x402-gateway-mcp

Server Quality Checklist

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Latest release: v0.2.0
Disambiguation5/5
Each tool targets a distinct domain or operation. For example, crypto_gas vs. crypto_price vs. defi_tvl are clearly different; dev_npm and dev_pypi differ by ecosystem; v1_preflight, v1_endpoint_scorecard, and v1_run serve unique purposes. No two tools have overlapping functionality.
Naming Consistency4/5
Most tools follow a descriptive pattern (domain_verb or domain_noun) like crypto_price, defi_tvl, web_extract. The v1_* and x402_market_pulse tools use a different prefix but are consistent within their group. Overall, naming is mostly predictable with minor deviations.
Tool Count5/5
23 tools is well-scoped for a multi-purpose gateway. The count covers a wide range of data and utility services without being overwhelming or insufficient. Each tool serves a clear purpose and earns its place.
Completeness4/5
The tool surface covers diverse domains (crypto, DeFi, packages, domains, weather, etc.) with good depth. Minor gaps exist, such as lack of historical price data for crypto or a token transfer tool, but these are not critical for the gateway's stated purpose. Overall, the set is reasonably complete.
Average 4.4/5 across 23 of 23 tools scored. Lowest: 3.9/5.
See the Tool Scores section below for per-tool breakdowns.
This repository is archived. Archived repositories automatically receive an F maintenance tier.
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It clearly states the tool performs deterministic reads from archived history with no upstream calls, and notes the cost per call. This is good disclosure, though it could mention error handling or permission needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with cost and purpose, followed by technical details and behavioral info. It is informative but slightly verbose; the cost note could be placed elsewhere. Overall, it is well-structured and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description should explain return values. It mentions 'time series' and sampling frequency but does not describe the output format (e.g., data structure, fields). Error handling and edge cases are also absent. The description is adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds significant value beyond the schema: it provides example values (Data, Inference, Search), explains the meaning of 'all' for catalog totals, and references the taxonomy endpoint for the full list. The days parameter is described as a 1-30 window.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves the x402 ecosystem listing trend as a time series per category or total. It specifies the data source and sampling frequency. However, it does not explicitly distinguish itself from the sibling tool 'x402_market_pulse', which is mentioned as a free snapshot, leaving some ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides query parameters (category and days) and examples, implicitly guiding usage. However, it does not specify when to use this tool versus alternatives like 'x402_market_pulse' or 'v1_category_breakdown', nor does it include when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It discloses cost ($0.05/call or metered), model (Claude Haiku 4.5), payment method (wallet), data handling (no storage), AI lane declaration, and spend caps. It does not detail rate limits or response structure beyond printing tokens and costs, but overall it is transparent and exceeds minimal expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph but front-loads the important cost detail. It covers pricing, model, data handling, and lane declaration without being excessively verbose. Some repetition (e.g., mentioning flat fee twice) could be tightened, but overall it is well-structured and earns its sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, no output schema, and no annotations, the description compensates well by explaining the payment model, model identity, data retention policy, and the presence of spend caps. It does not describe the exact output format beyond listing tokens, cost, margin, and charge, but this is sufficient for most use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 minimal extra meaning: it clarifies that the prompt is sent as a single user message and mentions the default of 256 (already in schema). The cost and model information are not parameter-specific. The added value is marginal.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs an LLM prompt over x402, specifying the model, cost structure, and that it returns raw model output. It effectively differentiates from sibling tools, which are all unrelated (crypto, weather, knowledge, etc.), so the 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.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus others. While the sibling tools are diverse, the description lacks direct guidance on when to use or avoid this tool, such as mentioning that it is only for single prompts with a fixed model and no streaming. The context of paying with a wallet is clear but not comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description adds important behavioral traits: cost per call ($0.01 USDC), caching up to 30 seconds, and that it reads via JSON-RPC directly from chains. It does not cover error handling or authentication, but it provides good transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with cost information. Each sentence adds value, though it could be slightly more structured. Overall, it is appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple 2-parameter tool with no output schema, the description covers expected returns, caching, and cost. Missing error handling, but completeness is good for the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description does not add significant meaning beyond the schema. The schema already defines chain as enum and poolAddress with a pattern. The description's mention of parameters is incidental.