for companies, FAERS/FDA for drugs). It distinguishes from sibling tools like entity_profile which likely handles single entity lookups.

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

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

The description provides explicit usage examples and strong guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It covers when to use this tool vs. 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, idempotentHint, and destructiveHint. The description supplements this by specifying the return content (full metadata, resources, resource_ids). 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 sentence that is front-loaded with the core action and includes essential details. No wasted words.

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

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

Given the absence of an output schema, the description adequately explains what is returned. It specifies that resources and their resource_ids are included. Minor improvement could hint at metadata structure.

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

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

Schema coverage is 100%, with the parameter 'id' described as 'Dataset id or name (from search_datasets).' The description echoes this but does not add format details or constraints. Baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'Show' and the resource 'full metadata for one Ukraine Open Data dataset'. It specifies the source of the identifier ('from search_datasets') and what is included ('all resources and their resource_ids'). This distinguishes it from sibling tool 'search_datasets'.

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

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

The description implies usage after 'search_datasets' by referencing it. It provides clear context for when to use (to get detailed metadata of one dataset). It does not explicitly exclude other use cases, but the sibling differentiation is evident.

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

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

The description adds behavioral context beyond annotations: account requirement, parallel decomposition of questions, return format (verbatim evidence, confidence, gaps), and that it returns empty gaps for uncatalogued topics. Annotations already declare readOnlyHint, openWorldHint, etc., and no contradiction exists.

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

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

The description is well-structured with critical info front-loaded (account requirement, when to use). It is slightly long but every sentence adds value, so a 4 is appropriate.

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

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

Given no output schema, the description thoroughly explains what to expect: findings packet with citations and gaps. It also covers edge cases (empty gaps for uncatalogued topics) and expected time (15-60s). The complexity of the tool is high, and the description is complete.

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

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

Both parameters are described in the schema, and the description adds value: it notes the paid plan constraint for 'thorough' depth and implies default depth is 'standard'. Schema coverage is 100%, so baseline is 3, but the extra context justifies a 4.

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

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

The description clearly states the tool performs grounded multi-source research across 1242 structured data sources, returning a findings packet. It distinguishes from sibling tools like ask_pipeworx by specifying this is for broad/multi-part questions, not single lookups or breaking news.

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 is given: use for broad/multi-part questions over structured data; use ask_pipeworx for single lookups or breaking news. Also notes account requirement and that 'thorough' depth needs a paid plan, with alternative tool listed.

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, idempotent true, destructive false. Description adds substantial behavioral context: returns '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'. 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 efficiently structured: purpose first, then usage guidance, then what returns. Every sentence adds value. No redundancy. Appropriate length for the complexity of the tool.

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

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

Given no output schema, description fully explains what the tool returns (names, descriptions, schemas with examples). It covers parameter aliases, examples, and when to call. No gaps for a discovery tool with 6 parameters and no nested objects.

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

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

Schema coverage is 100% with all 6 parameters well-described. Description reinforces the primary 'query' parameter with examples like 'look up FDA drug approvals' and notes the aliases. While the schema already covers aliases, the description adds usage context that helps the agent formulate queries.

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 uses specific verbs like 'browse, search, look up, discover'. It distinguishes itself from siblings by instructing 'Call this FIRST when you have many tools available', making its unique role explicit.

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

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

Description explicitly lists when to use: 'Use when you need to browse, search, look up, or discover what tools exist for:' followed by many domains. It also says 'Call this FIRST' for exploration. While no direct alternatives are named, the context is clear and useful.

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

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

Beyond annotations (readOnly, idempotent, etc.), the description details exactly which sources are queried (SEC EDGAR, XBRL, USPTO, news, GLEIF), what fields are returned (cik, company_name, recent_filings, fundamentals, patents, news, LEI), and notes limitations like patents sunset and fallback mechanisms. This gives the agent a comprehensive behavioral understanding.

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

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

The description is comprehensive but dense. It is front-loaded with purpose and usage, then details the returned data. Every sentence adds value, though it could be slightly more concise. Still, it earns a 4 for efficient communication.

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

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

Given the complexity of the tool (multiple data sources, various return fields, fallbacks), the description covers all essential aspects without an output schema. It explains what is returned, limitations, and fallbacks, making it complete for agent understanding.

