Reliable execution for agent tool calls, as an MCP server.
klanex is an async execution layer between an agent's tool-use intents and real third-party APIs: it schema-gates hallucinated payloads before anything runs, absorbs rate limits and outages with retries/backoff/circuit breakers, encrypts credentials, keeps an audit trail, and reports back via signed webhooks. With this MCP server, an agent submits a call once — klanex makes sure it lands.
Failures come back written for the model: every error carries an llm_hint telling the agent exactly what to fix (or that klanex is already handling it), so agents self-correct instead of guessing.
klanex's MCP server is hosted — no install needed if your client supports HTTP transports:
Endpoint | Environment | Keys |
| production |
|
| sandbox (free to try) |
|
Get an API key at klanexai.com — sandbox keys are free.
Claude Code
claude mcp add --transport http klanex https://api.klanexai.com/mcp \
--header "X-API-Key: klx_live_..."Cursor / Windsurf / VS Code (mcp.json)
{
"mcpServers": {
"klanex": {
"url": "https://api.klanexai.com/mcp",
"headers": { "X-API-Key": "klx_live_..." }
}
}
}Authorization: Bearer klx_... works too, for clients that can only set that header.
Related MCP server: AgentPay MCP Server
For clients that only speak stdio (e.g. Claude Desktop), this package proxies stdio to the hosted endpoint:
{
"mcpServers": {
"klanex": {
"command": "npx",
"args": ["-y", "klanex-mcp"],
"env": { "KLANEX_API_KEY": "klx_live_..." }
}
}
}The shim routes by key prefix — klx_test_… keys go to the sandbox automatically. Set KLANEX_MCP_URL to override.
Tool | What it does |
| Submit an HTTP call for reliable async execution: optional JSON Schema gate, retries/backoff/circuit breakers, idempotency keys, human approval gates, encrypted credentials. |
| Current status, attempts, target response, or classified error for an execution. |
| The account's executions, filterable by status/time, paginated. |
| Re-run a terminal execution byte-exact — outage recovery without re-prompting the LLM. |
| Plan and current-month usage/quota. |
| Stored credential connections (token vault) to reference via |
Hallucinated payloads are rejected before they execute — pass payload_schema and mismatches return synchronously with a correction hint.
Transient failures are not the agent's problem — 429s, timeouts, and 5xxs are retried with backoff behind a circuit breaker; the agent does nothing.
A network blip can never double-execute a refund — idempotency_key makes submits safe to retry.
Destructive actions can wait for a human — requires_approval pauses execution for an approve/reject decision (API, dashboard, or Slack buttons).
Credentials stay out of model context — store them once as a connection; klanex injects them at execution time.
Full API docs: api.klanexai.com/docs
MIT
Fetch the current state of an execution by ID: status (PENDING_APPROVAL|QUEUED|RUNNING|RETRYING|SUCCEEDED|FAILED), attempts, the target response on success, or a classified error on failure. Retryable failures need no action from you — klanex is still retrying. Header values always read back REDACTED.
| Name | Required | Description | Default |
|---|---|---|---|
| execution_id | Yes | the execution ID (exe_...) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses header redaction and retry handling behavior, which are critical for agent decision-making. No annotations exist, 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences, each providing distinct value: purpose/outputs, behavioral guidance, and constraint. No fluff, well 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 adequately explains return fields and key behaviors (redaction, retry). Adequate for a simple fetch tool with one parameter.
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 fully documents the single parameter with description 'the execution ID (exe_...)'. Description adds no additional semantic detail 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?
Clearly states the verb 'Fetch' and resource 'execution by ID', enumerates specific output fields (status, attempts, response/error), and implicitly distinguishes from siblings get_usage and replay_execution.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context for fetching execution state and includes an important 'when not to act' hint for retryable failures, but does not explicitly contrast with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Report the account's plan and execution usage for the current calendar month: used, included_executions, remaining, and whether overage is allowed. Check this before large batches to avoid hitting a hard quota.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that it returns usage data for the current month and includes fields like overage info. No annotations exist, so description carries full behavioral burden. Lacks details on authentication, rate limits, or side effects, but as a read-only query with no parameters, it is adequate.
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 efficient sentences: first defines output, second provides usage advice. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Describes output fields in plain language despite no output schema. Could be more precise about data format, but sufficient for agent to understand return value.
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?
No parameters exist (0 params, 100% schema coverage). Baseline of 4 applies as the description need not compensate for parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it reports account plan and execution usage for the current calendar month, listing specific metrics (used, included_executions, remaining, overage allowed). Distinct from sibling tools like get_execution which likely handle individual executions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises checking before large batches to avoid hitting a hard quota, providing a clear use case. Does not specify when not to use, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Re-run a terminal (SUCCEEDED or FAILED) execution as a brand-new execution: same target, byte-exact original payload, same sealed credentials, fresh attempt counter. This is the outage-recovery path — failed calls can be re-executed without regenerating the payload. Each call creates a new execution (deliberately not idempotent).
| Name | Required | Description | Default |
|---|---|---|---|
| execution_id | Yes | the execution ID (exe_...) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behavioral traits: it re-runs with same target, byte-exact original payload, same credentials, fresh attempt counter. It states it is deliberately not idempotent and only works for terminal (SUCCEEDED or FAILED) executions. Since no annotations are provided, these details are critical and well-provided.
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 that efficiently convey everything needed. No wasted words; front-loaded with core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool simplicity (single parameter, no output schema, no nested objects), the description covers purpose, constraints on input, behavioral implications, and even distinguishes from siblings implicitly via context. No critical gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers the parameter with minimal description. The tool description adds the requirement that the execution must be terminal (SUCCEEDED or FAILED), which is beyond the schema and adds semantic value. With 100% coverage, description provides meaningful additional context.
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 uses the specific verb 're-run' and identifies the resource as 'terminal execution'. It clearly states the tool creates a brand-new execution with the same target and payload, which distinguishes it from the sibling get_execution (read) and get_usage (read).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states this is the 'outage-recovery path' and that failed calls can be re-executed without regenerating payload. It also warns that each call creates a new execution and is not idempotent, guiding the user on when to use and the side effect. However, it does not explicitly exclude use cases like modifying the payload.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: get_execution fetches execution state, get_usage reports account usage, and replay_execution re-runs a terminal execution. No ambiguity.
All tool names follow a consistent verb_noun pattern in snake_case: get_execution, get_usage, replay_execution. No deviations.
Only 3 tools for an execution management platform is on the low end. While focused, typical CRUD operations would benefit from more tools (e.g., create, list, cancel).
Obvious gaps exist: no tool to create a new execution (only replay), no list/cancel functionalities. The workflow is incomplete for full lifecycle management.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Zero-trust gateway for AI agents: score tool calls, verify agent cards, enforce policy, audit.
Runtime permission, approval, and audit layer for AI agent tool execution.
Create and manage scheduled, guarded AI agent jobs with built-in quality control and 900+ connectors
See, price, and control every tool call your AI agents make: policy checks, cost, and audit tools.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/chrassy/klanex-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
