Datalastic

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

Annotations indicate read-only, idempotent, non-destructive. The description adds key behavioral details: it makes external API calls, requires an API key for Anthropic with direct billing, and returns per-model structured results including raw responses. This goes beyond annotations.

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

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

The description is concise (4 sentences) and front-loaded with the core action. Every sentence adds unique value: main purpose, model options, return format, use cases. No redundancy.

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

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

Despite no output schema, the description comprehensively outlines return values (per-model score, confidence, signals, raw_response, combined view) and cost implications. It adequately covers complexity for a 4-parameter probing 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% for 4 parameters. The description adds value beyond schema by explaining the default model, that '_apiKey' is only for anthropic probing, and that 'context' disambiguates. This provides practical context the schema lacks.

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 about an entity and scores visibility (0-100) per model. It specifies the default model and optional Anthropic probe, distinguishing it from sibling tools like 'scan_competitor_ai_presence' which likely focuses on competitors.

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 use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and provides guidance on model selection (default free vs. paid Anthropic via API key). It does not explicitly state when not to use the tool, but context is clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds transparency about the tool's internal routing to thousands of sources and return of structured answers with pipeworx:// citation URIs. However, it could elaborate on potential delays or failure modes for unsupported queries.

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

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

The description is moderately long but well-paced: it front-loads the primary purpose, then gives usage context, alternatives, and examples. Each sentence adds value. Could be slightly tighter, but no 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?

Given the complexity (no output schema, broad range of queries), the description covers purpose, examples, and differentiation from key siblings. It omits details about error handling or response structure, but the annotations mitigate the need for extensive behavioral disclosure.

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

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

Schema has 100% coverage with 6 parameters all aliases for 'question'. Description does not add meaning beyond the schema's description, but it provides example inputs and clarifies the parameter's role in the context of routing. Given high schema coverage, score is 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?

The description explicitly states the tool routes questions to a vast array of structured data sources, with specific examples like SEC filings, FDA data, and stock prices. It clearly distinguishes from sibling tools like ask_pipeworx_grounded (hallucination-resistant) and deep_research (broad multi-part queries). The verb 'routes' and resource '4,773 tools across 1242 verified sources' make the purpose concrete.

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

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

The description provides explicit when-to-use guidance: 'PREFER OVER WEB SEARCH' for factual queries, and gives clear conditions for alternatives (use ask_pipeworx_grounded for verbatim evidence, deep_research for broad questions). It also includes a list of query types and example questions, covering usage context comprehensively.

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 show safe read operation; description adds extra details like refusal reasons, extra LLM call cost, and that it extracts answer only from tool result. No contradiction.

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

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

Front-loaded with main purpose, informative but slightly long; every sentence adds value, but could be 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?

No output schema, but description fully explains return format (answer, evidence, confidence, source, etc.) and refusal reasons, making it complete for a high-complexity 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 all parameters being aliases for question; description repeats that 'question' accepts aliases, adding minimal 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 it is a 'Hallucination-resistant answer mode for high-stakes reads' and distinguishes itself from sibling tool 'ask_pipeworx' by noting its extra cost and reliability.

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

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

Explicitly tells when to use ('whenever an answer will be quoted, cited, or acted on') and when not to use ('prefer ask_pipeworx for casual lookups'). Also describes refusal scenarios.

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

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

Annotations already indicate read-only and idempotent behavior, but the description adds rich behavioral context: explanation of the resolution process (resolver contract, market_match_confidence), fan-out examples, safety handling (low_confidence_match, market_closed_or_inactive), tradeability notes, cancellation rule parsing, and news fallback details. No contradiction with annotations.

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

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

The description is thorough but verbose, spanning multiple paragraphs with dense information. It front-loads the main purpose but could benefit from better organization (e.g., bullet points for classifiers and fan-out examples). Every sentence adds value given the tool's complexity, but length impacts conciseness.

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

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

Given the tool's complexity (3 params, no output schema, extensive annotations), the description covers everything an agent needs: input formats, process flow, safety caveats, response shapes, error handling, and rule risks (cancellation rules). It is comprehensive and leaves no critical 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?