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

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

Schema coverage is 100%, but the description adds significant value: for 'type' it notes only 'company' is supported with future plans, and for 'value' it provides examples and explicitly states that names are not supported and to use 'resolve_entity' first. This goes beyond the schema's basic enum and string 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 starts with concrete example queries and explicitly states it returns a 'full cross-source profile of a US public company in ONE parallel call.' It also distinguishes itself from sibling tools by stating it should be preferred over chaining separate lookups, making the purpose unmistakable.

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

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

The description tells the agent to 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also warns that names are not supported and directs to use 'resolve_entity first' if only a name is available, providing clear when-to-use and when-not-to-use guidance.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true, indicating a destructive but idempotent operation. The description adds that it clears sensitive data, which is helpful context, but overall adds limited additional behavioral insight beyond the annotations.

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

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

Two sentences, no extraneous words. Front-loaded with the action and resource, followed by clear usage guidance. Highly efficient.

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

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

For a simple tool with one required parameter and no output schema, the description is complete. It covers when to use, the action, and points to sibling tools. Annotations cover behavioral traits adequately.

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

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

Schema coverage is 100%, and the description adds no extra meaning beyond the schema's description of the 'key' parameter as 'Memory key to delete'. Baseline score of 3 is appropriate.

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

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

The description clearly states the action ('Delete') and resource ('previously stored memory by key'). It also distinguishes from siblings by mentioning 'Pair with remember and recall,' making the tool's role clear.

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

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

Explicitly provides usage conditions: 'Use when context is stale, the task is done, or you want to clear sensitive data.' While it does not explicitly state when not to use, the positive guidance is specific and practical, and it references related tools.

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

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

The description discloses the core behavior (fetches page, extracts info, emits markdown) beyond the annotations (readOnlyHint, idempotentHint, etc.). It adds value by explaining the process and output format.

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

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

The description is concise with three sentences, front-loaded with the primary purpose, and each sentence adds necessary information without redundancy.

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

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

Given the rich annotations, 100% schema coverage, and simple output (single text blob), the description fully covers what the tool does, how it behaves, and what it returns, 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% with both parameters documented. The description restates the max_links default and limit but adds no new semantic information beyond the schema, resulting in a baseline score.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, using specific verbs and resources. It distinguishes from sibling tools, none of which perform this exact function.

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 explicit use cases (client's site, own project, competitor audit) providing clear context for when to use. It does not explicitly state exclusions, but the use cases are sufficient.

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

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

Annotations already declare safe, read-only, idempotent behavior. The description adds the specific fields returned, providing extra context without contradiction.

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

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

Two sentences, minimal and front-loaded with the core purpose. No extraneous 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 list tool with one optional parameter, the description adequately covers return fields, usage context, and default behavior. No gaps.

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

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

Schema coverage is 100% with clear parameter description. The tool description does not add additional meaning beyond what's in the schema.

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

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

The description clearly states the tool lists the caller's active subscriptions and specifies the returned fields. It distinguishes itself from sibling subscription management tools.

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

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

Provides explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells the agent when to invoke the tool.

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

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

Discloses rate limit (5 per identifier per day), that it doesn't count against tool-call quota, and that the team reads digests daily. No contradiction with annotations (which are neutral). Adds context beyond schema.

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

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

Concise yet comprehensive. Front-loaded with purpose, then usage, then specifics. Every sentence adds value without redundancy.

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

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

Despite no output schema, the description covers all necessary aspects: purpose, when to use, how to structure input, behavioral constraints. Complete for an agent to execute correctly.

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

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

Schema coverage is 100%, but description adds meaning: clarifies enum values with examples, explains context fields, and provides message length guidance (1-2 sentences, 2000 chars max). Adds value beyond schema.

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

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

The description clearly states the tool is for sending feedback to Pipeworx, listing specific categories (bug, feature, data_gap, praise) and examples. It distinguishes this from sibling tools by focusing on feedback rather than queries or actions.

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 each category (e.g., bug for wrong/stale data, feature for missing tools) and provides a negative instruction ('don't paste the end-user's prompt'). Also mentions rate limits and that it's free, 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?

