Valorant

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

Annotations already declare safe, idempotent, open-world behavior. The description adds behavioral context: it returns per-model {score, confidence, signals, raw_response} + combined view, explains default free model and BYO key for Anthropic, and implies non-deterministic results. 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 3-4 sentences, front-loaded with the core purpose, then optional details and use cases. Every sentence adds value with no redundancy.

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

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

With no output schema, the description adequately explains return structure (per-model and combined view) and cost implications. Param semantics are covered. It is complete enough for correct selection and 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 clear descriptions for all 4 parameters. The description adds marginal value by noting the default model and that _apiKey is only needed if anthropic is in models, and explains context's disambiguation purpose. This slightly exceeds 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 clearly states the tool 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model'. It uses specific verbs and resources, and distinguishes from siblings like scan_competitor_ai_presence by focusing on visibility scoring across multiple models.

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

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

The description provides clear usage context: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to pass _apiKey for Anthropic probing. However, it does not explicitly state when not to use this tool or compare it to siblings.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false. The description adds context about routing behavior, citation URIs (pipeworx://), and that it works on every tier with one fast call. This goes beyond annotations without contradicting them, though it could mention potential rate limits or error behaviors.

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 front-loaded with the most critical guidance ('PREFER OVER WEB SEARCH'). Every sentence earns its place, covering use cases, trigger phrases, examples, alternatives, and return behavior. No wasted words or repetition.

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

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

Given the tool's open-world nature and lack of output schema, the description is remarkably complete. It explains what the tool returns (structured answer with citations), when to use it, when to escalate to specific siblings, and even covers breaking-news use cases. It provides sufficient context for an agent to invoke the tool confidently.

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 six parameters are aliases of 'question' with clear descriptions. The description adds no extra parameter-level detail, but the schema already fully documents the input. Baseline 3 is appropriate since the schema does the heavy lifting.

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

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

The description clearly states the tool routes questions to 5,529 tools across 1,455 verified sources and returns structured answers with citations. It uses a specific verb ('routes', 'returns') and resource ('real-world data'), and distinguishes itself from sibling tools like ask_pipeworx_grounded and deep_research.

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

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

The description explicitly says to prefer this over web search, says 'START HERE for most questions' as default entry point, and gives clear step-up conditions: use ask_pipeworx_grounded for hallucination-resistant single answers, deep_research for broad/multi-part questions. It provides trigger phrases and examples, making usage guidance exceptionally strong.

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

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

Annotations already mark the tool as readOnly, openWorld, and idempotent. The description goes beyond by disclosing the beta/experimental nature, that candidate routing improvements may be active live, the current inactive state (retired 2026-07-26), and that results are compared against the stable router. This adds critical behavioral context not in annotations, and does not contradict them.

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

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

The description is three sentences, front-loaded with the core purpose, and every sentence adds essential information (beta status, current behavior, usage instruction, fallback clarification). No verbose or redundant content.

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

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

The description fully covers the beta nature, relationship to the stable router, experimental purpose, current equivalence to ask_pipeworx, and the fact that it is a complete working router. Even without an output schema, it references the known response shape of ask_pipeworx, providing sufficient context for an agent to use it correctly.

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

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

Schema description coverage is 100%, with the required 'question' parameter and five aliases all documented in the schema. The description adds no additional parameter-specific meaning beyond referring to 'same arguments' as ask_pipeworx. Per rubric, baseline 3 is appropriate when schema covers all parameters.

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

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

The description opens with 'Beta version of ask_pipeworx: identical universal router' and specifies the scope ('same 5,529 tools, same arguments, same response shape'). It clearly differentiates from the stable ask_pipeworx by noting it is the experimental edge with candidate routing improvements. This is a specific verb+resource+scope statement that distinguishes it from siblings.

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

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

The description states 'Use it exactly like ask_pipeworx when you want the newest routing; results are compared against the stable router to decide what merges.' This gives explicit when-to-use guidance and names the alternative (ask_pipeworx). It also clarifies 'Falls back to nothing' to prevent misuse as a fallback 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 declare readOnly, openWorld, idempotent, non-destructive. The description goes further by explaining the return structure, including evidence verbatim, confidence, source, and explicit refusal reasons. It also discloses the cost trade-off. No contradiction.

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

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

The description is information-dense without fluff. It front-loads the core purpose, then details routing, return shape, usage cases, and cost implications. Every sentence earns its place and the structure is logical.

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

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

With no output schema, the description fully covers return values including refusal reasons. It also provides enough context about when this is preferable to the sibling ask_pipeworx. The tool is complex, but the description accounts for it 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?

The schema already documents the single 'question' parameter and all aliases at 100% coverage. The description does not add parameter-level detail beyond that, so baseline 3 is appropriate.

