Lets AI agents autonomously pay for and call L402-protected APIs in Bitcoin Lightning. 12 tools across two layers: l402_fetch / l402_balance / l402_set_budget / l402_spending_report (generic L402 client) + 11 VERITY paid services (BTC price, web search, scrape, AI summarize/sentiment/translate, world state, domain intel, deep research, strategic alpha, integration). Sub-second settlement, no API keys, no chargebacks.
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Average 3.8/5 across 9 of 9 tools scored.
Each tool has a unique function: BTC price, domain intel, GitHub integration, scraping, search, sentiment, summarization, translation, and world state. No two tools could plausibly be confused for the same task, despite the shared verity_ prefix.
All tools share the consistent verity_ prefix, but the suffixes mix verbs (scrape, search, summarize, translate) with nouns (btc_price, domain_intel, worldstate). This is a minor inconsistency, but the pattern is still predictable and readable.
At 9 tools, the server is well within the ideal range for a diverse utility toolkit. Each tool provides a distinct capability, and none feel redundant or excessive.
The toolkit covers a broad range of common web and data processing tasks: real-time data, search, scraping, NLP, and geolocation. Some possible additions like account balance or usage history are absent, but these are not core to the primary functions.
verity_btc_priceAInspectReal-time BTC price in USD, EUR, and BRL. 10 sats per call.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosure. It adds a cost mention ('10 sats per call') and notes real-time data, providing some transparency. However, it does not describe the output format, error behavior, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences, front-loaded with the main purpose and enriched with the cost detail. Every word earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter price ticker tool, the description is nearly complete. It names the asset and currencies and notes the cost. The only minor gap is explicitly stating the output structure, but the implied response (prices for the three currencies) is sufficient given the simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, and the schema is empty. The description correctly highlights the fixed nature of the tool (returns prices in three specified currencies), so no parameter explanation is needed. Baseline of 4 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 clearly states the tool provides real-time BTC price in USD, EUR, and BRL. It is specific and distinct from sibling tools, which cover broader domains like search, sentiment, and translation.
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 explicit. The description indicates the tool is for obtaining BTC prices, but does not mention when to prefer it over alternatives or any exclusions. It is sufficient for a simple no-parameter tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verity_domain_intelAInspectWHOIS, DNS records, and SSL certificate intel for any domain. 500 sats per call.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain name to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden. It explicitly discloses the cost (500 sats per call) and enumerates the data categories (WHOIS, DNS, SSL). It does not mention error conditions or authentication, but for a simple read-only intel tool, the cost and scope provide reasonable transparency.
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 short, front-loaded sentences: the first states the core purpose, the second conveys the cost. No filler words; every element 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?
For a one-parameter, read-only intel tool with no output schema, the description adequately covers the core functionality and cost. The lack of return-format details is a minor gap, but the tool's simplicity keeps it sufficiently 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?
The input schema already describes 'domain' with 100% coverage. The tool description adds meaning by specifying that any domain is accepted and that the intel includes WHOIS, DNS, and SSL, enriching the parameter's purpose beyond the schema's minimal label.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: it provides WHOIS, DNS records, and SSL certificate intel for any domain. This specific verb+resource pairing distinguishes it from sibling tools like verity_sentiment or verity_btc_price.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for domain analysis ('any domain') but provides no explicit when-to-use or when-not-to-use guidance. The sibling list confirms no overlap, which helps, but the description itself lacks direct alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verity_integrationBInspectGenerate a full l402-kit integration for any GitHub repository. 10000 sats per call.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | GitHub repo URL or owner/repo |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full responsibility. It discloses the cost ('10000 sats per call'), which is helpful, but omits what 'full integration' entails, whether it writes to the repo, requires authentication, or has side effects. This is a significant gap for a generation 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?