Adds value beyond annotations: explains it's self-aggregating, no PII, cached (5min-1h), derived from CF analytics. Annotations already declare readOnlyHint and idempotentHint, but description enriches with behavioral specifics.

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 paragraphs, front-loaded with main result. Every sentence adds value, no fluff.

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

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

Given low complexity (one optional param, no output schema), description covers purpose, behavior, use cases, caching, and privacy. Complete 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 already covers 100% with enum and description. Description adds context: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This enhances meaning 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 it returns 'top tools, top packs, and total call volume' over configurable windows, distinguishing from sibling tools like 'ask_pipeworx' or 'discover_tools' by focusing on trending data.

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

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

Lists three specific use cases (discovering hot data sources, confirming canonical choices, aligning use cases with agent needs). Could be stronger with explicit when-not-to-use, but the use cases provide 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?

Discloses significant behavioral details: scanning strategies, partition check logic, Jaccard similarity anchor, placeholder filter, fill check using CLOB depth. Annotations (readOnlyHint, openWorldHint, idempotentHint) are consistent; description expands on what they imply. No contradiction 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?

Well-structured with clear sections (purpose, mode details, technical filters, response format). Though slightly verbose, each sentence adds value and no redundant information. Could be trimmed slightly without loss, but current length is acceptable 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 modes, partition checks, book depth analysis, similarity filters), the description is comprehensive. It explains output structure, details fill check conditions, and references sibling tools. No critical gaps remain despite absence of output schema.

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

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

Both parameters (event, topic) are described in schema (100% coverage), but the description adds substantial meaning: explains modes, shows example slugs, gives recommendation context, and details internal processing like market walking and partition sum check. Goes far 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?

Explicitly states 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks' and distinguishes two modes (event/topic) plus no-arg trending scan. Differentiates from siblings like polymarket_fill_risk and 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?

Provides clear when-to-use: 'Call with NO args for trending_scan', 'event (recommended for a specific market)', 'topic (for cross-event scanning)'. Includes when-not-to: 'realizable_edge_pp ≤ 0 means... do not trade it'. Mentions alternative tool for custom sizing (polymarket_fill_risk).

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive. The description adds significant behavioral details: caching ('Cached 1h at the KV level'), modeling methodology (crypto_price, news_momentum, etc.), exclusion of Fed bets from ranking, placeholder-slug filtering, and diagnostics for empty segments. No contradictions.

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

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

The description is well-structured with clear sections but is excessively verbose, containing detailed model explanations and internal logic that could be condensed. It front-loads the purpose but the length may overwhelm an AI agent. Not concise, but organized.

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

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

Given the complexity (9 parameters, no output schema, multiple segments), the description thoroughly explains the output structure (by_segment, fed_candidates, _diagnostics) and the meaning of response fields. It also covers why segments may be empty and includes diagnostics for debugging. This compensates for the lack of output schema.

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

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

Schema coverage is 100% (baseline 3). The description adds practical usage nuance beyond the schema descriptions, such as advice on slippage_pp ('Bump for very thin partitions; drop to 0 if you have a smarter fill model') and the purpose of min_partition_leg_kelly. This elevates it above baseline.

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

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

The description uses a specific verb ('scan... and return') and clearly identifies the resource ('top Polymarket markets') and the core action ('where Pipeworx data disagrees with market price'). It distinguishes from sibling tools by detailing the methodology (model families, segments, knobs), which is unique among the Polymarket siblings.

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

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

The description explicitly states the use case ('what should I bet on today') and provides context for when to use it (discovery without paging). It explains filters and tradeable-edge knobs. However, it does not explicitly state when not to use it or compare directly to siblings like polymarket_arbitrage.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds behavioral context: it reads snapshots, returns time-series data, and notes limits like 60-day TTL and snapshot enablement date. No contradictions.

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

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

Description is detailed but front-loaded with purpose. It is structured to explain response fields and limits. Could be slightly more concise, but overall well-organized.

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

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

Given no output schema, the description thoroughly explains the response structure (tracked[], expired[], snapshot_dates[]) and covers limitations (60-day TTL, snapshot enablement). For a complex data tool, this is complete.

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

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

Schema has 2 parameters with descriptions covering defaults and ranges (days clamp 2-30, window options). Schema coverage is 100%. Description adds context ('snapshot family') but does not significantly enhance meaning beyond schema.