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads live on-chain state of Uniswap v2/v3-compatible DEX pools, with specific details on auto-detecting pool type and returning token data, spot price, liquidity, etc. It distinguishes well from siblings like crypto_price and defi_tvl.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly mentions use cases: 'For price discovery, liquidity checks, and trading agents needing trustless pair state at the latest block.' It implies when to use but does not explicitly mention exclusions or alternatives, though context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It does: cost ($0.002 per call), resolver, return format (name, type code, TTL, data), NXDOMAIN behavior (unpaid 404), and caching (5 minutes). Missing details like rate limits or authentication, but the provided information is comprehensive for selection.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is packed with useful information and is front-loaded with cost and core action. Each sentence adds value, though the first two sentences overlap slightly. Still efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and annotations, the description adequately covers return format, caching, cost, and error behavior. It explains what the tool does and its response. Minor missing details like exact JSON structure or timeouts, but sufficient for a simple DNS lookup tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 no extra meaning beyond the schema: 'name' example and 'type' enumeration are already in schema. No additional syntax or format details are provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it resolves DNS records via DNS-over-HTTPS with DNSSEC validation. It lists supported record types (A, AAAA, etc.) and specifies the resolver (Cloudflare 1.1.1.1). The name 'network_dns' is self-explanatory and distinct from siblings like domains_whois (whois) and report_domain (DNS report). This is a specific verb+resource combination.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists use cases: infrastructure checks, email deliverability, domain verification, and security tooling. It mentions caching and cost, providing context for when to use. While it does not explicitly state when not to use or name alternatives, the sibling tools are clearly different, so no confusion arises.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It discloses the cost ($0.005 USDC per call), upstream latency (~10s), caching behavior (6-hour cache), and a response field (meta.fetchedAt). However, it does not explicitly state that the tool is read-only or non-destructive, which is implied but not confirmed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph that front-loads cost and purpose, then adds details and use cases. It is reasonably concise, though the list of use cases could be streamlined as it partially repeats the purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool without an output schema, the description covers the data source, output content (yields for 13 maturities, curve date), caching, cost, and use cases. It is sufficient for an agent to understand what the tool returns, though listing the exact maturities would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description adds value by explaining what the output contains (13 maturities, curve date) beyond the empty schema, partially compensating for the lack of an output schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves the latest US Treasury daily par yield curve for maturities 1M-30Y, specifying the data source (US Department of the Treasury) and scope (13 maturities). This distinguishes it from sibling tools like crypto_gas or fx, which cover different domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lists use cases (interest rate benchmarks, bond analysis, etc.) but does not explicitly state when to use this tool versus alternatives or provide any exclusion criteria. While sibling tools are distinct, no comparative guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears the full burden. It discloses cost ($0.01 USDC), probing behavior (SSRF-guarded, single probe), scoring rubric (starting at 50 with adjustments), and output nature ('Raw signals in every response; auditable. Not an endorsement.'). It does not mention side effects beyond cost, but is otherwise thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (under 100 words) and front-loaded with the main purpose. However, the rubric detail is somewhat dense and could be structured more clearly. Still, every sentence adds value and there is no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter, no output schema, and no annotations, the description covers all necessary aspects: purpose, cost, behavior, rubric, and output nature. There are no missing critical details for an AI agent to use this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 minimal extra meaning: it indicates the URL should be 'exactly as advertised', but the schema already has a pattern and description. The single parameter is well-defined, so no need for additional explanation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Score an x402 resource URL before paying it: reachability, terms, catalog presence, age.' The verb 'score' and resource 'x402 resource URL' are specific, and the tool is clearly distinct from siblings (no other tool does preflight assessment).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies when to use: 'before an agent pays it' and describes it as a 'spend-guard preflight'. It implies the tool is for evaluating resource URLs before payment. While it doesn't explicitly state when not to use or name alternatives, the usage context is clear and the tool is unique among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Reveals cost ($0.003/call), caching up to 10 minutes, shared cache for nearby coordinates, and public domain data. No annotations provided so description carries full burden; these disclosures are valuable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with cost and purpose. Every sentence adds value; no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Explains return values (temperature, wind, forecast text, place name) despite no output schema. Also covers caching, cost, and coverage boundaries. Could mention units but complete enough for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% coverage with descriptions for lat/lon. Description adds context that coordinates must be US-based but does not add detail beyond schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Describes getting official US NWS forecast for coordinates, specifying output includes next four periods with temperature, wind, and detailed text. Clearly distinguishes from sibling tools (none weather-related).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