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

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

The description clearly states a specific purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It distinguishes itself from ask_pipeworx by highlighting the extraction-only behavior and refusal mechanism, so the agent knows exactly what this variant adds.

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 when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on... and the agent must not invent facts.' It also provides an exclusion: 'prefer ask_pipeworx for casual lookups', and mentions the cost difference (extra LLM call). This is excellent usage guidance.

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

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

The description goes far beyond annotations by detailing internal behavior: market resolution, classification, fan-out to data packs, market match confidence, parent event extraction, news fallback handling, safety short-circuits for low confidence or closed markets, and cancellation rule parsing. Annotations already indicate read-only, open-world, idempotent; description adds rich behavioral 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?

The description is thorough but lengthy, spanning multiple paragraphs. While front-loaded with the core purpose, it includes extensive details (e.g., specific data sources, cancellation rule parsing) that could be summarized. The verbosity reduces conciseness, though every sentence serves a purpose.

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

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

Given the complexity and no output schema, the description provides complete context: response shapes, resolver contract, parent event extractor, news fields, safety behaviors, and resolution-rule risk. The agent has enough information to use the tool correctly and interpret results.

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

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

Schema coverage is 100% (all three parameters described). The description adds semantic value by explaining that 'market' can be a slug, URL, or question text, and provides examples. It clarifies 'depth' (quick vs thorough) and 'include_raw' (summarized vs full payloads), complementing the schema enumerations and descriptions.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies inputs (slug, URL, question text) and output (evidence packet + market-vs-model comparison). This distinguishes it from sibling tools like ask_pipeworx or polymarket_edges, which have different scopes.

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 for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It is clear about context but does not explicitly exclude alternatives or mention when not to use it. Given the detailed examples, it provides strong guidance.

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

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

Annotations declare readOnly, openWorld, idempotent, non-destructive. Description adds value by detailing data sources (SEC EDGAR for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and inclusion of citation URIs.

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

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

Description is moderately long but each sentence adds valuable information. Front-loaded with common query patterns. Could be slightly shortened without losing context.

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 simple schema (2 params, no nested) and no output schema, the description covers input formatting, data sources, sorting behavior, and output format (paired data + citation URIs). Sufficient for an agent to use correctly.

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

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

Schema has 100% coverage. Description adds meaning by explaining what each enum value does (company vs drug) and providing examples for the values parameter, beyond the schema descriptions.

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

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

The description clearly states the tool performs side-by-side comparison of 2–5 companies or drugs in one parallel call, distinguishes from sequential lookups, and provides specific examples of use cases.

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

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

Explicitly states to prefer this tool over sequential single-pack lookups when comparing entities, and gives usage examples. Lacks explicit when-not-to-use guidance but covers main 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 declare readOnly/openWorld/idempotent/non-destructive traits, but the description adds significant behavioral detail: expected latency (15-60s, up to 90s for thorough), the gaps[] mechanism guaranteeing no invented answers, contradictions[] for standard/thorough, semantic excerpting instead of head-truncation, and guarantee that citations are always fetchable. This goes well beyond the annotation baseline.

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 information-dense; every sentence serves a purpose, from account requirements to latency to citation integrity. It is front-loaded with the most critical action (account/sign-in warning). It could be considered verbose, but for a tool with this complexity and no output schema, the length is justified.

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

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

With no output schema, the description fully explains what the return packet contains: verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, gaps[], and contradictions[]. It also covers account tiers, latency, hops, and use-case boundaries, leaving no major contextual gap for an agent to make an informed call.

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 question and depth have rich descriptions), so the baseline is 3. The tool description reinforces the meaning by explaining depth levels in terms of gap-recovery and lead-chasing, and clarifies that broad/multi-part questions are fine, but this largely mirrors the schema's own description. It adds some practical context but does not significantly exceed 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 identifies the tool as a grounded multi-source research tool across Pipeworx's structured data sources, using the specific verb 'research' and distinguishing it from open-web search and sibling tools. It states exactly what it does in one call: decomposes questions, routes to tools in parallel, and returns evidence with citations and gaps.

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: best for broad/multi-part questions over structured data, not for single lookups or breaking current news. It explicitly names alternatives (ask_pipeworx for single lookup, live news APIs for current news) and even addresses account-tier fallback ('If you are not signed in, use ask_pipeworx instead').

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

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

Annotations already provide readOnlyHint=true, idempotentHint=true. The description adds that it returns top-N most relevant tools with full input schemas and curated examples, and that results are 'ready to call directly, no second schema lookup needed.' 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 front-loaded with 'Find tools by describing the data or task.' It lists many domains efficiently and ends with a call to action. While lengthy, every sentence earns its place for a discovery tool that needs to enumerate use cases.

