AgentDiet — Token Diet & Semantic De-bloater

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

No annotations are provided, so the description carries the burden. It usefully discloses the payment requirement (0.01 USDC via x402), which is real behavioral context, but omits auth needs, rate limits, response shape, and failure modes for a runtime-inspection 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?

A single front-loaded sentence that packs the resource, evaluated facets, pricing, and intended timing without filler. Slightly marred by the marketing-style 'Coding Agent' prefix, but no sentence is wasted.

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 one-parameter tool the description covers what is analyzed and the cost, but with no annotations and no output schema it does not describe the returned assessment at all. Adequate but with a clear gap on results and cost/permission behavior.

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

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

Schema description coverage is 100%, and the single 'repo' parameter is fully documented in the schema with an owner/name format and examples. The description adds nothing beyond the schema, 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 ('Audits/Evaluates') and resource ('any GitHub repository') and enumerates the evaluation dimensions: license risk, maintenance velocity, stars/forks, supply-chain security. This is clearly distinguishable from siblings like audit_x402_and_a2a_endpoint or scan_onchain_address_risk, which target different artifacts.

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 phrase 'before code execution' implies a use case, but there is no explicit when/when-not guidance or named alternative among the many audit/recon siblings. Usage is inferable from context 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 behavioral burden, and it does disclose a key trait: the tool costs 0.01 USDC via x402, which is meaningful payment/auth context. Beyond that it says nothing about read-vs-write safety, rate limits, timeouts, or failure behavior, leaving an important gap for an agent deciding whether to invoke it.

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?

A single front-loaded sentence that leads with the label and cost before unpacking the checks; no wasted filler. It is dense but each clause (cost, checks) earns its place, with only a slightly run-on construction holding it back from 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?

For a one-parameter tool with no annotations and no output schema, the description compensates by enumerating the four audit dimensions, effectively signaling what the return covers, and surfaces the payment requirement. A more explicit statement of the 402 payment flow or invocation prerequisites would complete 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?

There is a single parameter with 100% schema description coverage, so the schema already fully documents the 'url' target. The description restates that the URL must be an A2A/MCP/x402 service but adds no format, validation, or scope detail beyond the schema text.

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 ('Audits') and resource ('any remote agent or MCP URL'), then enumerates exactly what is checked: JSON-RPC compliance, latency SLA, OpenAPI schema, and HTTP 402 settlement headers. This distinguishes it from the discovery-oriented sibling discover_and_verify_x402_mcp_tools without opening either 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?

Usage is implied by the enumerated checks and the phrase 'Audits any remote agent or MCP URL', so an agent can infer the trigger condition. However, no explicit when-to-use/when-not guidance is given, and the closely related siblings (discover_and_verify_x402_mcp_tools, recon_webpage_skeleton) are never mentioned as 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?

With no annotations, the description carries the full burden, and it does disclose two useful behavioral traits no structured field provides: a paid cost of 0.01 USDC via x402 and proxy/firewall filtering via Prompt-Guard. However, it omits auth requirements, rate limits, pagination, and what a 'verification' actually returns.

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?

It is a single front-loaded sentence with no filler transitions, which is structurally sound. But it is dense with self-promotional jargon (85% token compression, Swarm Router) that crowds out operationally useful detail, so it is not maximally efficient.

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

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

For a simple one-parameter tool with no output schema, the essentials are present (what it discovers, the cost, the safety layer). It still leaves the result shape of 'verify' ambiguous and never routes the agent against overlapping siblings, so it is only adequate.

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

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

Schema coverage is 100% and the single parameter is fully documented in the schema, including both query and endpoint-URL modes. The description adds no syntax or format meaning beyond that, 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 does state a resource — a meta-router indexing 500+ MCP servers and A2A tools for discovery and verification — but it is buried under marketing jargon ('Universal', 'Safe-Proxy & Swarm Router', 'Prompt-Guard firewall'). It does not clearly distinguish itself from siblings such as audit_x402_and_a2a_endpoint or search_clean_agent_web.

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?

There is no explicit when-to-use statement and no named alternative, despite several overlapping siblings (audit_x402_and_a2a_endpoint, search_clean_agent_web). The only implied guidance ('capability search query or endpoint URL to verify') comes from the schema, not the description.

