TensorFeed

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

With no annotations, the description carries the full burden and delivers: cost semantics (1 credit/$0.02 for days 8-90), tier behavior, and three distinct auth paths (x402 payment via arguments.payment or X-PAYMENT header, Bearer tf_live_ token, or free trial credits). It even discloses the strict HTTP-402 transport endpoint, which is unusually rich operational detail.

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?

Purpose is front-loaded in the first sentence and the rest is dense but relevant. The payment/auth sentences with raw URLs are somewhat sprawling and could be tightened, but no sentence is pure filler.

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

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

For a data-fetch tool with no output schema and no annotations, the description covers purpose, parameters, cost, and authentication thoroughly. It does not describe the shape of the returned series, which is a minor gap given no output schema exists, but all call-critical operational information is present.

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

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

Schema description coverage is 100%, so the schema already documents all five parameters including the days free/paid split, the tier enum, and the payment payload format. The description largely restates these facts rather than adding new semantic meaning, so the baseline 3 applies.

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

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

The description opens with a precise verb+resource+scope: 'Daily benchmark scores for one model+benchmark over a window.' This is specific enough to distinguish it from siblings like compare_models (cross-model) and pricing_series (cost series). It also enumerates the concrete benchmark keys it serves.

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

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

It gives clear decision-relevant context on the free (days 1-7) vs paid (8-90) window and the tier='full' override, which effectively tells the agent when a call incurs cost. However, it never names an alternative tool or states an explicit when-not-to-use condition, so routing guidance is implied rather than stated.

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?

With no annotations, the description carries the full burden and does so well: it discloses rate limits (10 free calls/day per IP), pricing (1 credit / $0.02), the billing rule when tier is omitted, and the two auth paths (x402 payment vs Bearer credits token). It does not explicitly state the operation is read-only or side-effect free, which is the only notable gap.

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 purpose and output are front-loaded, but the final two sentences on payment mechanics are long and partly restate the tier schema description, adding density without new information. A complex tool justifies some length, but this could be tightened.

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?

There is no output schema, so the description must describe returns, and it does: trust band values, 0-100 trust score, and the extra per-signal detail, recommendation, and signed receipt in the full tier. For a payment-gated tool this 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 already 100%, so baseline is 3, but the description adds real meaning beyond the schema by explaining the conditional default for tier (preview unless a payment or credits token is attached) and how payment and benchmark/category interact with the request.

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?

States a specific verb and resource: a signed ruling on whether an AI benchmark is a trustworthy capability signal, returning a trust band and 0-100 trust score. This clearly separates it from siblings like benchmark_series or compare_models, which measure rather than judge.

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

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

Explicitly tells the agent how to scope the call (benchmark to narrow, category to filter, neither for the registry) and how tier selection drives cost and output. It stops short of naming sibling alternatives or stating when NOT to use this tool versus benchmark_series.

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?

No annotations are provided, so the description bears full responsibility for disclosing behavior. It covers: input formats (wallet or token_prefix), output fields (composite, sub-metrics, trust grade, flags, wallet age, etc.), rebuild schedule (daily at 04:50 UTC), data source (TF's own telemetry), and error handling (ok=false for unknown). All critical behavioral aspects are transparent.

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

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

The description is a single paragraph of about 120 words, front-loaded with the primary purpose. Every sentence adds value: input constraints, output details, schedule, error handling, and use cases. There is no wasted text, and the structure is logical and easy to follow.

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

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

Given the absence of an output schema and annotations, the description comprehensively covers all necessary context: input requirements, output structure (listing all major components), data freshness, error behavior, and practical use cases. It leaves no critical gaps for an agent to select and invoke the tool correctly.

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

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

Since the input schema already describes both parameters with 100% coverage (wallet: 'EIP-55 or lowercased EOA wallet address (0x + 40 hex). Mutually exclusive with token_prefix.'; token_prefix: 'First 16 chars of a tf_live_ bearer token (e.g. "tf_live_18e54f47"). Mutually exclusive with wallet.'), the tool description adds minimal additional semantic value beyond restating these details. Baseline 3 is appropriate.

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

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

The description clearly states the tool looks up an agent's public reputation card from the TensorFeed Agent Reputation Bureau. It specifies the input types (wallet or token_prefix) and the output structure in detail. Although it does not explicitly compare to sibling tools, its unique function (agent reputation lookup) is distinct from all listed siblings (e.g., check_ai_supply_chain_risk, get_agent_opportunities), making the purpose unambiguous.

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

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

The description provides explicit use cases: marketplaces routing work, peer agents deciding trust, ops dashboards, and operators inspecting their own reputation. It also clarifies the error handling (ok=false for unknown identities), which helps callers handle failures. However, it does not explicitly state when not to use the tool or mention alternative tools, which would strengthen the 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?

Describes return content (publicly-disclosed malicious packages), matching behavior (case-insensitive substring), and source attribution (GHSA). No annotations provided, but description adequately covers behavioral traits for a read-only tool.

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

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

Well-structured and informative, front-loading purpose. 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?

No output schema, and description does not detail the fields of returned entries (only mentions GHSA source). Adequate for a simple snapshot tool, but could be more complete.

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

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

Schema coverage is 100%, so baseline is 3. Description adds context: ecosystem filter, case-insensitive substring, and behavior when omitted (returns all). Adds usage hint ('before installing').