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

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

Given 6 parameters (mostly aliases), 100% schema coverage, and no output schema, the description adequately explains the return value (names, descriptions, schemas). It does not cover error handling or pagination, but for a discovery tool, this is sufficient.

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

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

Schema description coverage is 100%, so baseline is 3. The description does not add new parameter details beyond what the schema already provides; it mentions aliases but those are already in schema. The description includes examples, but that's not parameter semantics.

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

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

The description clearly states it finds tools by describing data or task, and lists many specific domains (SEC filings, FDA, etc.). It distinguishes itself from sibling tools by explicitly saying 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).'

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 you need to browse, search, look up, or discover what tools exist for: ...' and 'Call this FIRST...' providing clear usage context. It lacks explicit when-not-to-use guidance, but the context is sufficient.

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

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

Describes parallel fan-out across multiple sources, patent soft-fail deadline, and return structure in detail. Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint, and the description adds valuable behavioral 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?

The description is moderately long but packed with useful information, front-loaded with trigger phrases and examples. Each sentence earns its place, though slight trimming could improve conciseness.

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

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

Given the tool's complexity and absence of an output schema, the description covers key return fields, data sources, sorting, and limitations (patent sunset). It does not mention pagination or limits on news/patents, but overall it is fairly complete for a profile 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 clear descriptions for both parameters. The description reinforces meaning (e.g., 'value' can be ticker or zero-padded CIK) and adds context like unsupported names. Adds value beyond schema but not significantly more.

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 lists specific sources and return fields, and explicitly distinguishes from chaining single-pack lookups, making it easy for an agent to understand when to use this tool.

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

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

Provides explicit guidance on when to use ('holistic view') and when not to ('names not supported – use resolve_entity first'), along with examples. Contrasts with alternatives ('ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups'). Excellent usage 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?

Description aligns with annotations (destructiveHint true) and adds context about clearing sensitive data. This supplements the annotation 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: first states core action, second gives use cases and sibling tools. No extraneous words. Very 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?

For a simple tool with one parameter, good annotations, and no output schema, the description fully covers purpose, usage, and relationships.

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 documents 'key' as 'Memory key to delete'. Description simply says 'by key', adding no additional semantics beyond the schema.

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

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

Description clearly states the tool deletes a memory by key. It distinguishes from siblings 'remember' and 'recall' by using the verb 'delete', which is specific and unambiguous.

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

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

Explicitly states when to use: when context is stale, task done, or clearing sensitive data. Also mentions pairing with remember and recall. Lacks explicit when-not guidance, but sufficient for a simple 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 declare readOnlyHint and idempotentHint, indicating safe non-destructive operation. The description adds behavioral details: fetches page, extracts title/description/key links, emits standard markdown format. 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 two sentences plus a bullet list of use cases. Every sentence adds value, no fluff. Front-loaded with the core action and output format.

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 output format (single text blob, standard llms.txt markdown). Combined with strong annotations, the tool is complete enough 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 description coverage is 100%, so schema already documents both parameters well. The description does not add additional meaning beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description clearly states it generates a production-ready llms.txt file for any URL, explaining the fetch-extract-emit process. It distinguishes from siblings by being specific to llms.txt generation, which is unique among the listed 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 use cases: getting a client's site indexed, drafting for own project, auditing competitor visibility. While it doesn't state when not to use, the examples give clear context for appropriate invocation.

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. Description adds 'static game data, keyless' which reinforces safe read-only behavior but does not add significant new 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 sentence that efficiently conveys source, purpose, and scope. No wasted words; front-loaded with key information.

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

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

Given the tool's simplicity (1 param, no output schema, rich annotations), the description covers the essential context: source, keylessness, and return content. Minor gap: no indication of return format, but not critical for such a straightforward tool.

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

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

Schema description coverage is 100% for the single parameter 'uuid'. The description does not add additional semantic detail beyond what the schema provides, so 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?

Clearly states the tool retrieves full details for one agent by UUID, including description, role, role description, and abilities. Distinguishes from sibling 'list_agents' which returns a list.

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

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

Implied usage: use when you need detailed info for a specific agent after getting the UUID from list_agents. Context 'static game data, keyless' provides helpful guidance, but explicit when-to-use vs alternatives is missing.

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. Description adds key details: static game data, keyless access, and role filter option. 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, no redundant words, effectively 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?

Covers the essential purpose and filter option. Lacks details on output format, but given no output schema and simple list, it's sufficient.

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

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

Schema has full description for the single parameter. Description adds minimal new info beyond repeating 'optionally filter by role'. Baseline 3 is appropriate.

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

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

Clearly states it lists playable agents from VALORANT API, including roles and abilities. Differentiates from siblings like get_agent and list_maps.

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 context about static, keyless data and optional role filter. Implies use for retrieving all agents, but does not explicitly compare with get_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 declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false. The description adds value by specifying the data is static and keyless, reinforcing the read-only, safe behavior. 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?