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. It adds a useful quantitative reduction figure, but discloses nothing about network fetching, auth requirements, error handling, the relationship between the url and optional html inputs, or what each mode outputs.

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 tight sentences with the core effect front-loaded and the mode list appended. No filler; every clause is substantive, though the second sentence could be more informative rather than just enumerating names.

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 5-parameter tool with no annotations and no output schema, the description covers purpose and modes but omits mode semantics, return format, and interaction between url/html. It is minimally adequate but leaves meaningful gaps for correct invocation.

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

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

Schema description coverage is 80%, so the schema already documents url, html, query and max_tokens, and the enum constrains mode. The description restates the mode list but adds no explanation of how the modes differ semantically, matching the baseline 3 where the schema does the heavy lifting.

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

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

The description states a specific verb and effect ('Strips 85-97% of HTML/JS/CSS noise') operating on a URL or HTML string, and enumerates the supported modes. It does not, however, differentiate itself from close siblings like recon_webpage_skeleton or extract_structured_data, so an agent can't fully disambiguate from the description alone.

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?

There is no guidance on when to use this tool versus alternatives such as recon_webpage_skeleton or extract_structured_data, nor on which mode to choose for a given task. The modes are listed but their selection criteria are left entirely 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 present, the description carries the full behavioral burden. It usefully discloses the paid nature of the call ('0.02 USDC via x402'), which is real behavioral context beyond the schema. However, it omits auth/wallet prerequisites, latency, failure modes, and whether enrichment is cached or live.

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?

A single tightly packed sentence with the capability list front-loaded after the label. It is efficient, though the marketing tone ('GTM & research agents', '40+ tech stack signals') leans toward promotion over instruction.

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 one-parameter tool with no output schema and no annotations, the description does cover the output categories the agent will receive. Gaps remain around payment mechanics (wallet balance needed, what happens on failure) and no differentiation from the domain-recon sibling, leaving it only adequately 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% for the single 'domain' parameter, including a format example (stripe.com, linear.app), so the schema does the heavy lifting. The description adds no syntax or format detail beyond that, which is the expected baseline when schema documentation is complete.

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

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

The description names a specific verb-and-resource target (company domain enrichment) and enumerates concrete outputs: verified emails, social links, tech stack signals, pricing tiers, company summary. That is well beyond a restatement of the name. It stops short of distinguishing itself from the overlapping sibling recon_domain_osint, so it does not reach a 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 only usage cue is an audience tag ('for GTM & research agents'), with no when-to-use, when-not-to-use, or named alternative among siblings like recon_domain_osint or verify_email_deliverability. An agent must infer selection from the name alone.

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 behavioral burden. It usefully discloses the cost (0.02 USDC via x402), which is genuine behavioral context, but omits any statement about permissions, rate limits, failure modes for unsupported stores, or what the output looks like.

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?

A single front-loaded sentence with the cost disclosure included inline. Slight redundancy in 'E-Commerce & SaaS Product Catalog / Price Extractor' but no wasted sentences.

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

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

With no output schema and no annotations, the description does describe the return contents (normalized catalogs, prices, variants, availability), which helps. But it leaves gaps around supported store types, error behavior, and payment mechanics that an agent would need for correct invocation.

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

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

Schema description coverage is 100% for the single store_url parameter, so the schema already documents the input. The description adds no syntax, format, or constraint detail beyond implying e-commerce store URLs, so it neither compensates nor detracts.

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 states a specific verb (Extracts) and concrete resources (normalized product catalogs, live prices, variants, availability) from online stores. However, it does not differentiate itself from siblings like extract_structured_data or distill_webpage, so an agent could be unsure when to prefer this tool.

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

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

There is no indication of when to use this tool versus alternatives such as extract_structured_data, distill_webpage, or recon_webpage_skeleton. No prerequisites, exclusions, or store-type conditions are provided beyond the schema's mention of Shopify.

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 but only lists output artifact types. It says nothing about fetching behavior, JS rendering, rate limits, handling of non-HTML or blocked pages, or error cases, and does not state that it is a read-only operation.

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?

A single well-packed sentence, front-loaded with the verb and the highest-value content (the artifact types), with zero filler or redundancy.