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

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

The description clearly states it provides 'edge persistence and decay telemetry' from daily snapshots, answering a specific question about edge duration. It distinguishes itself from the sibling 'polymarket_edges' by focusing on temporal tracking.

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: to determine how long an edge has existed and if it is shrinking. Contrasts fresh vs old edges with a concrete example, but does not explicitly list when not to use or name 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 true, and destructiveHint false. The description adds significant behavioral context beyond annotations, such as the risk of partial fills, forced directional risk, and detailed return fields. No annotation contradiction.

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

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

The description is well-structured with clear sections (REQUIRES, SINGLE-MARKET, BASKET, USE THIS) and front-loaded purpose. It is slightly verbose but every sentence adds value. Could be trimmed slightly for brevity.

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

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

Despite no output schema, the description thoroughly details every return field for both modes, addresses edge cases (thin books, partial fills, forced directional risk), and explains usage context. Complete for a tool with 4 parameters and no output schema.

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

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

Schema description coverage is 100% (all parameters have descriptions). The description adds meaning by explaining how parameters behave in single-market vs basket modes, default values, and interpretation of size_usd. It enhances understanding beyond the schema.

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

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

The description clearly states the tool as a 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and explicitly distinguishes between single-market and basket modes. It differentiates from sibling tools like polymarket_arbitrage and polymarket_edges by specifying its role as a pre-trade 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 explicitly instructs 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500' and provides reasoning (theoretical overround on thin books, partial basket fills converting arb to directional position).

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 readOnlyHint annotations, the description details compatibility_warning, temporal_alignment, and skipped leg counters. It explains conditions under which spreads are meaningful, covering safety fields thoroughly.

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

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

The description is lengthy (~25 sentences) and contains some redundancy (e.g., compatibility_warning explained twice). However, it is well-structured with clear sections for modes and safety fields.

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

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

Given no output schema, the description thoroughly explains the response structure including spread, compatibility fields, and alignment. It covers edge cases and conditions, making it 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?

The input schema has 100% description coverage with param descriptions. The tool description adds semantics about how topic overrides work and lists the 10 shortcut values, enhancing understanding beyond the schema.

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

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

The description clearly states the tool computes cross-venue spreads between Kalshi and Polymarket for the same resolving question. It differentiates from siblings like polymarket_arbitrage by focusing on cross-venue, not single-venue.

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 two modes (topic shortcuts vs explicit IDs) and provides guidance on when to use each. It warns that most pre-mapped topics may not yield tradable spreads, but does not explicitly compare to sibling tools.

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

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

Annotations already declare readonly, idempotent, non-destructive. The description adds the crucial constraint that only DataStore-enabled resources are queryable, which provides value beyond annotations.

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

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

Two sentences, front-loaded with action, no extraneous words; every sentence earns its place.

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

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

Adequately complete for a simple query tool with no output schema; explains source of resource_id and DataStore constraint. Could add note on return format but not essential.

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

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

Schema coverage is 100%; description repeats parameter names (q, limit, offset) but adds no meaning beyond the schema's own property 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 pulls rows from a tabular Ukraine Open Data resource by resource_id, specifying the domain (Ukraine Open Data) and system (CKAN DataStore). It distinguishes from siblings like search_datasets.

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

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

It gives context about the resource_id from datasets and the DataStore requirement, but does not explicitly name alternatives; however, the purpose is clear for when to query a resource.

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. Description adds scoping to identifier and dual behavior (retrieve specific key vs list all keys), consistent with annotations.

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

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

Two sentences, front-loaded with main function, no wasted words. Each sentence serves a clear 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?

Single optional parameter and annotations cover safety. Description covers usage, scoping, and pairing. No output schema, but return behavior is implied. Could mention pagination for listing but not required.

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

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

Schema coverage is 100% with description 'Memory key to retrieve (omit to list all keys)'; tool description repeats the same info, adding no new meaning 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 verb 'retrieve' and resource 'value saved via remember' or list keys. Distinguishes from sibling tools remember and forget.

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

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

Explicitly says when to use: to look up context stored earlier without re-deriving. Mentions scoping and pairing with remember/forget, but doesn't explicitly exclude scenarios.