States usage for logistics, travel, event-planning. Explicitly notes US-only coverage and that non-US points return 404. No alternative tools mentioned but sibling list shows no weather tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes the tool as a read-only operation that refreshes ~3x/day, sourced from Stride20k, with no payment required. No contradictions with annotations (none provided); adds context beyond basic schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single paragraph front-loads key benefit (free, no wallet needed), lists data types concisely, and includes technical details (refresh rate, source, endpoint). Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description covers essential behavioral aspects (what data, how fresh, source). Could optionally mention return format, but not necessary for invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so baseline score of 4 applies. Description adds no parameter info but is not needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it provides a 'live x402 market feed for agents' and enumerates specific data categories (ecosystem snapshot, service listings, deltas, npm trends, protocol releases), making it distinct from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly notes it is free and requires no wallet, which implies usage when no financial transaction is needed, but does not provide guidance on when to use this tool versus alternatives like crypto_price or news_tech_pulse.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses cost, caching, attribution, and content license. No annotation provided, so description carries full burden. Missing rate limits or error details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Packed with useful info, front-loaded. Slightly verbose but each sentence adds value. Could be trimmed slightly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Describes return fields, licensing, caching, and attribution. No output schema, so description compensates well. Lacks error handling details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema provides basic description; description adds case-sensitivity and space/underscore flexibility. Adds meaningful value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Get the English Wikipedia summary for a topic' and lists response components. Distinct from sibling tools which cover unrelated domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit use cases ('knowledge lookup, entity enrichment, fact grounding') but no direct mention of alternatives or when not to use. Implicitly clear due to sibling diversity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description fully discloses behavioral traits: cost per call ($0.002 USDC), caching up to 15 seconds, direct JSON-RPC node reads with automatic fallbacks, and details of returned data (base fee, percentile priority fees). This is comprehensive 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with purpose and cost. Each sentence provides valuable information, though it could be slightly more concise by combining some points. Still efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description sufficiently describes the return values (gas price, base fee, percentile priority fees in gwei) and operational details (caching, fallbacks). The single parameter is well-covered. The tool is complete for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters with the enum values and description 'Chain to query.' The description reinforces the valid chains and adds context (EVM chain, three options), but does not add significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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 'current gas prices and EIP-1559 fee estimates for an EVM chain', and specifies the supported chains (Ethereum, Base, Polygon). It is distinct from sibling tools like crypto_price and defi_tvl.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states use cases: 'Use for transaction cost estimation, fee optimization, and timing on-chain actions.' Also provides context on data source and caching, but does not mention alternatives or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses cost ($0.003 USDC per call), caching behavior (up to 1 hour), normalization (PEP 503), and full list of returned data fields. Since no annotations provided, description fully covers 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is somewhat long but every sentence adds value: cost, purpose, use cases, data returned, caching. Front-loaded with cost and main purpose. Minor redundancy possible but still efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