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 checks a feed for malicious AI-related packages, specifies the feed name (TensorFeed) and ecosystems (npm/PyPI). Distinct from sibling tools like check_agent_reputation.

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

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

Explains when to use with no args (whole snapshot) vs with package_name (check specific package before installing). Lacks explicit when-not or alternative comparisons, but context is clear.

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

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

With no annotations, the description carries the full burden and does so well: it discloses cost (1 credit / $0.02), that payment is per call via x402 wallet or Bearer credits token, the strict HTTP-402 transport endpoint, and a free-trial fallback path. This is exactly the auth, cost, and transport context an agent needs before invoking.

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 cost caveat is correctly front-loaded and the core value proposition follows immediately, but the second half is a dense run-on string of payment alternatives (arguments.payment, X-PAYMENT header, strict 402 URL, Bearer token, trial URL) that is hard to parse and could be structured more cleanly.

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 annotations and no output schema, the description still covers the essentials: what it returns (pricing, benchmarks, status, news, cheapest-blended and per-benchmark rankings) plus cost, auth, and transport. Only the exact return shape and pagination/limits are left implicit, which is acceptable since the return content is enumerated.

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 both parameters are already documented, which sets the baseline at 3. The description restates the 2-5 model range and references arguments.payment but adds no syntax or format detail beyond what the schema examples already give.

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?

States a specific verb and resource: comparing 2-5 models side by side with pricing, benchmarks, status, news and rankings in one call. It clearly differentiates itself as the aggregated multi-model comparison, though it never names the narrower siblings (get_models, benchmark_series, pricing_series) it overlaps with.

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?

Implies when to use it via 'so you choose without scraping each provider', which frames it as the convenience aggregator over per-provider lookups. However, it never states when NOT to use it or which sibling to prefer for a single model, one benchmark series, or a pricing history, leaving the routing choice to inference.

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?

With no annotations, the description carries the full burden and does a good job: it discloses quotas (10 calls/day/IP), per-call cost (1 credit/$0.02), the exclusion of A and any already-failing provider, and the payment/auth mechanics (x402, Bearer token, strict transport, trial credits). It does not state whether the operation is read-only or has side effects, which is the main gap.

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 core purpose is front-loaded in the first two sentences, which are tight and clear. The trailing payment paragraph, however, is long and dense with URLs, token formats and tier edge cases, some of which duplicate the schema and dampen scannability.

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

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

No annotations and no output schema, so the description must cover behavior and returns, and it does: it enumerates what the free preview versus the paid full call yields (candidate detail, ranked alternatives, confirmed incident, signed receipt). Payment and auth paths are fully specified, leaving only return shape and pagination undetailed.

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

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

Schema description coverage is 100%, so the baseline is 3; the schema already documents from, task, tier and payment. The description adds some billing nuance (the tier/payment interaction and the strict x402 transport URL) but largely restates what the schema's field descriptions already cover.

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?

States a specific verb+resource: TensorFeed's signed ruling on the single best operational provider to fail over to, scoped to a task. It also positions itself relative to a sibling by noting it reuses the route-verdict fusion with the degraded provider excluded, so the agent can tell it apart from route_verdict or provider_reliability_verdict.

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 trigger condition is explicit: use it when Provider A is degraded and you need a failover target. It further routes by tier (free preview vs paid full) and explains what each tier returns. It never explicitly names when to prefer a sibling over this tool, so it stops short of 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?

No annotations are provided, so the description carries the full burden, and it does so thoroughly: it discloses the return shape (2-3 endpoints, each with HTTP path, what it returns, free/paid), the auth profile ('Free, no auth'), the access mechanisms for paid endpoints (x402 or credits token), and the scope of the catalog (100+ endpoints, plus unlisted callable tools). These are exactly the traits an agent needs and that no structured field supplies.

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?

Purpose and routing advice are front-loaded, which is correct. The prose is dense and slightly run-on across the access-mechanism clauses, but virtually every sentence earns its place by adding routing or access information rather than restating the schema.

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 discovery tool with no output schema and no annotations, the description is complete: it explains the query input, the number and content of returned matches, and the auth/paid-access model. Nothing an agent needs to invoke it correctly is missing.

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

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

Schema coverage is 100%, so the schema already documents both parameters, making 3 the baseline. The description adds value beyond that by giving concrete query examples ('trending AI papers', 'is OpenAI down', 'model price history') that clarify the intended plain-language input, though it adds nothing further about the limit parameter.

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?

States a specific verb+resource ('Discover which TensorFeed endpoint answers a data need') and immediately distinguishes itself from the many sibling data tools by positioning as the discovery/router layer. An agent can tell what it returns (2-3 best-matching endpoints) without opening the schema, and can tell it apart from the concrete siblings like get_research_papers or status_uptime.

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 the selecting condition: 'Use this first when no dedicated tool obviously fits,' and explains why (core endpoints are dedicated tools, the full catalog is reachable here). This gives a clear when-to-use rule against the concrete alternatives in the sibling list.

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?