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

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

The description describes mark_read:true as flagging events as read, which implies a state change (write operation). However, annotations include readOnlyHint:true, contradicting this behavior. According to scoring rules, a score of 1 is warranted for contradiction.

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

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

The description is information-dense but well-structured, front-loading the purpose and then detailing parameters and usage. No wasted sentences, though slightly longer than minimal. It earns a 4.

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

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

Despite no output schema, the description fully describes the return contents (source, citation_uri, raw payload), addresses polling suitability, and mentions an alternative API endpoint. This covers all necessary context for an agent to use the tool correctly.

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

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

Even though schema coverage is 100%, the description adds significant meaning beyond the schema: examples for type ('sec_8k'), explanation of since (ISO timestamp), and clear effect of mark_read. It provides concrete usage guidance that helps the agent use parameters correctly.

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

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

The description clearly states the tool pulls fired events from the subscription feed, specifying what each event carries (source, citation_uri, raw payload). It uses a specific verb+resource pattern and distinguishes from siblings by mentioning the alternative GET endpoint for scripts/dashboards.

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

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

The description provides guidance on when to use parameters like type, since, and mark_read, and notes that polling works fine. It also mentions an alternative access method. However, it lacks explicit when-not usage or comparisons with sibling tools beyond the GET endpoint.

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 significant behavioral context: fan-out to multiple APIs, fallback to GNews on errors, soft-fail for USPTO due to PatentsView sunset, and the parallel execution. 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 a single focused paragraph with every sentence adding value. It front-loads usage examples, then details sources, parameters, and output format. No redundancy or filler. Despite length, it is 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 the complexity (multiple data sources, fallback logic, soft-fail, output format) and no output schema, the description is remarkably complete. It explains what is returned (structured changes[], total_changes count, citation URIs), the fallback chain, and the limitation of the USPTO source. Nothing essential seems missing.

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

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

Schema coverage is 100% (all 3 parameters described). The description enriches each: `since` explains ISO date and relative shorthand with examples ('7d', '30d', '3m', '1y'), `type` clarifies only 'company' is supported, and `value` specifies ticker or CIK format. This adds meaning beyond the schema.

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

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

The description clearly states it returns a change feed for a company in the last N days/weeks/months in one parallel call, listing specific sources (SEC, GDELT, GNews, USPTO). It distinguishes from sibling tool entity_profile which provides static profiles, making its purpose very 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?

The description provides explicit usage examples and conditions: 'What's new with X', 'latest on Y', etc. It tells when to use entity_profile instead, and explains fallback behavior (GNews when GDELT rate-limited or 5xx). This gives clear guidance for when to invoke this tool vs alternatives.

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

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

Annotations declare idempotentHint=true and destructiveHint=false. The description adds behavioral details: 'Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours.' This explains the scope and duration beyond annotations, though it doesn't explicitly mention idempotency implications.

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

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

Three sentences, each earning its place: first states purpose, second gives usage guidance, third adds behavioral details. Front-loaded with key purpose. No redundancy or filler.

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

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

For a simple key-value store with 2 parameters, the description covers purpose, usage, pairing with siblings, persistence details, and example keys. No output schema exists, but the return behavior (stored) is implicitly understood. 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 covers both parameters (key, value) with descriptions. The description adds naming conventions (e.g., 'subject_property', 'target_ticker') and clarifies value as 'any text — findings, addresses, preferences, notes', providing useful context beyond the schema.

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

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

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later' with specific verbs and resources ('key-value pair'). It distinguishes from siblings like 'recall' and 'forget' by mentioning pairing, and provides concrete examples of what to store.

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 is provided on when to use: 'when you discover something worth carrying forward'. Examples (resolved ticker, target address, etc.) clarify use cases. It also mentions pairing with recall/forget and differentiates session persistence.

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 that the tool cascades through multiple endpoints and returns specific fields (e.g., ticker, CIK, RxCUI) with citation URIs. It mentions auto-disambiguation but does not fully detail error handling or edge cases.

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

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

The description is well-structured with clear examples upfront, a succinct purpose statement, and organized details for each supported entity type. It is slightly verbose but each sentence adds value.