Single sentence that front-loads the source and purpose, covering all essential information without fluff. Every word contributes 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 list tool with no parameters and no output schema, the description adequately states what the tool returns (maps with tactical description and icon). However, it could be more explicit about the output structure (e.g., field names). Still, it is sufficient for an agent to understand the tool's purpose.

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?

Tool has zero parameters, so schema coverage is 100% by default. The description correctly implies no input is needed, which aligns with the empty schema. No additional parameter documentation is necessary.

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

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

Description clearly specifies verb+resource: 'list maps' for VALORANT game data. It identifies the data source (valorant-api.com) and what it returns (tactical description, icon). This differentiates it from siblings like list_weapons which also deal with game data but a different 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?

Context is clear: it's for retrieving static VALORANT map data without authentication (keyless). While no explicit when-not-to-use or alternatives are given, the nature of the tool (read-only static data) and the sibling list show no direct overlap, making exclusions unnecessary.

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

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

Annotations already indicate readOnlyHint, idempotentHint, destructiveHint. The description adds context on return fields and scope (caller's subscriptions, active by default). 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 efficient sentences: first states purpose and return fields, second gives usage guidance. 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?

Simple tool with clear annotations and usage context. Description sufficiently covers return fields and usage hints. No need for 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 has 100% coverage for the single parameter include_inactive. Description does not add meaning beyond the schema, just restates the overall purpose.

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 the caller's active subscriptions and lists specific return fields. It distinguishes from sibling tools like subscribe and unsubscribe.

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

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

Explicitly says 'Use this to review what you're monitoring before adding more or to find an id to cancel' providing clear context for when to use this 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 declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds valuable context: it is 'keyless' (no authentication needed) and 'static game data', which goes beyond the annotations.

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

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

The description is a single, efficient sentence that front-loads the purpose and includes only essential 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 zero-parameter tool with no output schema, the description sufficiently explains what data is returned and its source, making it complete for an agent to invoke.

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?

There are no parameters, and schema coverage is 100%. The description does not need to add parameter info, so baseline 4 is appropriate.

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

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

The description clearly states the tool lists weapons from VALORANT reference data, specifying the attributes included (category, cost, fire-rate, magazine size). This distinguishes it from sibling tools like list_maps and list_agents.

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 implicitly indicates usage for retrieving static game data without authentication, but does not explicitly state when to use this tool over siblings or when not to use it.

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

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

Beyond the neutral all-false annotations, the description discloses key behavioral traits: rate limit (5 per identifier per day), free usage with no quota impact, account-free submission, claim_token return, and the team's daily digest/review cadence. This adds substantial context beyond the structured annotations.

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

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

The description is dense yet focused. Every sentence contributes: purpose, usage categories, exclusion criteria, reporting rules, claim-token mechanics, quota info, and roadmap impact. No wasted words, well-structured for agent consumption.

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 params, a nested context object, and no output schema, the description covers the full submission lifecycle, the claim_token response/retrieval loop, rate limits, and the relationship to the Pipeworx catalog. It also preempts common mistakes (reporting non-Pipeworx tools, pasting user prompts).

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 documented. The description adds genuine value for claim_token by showing an example invocation and explaining that it reads a previously filed report. It also clarifies that context is optional structured metadata. Slightly more detail on message formatting could push this to 5, but it is already strong.

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

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

The description opens with a specific action ('Tell the Pipeworx team something is broken, missing, or needs to exist') and clearly distinguishes this from sibling tools by naming concrete feedback categories (bug, feature, data_gap, praise) and explicitly excluding feedback for non-Pipeworx MCP servers.

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 provides explicit when-to-use triggers (wrong/stale data, missing tool, positive feedback), a when-not-to-use exclusion (different MCP server), and practical guidance (don't paste user prompt, describe in terms of Pipeworx tools). It also explains the claim_token workflow for following up on filed reports.

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 it as read-only, open-world, idempotent, non-destructive. Description adds valuable context: derived from CF analytics-engine, no PII, caching duration (5min-1h). 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?

Concise, front-loaded with purpose, followed by bulleted use cases and technical details. Every sentence adds value with no redundancy.

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

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

For a simple read-only analytics tool with one optional parameter and no output schema, the description covers purpose, usage, behavioral traits, and data origin. 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 covers window parameter with enum and description. Description adds semantic nuance: 'shorter windows surface what's hot right now; longer windows show steady-state demand,' which 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?

Clearly states it returns top tools, top packs, and total call volume over a recent window. Uses specific verbs and resource, and the use cases distinguish it from sibling tools like discover_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?

Lists three explicit use cases (discovering hot data, confirming canonical tool, aligning use case). Does not provide contraindications or alternatives, but the context is clearly defined.