The input schema has 100% description coverage, providing baseline understanding. The description adds context for the tool's overall process but does not significantly enhance per-parameter meaning beyond what the schema already provides. Examples are given in the schema's 'examples' field, not the description.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data, specifying input types (slug, URL, question text) and output (evidence packet + comparison). It distinguishes from siblings by listing specific use cases like 'should I bet on X' and mentions edge calculation, which overlaps with polymarket_edges but is framed as broader 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?

The description provides a clear 'Use for' section with example queries and extensively covers when to inspect results (e.g., low-confidence matches, closed markets, wide spreads). However, it does not explicitly state when not to use this tool or provide direct alternatives, so it lacks explicit exclusions.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. Description adds behavioral details: sorted by primary metric, returns paired data with citation URIs, explains fiscal year handling for companies, and data sources.

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?

Somewhat verbose but well-structured: starts with trigger phrases, then explanation, then details. Every sentence adds value, though could be slightly more concise.

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

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

Despite no output schema, description thoroughly explains return format, data sources, fiscal year handling, and constraints. Covers all necessary aspects 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 description adds significant meaning: explains enum values (company vs drug) and their data sources, gives examples for values parameter, and describes behavior per type 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 uses specific trigger phrases like 'Compare X and Y' and 'head to head', clearly states the verb 'compare' and resource 'entities' (companies or drugs), and distinguishes from sibling tools by noting it replaces 8-15 sequential 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?

Explicitly advises to prefer this tool over sequential single-pack lookups when comparing entities. Provides clear context for when to use, though does not explicitly state when not to use.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by listing the specific returned fields (lat/lon, speed, course, etc.), going 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 plus a concise example; the most critical information (purpose, inputs, outputs) is presented first and without unnecessary fluff.

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

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

Given no output schema, the description adequately lists the return fields. For a simple single-vessel lookup, it is complete enough, though it omits potential edge cases or 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%, so the baseline is 3. The description mentions the identifiers and includes an example, but does not add significant new meaning beyond what the schema already 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 starts with 'Where is ship <imo/mmsi>' and explicitly states it returns live AIS position for a single vessel, clearly distinguishing it from the sibling tool datalastic_vessels_in_radius which handles multiple vessels.

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

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

The description explains the identifiers (IMO, MMSI, UUID) and provides an example, but does not explicitly state when to use this tool versus alternatives like datalastic_vessels_in_radius. The context is clear enough for the intended use case.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so safety is clear. Description adds that it returns live AIS positions but lacks details on data freshness, pagination, or rate limits. Adequate but no extra depth 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?

Single sentence plus example. Every word earns its place. No fluff. Front-loaded with the core purpose. Excellent structure.

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

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

For a tool with 4 parameters and no output schema, the description explains the return data (ship name, IMO, MMSI, etc.). Provides a concrete example. Could mention pagination or maximum radius but overall sufficient for usage.

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

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

Schema coverage is 100%, so parameters are already described. The description adds an example call with example values, clarifying units (nautical miles, decimal degrees) and the role of _apiKey. This adds meaningful 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 action ('Find vessels within radius nm of a lat/lon') and the resource ('live AIS positions of all ships near a point'). It lists the returned fields, distinguishing it from the singular sibling 'datalastic_vessel'.

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?

No guidance on when to use this tool versus alternatives. Does not mention specific use cases or when not to use it. An example is provided but no context for selection.

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

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

Annotations already mark readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false. Description goes beyond by disclosing 15-60s latency, return format (findings packet with evidence, confidence, gaps[]), and the guarantee that it never invents answers. No contradiction with annotations.

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

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

The description is relatively long but well-structured, front-loading the critical account requirement and alternative tool. Each clause provides distinct value—use cases, limitations, output description, timing. Slightly verbose in listing example question types, but no redundancy.

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

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

Given the moderate complexity (2 params, no output schema), the description fully covers: prerequisites (account), exclusivity conditions, behavior, output structure, performance expectations, and edge cases (empty gaps). No missing information for an agent to decide or invoke this 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?

Input schema covers both parameters with descriptions (100% coverage). Description adds meaning by explaining depth options ('quick=3, standard=5, thorough=8 (paid plans)') and clarifying that the question can be broad/multi-part. This is valuable context beyond the schema's enum lists.