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

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

In the absence of an output schema, the description adequately explains the return values and internal behavior. It covers input format, auto-disambiguation, and the tool's role as a shortcut. Minor gaps exist regarding error cases and rate limits.

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 already descriptive parameter descriptions. The tool description reiterates examples and adds context about return values, but does not significantly enhance understanding of individual parameters beyond what the schema provides.

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

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

The description clearly states the tool resolves names to canonical identifiers, provides example queries, and distinguishes its role as the first step for other tools. It explicitly says 'Use FIRST whenever you have a name but need an ID.'

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 ('Use FIRST whenever you have a name but need an ID'), and the context of sibling tools implies alternatives for further details. It also explains it replaces multiple manual lookups.

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=false. The description adds behavioral context about probing each entity, ranking by score, and returning scored results, which complements the annotations well.

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 concise: two sentences plus a brief note. It front-loads the core purpose and efficiently communicates the tool's value without extraneous information.

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

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

Given no output schema, the description adequately explains the return format (ranked list with score, confidence, signal density). It covers the key aspects of the tool's behavior and input requirements, making it complete for a comparison audit 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 each parameter described. The description adds context about the 'entities' array's first element being the subject and explains the 'models' parameter defaults. This enhances understanding beyond the schema alone.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, using the verb 'compare' and specifying the resource (AI presence). It distinguishes itself from the sibling ai_visibility_check by performing batch comparison and ranking.

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') and implies when to use this tool over single entity checks (ai_visibility_check). However, it does not explicitly state when not to use it or provide alternative tools.

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

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

Adds value beyond annotations: partial degradation, bundlephobia timing (5-30s), sources_failed field. Annotations already declare readOnly, idempotent, non-destructive.

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

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

Single paragraph but well-organized with clear list of returned fields and failure behavior. Slightly lengthy but justified by complexity.

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

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

No output schema but description details all returned data points and handling of partial failures. Fully adequate 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%, but description adds useful context: scoped packages accepted, default to latest version. Slightly above baseline 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?

Clearly states composite check for adding npm packages, mentions specific data sources and return summary. Distinguishes from sibling tools by focusing on npm ecosystem and one-call composite.

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 (e.g., 'is X safe / popular / small') and distinguishes from sibling tools by noting NPM-only scope and alternative for other ecosystems.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context about the output structure, including resources and the link to query_resource, and notes that blank/'*' lists all datasets. No contradictions.

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

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

Two sentences, front-loaded with the core purpose, and no wasted words. Efficient and clear.

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

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

Given 3 parameters, no output schema, and annotations covering safety, the description is complete enough: it covers purpose, output structure, and cross-tool linking. Could mention pagination behavior but not necessary.

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

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

Schema coverage is 100% with parameter descriptions. The description adds semantic value by explaining that blank/'*' lists all datasets for the query parameter, which is not in 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 the tool searches Ukraine Open Data by keyword, specifies the data source, and details the return structure including dataset id/name, title, organization, and resources with resource_id linking to query_resource. This distinguishes it from siblings like query_resource.

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

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

Description implies usage for discovering datasets, but does not explicitly state when to use this tool versus alternatives or provide exclusions. The link to query_resource suggests a workflow, but no direct usage guidelines.

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

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

Beyond annotations (readOnly, idempotent, etc.), the description adds operational details: embedding model (BGE-base-en), window size (500-char overlapping), max input (200K chars with truncation flagging), and output specifics (character offsets, similarity scores). 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 well-structured paragraphs: first covers core functionality and usage, second provides technical specifics and limitations. Every sentence adds useful information without redundancy, fitting the tool's moderate complexity.

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

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

Given no output schema, the description fully explains return values (top-N passages, offsets, scores). It covers boundaries (200K char cap, truncation), mentions pairing with a sibling tool, and provides sufficient context for correct invocation.

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

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

Schema coverage is 100% (all parameters have descriptions), raising baseline to 3. The description adds value by explaining the purpose of the 'text' parameter ('Pass the text you already pulled') and emphasizing the utility of character offsets for verification, going beyond schema detail.

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

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

The description clearly states the tool performs semantic search inside a fetched record, returning top-N passages with offsets and scores. It distinguishes from siblings by contrasting with using whole documents and by explicitly pairing with ask_pipeworx_grounded.