Despite no annotations, the description fully discloses behavioral traits: refresh schedule ('Refreshed daily at 13:30 UTC'), data source ('GitHub data via the public Search API'), licensing, and the composite score formula. It also implies read-only behavior without explicit 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 concise (several sentences) but each sentence contributes essential information (frequency, output fields, use cases, license). It could be slightly trimmed without losing clarity, 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 no output schema, the description fully explains the return fields (GitHub repo path, description, stars, last update, source signal, composite score) and the scoring formula. It also covers data source and refresh schedule, leaving no major gaps.

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

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

Schema coverage is 100% with both parameters described. The description adds value by enumerating valid signal values (e.g., 'anthropic-org', 'openai-org') and explaining how the 'limit' parameter caps results (1-25). This goes beyond the schema's minimal description.

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

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

The description clearly states the tool returns a daily scan of new repositories across the AI agent ecosystem. It uses specific verbs ('Get') and identifies the resource ('TensorFeed's daily scan'). The description distinguishes this from sibling tools like 'get_hf_daily_papers' or 'get_models' by focusing on opportunities with composite scores.

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

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

The description provides clear use cases: 'surfacing distribution targets, integration ideas, or just a daily digest'. However, it does not explicitly state when not to use this tool or mention alternative tools for related tasks.

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?

No annotations provided, so description carries full burden. Discloses lazy-fetching, 7-day caching, license terms, and attribution requirement, which are helpful behavioral details.

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 succinct, front-loaded sentences with no wasted words. Every sentence provides essential information: purpose, example, caching, license, and attribution.

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 lookup with one parameter and no output schema, the description adequately covers purpose, caching, and license. It lacks detail about the response format but is otherwise 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 a clear description of the cve_id parameter. The tool description adds an example and mentions case-insensitivity, but this closely mirrors the schema, adding no significant new meaning.

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

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

Description clearly states the action ('look up'), specific resource ('CVE Record v5.2 from the MITRE CVE List'), and provides an example ID. It distinguishes from siblings like get_kev_catalog and get_epss_score.

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 for retrieving a single CVE by ID, but no explicit guidance on when to use this vs. alternatives or prerequisites. Adequate for a simple lookup.

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?

No annotations provided, so description carries burden. It discloses source and license but does not explicitly state that it is read-only or mention any authentication or rate limits. The return values are described, but behavioral traits like non-destructiveness are implied.

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 fluff. Each sentence adds value: first states purpose and source, second describes return and license.

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

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

For a simple tool with one parameter and no output schema, the description adequately covers purpose, return values, and data source. No missing critical context.

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

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

Schema coverage is 100% with a clear description of cve_id. Description adds no extra meaning beyond the schema, 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?

Description uses specific verb 'Get' and identifies the resource as 'EPSS probability for one CVE', sourced from FIRST.org. This clearly distinguishes from siblings like get_cve_record or get_kev_catalog.

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

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

Description states what the tool returns (probability and percentile rank) and implies usage when exploit prediction score is needed. No explicit when-not-to-use or alternatives, but the context is clear.

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

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

With no annotations provided, the description fully carries the burden. It discloses that the tool is read-only (getting catalog), non-destructive, publicly available, and has a defined update frequency. Mentions approximate catalog size ('~1500 actively-exploited CVEs') and license, giving the agent a clear understanding of behavioral traits.

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

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

The description is extremely concise, with three sentences that are front-loaded with the main purpose. Each sentence provides unique value: purpose, behavior (returns N entries + metadata), schedule, license, and scope. No wasted words.

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

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

Given the tool's low complexity (one parameter, no output schema), the description is complete. It covers what the tool does, what it returns, when it is updated, and the data's public domain nature. The agent has enough information to use the tool correctly.

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

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

The input schema has 100% coverage for the only parameter (limit) with a clear description ('Top-N most-recent entries (1-50)'). The tool description does not add any additional meaning beyond what the schema provides, so the 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 'Get the CISA Known Exploited Vulnerabilities (KEV) catalog' with a specific verb and resource. The description distinguishes it from sibling tools like get_cve_record and get_epss_score by focusing on the KEV catalog. It also specifies what is returned (most recent N entries and catalog metadata).

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

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

Provides clear context on when to use (to get recent entries) and includes refresh schedule ('Refreshed daily at 06:30 UTC') and license ('US Government public domain'). It implicitly differentiates from siblings by focusing on actively exploited CVEs, but does not explicitly state when not to use or suggest 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?

No annotations are provided, so the description carries the full burden. It discloses the daily refresh frequency, which adds context, but does not mention authentication, rate limits, or error behavior. For a read-only catalog, this is adequate but not thorough.

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: the first states the core function, the second adds specifics and update frequency. No wasted words, and the key information is front-loaded.

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

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

Given the tool has no parameters, no output schema, and simple functionality (retrieving a catalog), the description fully covers what the tool does, what data it includes, and its freshness. No missing critical aspects.

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

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

The input schema has zero parameters (100% schema description coverage), so the baseline is 4. The description adds meaning by detailing the contents (per-token pricing, context windows, capabilities, deprecation flags), which goes beyond the empty 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 verb 'Get' and the resource 'model pricing and specs catalog across providers'. It lists specific providers and data types, distinguishing it from sibling tools like get_eia_series (energy data) or get_arxiv_recent (papers).

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

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

The description implies usage when model pricing/specs are needed but provides no explicit guidance on when not to use it or alternatives. No exclusions or comparisons to siblings are mentioned.

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?