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

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

There is no output schema and no annotations, so the description's enumeration of artifact types usefully doubles as a partial return-value contract. However, it omits the fallback behavior when none of those artifacts exist, the role of the optional 'html' parameter, and any error semantics, leaving real 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 description coverage is 100%, so the schema already documents 'url' and the optional 'html' string. The description adds no parameter-level meaning, such as what happens when 'html' is provided alongside 'url' or whether 'html' overrides fetching; 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 names a specific verb (extracts) and enumerates the exact artifact types it returns (JSON-LD schemas, OpenGraph tags, HTML tables as JSON, code blocks) from a webpage URL. That is far more precise than a generic 'extract data' tool, though it never explicitly differentiates itself from siblings like distill_webpage or recon_webpage_skeleton.

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?

There is no when-to-use guidance and no mention of alternatives, despite several sibling tools that also process webpages (distill_webpage, recon_webpage_skeleton, extract_ecommerce_catalog). The description also gives no hint about when to supply the optional 'html' parameter versus letting the tool fetch the URL.

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 it does disclose the key behavioral trait of a 0.01 USDC x402 payment and the shape of the response. However, it omits rate limits, error behavior, and any auth or wallet prerequisites beyond the bare cost mention.

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?

A single efficient sentence with the scope and return contents front-loaded. The marketing-style prefix 'Structured Token-Diet Digest' is slightly noisy but costs little.

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

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

With no output schema, the description compensates by naming the returned artifacts, and the payment model is surfaced. Only pagination/result-limit behavior is unaddressed, which is a minor gap for a 1-param 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% and the single 'query' parameter already documents that it accepts an ArXiv ID (e.g. 2308.11432) or a topic query. The description adds nothing beyond the schema, so baseline 3 applies.

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

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

States a specific verb (Queries) and resource (ArXiv AI/CS/math papers) and enumerates the returned fields (abstracts, authors, PDF links, BibTeX). It is clearly distinguishable from every sibling, none of which do academic literature retrieval.

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 says what it queries but never states when to prefer it over alternatives or any preconditions for use. There is no 'when-not-to-use' or routing guidance, leaving usage entirely 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 disclosure burden, and it does surface the paid x402 mechanism (0.015 USDC) and that the operation is an automated crawl. It omits other behavioral traits an agent would want: crawl depth/limits, latency, idempotency, and failure behavior on large or blocked sites.

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?

A single front-loaded sentence packs the core action, the pricing model, and the output artifacts with no filler. It is dense but coherent, though the parenthetical pricing interrupts the flow 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 one-parameter tool with no output schema, the description is largely sufficient: it names the deliverable artifacts (llms.txt manifest, JSON-LD bundle) so the agent can anticipate the return. The remaining gap is the absence of any note on scope/limits of the crawl, which matters for a network-heavy operation.

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

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

Schema coverage is 100% and the single url parameter is fully documented. The description restates the same crawl-target semantics without adding syntax, format, or constraint detail, so the baseline of 3 is appropriate.

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

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

States specific verbs (crawls, compiles) and concrete artifacts (/llms.txt manifest, JSON-LD schema bundle) with a clear resource scope, so an agent immediately knows what the tool produces. No sibling tool generates llms.txt manifests, so it is easily differentiated from the web-scraping alternatives like distill_webpage or extract_structured_data.

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

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

The description implies context ('Crawls any documentation or SaaS site') but never states when to choose this over siblings such as distill_webpage or recon_webpage_skeleton, nor any prerequisites or exclusions. Usage is only inferable from the purpose statement.

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. 'Returns' implies a read-only, non-mutating operation and 'the calling agent's' clarifies it reports the caller's own wallet, which is useful behavioral context, but it omits any statement about side effects, auth requirements, rate limits, or freshness of the quota/balance figures.

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?

One front-loaded sentence with zero filler; the returned fields are enumerated immediately and nothing is wasted.

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

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

For a simple read tool with no output schema, the description covers the key return values (quota, balance, tokens saved) and the absence of annotations is acceptable for a pure read. The only real gap is the undocumented agent_id parameter and the missing note on when to 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 description coverage is 0% and the description never mentions the single agent_id parameter. Worse, 'the calling agent's' suggests identity comes from context/auth, which leaves the purpose of agent_id (optional override? ignored?) unresolved rather than clarified.