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, description covers cost, caching, normalization, use cases, and return fields. Fully informs an AI agent about what the tool does and how to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% but description for packageName repeats schema description ('PyPI package name (PEP 503 normalized, e.g. 'requests', 'scikit-learn').') without adding further meaning beyond what schema already provides. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description explicitly states 'Check a PyPI package's version freshness, yank status, and popularity.' with specific verb 'Check' and resource 'PyPI package'. Clearly distinguishes from sibling dev_npm by specifying PyPI.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists use cases: 'auditing requirements.txt and pyproject.toml, supply-chain security checks, and version freshness agents.' Also mentions caching. No explicit when-not-to-use, but context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses cost ($0.005 per call), data source (authoritative registry's RDAP server), caching (up to 24 hours), and error behavior (unpaid 404 for unregistered domains). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with cost and purpose, then lists data fields, use cases, and limitations. While informative, it is somewhat verbose and could be more concise without losing value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple parameter structure, lack of output schema, and no annotations, the description covers input requirements, behavior, use cases, limitations, and caching. It is fully self-contained and complete for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'domain' is fully described in the input schema (pattern, description). The tool description adds an example ('example.com') but no additional semantic value beyond the schema. With 100% schema coverage, baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it retrieves normalized WHOIS/RDAP data for a domain, listing specific data points (registration/expiry/update dates, registrar, EPP status codes, nameservers, DNSSEC) and TLD coverage (all gTLDs, most ccTLDs). This clearly differentiates it from sibling tools, none of which are domain-related.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description enumerates specific use cases (domain availability checks, ownership research, expiry monitoring, security screening) and notes limitations (unregistered or RDAP-less domains return a 404). However, it does not differentiate from the sibling tool 'report_domain', which may serve a similar purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description fully discloses behavior: cost ($0.15 per call), deterministic and auditable nature, rubric details (start at 50, adjustments for bytecode, price, verification, supply, decimals, age), and disclaimers (NOT financial advice). No annotations provided, so description carries full burden and excels.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single well-structured paragraph that front-loads the cost and main purpose. It includes detailed rubric information that is valuable but could be slightly more concise. Overall, it is efficient and informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description clearly states the return values (score, flags[], every rubric input) and explains the rubric and inputs. For a tool with 2 parameters, this is very complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for both 'chain' (enum) and 'address' (pattern). The description does not add additional parameter semantics 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool gets a deterministic, auditable risk score (0-100) for an ERC-20 token on Ethereum, Base, or Polygon. The verb and resource are clear, and the scope distinguishes it from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use (when needing a token risk score) but does not explicitly mention alternatives or when not to use. Given the unique sibling tools, the context is sufficient for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses cost per call, caching behavior (up to 5 minutes), data freshness indicator (meta.fetchedAt), and that it aggregates across chains. No annotations, so description fully carries burden.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise at ~100 words, front-loaded with purpose. Includes key details (cost, caching) without excessive verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Simple tool with one parameter. Describes return value (TVL in USD) and freshness indicator. No output schema, but explanation suffices for agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage 100% with one parameter. Description adds value by specifying slug source (defillama.com/protocols), lowercase requirement, and examples beyond schema description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb 'Get' and resource 'total value locked (TVL) in USD for a DeFi protocol'. Specific about using DefiLlama slug. Distinguishes from siblings like crypto_price or dex_pool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit use cases (protocol health checks, DeFi rankings, risk screening, market research). Does not explicitly exclude alternatives but context signals indicate distinct purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 cost ($0.003/call), caching (1 hour), and specifics of returned data (latest version, dist-tags, deprecation message, etc.). This provides good insight into behavior beyond mere functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two paragraphs. It front-loads the cost and purpose, then details. Every sentence adds value—no fluff. Well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
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 thoroughly explains what data the tool returns, including version details, deprecation status, and popularity metrics. It also covers caching and cost, making the tool's behavior fully transparent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 extra guidance for URL-encoding scoped package names, which adds value beyond the schema's description. The cost and caching info also help understand parameter usage context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks npm package version freshness, deprecation, and popularity. It uses a specific verb and resource, and the domain (npm) distinguishes it from sibling tools like dev_pypi.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists use cases (dependency auditing, supply-chain security, etc.) and provides encoding guidance for scoped packages. While it doesn't explicitly mention when not to use, the sibling tools are from different domains, making the context clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