With no annotations, the description carries full burden. It discloses polling every 10 min, deduplication, sanitization for prompt injection, and return fields. This is comprehensive for a read tool, though no mention of rate limits or authorization.

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, front-loaded with the main purpose, and every sentence adds value without unnecessary words. It uses bullet points effectively to convey key features.

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

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

Given the simple retrieval nature, 2 parameters, and 100% schema coverage, the description is complete. It covers source list, update frequency, data processing, limits, and output schema indirectly, fully meeting the agent's needs.

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

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

Schema coverage is 100% with descriptions provided. The description adds value by giving example categories ('models, business, research') and clarifying the return fields (title, snippet, source, publishedAt), going 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 gets the latest AI news articles from 12+ sources, listing specific providers. It distinguishes from sibling tools like get_ai_trending_papers and get_arxiv_recent by focusing on general AI news aggregation.

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

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

The description implies usage for obtaining AI news but does not explicitly state when to use or not use this tool versus siblings. It provides context (poll frequency, dedup) but lacks direct alternatives or exclusions.

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

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

No annotations provided, so description carries full burden. Only states it returns advisories and the license. No disclosure of rate limits, error handling, auth requirements, or side effects.

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

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

Three concise sentences with no wasted words. Front-loaded with purpose and ecosystem examples.

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

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

No output schema; description does not specify structure of returned advisories (e.g., fields like advisory ID, summary). Tool complexity is low, but completeness is weak for a lookup 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?

Adds value beyond schema by listing example ecosystems and clarifying version as optional. Schema already provides parameter descriptions.

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

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

Clearly states it is a cross-ecosystem vulnerability advisory lookup via OSV.dev, lists supported ecosystems, and specifies inputs and outputs. Distinguishes from sibling get_osv_advisory_by_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?

No explicit guidance on when to use this tool versus alternatives like get_osv_advisory_by_id. Usage is implied but not formally stated.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It goes beyond simple output by detailing each feed's nature (firehose, citation-ranked, editor-curated), refresh times (11:30 UTC, 11:00 UTC, 14:15 UTC), deduplication for trending, and licensing/attribution requirements ('standard attribution block ships on every response'). These are meaningful behavioral traits that help the agent anticipate what the tool actually does.

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 efficiently structured: a clear purpose sentence, then a per-feed breakdown, followed by guidance and licensing. Each sentence serves a purpose, and the layout allows an agent to quickly parse the key distinctions. It earns a 4 rather than 5 because it is somewhat verbose and could be tightened without losing essential information, but it is not wasteful.

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

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

Given the absence of an output schema, the description does a good job of explaining what the tool provides (recent papers, ranked/curated feeds) and the licensing constraints. It covers refresh times, source selection, and attribution. A minor gap is the lack of explicit detail on the return format (e.g., fields like title, abstract, URL), but this is not critical since the description already conveys the tool's purpose and behavior well enough for an agent to invoke it correctly.

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

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

Although schema coverage is 100% and the schema already documents both parameters, the description adds substantial meaning. For the 'source' parameter, it explains that arxiv_recent covers five specific arXiv categories, trending uses 'five fan-out queries' and is 'deduped', and hf_daily includes 'community upvotes and discussion counts' – details absent from the schema. It also reinforces the limit caps by tying them to each source. This significantly enriches the parameter semantics beyond the structured data.

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 'Get recent AI/ML research papers from one of three feeds' with a specific verb and resource. It distinguishes among the three feed options (arxiv_recent, trending, hf_daily) and effectively separates this tool from siblings like get_news_articles or get_cve_record, which serve entirely different domains.

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 usage guidance is provided: 'Pick arxiv_recent for what is brand new, trending for what is influential, hf_daily for what practitioners are discussing.' This tells the agent exactly when to choose each source. It also describes the refresh schedules and feed characteristics, helping the agent decide based on freshness or ranking preferences. Although no sibling exclusions are needed, the guidance is unambiguous and actionable.

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?

With no annotations, the description carries the full burden and does well: it discloses the 2-minute polling cadence, the possible result values (operational | degraded | down | unknown), and the last-checked timestamp, which warns the agent about staleness. It omits auth requirements and error behavior, but the disclosure of freshness semantics is substantive.

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

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

Three tight sentences, front-loaded with the resource and scope, followed by freshness cadence, then the return contract. Every sentence carries information an agent needs.

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

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

No output schema and no annotations exist, so the description must cover the return contract, and it does: enumerated values plus the last-checked field. For a zero-param read tool this is sufficient to invoke and interpret 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?

The tool takes zero parameters, so per the rubric the baseline is 4. The description adds no parameter confusion and the empty schema is self-consistent with the global 'every major AI service' scope.

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?

States a specific verb (Get) and resource (live operational status of every major AI service), and names the concrete set of services tracked, which distinguishes it from siblings like check_free_tier_status or check_ai_supply_chain_risk. An agent can identify the tool's output without opening the schema.

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

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

The description gives clear context (live operational status, polled every 2 min) and points to /api/status for per-component detail, effectively routing granular queries elsewhere. It stops short of explicitly naming when not to use this tool or a sibling alternative.

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?

With no annotations present, the description carries the full burden and does so thoroughly: it discloses the cost model, free vs paid window boundaries, what the paid tier adds, three distinct auth/payment paths (x402 payload, X-PAYMENT header, Bearer token), the strict 402 transport URL, and how to obtain free trial credits.