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 ('Returns') and resource ('the calling agent's remaining free quota, USD balance, and total tokens saved'), enumerating the exact data returned. It is clearly distinguishable from the unrelated sibling tools (github audit, domain recon, etc.), though it does not explicitly reference any sibling.

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?

There is no when-to-use guidance, no prerequisites, and no mention of alternatives. An agent can infer this is a self-status check, but the description never says when to call it (e.g., before paid operations) or whether it is safe to poll.

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 it does disclose one important trait: a 0.015 USDC x402 payment requirement. It does not cover rate limits, whether the call can fail for unsupported chains, or the freshness/latency of the 'live' data, so disclosure is partial rather than rich.

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?

A single dense sentence that front-loads the resource category and enumerates the returned metrics. It is efficient with no wasted clauses, though packing the pricing and source into one line makes it slightly crowded.

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 one-parameter tool with no output schema and no annotations, the description compensates by enumerating the returned fields (liquidity, FDV, volume, orderflow) and the payment cost. Chain support is only implicit via the parameter text, leaving a minor gap.

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

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

Schema description coverage is 100% for the single `token` parameter, so the baseline is 3. The description adds only the DexScreener data source, which is not in the schema, but contributes no format, chain, or validity nuance beyond what the parameter description already states.

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 states a specific verb and resource: it fetches live DexScreener liquidity pools, FDV, 24h volume, and 1h buy/sell orderflow for a token. This is clearly distinguishable from siblings like scan_onchain_address_risk or get_public_company_sec_intel, though it never names a sibling to sharpen the contrast.

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?

Usage is only implied by the content (get DEX market metrics for a token address/symbol). There is no explicit when-to-use statement, no prerequisites, and no guidance on when to prefer a sibling tool such as scan_onchain_address_risk. Adequate but leaves routing 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 behavioral burden, and it does disclose a genuinely important trait the schema cannot: the call costs 0.02 USDC via x402, so the agent knows this is a paid endpoint. However it omits other relevant traits such as whether an invalid ticker errors or returns empty, rate limits, caching of filings, or data freshness.

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?

One dense sentence that front-loads the domain and the covered artifact types before the pricing and resolution details. Every clause earns its place, though the parenthetical price fragment slightly interrupts the main clause flow.

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 single-parameter tool with no output schema and no annotations, the description does state what is returned (CIK, recent filings, entity metadata), which covers the essentials. It stops short of describing response shape, filing lookback window, or error behavior for unrecognized tickers, which an agent would want before spending USDC.

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

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

Schema coverage is 100% and the single ticker parameter is already documented with examples (NVDA, MSFT, AAPL, PLTR). The description's '10,000+ US public stock tickers' adds scope context about valid inputs but no syntax or format detail beyond the schema, 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 names a specific domain (SEC EDGAR), specific artifacts (10-K/10-Q/8-K filings, CIK numbers, entity metadata), and the action performed (resolves tickers to CIK and filings). No sibling tool touches SEC EDGAR data, so an agent can route to it unambiguously 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?

Usage is only implied: the tool clearly applies when you have a US public stock ticker and want SEC filings, but there is no explicit when-to-use statement, no exclusions (e.g. private companies, non-US listings), and no named alternative. Adequate but leaves the boundary conditions 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. It does disclose two useful behavioral traits: the operation is a live network inspection, and it requires payment of 0.01 USDC via x402. It does not mention rate limits, failure modes, or auth beyond the payment hint, so it is adequate but incomplete.

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?

One compact sentence with the inspected checks front-loaded after the colon; every clause earns its place. The parenthetical cost is tucked in but does not obscure the primary purpose.

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

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

With no output schema and no annotations, the description should ideally convey what the agent receives back, such as grade format or raw JSON. It names the inspection targets but leaves the return shape and payment flow mechanics underspecified for a paid recon tool.

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

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

Schema description coverage is 100% and the single 'domain' parameter is fully documented in the schema, so the baseline is 3. The description adds only the target scope (DNS/TLS/security header OSINT), not format or syntax details 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 pairs a specific verb ('Inspects') with a precisely enumerated resource set: live IPv4/IPv6 DNS resolution, TLS certificate issuer/expiry, and HTTP security grade. This clearly distinguishes it from adjacent siblings like enrich_company_domain and recon_webpage_skeleton, which operate at different scopes.