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 annotations (readOnly, idempotent, openWorld). It reveals internal algorithms (monotonicity violations, Jaccard similarity threshold, placeholder filtering), the fill check mechanism, and trade signals like 'BUY/SELL EVERY LEG'. It warns about conditions under which the tool returns null signals or untradeable results, providing critical behavioral context not available from annotations alone.

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

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

The description is fairly long but every sentence serves a purpose, introducing modes, algorithms, filters, and output format in a logical order. It is not tautological and avoids redundancy. Some minor reorganization could improve readability, but overall it is well-structured and front-loaded with the core purpose.

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

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

For a complex tool with multiple modes and a detailed trade recommendation pipeline, the description covers all necessary aspects: how to invoke each mode, what the response contains (including field names like gap_pp, suggested_trade), fill check behavior, and edge cases like placeholder filtering and low similarity. Despite lacking an output schema, the prose description of the response structure is sufficient for an agent to consume the output correctly.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds significant value by explaining what constitutes a valid event slug ('fed-decision-may-2026') and topic seed question, and that full Polymarket URLs are accepted. It also provides usage examples, which enriches the parameter semantics beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes three distinct call modes (no args, event, topic) and uses specific verbs like 'find', 'scan', 'pass', making it easy for an agent to understand what the tool does and how it differs from siblings like polymarket_edges or polymarket_fill_risk.

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 for each mode: 'Call with NO args for a trending_scan...', 'event (recommended for a specific market)...', 'topic (for cross-event scanning)...'. It also explains when to use event vs topic, and advises against trading when fill check shows unrealizable edge. The mention of 'For custom sizing use polymarket_fill_risk' directly points to an alternative sibling 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 declare readOnlyHint true and destructiveHint false. Description adds extensive detail: explains five model families, caching (1h at KV level), response segments, diagnostics for empty results, and nuance like fed_candidates exclusions. 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 verbose, covering many details in a dense paragraph. While well-structured with sections, it could be more concise. Front-loaded with purpose, but the length may overwhelm agents.

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), description covers output structure, edge metrics, segment definitions, diagnostic fields, and caching. It explains why fed bets are excluded and how to interpret empty results. Very 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% with detailed parameter descriptions. The tool description provides general context (e.g., knobs for tradeability) but does not significantly add meaning beyond the schema. Baseline of 3 is appropriate.

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

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

Description explicitly states scanning Polymarket markets for opportunities where Pipeworx disagrees with market price. Verb is 'scan', resource is 'Polymarket markets', and the unique value (disagreement) is clear. Differentiates from siblings like polymarket_arbitrage by focusing on model-driven edge detection.

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?

States 'Built for "what should I bet on today"' clearly indicating usage context. Describes tradeable-edge knobs for filtering. Although it doesn't explicitly list sibling alternatives, the purpose is specific enough to guide appropriate use.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description details data sources (daily snapshots), response structure (tracked, expired, snapshot_dates), constraints (60-day TTL, decay from daily closes), and behaviors like gaps. 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; it includes a detailed response format and constraints. While well-structured, it could be more concise without losing key information.

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

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

The description comprehensively explains the return format, limits, and data semantics, compensating for the lack of an output schema. It provides sufficient context for an AI agent to understand inputs, outputs, and constraints.

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

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

All two parameters are fully described in the input schema with defaults and ranges. The description repeats this information but adds no semantic value beyond the schema, warranting the baseline score for full coverage.

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

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

The description clearly states the tool tracks edge persistence and decay from daily snapshots, answering how long an edge has existed and whether it is shrinking. It distinguishes from sibling polymarket_edges by focusing on historical trends rather than current edges.

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

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

The description explains the tool's purpose and contrasts trade implications of fresh vs old edges, providing context for when to use it. However, it does not explicitly mention when not to use it or name alternative tools.

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

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

Annotations already indicate readOnlyHint, idempotentHint, and openWorldHint. Description adds rich behavioral context: walks the order ladder, returns specific fields (top_of_book, vwap_fill_price, slippage_pp, etc.), warns about thin legs and forced directional risk from partial basket fills. These details go beyond annotations.

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

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

Description is fairly long but well-structured with labeled sections (SINGLE-MARKET, BASKET) and front-loads the core purpose. Every sentence adds value given the tool's complexity. Minor verbosity but justified.

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 details all return fields for both modes, covers edge cases (thin legs, forced directional risk), and explains the risk of partial fills. Given tool complexity, this is very 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%, but description adds substantial meaning: explains differential interpretation of `size_usd` (spend vs. target proceeds for single-market; settlement notional for basket), auto-detection of `side` in basket mode, and clamping range. This goes beyond the schema's basic descriptions.