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?

Purpose is front-loaded in the first sentence, followed by pricing then auth. The payment detail is dense and somewhat verbose, but each clause addresses a real decision the caller must make before invoking, so little is pure waste.

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?

There is no output schema, so the description must fend for itself; it partially covers returns by mentioning the min/max/delta summary and the changes-detected count. Combined with full coverage of the payment gate, an agent has enough to call it correctly, though the exact response shape remains unspecified.

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

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

Schema description coverage is 100%, so the schema already documents all four parameters including ranges, enum, and examples. The description restates the days/tier/payment semantics rather than adding new meaning, so the baseline of 3 applies.

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

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

The opening sentence states a specific resource and scope: 'Daily price points for one AI model over a window.' The 'one AI model' framing distinguishes it from multi-model siblings like compare_models, but it never names or contrasts an actual sibling (e.g. benchmark_series) explicitly, so differentiation is left to inference.

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

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

The description gives strong guidance on payment/auth context (free 1-7 days, paid 8-90, tier='full' forces paid, x402 vs Bearer vs trial credits), but says nothing about when to choose this tool over alternatives such as benchmark_series or compare_models. Usage is implied by the resource, not stated.

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?

With no annotations, the description carries the burden and does so well: it discloses cost (5 credits / $0.10), that it is strict premium with no trial, the two auth paths (x402 wallet payment via arguments.payment or X-PAYMENT header, or a Bearer tf_live_... token), the strict HTTP-402 transport endpoint, and the fallback for obtaining trial credits. What it omits is depth or freshness of the joined data (how much news, what period of agent traffic), and there is no output schema to cover that.

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

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

Front-loaded correctly, with purpose first and payment mechanics second. However, the second half is a dense run-on packing two full URLs, two auth schemes, and a trial-credit recovery path into one sentence, which is harder to scan than a short structured list would be.

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 paid tool with non-standard auth and no output schema, the description covers the essentials an agent needs to call it correctly: price, both payment routes, the 402 failure mode, and an enumeration of returned data. The one gap is return-shape specifics (counts, freshness windows) for the joined sections, which nothing else documents.

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 both parameters are self-documenting, so the baseline is 3. The description's only added detail is that arguments.payment carries the same string as an X-PAYMENT header and is an alternative to the Bearer token, which largely restates the schema; it adds nothing new about the provider identifier beyond the schema's examples.

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?

States precisely what the tool returns for a single provider: live status, every model with pricing/tier/benchmarks joined in, recent news, and agent traffic. It also frames the tool as a bundle that replaces about four separate lookups, which implicitly distinguishes it from narrower siblings like get_models or get_status_summary, though it never names them.

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?

Clear usage context: use this for a comprehensive single-provider snapshot instead of multiple lookups, and it is explicitly premium-only with no free trial. It stops short of naming the cheaper siblings to use when you only need one slice (models, status, news), so the when-not guidance is only implied.

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

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

With no annotations, the description carries the full burden and does so well: it discloses rate limits (10 calls/day per IP on preview), credit cost (1 credit / $0.02), the non-obvious billing rule (omitting tier bills as 'full' when a payment or credits token is attached), the strict x402 transport option, and the trial-credit path. This is exactly the auth, cost, and rate-limit context an agent needs before calling.

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 purpose and scoring rationale are front-loaded before the pricing/auth detail, so an agent gets the 'what' first. It is dense and the auth/payment sentences are lengthy, but nearly every sentence carries decision-relevant information (cost, rate limits, billing edge case) rather than filler.

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

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

There is no output schema, so the description must convey the return content, and it does: the preview yields the top verdict only, full adds the per-provider ranking with measured availability and p50/p95/p99 plus a signed receipt. Combined with complete cost and auth coverage, an agent can call this correctly without further context.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds genuine meaning beyond the schema by spelling out what 'full' unlocks (full per-provider ranking with p50/p95/p99 and tail spread plus a signed receipt) and how 'payment' interacts with the default tier, going past the schema's own phrasing.

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?

States a specific resource and output concretely: 'dependability ruling over its OWN measured latency and availability probes of the frontier AI providers,' returning 'the single most-dependable provider to build on and the riskiest,' with scoring method (p50 over p95). This is far more specific than a generic verdict tool. It does not explicitly name a sibling to differentiate from (e.g. failover_verdict, route_verdict, status_leaderboard), which keeps it at 4 rather than 5.

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

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

The description gives clear guidance on which TIER to use ('preview' free vs 'full' paid) and what each returns, which is valuable. However, it never says when to reach for this tool versus its many similar verdict siblings (failover_verdict, route_verdict, status_leaderboard), so usage relative to alternatives is only implied.

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

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

With no annotations, the description carries the full behavioral burden and does much of it: quota limits, 30-day TTL, one-time secret return that cannot be retrieved again, and the delivery/management model. It omits auth requirements for registration and what happens when quotas are exhausted, but the disclosure of limits and irreversible secret handling is well above average.

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 what-and-why is front-loaded in sentence one, followed by trigger semantics, limits, secret handling, and management in a compact block. Every sentence carries information, though the middle sentences could be tightened slightly.

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 mutation tool with no annotations and no output schema, the definition covers triggers, quotas, TTL, secret lifecycle, and follow-up management paths. It stops short of stating registration prerequisites (auth) or the shape of the registration response, but an agent has enough to invoke it correctly.