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

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

It explains when to use this tool ('when the record is too big to cram into the prompt') and provides an alternative workflow by pairing with ask_pipeworx_grounded, giving explicit context and usage direction.

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

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

The description adds value beyond annotations by disclosing the need for phone verification, a 10/day SMS cap, and the one-time returning of webhook secret. It does not contradict any annotations.

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

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

The description is lengthy but well-structured, starting with the core action and then detailing types and delivery. All sentences add necessary information, though some detail (e.g., webhook signing) could be streamlined.

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

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

Given the tool's complexity (3 params, nested objects, no output schema), the description is remarkably complete. It covers subscription creation, types, delivery channels, constraints, and what to expect upon return.

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

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

The description significantly enriches the schema by providing concrete examples for each type's params and explaining delivery options in detail, including the webhook signing secret. Schema coverage is 100%, but the description adds context not present in the schema.

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

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

The description clearly starts with 'Create a proactive monitoring subscription to a live-data event stream,' which is a specific verb and resource. It distinguishes itself from siblings like 'unsubscribe' and 'list_subscriptions' by focusing on creation.

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

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

It explicitly states the requirement for a Pipeworx OAuth account and that anonymous/BYO cannot persist subscriptions. It provides detailed guidance on subscription types and delivery channels, though it doesn't explicitly state when not to use this tool (e.g., versus 'recent_alerts').

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds value by explaining the output structure (category-bucketed example questions with tool+argument shape) and the live catalog source.

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 user queries for quick scanning, followed by purpose and usage. While slightly long, every sentence serves a purpose and information is well-organized.

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

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

Given the simple input (one optional param) and no output schema, the description fully covers when to use, how to call, and what to expect. No gaps identified.

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

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

The schema already describes the topic parameter with enum list. The description adds context about focusing the output ('to focus') and the behavior when omitted ('full spread'), enriching the schema's 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 identifies the tool as an onboarding entry point that suggests example questions about Pipeworx's capabilities. It distinguishes itself from siblings like ask_pipeworx and discover_tools by targeting the 'what to ask' scenario.

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 this FIRST when you do not yet know what Pipeworx can do for you', and provides calling instructions with optional topic parameter. Also mentions alternatives like ask_pipeworx, entity_profile, etc.

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

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

Annotations indicate write, idempotent, non-destructive; description confirms ownership enforcement and deactivation (vs deletion), adding valuable behavioral context beyond annotations.

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

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

Two clear, front-loaded sentences with no redundant information; every sentence adds value.

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

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

For a simple tool with one parameter and no output schema, the description covers purpose, behavior, ownership, and lifecycle impact completely.

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

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

Schema already fully documents the 'id' parameter. Description adds ownership enforcement context, exceeding baseline 3.

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

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

The description explicitly states the action ('Cancel a subscription by id') and distinguishes it from the sibling tool 'subscribe' by mentioning ownership enforcement and deactivation.

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 on when to use (only cancel own subscriptions) and what happens (deactivation, not deletion), but does not explicitly mention when not to use or provide alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral details: returns a verdict (with specific types), extracted structured form, actual value with citation, and percent delta. It also mentions v1 limitations to company-financial claims, providing helpful transparency.

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

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

The description is front-loaded with trigger phrases but is fairly long. It packs a lot of information efficiently, though some redundancy exists (e.g., mentions 'verdict' and then lists types). Still, it earns its place.

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

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

Despite having no output schema, the description fully explains what the tool returns (verdict types, structured form, actual value, citation, delta). It covers the domain scope, limitations, and the value proposition. With only one parameter, it is complete.

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

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

Schema description coverage is 100% for the single parameter 'claim', which already has a detailed description. The tool description adds value by providing example claims and explaining the underlying data sources (SEC EDGAR + XBRL), enhancing understanding beyond the schema.

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

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

The description starts with multiple trigger phrases ('Is it true that…', 'fact check', 'verify the claim that…') and explicitly states the verb 'validate' and resource 'claim'. It clearly specifies the domain (company-financial claims via SEC EDGAR + XBRL) and distinguishes from sibling tools by noting it replaces 4-6 sequential calls.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and provides the scope (company-financial claims). It does not explicitly state when not to use, but the context is clear enough.

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