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 cost ($0.005 per call), technical method (DNS-over-HTTPS), data persistence (nothing stored), caching (up to 1 hour), and the meaning of the 'deliverable' flag. This is comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Every sentence is informative: cost, purpose, technical detail, limitation, and use cases. The description is front-loaded with the most critical information (cost and action) and contains no filler. It is appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description explains the return values (parsed domain, sorted MX hosts, deliverable flag) and their meaning. It covers cache behavior, cost, and limitations. With only one simple parameter, the description is fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the single required parameter 'email' as 'Email address to validate.' The description does not add new semantic details about the parameter beyond what the schema provides. With 100% schema coverage, a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Validate an email address: syntax check plus live MX-record lookup.' It specifies the verb (validate) and resource (email address), and the sibling tools are all unrelated (crypto, finance, weather, etc.), so there is no confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists use cases: 'Use for lead cleaning, signup validation, and contact verification by agents.' It also notes a key non-use: 'It does NOT verify the mailbox exists.' However, no explicit alternatives are given, though there are no competing email tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and excels: it discloses cost ($0.002 per call), data source (ECB via Frankfurter API), update frequency (once per business day, 16:00 CET), caching (up to 1 hour), and scope (daily reference rate, not live).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (roughly 100 words), front-loaded with critical info (cost and purpose), and well-structured. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description covers key aspects: input format, data source, update cadence, caching, cost, and typical use cases. It is sufficiently complete for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% parameter description coverage (each param gets a clear description). The description adds marginal value: it notes ISO 4217 codes and mentions ~30 major currencies, but the schema already specifies the pattern and example.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves 'the ECB reference exchange rate between two fiat currencies,' which is a specific verb+resource. It distinguishes itself from sibling tools like crypto_price and defi_tvl by focusing on fiat currencies via official ECB rates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases ('currency conversion, pricing normalization, and financial reporting by agents') and clarifies it's not a live trading feed. However, it does not explicitly state when not to use this tool or compare it to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses pricing ($1.00 USDC per call), caching (up to 1 hour), partial-success charging model, and the composed nature of the report, providing full transparency for an AI agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise but comprehensive, front-loading the pricing and purpose, then listing components, use cases, and caching. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description provides ample context: pricing, data components, use cases, caching, and charging behavior. An agent can fully understand what to expect from the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'domain' is fully described in the schema (pattern, explanation). The description adds minimal extra meaning beyond stating that the domain is the input for the dossier, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool composes a due-diligence dossier for a domain, listing specific data points (WHOIS, DNS, email, homepage, risk flags) and distinguishing it from sibling tools like domains_whois or network_dns that provide only single aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lists several use cases (vendor vetting, KYC, phishing triage, onboarding) and mentions partial-success charging, but does not explicitly state when not to use this tool or suggest alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully covers behavior: it is a deterministic read from archived observations, no upstream calls, no side effects. Update frequency (~3x/day) and data source are explained. No destructive or mutable behavior is indicated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively long but front-loaded with cost and main verb. Each sentence adds value (cost, purpose, data details, free alternative). Slightly verbose but appropriate for the level of detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description thoroughly explains return data (services per category, catalog share, 7-day delta, taxonomy). Update frequency, cost, and free alternative are covered. For a 0-parameter read tool, this is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist (0 required, schema coverage 100%), so baseline 4 applies. The description adds no parameter details, which is acceptable since there are none.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the tool retrieves the current x402 service catalog broken down by category with 7-day deltas. It explicitly mentions the taxonomy (Agentic.Market) and data points (catalog share, delta). While sibling tool x402_market_pulse exists, the description subtly differentiates by noting a free snapshot at /v1/market-pulse, indicating alternative access.