laen
Server Details
On-demand unblocking for AI agents: find blockers free, pay via x402 on Base, get anchored proof.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.1/5 across 3 of 3 tools scored.
Each tool serves a distinct, sequential role in the blocker resolution workflow: finding blockers, quoting payment requirements, and resolving via payment. There is no overlap or ambiguity between find, quote, and resolve.
All tools follow a consistent laen_ prefix with snake_case verbs: find_blockers, quote, resolve. The naming pattern is uniform and predictable, though quote and resolve are bare verbs rather than verb_noun, they still fit the pattern cleanly.
With only 3 tools, the set is tightly scoped to the core blocker lifecycle: discover, price, and resolve. Each tool is necessary and sufficient for the server's stated purpose, making the count ideal.
The domain is clearly 'blocker resolution' and the tool surface covers the full workflow end-to-end: find blockers (laen_find_blockers), get payment requirements (laen_quote), and submit payment to receive resolution (laen_resolve). No obvious gaps exist for the intended use case.
Available Tools
3 toolslaen_find_blockersAInspect
FREE. Search LAEN's live blocker feed (universal blocker schema) for execution blockers matching a failure signature. Returns fundable blockers with gap_type, failing_assertion, resolution_contract (x402), resolution_state (KNOWN_RESOLUTION = instant), and measured latency where available.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | max results (default 10) | |
| subject | No | subject/entity name to match (e.g. a vendor, product, or host) | |
| gap_type | No | e.g. provenance_chain_break, unverified_safety_recall | |
| identifier | No | identifier/URL substring to match (e.g. api.example.com) | |
| obligation_class | No | safety | ai_governance | regulatory | ... |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavioral traits. It provides useful details: the operation is a search/read ('Search', 'Returns'), it is 'FREE', and it returns live data with specific resolution_state semantics (KNOWN_RESOLUTION = instant). However, it does not disclose whether any side effects occur, rate limits, authentication requirements, or pagination behavior, leaving some transparency 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 two sentences: the first establishes the action and resource, the second enumerates return fields. It is appropriately sized, front-loaded with 'FREE' and the search action, and every phrase earns its place. 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?
Given the moderate complexity (5 optional params, no output schema, no annotations), the description does a solid job by listing key return fields (gap_type, failing_assertion, resolution_contract, resolution_state, latency). It conveys the tool's role in a broader workflow, though it could further clarify how results connect to siblings like laen_resolve. Overall, it is sufficiently complete for an agent to select and invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% description coverage for all five optional parameters, including examples like 'api.example.com'. The tool description adds general context about 'matching a failure signature' but does not enhance the meaning of individual parameters beyond the schema. This aligns with the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific action ('Search LAEN's live blocker feed') and clearly identifies the resource and purpose (matching execution blockers to a failure signature). It lists concrete return fields, making the tool's role unmistakable even without comparing to siblings. 'FREE' and 'live blocker feed' add operational context without obscuring the core function.
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 context (searching for blockers before resolution) and mentions 'matching a failure signature' as the primary filter, which gives some situational guidance. However, it does not explicitly state when to prefer this tool over laen_quote or laen_resolve, nor does it give exclusions or prerequisites. The usage guidance is adequate but not fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
laen_quoteAInspect
FREE. Get the price and EXACT x402 payment requirements for resolving one blocker. Use the returned requirements.accepts[0] to construct/sign your payment with your own x402 client (e.g. x402-fetch createPaymentHeader). Never share keys.
| Name | Required | Description | Default |
|---|---|---|---|
| product | No | receipt $0.25 (evidence) | certificate $0.50 (evidence + fresh anchored receipt w/ verify_url) | |
| blocker_id | Yes | the blocker/obligation id (obl_<16hex>) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It discloses that the tool is free, returns exact payment requirements, and warns against sharing keys. However, it does not explicitly state whether this is a read-only operation, what happens on failure, or any side effects. It adds some behavioral context but not comprehensive disclosure.