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

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

Schema description coverage is 100%, so the schema already documents spec, secret, fire_cap, and callback_url, including detailed spec examples and the SSRF guard. The description only marginally reinforces the one-time secret behavior, adding little beyond the structured fields — the baseline 3 applies.

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

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

The first sentence states a specific verb and resource: 'Register a free webhook subscription.' It immediately distinguishes itself from every sibling tool, none of which deal with webhooks, and the parenthetical list of trigger types (price drop, status change, rank shift, macro threshold) further pins down what the watch does.

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

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

It clearly establishes the context of use (subscribe to push deliveries when a spec fires) and reveals the free-tier constraints (5 watches per IP, 25 fires, 30-day TTL) plus a pointer to management endpoints. It gestures at a paid alternative ('same delivery infrastructure as paid /api/premium/watches') but never states when an agent should prefer the paid path, so the alternative routing is implied rather than explicit.

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

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

With no annotations, the description carries the full burden and does so well: it discloses cost (1 credit / $0.02), rate limits (10 calls/day/IP on preview), billing triggers when a payment or credits token is attached, auth mechanisms (x402 payment, X-PAYMENT header, Bearer tf_live_ token, strict transport URL), and a free-trial path. These are exactly the pre-invocation risks an agent must know.

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?

Purpose is front-loaded and the billing/payment detail, though dense, is invocation-relevant rather than filler. There is some redundancy (preview's freeness and the omitted-tier default are stated twice), and the run-on payment sentence is hard to parse, keeping it below 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 9 parameters, no annotations, and no output schema, the description does the heavy lifting on billing, auth, and tier behavior, and sketches the return (top verdict with reasoning, ranked runners-up, signed receipt). It stops short of fully describing the verdict payload, but covers everything needed to invoke it without surprise charges.

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, but the description adds real value beyond the schema: the cross-parameter default logic (omitting tier yields preview unless a payment/credits token is present, which bills as full) is not derivable from the individual field descriptions. It does not, however, restate or clarify the full-tier-only constraint fields beyond what the schema already says.

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

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

The opening sentence gives a precise verb+resource: a signed model-routing decision returning the single best model for a task or named model, fused from named signals. The scope ('single best') is inherently distinct from siblings like compare_models or failover_verdict, so an agent can route correctly without opening the schema.

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?

Strong guidance on tier selection: preview vs full, what each returns, and the default behavior when tier is omitted. However, it never states when to prefer this tool over sibling verdict tools (compare_models, provider_reliability_verdict, failover_verdict), so the cross-tool selection is left to inference.

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?

With no annotations, the description must disclose behavior. It mentions Lucene-style search and public domain license but lacks details on rate limits, pagination, response format, or query constraints. Adequate but not thorough.

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-loading purpose and form examples. The last sentence is slightly marketing-flavored but still informative. Could be slightly more structured.

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

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

Given 5 parameters, no output schema, and no annotations, the description covers purpose, data source, and form examples. Missing date range syntax details and response structure, but adequate for a search tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by noting Lucene syntax for 'q' and listing example forms, which aids understanding beyond the schema alone.

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

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

The description clearly states it performs full-text search on SEC EDGAR filings since the 1990s, lists specific form types, and targets finance/equity-research agents. It distinguishes itself from siblings like 'get_sec_submissions' (which retrieves specific company submissions) and 'lookup_sec_company_ticker'.

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

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

The description implies usage for financial research but does not explicitly state when not to use or provide comparisons to alternatives. The context from sibling tools helps, but explicit guidelines are absent.

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?

With no annotations, the description carries the full burden and does so: free-tier quota (10 calls/day/IP), exactly what preview omits vs. what full adds (computed decision, envelope, reasoning, KEV cross-check, signed receipt), the billing default trap, both payment transports (x402 wallet payment, Bearer credits token), and a trial-credit path. This is far more than a restatement of the name.

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

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

Front-loaded with the purpose and outcome, then the tier economics, then payment mechanics. Nearly every sentence carries non-schema information (trial URL, strict transport URL), though the pricing/credit figures and tier contents partially duplicate the schema descriptions, adding some redundancy to an already dense block.

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 paid 3-param tool with no output schema and no annotations, the description covers what an agent needs: the required input, the two tiers and their verdict contents, the default/billing edge case, and how to pay. Nothing essential to making a correct call is missing.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the schema: the conditional billing behavior of an omitted tier, and the relationship between the payment argument and the alternate Bearer-token / strict x402 transports. It stops short of syntax examples for the payment payload.

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?

States a specific verb+resource+scope: a signed SSVC patch-urgency decision for one CVE, built on the CISA SSVC Coordinator tree and Vulnrichment decision points. This clearly separates it from siblings like get_cve_record or get_kev_catalog without the agent needing to open a schema.

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?

Gives clear context for when to call it (a single-CVE urgency decision) and exhaustively explains the preview-vs-full choice, including the non-obvious default that omitting tier bills as full when a payment/credits token is attached. It never names a sibling alternative or states when NOT to use this tool, so it falls short of fully explicit routing.

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?