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 precisely states it performs grounded multi-source research across 1242 structured data sources, decomposing questions into facets and routing to 4,773 tools in parallel. It clearly distinguishes from siblings like ask_pipeworx by emphasizing structured data vs news, and from single-lookup alternatives.

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

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

Explicitly tells when to use (broad/multi-part structured data questions) and when not (single lookup -> ask_pipeworx; breaking news -> ask_pipeworx). Also notes account requirement and paid plan need for 'thorough' depth, providing clear decision criteria.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds key behavioral details: returns top-N relevant tools with full input schemas and curated examples, and that results are 'ready to call directly, no second schema lookup needed.' This complements the annotations without contradiction.

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

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

The description is concise (about 100 words) and well-structured: purpose, use cases, return details, and a call to action. Every sentence adds value, and no unnecessary repetition.

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

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

Given no output schema, the description adequately explains what is returned (tools with schemas). It covers the query parameter well and includes a limit parameter. However, it does not mention error handling, pagination beyond max limit, or what happens if no tools match, which would make it more complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the query parameter accepts multiple aliases (task, q, description, search) and provides two concrete examples. This goes beyond the schema, which only has generic descriptions. A higher score would require more detailed semantics, but this is sufficient.

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 'Find tools by describing the data or task' and lists numerous specific use cases (SEC filings, FDA drugs, etc.). It distinguishes itself from sibling tools by explicitly instructing 'Call this FIRST when you have many tools available and want to see the option set,' making it clear that this is a meta-tool for discovering other tools.

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

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

The description explicitly states when to use: 'when you need to browse, search, look up, or discover what tools exist for' and 'Call this FIRST...' It also implies that for specific tasks, the returned tools should be called instead, providing a clear decision rule.

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. The description adds substantial behavioral context: lists specific returned fields, mentions patent source status (soft-fails after May 2025), and explains multi-source fan-out. No contradiction.

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

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

The description is relatively long but well-organized, starting with example queries and then detailing behavior. Every sentence adds value, though some could be streamlined slightly. Still very effective.

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

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

No output schema exists, so the description carries full burden. It thoroughly explains the return structure (cik, recent_filings, fundamentals, patents, news, LEI) with specifics like URI format and sorting. Input constraints and fallback behavior are also covered. Complete for a complex tool.

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

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

Schema coverage is 100%, baseline 3. Description adds crucial meaning: type is limited to 'company' with future plans, value must be ticker or zero-padded CIK, and names are not supported (directing to resolve_entity). This greatly enhances agent understanding.

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

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

The description clearly states the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call.' It specifies the verb (profile), resource (company), and scope (US public company). It also differentiates from sibling tools like resolve_entity and chaining 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?

Explicit guidance is provided: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Also states to use resolve_entity first if only a name is available. This clearly indicates when and when not to use 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?

Annotations already include destructiveHint=true and readOnlyHint=false, so the destructive nature is clear. Description adds 'clear sensitive data' but doesn't elaborate on permanence or side effects beyond annotation coverage.

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 front-loaded sentences: first defines action, second provides usage context. No extraneous information.

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

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

For a simple destroy operation with no output schema, description covers the essential functional context and sibling pairing. Minor gap: no mention of return behavior.

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

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

Schema coverage is 100%, with the parameter described as 'Memory key to delete'. Description adds no extra parameter meaning beyond 'by key', so baseline 3 applies.

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

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

Description explicitly states 'Delete a previously stored memory by key' – specific verb (delete), resource (memory), and method (by key). Clearly distinguishes from sibling tools remember and recall.

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

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

Provides explicit use cases: stale context, completed task, clearing sensitive data. Advises pairing with remember and recall. Lacks explicit when-not but highly contextual.

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

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

Annotations indicate readOnly, openWorld, idempotent, and non-destructive. Description adds process details: fetches page, extracts title/description/key links, emits standard markdown. No contradiction with annotations.

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

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

Three sentences efficiently cover purpose, process, and use cases. Front-loaded with key verb and resource. No superfluous content.

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

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

Covers purpose, behavior, output format, and usage scenarios. No output schema, but output is well-described. Could mention limitations (e.g., site complexity) but not essential for clarity.