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

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

Usage is implied by the recon framing and the enumerated checks, so an agent can infer this is for a domain-level security/DNS audit. However, it offers no explicit when-to-use, when-not, or named alternative against the many sibling recon/enrichment tools.

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

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

No annotations, so the description carries the full burden. It discloses the output shape (structural outline, headings, token counts) and the lightweight nature (<200 tokens), which is genuinely useful. It does not explain fetching behavior, failure modes, or how the optional html parameter changes behavior.

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

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

A single tight sentence with the value proposition and the <200-token constraint front-loaded. No filler. Slightly dense but every clause earns its place.

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

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

With no output schema, the description takes on return-value duty and does describe the outline, headings, and token counts. For a simple 2-param read tool this is close to sufficient; only the html-vs-url interaction and safety profile remain unstated.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds no parameter-level meaning: it doesn't say when to supply raw 'html' instead of a 'url', nor whether 'url' triggers a fetch. No lift over 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 and resource: 'Returns a <200-token structural outline of any webpage with section headings and token counts.' This is concrete and distinguishable from a full-content tool. However, it never names the obvious sibling (distill_webpage), so the agent must infer the boundary.

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?

'before committing context window budget' implies a recon-first workflow, which is useful usage signaling. But there are no explicit when-to-use/when-not rules and no named alternative, so the choice between this and distill_webpage 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 carries the full burden. It usefully discloses determinism and, importantly, a payment gate (0.002 USDC via x402), which an agent must know before invoking. However it omits failure behavior on unrepairable input and does not state what the tool returns, leaving significant behavioral gaps.

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?

One front-loaded sentence that leads with the capability and the input types. Some marketing residue ('so multi-agent pipelines never crash') and the pricing parenthetical add noise, but nothing is redundant enough to obstruct scanning.

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

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

There is no output schema, so the description should ideally say what a successful call returns (the repaired JSON string?) and what happens when repair is impossible. Payment and determinism are covered, but the return contract and failure semantics are not, leaving the definition only partially complete for a zero-annotation tool.

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

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

The single parameter has 100% schema description coverage, so baseline is 3. The description goes further by enumerating the malformation classes it accepts (trailing commas, markdown fences, single quotes), which sharpens the agent's understanding of what belongs in raw_json.

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 names a concrete verb (repairs) and resource (truncated/malformed LLM JSON outputs) and gives concrete failure examples (trailing commas, markdown fences, single quotes). It is clearly distinguishable from all listed siblings, none of which touch JSON repair. The 'Schema Validator' label is slightly muddled because no schema parameter exists, but the core purpose is 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?

Usage context is implied rather than stated: repairing LLM-generated JSON inside multi-agent pipelines. There is no explicit when-not guidance (e.g., already-valid JSON, non-JSON payloads) and no named alternative among siblings. Adequate but leaves the agent to infer the trigger condition.

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 it does disclose two meaningful traits: a cost (0.02 USDC) and a payment mechanism (x402). However, it omits auth/allowance requirements, failure behavior for invalid addresses, latency, and whether the scan is read-only versus state-changing, leaving real behavioral gaps.

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?

A single dense sentence that front-loads the tool's identity and the payment model before the check list. Parenthetical stacking of cost and chain qualifiers makes it slightly harder to parse than a clean 5, but there is no wasted clause.

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 one-parameter tool with no annotations and no output schema, the description should explain at least the shape of the result (risk verdict, flags found, confidence). It covers inputs and payment but says nothing about what the agent receives back, so an agent cannot set expectations for the response.

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 single parameter already documents the accepted 0x/Base58 formats. The description restates the same address formats without adding syntax, validation, or chain-selection nuance, 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?

States a specific verb (Audits/Scans) and resource (Base EVM 0x contract or Solana SPL Mint), and enumerates the exact checks performed: proxy patterns, GoPlus honeypot flags, freeze authority. This is clearly distinguishable from siblings like get_dex_token_intelligence or audit_github_repository.