With no annotations, the description carries the full burden and does it well: it discloses the exact gating logic (BLOCK only on an exploited CVE with no fix, HOLD when a known CVE applies, PASS on no match, UNKNOWN outside the cohort), rate limits (10 calls/day/IP), package caps (3 preview, 10 full), billing triggers, and cost ($0.02/credit). This is far beyond what any structured field provides.

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 content is dense and almost every sentence earns its place for a payment-metered tool, but it is delivered as one long unstructured paragraph. Payment mechanics, tier semantics, and gating logic are interleaved rather than sectioned, hurting scannability and front-loading.

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

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

No output schema exists, and the description compensates by describing the return shape: an overall gate plus per-package verdict, with full tier adding matched-CVE evidence (ids, affected ranges, fixed versions, KEV status) and a signed receipt. For a tool of this complexity, an agent has everything needed to invoke it correctly.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds real value beyond the schema: it names the x402 payment transport, the X-PAYMENT header equivalence, the strict HTTP-402 endpoint URL, the Bearer tf_live_ token form, and the trial-credit claim URL. These are operational details the schema does not carry.

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?

States a specific verb+resource: it fuses the ingested AI-stack CVE batch with the CISA KEV catalog to produce a BLOCK/HOLD/PASS/UNKNOWN deploy gate. The scope (AI software stack, per-package name@version) is explicit and clearly separable from siblings like get_cve_record or get_kev_catalog, which supply raw data rather than a fused gate verdict.

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

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

Explains the conditions that select each tier (omit tier → free preview unless a payment/credits token is present; full costs 1 credit and raises the cap). It gives clear operating context but does not explicitly compare itself against the many sibling verdict tools (ssvc_verdict, check_ai_supply_chain_risk), so an agent gets no stated 'use this instead of X' routing.

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?

With no annotations, the description carries the full burden and does so well: it discloses the minute-resolution counter basis (~720 samples/provider/day), which fields require payment, the x402 transport URL, Bearer token alternative, and the free-trial credit path. It is thin only on return shape beyond the two paid fields.

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

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

The core ranking and window semantics are front-loaded, and pricing/auth details follow in the right order. It is dense and packs two long URLs, but each sentence carries operational information an agent needs to call it correctly.

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 paid read tool with no output schema and no annotations, the description covers computation, cost, and auth completely. The remaining gap is the default (unpaid) response fields, which an agent can only partially infer from the incident_count/mttr_minutes mention.

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 schema already documents days, tier, and payment. The description largely restates the same tier/pricing semantics and adds only the note that payment can arrive via arguments.payment or an X-PAYMENT header — useful but marginal, so the baseline 3 applies.

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

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

States a specific verb+resource: a cross-provider uptime leaderboard ranked by uptime % DESC, which is meaningfully distinct from single-provider siblings like status_uptime or get_status_summary. It falls short of a 5 only because it never names the adjacent tools it should be chosen over.

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?

Gives clear conditions for the paid path: days 1-7 free, 8-90 costs 5 credits, and tier='full' forces paid fields at any window. It explains payment/token prerequisites but offers no explicit when-not or sibling routing (e.g., when to use status_uptime 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?

With no annotations, the description carries the full behavioral burden and does so unusually well: exact credit cost ($0.02), the free/paid window boundary, both auth mechanisms (x402 wallet payment via arguments.payment or X-PAYMENT header, plus Bearer tf_live_ token), the strict HTTP-402 transport endpoint, and a free-trial remediation path when no token or USDC exists. Only failure/error behavior is left unstated, which is minor for a pay-per-call read endpoint.

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 paragraph is dense but front-loaded: purpose and returned metrics come first, then pricing, then payment mechanics. Every sentence carries operational weight, though the payment/transport detail is heavy enough that it could be split for scanability.

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 paid x402 endpoint with no annotations and no output schema, the description covers everything needed to invoke it correctly: what it returns, what each window costs, the tier override, both credential forms, and how to obtain credits. Nothing an agent needs before calling is missing.

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

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

Schema description coverage is 100%, and the description largely restates it — the days free/paid boundary, tier='full' semantics, and the payment payload explanation all already appear in the schema. The only added value is the $0.02 price figure; the 'per-incident-day detail' clause is also present in the schema's tier description. Baseline 3 is correct when 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?

It names a specific verb and resource — a daily uptime rollup scoped to one provider over a window — and spells out the exact metrics returned (operational/degraded/down day counts, uptime %, degraded at half credit). The single-provider, day-rollup scope implicitly separates it from aggregate siblings like get_status_summary and status_leaderboard.

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

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

It gives concrete conditions for parameter choice: days 1–7 is free, 8–90 costs 1 credit, and tier='full' forces the paid tier at any window. It also explains the payment paths available. It never names an alternative sibling tool or states when NOT to use this versus them, so it falls short of an explicit when/when-not rubric.

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

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

Discloses anonymity, no PII collection, 30-day expiry, per-IP rate limit (5/24h), and nature as signal collector. No annotations provided, so description carries full burden and does it well.

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

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

Three sentences, front-loaded with main action, no redundant information. Efficient and clear.

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

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

Covers purpose, behavior, constraints, and intent. Could mention expected response or error handling, but not critical for a submission tool.

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

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

Schema has 100% coverage with detailed parameter descriptions. Description adds no extra parameter-level meaning, 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?