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

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

Schema coverage is 100%, so baseline is 3. Description adds context about output format but does not elaborate on parameter specifics beyond what schema already 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 verb (generate), resource (llms.txt), and target audience (AI crawlers). It distinguishes itself from 21 sibling tools, none of which generate llms.txt.

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: getting client's site indexed, drafting for own project, auditing competitor. It does not specify when not to use or alternatives, but given no similar sibling tools, this is clear enough.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds return fields and default behavior (only active subscriptions), which goes beyond annotations.

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

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

Two concise sentences that are front-loaded with purpose and usage. 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?

For a simple list tool with one optional parameter, the description covers return fields, default behavior, and intended use. No output schema needed given the explicit field listing.

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

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

Schema has 100% coverage for the single parameter. Description adds context by explaining default behavior (active only) and the parameter's effect, enhancing understanding.

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

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

The description clearly states it lists active subscriptions with specific verb and resource. It distinguishes from sibling tools like subscribe and unsubscribe by focusing on listing.

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: to review monitoring before adding more or find an id to cancel. This guides the agent on context relative 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 are all false (non-destructive, etc.), and the description adds rate limits (5 per identifier per day) and that it's free. No contradictions with annotations.

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

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

The description is concise (4 sentences), front-loaded with purpose, then usage, then constraints. Every sentence adds essential information without redundancy.

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

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

For a simple feedback tool with 3 parameters (all fully described in schema), the description covers purpose, usage, rate limits, and cost. No output schema needed, and the tool is self-contained.

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

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

Schema coverage is 100% with good descriptions. The description adds value by explaining how to construct the message (describe in terms of tools/packs, be specific) and giving typical length, which goes beyond the schema.

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

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

The description clearly states the tool is for sending feedback (bugs, missing features, praise) and distinguishes it from sibling tools, none of which serve this purpose. The verb 'tell' and specific categories make purpose unambiguous.

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

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

The description explicitly specifies when to use (bug, feature/data_gap, praise) and when not to (don't paste user prompt). It also mentions rate limits and free access, providing complete guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds behavioral details: self-aggregating, no PII, derived from CF analytics-engine, and caching 5min-1h, complementing 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?

Three sentences, front-loaded with key output, efficient use cases, and no wasted words. Excellent structure.

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 is complete: it explains output, use cases, data source, privacy, and caching. 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 input schema covers the single parameter with enum and description (100% coverage). The description adds value by explaining window trade-offs: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.'

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

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

The description clearly states it returns top tools, top packs, and total call volume over windows, with three explicit use cases. It distinguishes from sibling tools like 'ask_pipeworx' and 'discover_tools' by focusing on trending/call volume.

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 three specific scenarios for use: discovering hot data sources, confirming canonical tool, and seeing use case alignment. It lacks explicit when-not-to-use but the context is clear.

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

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

Beyond annotations (readOnly, idempotent, non-destructive), the description adds rich behavioral context: partition checks, fill checks against CLOB depth, semantic anchor (Jaccard similarity), partition filter for placeholder slugs, and conditions for not trading. No contradiction with annotations.

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

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

Well-structured with clear sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). While somewhat long, it is dense with information and front-loads the key use cases. Minor redundancy could be trimmed, but overall efficient.

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

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

Provides comprehensive coverage of inputs, algorithms, output structure (opportunities[], partition_check details), and even post-processing (fill check). No output schema exists, but the response fields are described. Completeness is excellent for a tool with this complexity.

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

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

Schema coverage is 100% (both event and topic described). The description adds significant meaning: explains the difference between single-event and cross-event modes, provides example slugs, and describes how each mode works internally. It augments the schema effectively.

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

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

The description clearly states the tool's purpose: finding arbitrage opportunities on Polymarket. It specifies the methods (monotonicity violations, partition-sum checks) and distinguishes three modes (trending_scan, event, topic). This differentiates it from siblings like polymarket_edges.

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

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

Explicit guidance on when to use each mode: no args for trending_scan, event for a specific market slug, topic for cross-event scanning. Provides examples. Does not explicitly mention when not to use, but the instructions are clear enough to infer appropriate usage.

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

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

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) are provided and consistent. The description adds substantial behavioral context: caching (1h KV), model families (crypto_price, news_momentum, etc.), per-sport overround corrections, placeholder filters, and diagnostic counters. No contradiction.