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

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

Description clearly states it performs a realizable-vs-theoretical edge check against live CLOB order-book depth, with distinct single-market and basket modes. It explicitly differentiates from sibling tools like polymarket_arbitrage and polymarket_edges by stating when to use it first.

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 before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It explains the required parameter (`market` or `event`) and default behaviors for each mode. The rationale for usage (uncapturable overround, partial fill risk) 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 readOnly, idempotent, non-destructive. Description adds extensive behavioral context: response structure, safety fields (compatibility_warning, temporal_alignment), skipped leg counters, and scenarios where spreads are meaningless. 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?

Well-organized with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS). Every sentence adds unique value. Length is appropriate for the tool's complexity.

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

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

Covers all aspects: modes, safety fields, temporal alignment, compatibility conditions. Despite no output schema, response fields are described sufficiently. Comprehensive for a cross-venue spread tool.

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

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

Schema coverage is 100%, but description adds rich meaning: explains the two modes, provides full list of topic shortcuts, gives example tickers/slugs, and describes how parameters interact. This adds significant 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 computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes from siblings like polymarket_arbitrage by focusing on two-venue 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?

Explicitly describes TWO MODES: topic shortcuts (list of 10) and explicit tickers. Provides guidance on when to use each and warns that most pre-mapped topics return compatibility warnings, telling when not to rely on results.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds value by explaining scoping to identifier and pairing with sibling tools, without contradicting annotations.

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

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

Description is concise and well-structured: states main purpose first, then usage guidelines and scoping, with no unnecessary words.

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

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

Given the rich annotations and schema, the description fully covers purpose, usage, scoping, and relationship to siblings, with 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?

The description clarifies that omitting 'key' lists all keys, adding meaning beyond the schema's property description, and the schema coverage is complete.

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 retrieves a value saved via 'remember' or lists all keys if omitted, distinguishing it from siblings '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 describes when to use (look up stored context) and how to list keys, with mention of pairing with remember/forget, but lacks explicit when-not-to-use scenarios beyond the listing behavior.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds critical behavioral context: the mark_read parameter will flag events as read, affecting future calls ('so the next call only shows newer ones'). It also mentions the GET endpoint alternative, providing additional transparency 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?

Four sentences, front-loaded with the primary purpose. Every sentence adds unique value: purpose, return contents, filtering, mark_read behavior, and a cross-reference to an alternative access method. No filler or repetition.

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

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

For a tool with 5 parameters, no output schema, and moderate annotations, the description explains the return structure, filtering, state-modifying parameter, and polling suitability. It does not list all possible type values or describe the output schema in detail, but given the lack of output schema, the provided fields suffice. The mention of the GET endpoint adds completeness for alternative use cases.

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 5 parameters documented). The description adds meaning by explaining the type filter with an example ('sec_8k'), clarifying that 'since' is an ISO timestamp, and detailing the mark_read behavior. It also notes the return fields ('source, citation_uri... raw event payload'), which complements the schema without repeating 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 uses strong verbs ('Pull fired events') and clearly specifies the resource: 'fired events from your subscription feed.' It details what each alert carries (source, citation_uri, raw payload), making the tool's purpose unambiguous. Among siblings, this is the only alert retrieval tool, so no confusion.

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 usage context: polls work fine, and mentions an alternative GET endpoint for scripts/dashboards. It specifies filtering by type and since, and explains the mark_read flag's effect on subsequent calls. However, it does not explicitly state when not to use this tool or compare it directly to siblings, although siblings are diverse.

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 details about parallel fan-out to multiple sources, GDELT→GNews fallback, and USPTO soft-fail until reactivated, going beyond annotations.

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

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

The description is detailed and front-loaded with examples, but somewhat lengthy. Every sentence is informative, though slight trimming could improve conciseness.

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

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

Despite no output schema, the description explains the return structure (changes[], total_changes, citation URIs). It covers input semantics, behavioral edge cases, and fallback logic, making it 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?

All three parameters are described in the schema (100% coverage). The description adds value by explaining relative shorthand for 'since', example values, and the constraint that 'type' only supports 'company'.

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

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

The description clearly states the tool provides a change feed for a company, covering SEC EDGAR filings, news, and patents. It distinguishes from the sibling tool entity_profile 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?

Explicit examples of when to use (e.g., 'What's new with X') and a clear directive to use entity_profile instead for static profiles regardless of window. Also explains fallback behavior and window formats.

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

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

Annotations indicate idempotent and not destructive. The description adds value by explaining scoping by identifier, persistence differences between authenticated and anonymous sessions, and pairing with recall/forget. No contradictions.

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

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

The description is front-loaded with purpose, followed by usage guidelines, then persistence details, all in a concise 5-sentence paragraph. 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 key-value storage tool with 2 params and no output schema, the description covers purpose, when to use, scoping, persistence, and complementary tools. 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 coverage is 100% with good descriptions for both key and value. The description provides example keys, adding modest context beyond the schema. Baseline is 3.