The description is two short sentences: the purpose and the cost. Both earn their place, with no unnecessary filler. The main point is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema or annotations, the description should explain what the generated integration looks like or what the user receives. It only says 'full l402-kit integration' without details, leaving significant ambiguity about the output and process. Complexity is moderate, but the description fails to cover it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description for 'repo' ('GitHub repo URL or owner/repo'). The tool description adds 'any GitHub repository,' which is redundant. Baseline 3 applies since the schema adequately explains the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: 'Generate a full l402-kit integration for any GitHub repository.' This is a specific verb-resource pair that distinguishes it from sibling tools like price, search, or sentiment. The scope 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?
The description implies when to use it (whenever you need an l402-kit integration for a repo), but provides no explicit alternatives or exclusions. It doesn't compare to siblings or mention prerequisites, so guidance is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verity_scrapeAInspectScrape any public URL and return clean markdown. 200 sats per call.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to scrape |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It adds useful context by stating 'public URL' (implying non-public will fail) and 'clean markdown' (output format), plus the cost in sats. However, it does not disclose behavior on failures, dynamic content, or rate limits, which would be valuable for a scrape 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?
The description is a single sentence with an additional cost note, achieving maximum conciseness. The essential information (action, target, output, pricing) is front-loaded and every word 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?
For a simple one-parameter tool with no output schema and no annotations, the description covers the core functionality, output format, and cost. It does not mention error cases or limitations beyond 'public', but given the low complexity, the level of detail is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the 'url' parameter as 'URL to scrape', giving 100% coverage. The description adds the qualifier 'public' but does not elaborate on parameter format or constraints, so it adds minimal value 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 explicitly states the verb 'Scrape', the resource 'any public URL', and the output 'clean markdown'. This clearly distinguishes it from sibling tools like verity_search or verity_sentiment, which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for extracting markdown from a public URL, but it does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. The context is clear enough for a distinct function but lacks direct guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verity_searchBInspectWeb search returning top 10 results. 100 sats per call.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It adds useful behavioral details: returns exactly top 10 results and costs 100 sats per call. However, it omits other aspects like response format, error handling, or whether the search is read-only. For a simple search tool, this is adequate but not 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?
The description is extremely concise — one sentence of ten words — and every word adds value. It includes the operation, the result count, and the cost without any fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description should explain what the returned results contain (e.g., titles, URLs, snippets). It only states 'top 10 results' but not the structure. Cost is mentioned but not the payment context. For a search tool, missing return format is a significant 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?
The schema provides 100% coverage for the single 'query' parameter with description 'Search query'. The tool description adds no further semantics about query formatting, length, or special syntax. Since schema already fully covers the parameter, the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as a web search that returns top 10 results. While it lacks an explicit verb like 'perform', the noun phrase 'Web search' combined with 'returning top 10 results' effectively communicates the function. It distinguishes from sibling tools like verity_btc_price and verity_scrape, which are not general search.
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 usage is implied: use this when you need web search results. However, no explicit when-to-use guidance or alternatives are mentioned. The sibling tools are distinct enough that an agent can infer this is the general search tool, but the description itself offers no context on when it is preferable or when to avoid it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verity_sentimentAInspectSentiment analysis with score and keywords. 30 sats per call.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses a cost of 30 sats per call and mentions output components (score and keywords), adding some transparency. However, it does not describe score range, response format, or potential side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences convey purpose, output elements, and cost without waste. The description is front-loaded and easily scannable.
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 a single parameter and no output schema, the description mentions outputs but lacks details on score interpretation or response structure. It is minimally complete for a simple tool, but could be more informative.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers the only parameter 'text' with a description, giving 100% schema description coverage. The description adds no extra meaning beyond the schema, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs sentiment analysis and returns a score and keywords. This differentiates it from siblings like verity_translate or verity_search, though it lacks an explicit verb phrase. The intent 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?
No explicit when-to-use or alternative guidance is provided. The purpose itself implies usage for sentiment analysis tasks, but there are no exclusions or named alternatives. This is implied usage rather than clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verity_summarizeAInspectAI summarization of up to 50k characters of text. 50 sats per call.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to summarize |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It mentions the 50k character limit and the 50 sats per call cost, which are useful behavioral traits. However, it does not disclose whether the operation is read-only, sends data externally, or what happens if the limit is exceeded.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, comprising two short sentences. The first sentence front-loads the purpose, and the second adds cost information. Every word contributes value, with no 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?