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

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

The description is very long and detailed, covering every segment and knob. It is structured with sections but not front-loaded; the first sentence states purpose but then dives into technical details immediately. Some repetition (e.g., filter descriptions) could be condensed.

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

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

Given 9 parameters, no output schema, and complex filtering logic, the description is exceptionally complete. It explains the response structure (by_segment, top-level fields, diagnostics), caching, and every knob's effect. It covers what would be in an output schema.

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

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

Schema coverage is 100% so baseline is 3. The description adds valuable context beyond parameter descriptions: explains tradeable-edge knobs (min_liquidity, max_spread_pp), distinguishes min_kelly vs min_partition_leg_kelly, and clarifies slippage assumptions.

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

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

The description clearly states the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It distinguishes from sibling tools like polymarket_arbitrage and polymarket_edge_tracker by focusing on Pipeworx disagreement and offering specific segments.

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 targets 'what should I bet on today' and mentions that agents discover opportunities without paging hundreds of markets. It explains usage of knobs but does not explicitly contrast with alternatives like polymarket_arbitrage or polymarket_fill_risk.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds important behavioral details: decay from daily closes, snapshot gaps from cache-misses, and bounded history. 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 long but well-structured: core purpose first, then detailed response format, then limitations. It is front-loaded with the key question. 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?

Without an output schema, the description thoroughly explains the response structure (tracked[], expired[], snapshot_dates[]) and covers all important aspects for an agent to use the tool effectively.

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

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

With 100% schema coverage, baseline is 3. The description restates defaults and roles but adds little beyond the schema (e.g., window values are listed in 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 provides 'edge persistence and decay telemetry' from daily snapshots, answering a specific question about edge age and shrinkage. It is distinct from sibling tools like polymarket_edges and polymarket_arbitrage.

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

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

The description explains when to use it by contrasting fresh vs. old edges, implying you'd use it alongside polymarket_edges for deeper analysis. However, it doesn't explicitly state when not to use it 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?

The description adds substantial behavioral context beyond annotations (readOnly, idempotent, non-destructive). It explains the tool walks the order book ladder, returns fill metrics and verdict, and warns about thin books and unhedged directional risk. No contradiction with annotations.

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

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

The description is well-structured with clear sections (intro, requirements, modes, usage note) and every sentence adds value. However, it is relatively lengthy; slight trimming could improve conciseness 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 no output schema, the description lists key return fields (e.g., top_of_book, vwap_fill_price, verdict) and explains both modes thoroughly. It addresses edge cases (partial fills, thin legs, forced directional risk). This provides a complete understanding of the tool's capabilities and limitations.

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

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

Even with 100% schema coverage, the description adds critical meaning: clarifies size_usd as 'max spend on buys, target proceeds on sells' for single-market and 'settlement notional S (shares per leg; each share pays $1)' for basket, plus explains default side auto-selection. This significantly aids correct usage.

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

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

The description clearly states the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes, and explicitly says 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500', differentiating it from sibling tools.

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

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

The description provides explicit when-to-use guidance: before acting on arbitrage signals or trades above ~$500. It explains the risks of partial basket fills converting arb into directional positions, and implies when not to use (e.g., small trades). This directly helps the agent decide contextually.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds significant behavioral details: compatibility_warning conditions (two cases), temporal_alignment field, skipped_cross_type counters, and explains when spreads are mathematically meaningless.

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: concise summary at start, then modes, response fields, safety warnings. Some slight redundancy (e.g., 'pre-mapped ≠ tradeable' restates earlier warning), but overall efficient for the complexity.

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

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

Given no output schema, the description adequately explains return fields (leg prices, spreads, safety fields). It covers matching logic, temporal alignment, and compatibility checks. Lacks explicit error handling or rate limit info, but sufficient 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% with descriptive comments for all three parameters. The description adds context: lists the 10 topic options, explains override behavior for explicit tickers, and provides examples of ticker formats, adding 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 computes cross-venue spread between Kalshi and Polymarket for the same question. It distinguishes from sibling tools like polymarket_arbitrage by focusing on cross-venue rather than intra-venue, and provides specific verbs like 'compute', 'fetch', 'return'.