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

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

The description clearly states the tool saves data for later reuse, with specific examples like 'resolved ticker, target address, user preference'. It distinguishes from siblings recall and forget, and provides concrete use cases.

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

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

The description tells when to use ('when you discover something worth carrying forward') and mentions pairing with recall/forget. It does not explicitly say when not to use, but the context is sufficient for an agent to decide.

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 read-only, open-world, and idempotent behavior, but the description adds significant behavioral context: it cascades through multiple endpoints, degrades gracefully when GLEIF/OpenFIGI are unavailable, explicitly reports unresolved identifiers, and labels each identifier with its source. This goes beyond the structured hints.

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 verbose, especially the long SUPPORTED TYPES paragraph that mixes multiple details in a run-on fashion. It is front-loaded with a clear purpose statement, but the structure could be improved with clearer breaks or bullet points to enhance readability.

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

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

The description covers all critical aspects: inputs, outputs, error handling (unresolved fields), service degradation, internal cascading behavior, and how it relates to other tool needs. Since there is no output schema, this level of detail is both necessary and sufficient for an agent to correctly invoke the tool.

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

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

Even though schema coverage is 100%, the description enriches both parameters with concrete examples (e.g., 'AAPL', '0000320193'), clarifies accepted formats for company vs. drug, and explains how 'value' is interpreted for each type. This adds meaning beyond the schema's basic field descriptions.

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

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

The description clearly states the tool resolves user-spoken names to canonical identifiers, lists supported entity types (company, drug), and specifies the output fields (CIK, ticker, LEI, etc.). It explicitly differentiates itself from sibling tools with '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 provides clear usage context ('Use FIRST whenever you have a name but need an ID') and details the types of inputs accepted for each entity type. However, it does not explicitly name alternative tools or provide 'when not to use' guidance, so it stops short of a 5.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, ensuring safety. Description adds valuable behavioral context: it uses ai_visibility_check as subtool, ranks by score, and returns 'score, confidence, signal density per entity.' 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 three sentences, front-loaded with purpose and mechanism. It efficiently conveys the tool's value and output. Could trim the example quote slightly, but overall very 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 four parameters (one required), no output schema, and rich annotations, description covers purpose, process, output structure, and use case. It mentions subtool and ranking details. Lacks explicit ordering direction (e.g., highest to lowest score), but is largely 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. Description mentions the use of ai_visibility_check but does not add parameter-level meaning beyond schema descriptions. Schema already explains 'entities' first entry is subject, which is adequate.

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 tool compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. Distinguishes from sibling ai_visibility_check (single entity) and compare_entities (generic comparison) by specifying the AI-presence context and ranking mechanism.

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

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

Description provides explicit use case: 'competitive AI-marketing audits: does Claude know about us as well as our competitors?' It implies when to use (multi-entity comparison) and contrasts with single-entity sibling ai_visibility_check. Could be improved by explicitly stating when not to use or naming 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?

Beyond annotations (readOnly, idempotent), description adds critical behavioral details: composite fan-out across two services, bundlephobia's first measurement can take 5-30s, and partial failures degrade gracefully with sources_failed reported. 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 dense but well-structured: it explains what, when, outcome, limitations, and error handling. It is front-loaded with the core function. Could be slightly more concise, but every sentence adds value.

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

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

Given the tool's complexity (multiple sources, partial failures, timeout) and no output schema, the description fully details the output format (summary block, advisories, links, alternatives) and behavior (graceful degradation). No gaps.

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

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

Schema coverage is 100%, so baseline is 3. Description adds minor value by noting scoped packages are accepted, but does not significantly enhance 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 is a composite check for adding npm packages, covering license, advisories, version history, and bundle size. It uses a specific verb 'fan out' and distinguishes from sibling tools, none of which are dependency-related.

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: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also notes NPM-only in v1 and fallback for other ecosystems, providing clear usage 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?

The description adds significant behavioral context beyond the annotations: explains embedding model (BGE-base-en), windowing (500-char overlapping windows), character cap (200K chars with truncation and flagging), and that passages include offsets for verification. 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 concise with no wasted words. It front-loads the core purpose and then efficiently adds technical details. Each sentence earns its place, making it easy for an agent to parse quickly.

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 no output schema, the description fully explains what the tool returns (top-N passages with character offsets and similarity scores). It also covers limitations (200K char cap) and technical details, providing a complete picture for correct usage.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds some context (e.g., truncation for text, examples for query) but does not substantially enhance 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's function: semantic search inside a fetched record. It provides specific examples (SEC 10-K, article) and distinguishes itself by mentioning pairing with ask_pipeworx_grounded, setting it apart 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?