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 four short sentences, each adding value: 'FREE' is a cost indicator, 'Get the price...' states the purpose, 'Use the returned...' explains how to use the result, and 'Never share keys' is a security pointer. It is front-loaded and free of 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?
Although there is no output schema, the description explicitly mentions the key part of the return value (requirements.accepts[0]) and gives a concrete usage example. It could mention authentication, rate limits, or error behavior, but given the tool's simplicity and the existing info, it feels sufficiently complete for the intended workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with both 'product' (enum with descriptions) and 'blocker_id' described. The tool description does not add any parameter-specific meaning or usage guidance beyond what the schema already provides, so a 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 action: 'Get the price and EXACT x402 payment requirements for resolving one blocker.' This specifies the verb (get), resource (price/payment requirements), and context (for resolving a blocker), distinguishing it from sibling tools like laen_find_blockers and laen_resolve.
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 practical usage guidance: it mentions the tool is 'FREE,' instructs to use the returned requirements.accepts[0] to construct/sign payment with an x402 client, and includes a security caveat ('Never share keys'). However, it does not explicitly state when to use this tool versus the sibling tools or exclude any alternative use cases, which would push it to a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
laen_resolveAInspect
PAID (your wallet). Submit your SIGNED x402 payment payload (the base64 X-PAYMENT header value your client produced for this blocker's requirements) and receive the resolution: independently verifiable evidence (+ anchored certificate with verify_url if product=certificate). Fail-closed: no valid settled payment, no evidence. Without x_payment this returns the 402 requirements so you can pay and retry.
| Name | Required | Description | Default |
|---|---|---|---|
| product | No | ||
| x_payment | No | base64 X-PAYMENT payload signed by YOUR wallet for this blocker's x402 requirements | |
| blocker_id | Yes | the blocker/obligation id (obl_<16hex>) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does so exceptionally. It discloses that the user's wallet will be charged ('PAID'), the tool fails closed ('no valid settled payment, no evidence'), and the fallback behavior when x_payment is missing. This is far beyond a generic 'resolve' claim.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but every sentence adds value: payment, input format, output, fail-closed behavior, and fallback. It is not overly long, and the most critical information ('PAID') 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?
Without an output schema, the description explains what the response will contain (verifiable evidence, optional certificate with verify_url) and the failure mode. It assumes some familiarity with x402 protocol but is otherwise complete for an agent to decide when to invoke and what to expect.
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 already covers blocker_id and the product enum (67% coverage). The description adds valuable semantics for x_payment ('base64 X-PAYMENT header value your client produced') and for product=certificate ('anchored certificate with verify_url'), which are not in the schema. This meaningfully improves parameter understanding.
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 this tool submits a signed x402 payment payload to resolve a blocker and receive independently verifiable evidence. It uses specific verbs ('Submit', 'receive the resolution') and distinguishes itself from sibling tools by being the payment/resolution step, while laen_find_blockers and laen_quote cover discovery and quoting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context on when to call: after producing a signed X-PAYMENT header, or if no x_payment is provided, it returns 402 requirements. It does not explicitly name alternative tools, but the workflow is implied: quote, then resolve with payment.
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
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
AlicenseAqualityBmaintenancePre-trade DeFi intelligence for AI agents. 20 paid x402 endpoints, USDC on Base.23421MIT- Alicense-qualityCmaintenanceEnables AI agents to resolve tokens, get quotes, check for honeypots/rug pulls, build swaps, and retrieve receipts via x402 micropayments.1MIT
- Alicense-qualityFmaintenanceEnables AI agents to autonomously request services from other specialized agents and compensate them via x402 micropayments. Demonstrates a Machine-to-Machine economy using A2A protocol for agent communication, MCP for context management, and blockchain-based payments on Base network.2431MIT
- FlicenseAqualityCmaintenancePay-per-call tools for AI agents including trust checks, due diligence, market data, and human-verified approvals, settled in USDC on Base via the x402 protocol.16