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 and explicit tickers) and warns that pre-mapped topics often return compatibility warnings, implying when spreads are unreliable. However, it does not explicitly contrast with sibling tools or state when to choose this tool over others.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds scoping detail (anonymous IP, BYO key hash, account ID) and that omitting key lists all keys, but does not significantly extend beyond the annotations' safety profile. 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 three sentences, front-loaded with the main action, and efficient. Slightly verbose but all sentences add 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 retrieval tool with one optional parameter, good annotations, and no output schema, the description covers purpose, usage, scoping, and relationship to sibling tools. 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% for the single optional parameter, and the schema description already states 'omit to list all keys'. The description repeats this but adds no new semantic detail about the parameter format or usage 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 verb ('Retrieve' or 'list') and the resource ('a value previously saved via remember' or 'all saved keys'). It distinguishes from sibling tools by explicitly naming 'remember' and 'forget' and explaining the pairing.

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

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

The description explains when to use this tool ('to look up context the agent stored earlier... without re-deriving it from scratch'). It implies not using it for saving or deleting, but lacks explicit when-not-to-use statements. Still clear overall.

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

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

Annotations declare readOnlyHint=true and destructiveHint=false; description adds that mark_read:true flags events as read, a mutation. This extra detail compensates for the annotation's broad safety claim.

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

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

Three sentences, no filler. Front-loaded with main action, then key details (return fields, filter options, side effect), ending with alternative access.

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

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

Describes inputs, return fields, and alternative endpoint. Lacks error handling or pagination info, but sufficient for a read-heavy tool with well-documented schemas.

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; description adds examples (e.g., 'sec_8k' for type, ISO timestamp for since) and explains mark_read's effect on subsequent calls, enriching bare schema.

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

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

Starts with 'Pull fired events from your subscription feed,' clearly stating verb and resource. Distinguishes from sibling tools like list_subscriptions and subscribe by specifying it retrieves fired events.

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 filtering guidance (type, since) and mentions alternative access (GET endpoint). Lacks explicit when-not-to-use or comparison to siblings, but context is clear.

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

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. Description adds behavioral details: fan-out to multiple sources, fallback logic, USPTO deprecation note, return format (changes[] grouped by source). No contradiction with annotations.

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

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

Single paragraph but well-structured with examples and fallback info. Every sentence adds value; slightly long but not excessive. Could be more concise, but highly informative.

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

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

Despite no output schema, description explains return format (changes[] grouped by source, total_changes count, citation URIs). Covers all parameters, sources, fallback, and sibling distinction. Fully adequate for an agent to understand the tool.

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

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

Schema has 100% coverage with descriptions. Description adds examples for 'since' (ISO date and relative shorthand like '30d') and for 'value' (ticker or CIK). Explains parameter purpose in context, adding value beyond schema.

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

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

Description clearly states it's a change feed for a company, fans out to multiple sources (SEC, GDELT/GNews, USPTO), and provides example queries. It distinguishes from sibling tool 'entity_profile' by specifying scope (dynamic changes vs. static profile).

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

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

Explicitly provides example queries ('What's new with X', 'updates on Acme') and directs to use 'entity_profile' for static profile. Includes fallback behavior (GDELT preferred, GNews on rate limit) and notes USPTO soft-fail. Clear when-to-use and when-not-to.

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 provide idempotentHint=true but no destructive or readOnly hints. Description adds that data is scoped by identifier, authenticated users get persistent memory, and anonymous sessions retain for 24 hours. This adds useful 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?

Single paragraph front-loaded with purpose. Every sentence adds value. No wasted words. Appropriate length.

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

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

For a simple key-value store tool, description covers purpose, usage context, parameter semantics, behavioral nuances (persistence, scoping), and pairing with sibling tools. No output schema, but return behavior is implicitly clear.

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

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

Input schema has 100% coverage with descriptions. Description adds example key patterns ('subject_property', 'target_ticker') and explains value as 'any text', reinforcing schema meaning.

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

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

The description clearly states 'Save data the agent will need to reuse later' with specific examples (resolved ticker, address, preference, research subject). It distinguishes from sibling tools by mentioning 'recall' 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 'Use when you discover something worth carrying forward' and provides guidance on pairing with recall/forget. Also clarifies authentication scoping and retention limits.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds value by noting internal cascading lookups and that it replaces 2-3 manual calls. No contradiction.