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 implicitly scopes usage by naming the accepted inputs (Base EVM 0x contract or Solana SPL Mint), but never states when to prefer this over alternatives, nor any prerequisites beyond payment. Usage must be inferred rather than read.

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 present, so the description must carry the full behavioral burden. It discloses the payment requirement (0.005 USDC via x402) and that it returns sanitized text, but omits permissions, side effects, rate limits, and what sanitization entails. For a security scanner with zero annotation coverage, this is a moderate 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?

A single, front-loaded sentence that names the tool, price, action, input types, and output without wasted words. The parenthetical price is integrated efficiently.

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

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

With no output schema, the description supplies the return type ('sanitized text'), and it states the cost. Given one required parameter and no annotations, this covers the essentials for invocation, though it could clarify safety profile or response structure.

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

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

The schema documents the single 'text' parameter at 100% coverage. The description adds source examples (web pages, emails, MCP responses, A2A payloads) beyond the schema's 'text, HTML, or tool output', providing modest extra semantic context, though it does not specify format constraints.

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 ('Scans') and resources ('untrusted web pages, emails, MCP responses, or A2A payloads') and specifies the output ('returns sanitized text'). It is clearly distinguishable from all listed siblings, none of which perform prompt-injection scanning.

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?

Establishes context by listing the categories of untrusted input it handles, so an agent knows to use it when it encounters such content. However, it does not name alternatives or state when not to use it, and omits prerequisites like the x402 payment requirement implied by the parenthetical cost.

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 it usefully discloses the data sources (Google News RSS + HackerNews Algolia), the paid cost model (0.01 USDC via x402), and that results are token-pruned. However, it omits auth requirements, rate limits, result limits, and error/failure behavior, leaving meaningful gaps.

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?

It is a single front-loaded sentence, but a notable share of it is marketing filler ('Zero-Bloat', 'zero ad bloat') that repeats the same claim twice, slightly diluting the useful signal about sources and cost.

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

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

For a simple one-parameter search tool with no output schema, the description covers the key facts an agent needs: what it searches, where results come from, that it costs USDC via x402, and that returns are clean/pruned. Only result format and volume details are left 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 coverage is 100% and the single 'query' parameter is already documented as a search query for live news, technical articles, and web results. The description adds no syntax, format, or operator guidance beyond the schema, so the 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 states a specific action (executes live Google News RSS + HackerNews Algolia search queries) and the nature of the output (clean, token-pruned results). It distinguishes itself from generic siblings like distill_webpage or recon_webpage_skeleton by specifying live web/tech-news grounding, though it does not name any sibling directly.

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?

Usage context is implied by 'Live Web Search & Tech News Grounding,' suggesting it is for current news/tech lookups, but there is no explicit when-to-use, when-not-to-use, or routing to alternatives like distill_webpage or fetch_arxiv_research_digest.

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 it does disclose two useful traits: the x402 payment requirement (0.005 USDC) and that checks are 'live DNS' lookups, implying network calls and latency. It does not cover rate limits, error behavior, or whether disposable results are boolean vs. scored, so the disclosure is partial.

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?

One efficient sentence with the check list front-loaded after the naming/pricing prefix. The 'B2A Email Deliverability, MX, SPF & DMARC Verifier' label is somewhat redundant with the following 'Validates ... MX/SPF/DMARC', but overall the text is tight.

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, but enumerating the four check categories effectively tells the agent what results to expect, and the payment mechanism is disclosed. Missing details such as result format or latency keep it shy of a 5, but it is adequate for a single-parameter verification tool.

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

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

Schema coverage is 100% and the single parameter is already documented in the schema as the email to validate. The description adds only marginal meaning (role-account and SPF/DMARC checks not mentioned in the schema), which is the expected baseline 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?

The description pairs a specific verb ('Validates') with a precise resource ('email') and enumerates the exact checks performed: syntax, disposable/burner status, role accounts, and live MX/SPF/DMARC records. No sibling tool overlaps with email deliverability verification, so the agent can select it unambiguously.

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?

Usage is implied (validate an address before relying on it), but the description never states when to reach for this tool, whether it is safe for bulk use, or any prerequisites. Since no sibling competes for the same job, the absence of alternatives is less costly, but guidance remains inferred rather than stated.

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