For a simple tool with one parameter and no output schema, the description is mostly complete: it covers purpose, input limit, and cost. However, it does not describe the return value (presumably a summary) or handle edge cases like exceeding the character limit. This is a minor gap for a tool of this simplicity.
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 provides a description for the 'text' parameter ('Text to summarize'), achieving 100% coverage, which gives a baseline of 3. The description adds the constraint 'up to 50k characters,' which provides meaningful semantic information beyond the schema by setting a size limit on the input.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: AI summarization of text. It identifies the resource (text) and the action (summarization), and the character limit distinguishes it from other text-processing tools like sentiment or translation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for summarizing text but does not explicitly state when to use this tool over alternatives like verity_sentiment or verity_translate. No exclusion criteria or alternative suggestions are provided, but the character limit and cost give some context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verity_translateAInspectAI translation to 11 locales, MDX-aware (preserves code blocks and URLs). 50 sats per call.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to translate | |
| format | No | Input format (default: plain) | |
| locale | Yes | Target locale (e.g. es, zh, ar, pt, fr, de, ja, ko, hi, ru, it) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the key behaviors: AI-powered translation, preservation of code blocks/URLs when MDX-aware, and a per-call cost of 50 sats. This gives the agent a solid understanding of side effects and resource usage. It stops short of mentioning rate limits or error behavior, but for a translation tool these are minor 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?
The description is one sentence, 15 words, front-loaded with the core action and target, then adding the differentiator and cost. Every element earns its place; no fluff 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?
For a simple tool with 3 parameters and no output schema, the description covers the main purpose, the MDX handling, and the cost. It does not specify the source language assumption or character limits, but these are not critical for basic invocation. The presence of the schema and the description together make the tool sufficiently complete for an agent to select and call it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers all three parameters with descriptions (100% coverage), so the baseline is 3. The description adds value by clarifying the 'format' parameter's purpose ('MDX-aware (preserves code blocks and URLs)') and specifying the locale count, which reinforces the 'locale' parameter's valid values. This elevates the semantic clarity beyond the raw 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 states a specific verb ('AI translation') and resource ('11 locales'), clearly distinguishing it from the sibling tools which cover pricing, scraping, search, etc. It also adds a distinctive capability (MDX-aware) that further clarifies its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: translate to 11 locales, with MDX support for preserving code blocks and URLs. It implies when to use it (any translation need) and hints at format behavior, but does not explicitly discuss alternatives or when not to use it. Since no sibling tool is a translation service, the intended usage is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verity_worldstateAInspectUTC time, geolocation, and weather for any IP address. 80 sats per call.
| Name | Required | Description | Default |
|---|---|---|---|
| ip | No | IP address (optional, defaults to caller IP) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the cost of 80 sats per call, which is a behavioral trait. The description implies a read-only lookup, but does not explicitly state side effects, error handling, or dependencies. The cost mention adds value, but the overall behavioral disclosure is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the core functionality and followed by the cost. Every word earns its place, with no redundancy or filler. It is an ideal length for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, has one optional parameter, and no output schema. The description explains what the tool returns (time, geolocation, weather) and covers the input scope. It lacks details like error cases or output formatting, but for a straightforward lookup, it is sufficiently 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?
The input schema is fully self-descriptive: the only parameter 'ip' is explained as optional and defaults to the caller IP. The description adds no additional parameter semantics beyond what the schema already provides. With schema coverage at 100%, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: providing UTC time, geolocation, and weather for any IP address. It distinguishes itself from sibling tools by focusing on worldstate data rather than prices, domain intel, or translation. However, it lacks an explicit verb like 'get' or 'retrieve', so it stops short of a perfect 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 usage is implied: if you need time, location, or weather information for an IP address, this is the tool to use. There is no explicit comparison to alternative tools or exclusion of when not to use it. The description gives a clear context but no direct guidance on choosing this over a sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
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