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

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

The description is a single paragraph, front-loaded with usage triggers and examples. Every sentence adds value; no wasted words.

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

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

Given 2 parameters, no output schema, and strong annotations, the description provides a complete picture: purpose, types, return info, and behavioral note. No gaps remain.

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

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

Schema coverage is 100%, but the description enriches both parameters: 'type' is linked to supported values (enum), and 'value' explains auto-disambiguation and what each type returns (ticker, CIK, RxCUI, etc.).

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 IDs with specific examples (ticker, CIK, RxCUI). It distinguishes from siblings by stating '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 says 'Use FIRST whenever you have a name but need an ID.' It provides clear context for use cases but does not explicitly state when not to use it, though it is implied.

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 guarantee safety (readOnlyHint, idempotentHint, etc.). The description adds behavioral details: it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. It also describes the return format (score, confidence, signal density).

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 efficiently convey purpose, usage example, and return details. No wasted 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?

Given no output schema, the description adequately explains the ranked return list with key fields (score, confidence, signal density). It also mentions internal use of ai_visibility_check, making the tool's behavior fully understandable.

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 extra context beyond schema, notably that the first entity is treated as the 'subject' for narrative and explains the models parameter and _apiKey usage.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, with a specific verb ('Compare') and resource ('AI visibility'). It distinguishes from sibling tools like ai_visibility_check (single entity) by emphasizing multi-entity comparison.

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

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

The description provides a clear use case ('competitive AI-marketing audits') and an example query. It implies when to use but does not explicitly state when not to use or mention alternatives like ai_visibility_check for single entities.

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 important behavioral details beyond annotations: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30 seconds, and the sources_failed field will list any timeouts. Also describes the return structure in detail (summary block, per-advisory detail, links, alternative versions).

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

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

The description is detailed but well-structured, starting with the main purpose, then use cases, then return fields, then alternatives. Every sentence adds value, though it could be slightly more condensed. It remains clear and front-loaded.

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

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

Given no output schema and the tool's complexity, the description thoroughly covers what the tool returns (specific fields in summary block, per-advisory detail, links, alternative versions) and behavioral notes (latency, partial failures). It is complete for an agent to understand and invoke correctly.

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

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

The description adds meaning beyond the schema by explaining that scoped packages are accepted, version defaults to latest, and that partial failures may occur. Since schema coverage is 100%, baseline is 3, but the added context justifies a 4.

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

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

The description clearly states the tool's purpose: a composite check for npm packages using deps.dev and bundlephobia to answer questions about safety, popularity, and size. It uses specific verbs and resource, and distinguishes from any sibling tools by focusing on npm packages.

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 on when to use: when an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. Also specifies limitations: NPM only in v1, and directs users to deps.dev:version for other ecosystems. Provides clear when-to-use and when-not-to-use context.

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

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

Beyond annotations (readOnly, idempotent, non-destructive), the description adds critical details: the embedding model (BGE-base-en), similarity measure (cosine), windowing (500-char overlapping), truncation cap (200K chars with flagging), and output specifics (top-N passages with offsets and scores). This fully informs the agent of behavior.

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

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

The description is three focused sentences, front-loaded with the core action. Each sentence adds essential information (usage, output details, technical specifics) without redundancy. Proportional to the tool's complexity.

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

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

Despite no output schema, the description explains return values (passages, offsets, scores). It covers input constraints (200K cap, truncation), pairing with a sibling tool, and algorithmic details. The agent has enough to use the tool correctly and safely.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds value by clarifying the intended use of 'text' (previously fetched record), providing concrete examples for 'query', and noting the range and default for 'limit'. This enhances 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 performs semantic search inside a fetched record, with specific verbs ('search', 'returns') and resource ('text'). It distinguishes from siblings by explicitly pairing with ask_pipeworx_grounded and emphasizing the use case of saving context when records are too large.

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 when the record is too big to cram into the prompt' and explains how it pairs with ask_pipeworx_grounded. This provides clear when-to-use and a complementary alternative, leaving no ambiguity about appropriate contexts.