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides cost ($0.005 USDC per call) and mentions a free snapshot alternative (x402_market_pulse), guiding cost-sensitive use. It implies use for analyzing x402 supply concentration and category growth but lacks explicit when-to-use vs when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description fully discloses behavioral traits: cost per call, caching (up to 2 minutes), single upstream call via Algolia HN API, and read-only operation. This is comprehensive and leaves no ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (4 sentences) and front-loaded with cost and core action. Every sentence adds value: cost, action, return format, use cases, caching behavior. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description is complete. It details the return format (rank, story id, title, etc.), data source, caching, and cost. Even without a schema, an agent has enough to invoke and interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has zero parameters and 100% schema coverage. Since there are no parameters, the description cannot add parameter semantics. The description correctly implies no user input is needed, which aligns with the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it gets the current Hacker News front page (30 stories) as normalized JSON, specifying the exact fields (rank, story id, title, etc.). This distinguishes it from all sibling tools which cover unrelated domains like crypto, weather, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists use cases: tech trend detection, topic monitoring, news agents, content-pipeline seeding. While it doesn't explicitly state when not to use it, the specific use cases sufficiently guide the agent. The cost warning ($0.005 per call) adds practical usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses key behaviors: cost ($0.02 USDC), determinism, use of public-source data only, collection frequency (~3x/day since a specific date), and that it provides presence/persistence data, not an endorsement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with three sentences that front-load the cost and purpose. Every sentence adds essential information without redundancy. The structure efficiently communicates what the tool does, when to use it, and its constraints.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter and no output schema, the description comprehensively explains the tool's purpose, output expectations (listing history, observed age, first observation, consistency), data source, limitations, and cost. It leaves no major gaps for an agent to understand the tool's utility.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'resource' is described in the schema with a pattern and description. The tool description adds value by specifying the requirement for the URL to be 'exactly as it appears in public catalogs,' providing clarity beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides public listing history and observed age for an x402 resource URL, explicitly acting as a due-diligence scorecard. It distinguishes itself from sibling tools by focusing on x402 resources and public catalog data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises using the tool 'before an agent pays' a resource, indicating the context. It also specifies the input format and notes the data source (public catalogs, no internal data). While it doesn't explicitly mention when not to use or provide alternatives, 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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavioral traits: cost ($0.03 per call), honest User-Agent, robots.txt adherence, redirect limits (3), size caps (1MB input, 100k output), caching (5 min), and error conditions (private/internal targets refused). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (three short sentences) and front-loaded with the core action and cost. Every sentence adds necessary detail without repetition. Perfectly structured for quick parsing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter and no output schema, the description covers all essential aspects: input requirements, behavior, caching, caps, and error cases. The tool is simple and the description is fully sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description for the 'url' parameter. The description adds value by specifying additional constraints (must be public HTML page, http/https), which enhances understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches a web page and extracts readable content as clean markdown, specifying verb and resource. It also lists use cases (research agents, summarization, RAG) which distinguishes it from unrelated sibling tools like crypto_gas or weather_us.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use for research agents, content extraction, summarization pipelines, and RAG ingestion' and includes constraints (HTML only, private/internal refused). It does not explicitly mention alternatives but the context of siblings shows no overlap, so guidance is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses cost, caching behavior, data source with confidence score and timestamp. No annotations provided, so description carries full burden and does so well.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise, front-loaded with cost and action, every sentence adds value. No repetition or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter and no output schema, description explains output attributes (confidence score, timestamp) and caching, making it complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% but description adds value with examples of acceptable formats (CoinGecko id, DefiLlama id with chain:address), going beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it gets the current USD price of a cryptocurrency by ID. It specifies sources and use cases, and distinguishes from siblings like 'crypto_gas' and 'defi_tvl'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit use cases (spot prices, portfolio valuation, price checks) and cost warning. Does not explicitly state when not to use or name sibling alternatives, 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.

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