First sentence clearly states verb 'submit', resource 'wantlist entry', and purpose 'telling TensorFeed what data you wish was served'. Distinguishes from sibling read-only tools (all 'get_' or 'check_' etc.).

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 usage context: 'Aggregated patterns inform TF's pipeline priorities', 'Signal collector, not a contract', and rate limit. No explicit exclusion or alternative mention, but siblings are all read-only, so this is the only write 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?

Without annotations, the description carries the burden. It discloses the tool is paid, requires credits or payment, supports polling for free unchanged briefs, and returns a signed brief. It does not explicitly state it is a read-only operation, but the context implies it. Could be improved by stating safety characteristics.

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 due to necessary complexity around payment and polling, but it is front-loaded with the premium nature. Every sentence adds value. Minor redundancy could be trimmed, but overall well-structured.

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

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

Given the lack of output schema, the description mentions what the brief includes (headlines, model pricing deltas, incident detail, AFTA-signed) but not the structure. It covers payment, polling, and distinction from siblings well. Lacks explicit response format, but overall 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 coverage is 100%, so baseline is 3. The description adds valuable context beyond the schema, especially for the 'since' parameter (explaining free polling) and 'payment' parameter. This enhances agent understanding of parameter usage.

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

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

The description clearly states it returns the full TensorFeed morning brief with headlines, model pricing deltas, and incident detail. It distinguishes from the free preview sibling. However, it lacks an explicit verb like 'retrieve' or 'get', so purpose is slightly implicit.

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 explains when to use the paid tool versus the free preview sibling, details the polling mechanism with the since parameter to avoid charges for unchanged briefs, and covers all payment options (x402 wallet, Bearer token, free trial credits). This is comprehensive and actionable.

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?

With no annotations, the description carries the full burden and does so unusually well: it discloses rate limits (10 calls/day/IP on preview), cost (1 credit / $0.02), the tier defaulting rule under payment or strict transport, the auth mechanisms (x402 payment, X-PAYMENT header, Bearer credits token), and the trial-credit fallback. The main risk surface of a paid tool is covered.

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

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

Purpose and scope are front-loaded in the first clause, then tiers/pricing, then auth. Sized long for a 3-parameter tool and repeats some tier/payment detail already present in the schema, but nearly every sentence carries substantive, non-filler 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?

There is no output schema, yet the description enumerates the verdict outcomes and specifies exactly what the 'full' tier adds (momentum, shared-wallet risk flag, settlement evidence, AFTA-signed receipt). Combined with the auth and billing paths, an agent has everything needed to select and call it.

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

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

Schema coverage is 100%, so baseline is 3, but the description adds meaning the schema does not: the interaction between omitted tier, attached payment, and strict x402 transport resolving to paid billing, and where the payment payload is sourced. It does partly duplicate the tier description already in the schema.

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

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

States a specific verb+resource pair: a 'signed trust verdict on one x402 publisher, over its OWN on-chain settlement index', and enumerates the exact verdict states (settling, quiet, registered-no-settlement, unreachable, missing Base payTo, not indexed). An agent can immediately tell this is a per-publisher ruling, distinct from the broader settlement tooling in the sibling 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?

Gives clear context: 'Requires a domain', plus the tier decision rules and what each tier returns. However it never explicitly routes against siblings such as x402_settlement_verdict or benchmark_trust_verdict, so the agent must infer this is the single-publisher case.

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

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

Annotations are absent, so the description carries the full burden and does so well: it discloses pricing (10 free calls/day, 1 credit = $0.02), auth mechanisms (x402 wallet payment via arguments.payment or X-PAYMENT header, strict HTTP-402 transport URL, Bearer tf_live_... token), a trial-credit path, and the subtle billing default when tier is omitted. Coverage limits (forward-only from launch, only publishers TensorFeed indexes on Base) are also stated.

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?

Opening sentence front-loads the resource and scope before any billing detail, and most of the length is load-bearing (pricing, auth, coverage limits). It still repeats the tier/payment explanation already present in the schema descriptions, and the final sentence bundles a URL-heavy trial-credit pitch that could be trimmed.

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

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

With no annotations and no output schema, the description covers the essentials an agent needs to call it correctly: what is returned at each tier, the default tier, cost, and both payment paths. The main residual gap is the shape of the returned verdict/receipt fields, which is implied ('headline verdict', 'per-publisher ranking with volume share', 'AFTA-signed receipt') but not specified.

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 tier, window, and payment are already fully documented in the schema; the description's tier/window prose largely restates those semantics. It adds the cross-cutting rule that a payment or credits token silently promotes the tier to 'full', but otherwise is baseline-level against a complete 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?

Names a specific verb+resource (a signed settlement-market ruling over TensorFeed's own on-chain x402/USDC settlement index on Base) and enumerates the dimensions it rules on: momentum vs. prior equal-length window, concentration, and leading publisher. It also scopes coverage ('publishers TensorFeed indexes on Base, forward-only from launch'), which distinguishes it from siblings like x402_publisher_verdict and benchmark_trust_verdict.

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?

Gives explicit conditions for each mode: 'preview' is free with 10 calls/day/IP, 'full' costs 1 credit ($0.02) and adds the ranking/receipt, and the omit-tier auto-billing rule is spelled out. However, it never names an alternative sibling tool or says when to reach for this verdict versus x402_publisher_verdict, so routing between tools is left partly to inference.

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