Explicit guidance on when to use: 'when the record is too big to cram into the prompt.' It also suggests pairing with ask_pipeworx_grounded for grounding. Missing explicit when-not-to-use, but context is clear enough.

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

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

Annotations indicate readOnlyHint=false (writes), idempotentHint=true (safe to retry), destructiveHint=false. Description adds critical behavioral details: requires OAuth account, subscription persistence, delivery channels with limits (SMS 10/day), webhook auto-disable after 10 failures, and verification steps. 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 moderately long but efficient. It is front-loaded with the main purpose and prerequisite. Each sentence adds information; however, it could be slightly more streamlined 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?

For a tool with 3 parameters (including nested objects) and no output schema, the description adequately covers purpose, parameters, return value (id), and behavioral notes. It does not detail error handling or idempotency implications, but overall it provides sufficient context for an agent to use 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?

The input schema already has 100% coverage with descriptions for all parameters. The description adds value by providing concrete examples for each type and detailed delivery channel behaviors (e.g., webhook signing). This goes beyond the schema but is not essential given the schema's completeness.

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-resource pair: 'Create a proactive monitoring subscription'. It clearly states the action and return value (subscription id). Supported types and examples differentiate it from sibling tools like unsubscribe and list_subscriptions.

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

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

The description specifies when to use the tool: to create subscriptions for event streams. It states prerequisites (Pipeworx OAuth account) and implies that anonymous users cannot use it. However, it does not explicitly compare with alternatives or provide explicit when-not-to-use guidance.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds value by explaining that the tool returns 'example questions with exact tool + argument shape drawn from the live catalog'. 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 relatively long but well-structured: it starts with alternative phrasings, then explains purpose, return format, and usage. It is front-loaded with core information. A few words could be trimmed, but it remains efficient.

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

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

Given the tool's simplicity (one optional parameter, no output schema, and clear annotations), the description is fully complete. It covers input, output, and use cases 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 coverage is 100% for the single parameter 'topic'. The description adds nuance beyond the schema by listing example values and explaining the default behavior (full spread if omitted). This provides useful context without redundancy.

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

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

The description clearly states the tool's purpose: it returns category-bucketed example questions about what Pipeworx can do, positioning it as the onboarding entry point. It lists a variety of user intents and distinguishes itself from sibling tools by being the first stop for discovering capabilities.

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

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

The description explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools', providing clear context for when to use. However, it does not explicitly state when not to use it or list alternatives, which slightly reduces the score from a 5.

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

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

Describes deactivation (not deletion) and availability via recent_alerts, adding context beyond annotations. No contradiction with annotations.

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

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

Two sentences, no wasted words, front-loaded action and key constraint.

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 single-parameter mutation tool without output schema, the description covers what happens and links to related tool (recent_alerts).

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 id parameter fully (100% coverage). Description adds value by noting id comes from subscribe endpoint, slightly enhancing semantics.

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

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

Clearly states 'Cancel a subscription by id' with verb and resource. Distinguishes from sibling 'subscribe' and 'list_subscriptions' 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?

Explicitly says ownership is enforced, informing when to use. Does not explicitly list alternatives but context with siblings 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 note readOnly/openWorld/idempotent, but the description adds substantial behavioral context: two execution paths, the verdict vocabulary, and critical caller instructions that 'could_not_verify' means the check did not happen and must not be presented as evidence. It also explains the 'unsupported' verdict. This is exactly the kind of disclosure beyond annotations that agents need.

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 dense paragraph but well-organized: example triggers → usage → financial vs. other claims → return values → caveats. Every sentence earns its place. It is slightly long, but the complexity justifies the length, making it a 4 rather than a 5.

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

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

With no output schema, the description carries the burden of explaining return values (verdict, actual value, citation, reasoning) and error semantics. It covers both the financial fast path and the grounded pipeline, explains special verdicts, and tells callers how to handle 'could_not_verify'. This is fully complete for a two-parameter tool.

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

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

Schema coverage is 100%, but the description enriches both parameters. It explains that 'claim' is a natural-language statement and provides examples. For 'tolerance_pct', it adds semantics not in the schema: it overrides the tolerance implied by the claim wording, has a default capped at 5, and suggests 1–2 for hallucination detection. This exceeds the baseline.

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

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

The description clearly identifies the tool as natural-language claim verification against authoritative sources, with specific example phrasings. It distinguishes itself from sibling tools by framing it as a replacement for a multi-step pipeline (NL parsing → entity resolution → data lookup → comparison), even though it doesn't name siblings explicitly.

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

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

Explicit guidance is provided: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates between company-financial claims (SEC EDGAR path) and any other claim (grounded pipeline). However, it doesn't explicitly state when NOT to use this tool relative to alternatives, so it falls short of a 5.

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