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 the tool is mutating (readOnlyHint=false) and idempotent (idempotentHint=true). The description adds detailed behavioral context: returns new subscription ID, OAuth requirement, delivery channel specifics, email validation, SMS cap, and webhook signing details. 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 detailed but well-structured, starting with the primary purpose and then listing types and delivery options. Every sentence adds value, though it could be slightly more concise.

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

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

Given the complexity (multiple subscription types, delivery channels, constraints) and the absence of an output schema, the description is remarkably complete. It covers requirements, types, filtering params, delivery mechanisms, limitations, and even webhook security.

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

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

Although schema coverage is 100%, the description adds significant value by explaining type-specific params with examples and delivery channel constraints (e.g., 'sms: phone must be verified; 10/day cap'). This goes beyond the schema's 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 it creates a proactive monitoring subscription and returns the subscription ID. It differentiates from sibling tools like list_subscriptions and unsubscribe by focusing on creation and providing detailed type-specific examples.

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

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

The description explains when to use the tool (to subscribe to alerts), specifies the requirement for a Pipeworx OAuth account, and notes that anonymous/BYO cannot persist subscriptions. It does not explicitly state when not to use it, but the context is clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds that it returns live catalog examples with tool shapes, which is useful 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?

Well-structured with front-loaded alternative phrasings. Slightly long but every sentence adds necessary context. Could be tightened but still effective.

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

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

Despite no output schema, description thoroughly explains what is returned (category-bucketed examples with tool shapes). Covers all necessary context for a first-time user.

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

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

Schema has 100% coverage. Description adds value by listing example topics, explaining that omitting gives cross-category spread, and linking topic to narrowing the focus.

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 category-bucketed example questions with exact tool+argument shapes. It distinguishes itself from siblings like discover_tools and ask_pipeworx by specifying it is the onboarding entry point.

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and mentions alternatives like meta-tools. Also explains when to pass topic vs no arguments.

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

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

Beyond annotations (idempotent, not destructive, not read-only), the description adds key behaviors: ownership enforcement, soft delete (deactivation), and link to 'recent_alerts'. This provides valuable context about side effects and data persistence.

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

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

The description is two sentences, front-loaded with the core purpose, and each sentence adds distinct value. No redundant or unnecessary text. 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 parameter and no output schema, the description covers purpose, precondition (ownership), postcondition (deactivation with historical data retention), and a relevant sibling tool ('recent_alerts'). Completely sufficient.

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

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

The schema covers the sole parameter 'id' with description 'Subscription id (uuid) returned by subscribe.' The description reinforces that the id comes from subscribe but adds no new semantic details. With 100% schema coverage, a baseline of 3 is appropriate.

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

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

The description clearly states the action: 'Cancel a subscription by id.' It specifies the resource (subscription) and the operation (cancel), differentiating it from sibling tools like 'subscribe' (create) and 'list_subscriptions' (list). This is a specific verb+resource combination.

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 ownership enforcement ('you can only cancel your own subscriptions'), guiding when to use. It also explains the effect (deactivation not deletion) and mentions 'recent_alerts' for historical context. While it doesn't explicitly state when not to use, the context is clear enough for an agent.

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

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

Annotations already provide readOnlyHint, idempotentHint, and other hints. The description adds value by detailing the return format (verdict, extracted structure, actual value, citation, percent delta) and noting it replaces multiple sequential calls. 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 slightly verbose but well-structured, with examples, domain specification, and return details. Every sentence contributes value, though it could be tightened 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?

Given a single required parameter and no output schema, the description fully covers what the tool does, what it returns, and its advantages. It is complete for an agent to understand usage and expectations.

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

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

With 100% schema description coverage, the baseline is 3. The description adds example claims ('Apple's FY2024 revenue was $400 billion') and clarifies the domain (company-financial claims), but these are minor enhancements over the schema's existing natural-language description.

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

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

The description clearly states the tool's purpose: natural-language claim verification against authoritative sources, with specific examples like 'Is it true that…' and domain focus on company-financial claims. It distinguishes itself from generic fact-checking tools by specifying the supported scope.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also implies the tool is for financial claims of US public companies, but does not explicitly state when not to use it or list alternatives among siblings.

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