Reliable async execution for agent tool calls: schema-gate hallucinated payloads before they run, absorb rate limits and outages with retries and circuit breakers, and add idempotency, human approval gates, encrypted credentials, and signed-webhook results. Failed calls return an llm_hint the agent can self-correct from.
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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.
Average 4.6/5 across 6 of 6 tools scored.
Each tool has a clearly distinct purpose: execute creates executions, get_execution reads one, list_executions lists them, replay_execution re-runs them, get_usage reports account usage, and list_connections manages credentials. There is no overlap or ambiguity.
All tool names follow the same verb_noun snake_case pattern: execute, get_execution, get_usage, list_connections, list_executions, replay_execution. The only tool without a noun is 'execute', but it is the primary verb and fits the imperative style consistently.
Six tools is well-scoped for an HTTP execution management server. Each tool covers a distinct aspect of the lifecycle (create, read, list, replay, usage, credentials) without redundancy or bloat.
The core execution workflow is well covered: submit, fetch, list, replay, plus account usage and credential listing. A minor gap is the absence of explicit tools for approval actions (given the PENDING_APPROVAL status) or connection management beyond listing, but these are arguably external concerns.
executeAInspectSubmit an HTTP call to a third-party API for reliable asynchronous execution. klanex validates the payload (optionally against payload_schema), stores credentials encrypted, executes with automatic retries/backoff/circuit breakers, and keeps an audit trail — you submit once and never retry transient failures yourself.
Call this instead of making a direct HTTP request whenever the call has side effects (payments, messages, tickets, writes) or the target may be flaky. Prefer connection_id over headers for credentials. Always set idempotency_key for side-effecting calls so resubmitting can never double-execute. Set wait_seconds to block for the result; otherwise poll get_execution with the returned execution_id.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | absolute http(s) URL of the target endpoint | |
| method | No | HTTP method: GET, POST, PUT, PATCH, or DELETE (default POST) | |
| headers | No | request headers, typically third-party credentials; encrypted at rest and never readable back. Prefer connection_id so secrets never pass through your context | |
| payload | No | JSON body to send to the target | |
| seal_url | No | set true when the URL itself is a secret (webhook URL, presigned URL); it is encrypted and reads back redacted | |
| timeout_ms | No | per-attempt timeout in milliseconds (default 30000) | |
| max_attempts | No | maximum delivery attempts, 1-10 (default 5) | |
| wait_seconds | No | block up to this many seconds (max 55) for a terminal result instead of returning immediately after queueing | |
| connection_id | No | ID of a stored credential connection (con_...) to authenticate the call; see list_connections | |
| payload_schema | No | JSON Schema to validate payload against before anything executes; mismatches are rejected synchronously with a correction hint | |
| idempotency_key | No | stable key (max 255 chars) making this submit safe to retry; the same key returns the original execution instead of creating a new one. Always set this for side-effecting calls | |
| requires_approval | No | pause for a human approve/reject decision before anything executes; use for destructive or high-stakes actions |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full transparency responsibility. It discloses validation, encrypted credential storage, automatic retries/backoff/circuit breakers, audit trail, idempotent resubmission, blocking wait behavior, and approval gating—far beyond the schema's field names.
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 front-loaded with a one-sentence purpose, followed by when-to-use guidance and parameter-specific notes. Three compact paragraphs contain no filler; every sentence carries operational value.
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 12 parameters, no output schema, and side-effecting behavior, the description explains the full lifecycle: submit, receive execution_id, optionally wait or poll get_execution. It covers validation, retries, credential handling, idempotency, approval, and audit trail, making it complete enough for safe invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although the schema already has 100% parameter coverage, the description adds strategic usage meaning: prefer connection_id over headers for credentials, set idempotency_key for side effects to prevent double execution, and use wait_seconds to block or poll get_execution. This materially improves the agent's ability to choose parameter values.
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?
First sentence states the specific action: submitting an HTTP call to a third-party API with reliable asynchronous execution. It distinguishes this tool from siblings by framing it as the execution entry point, contrasted with get_execution 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?
Explicitly says when to use this tool instead of a direct HTTP request: when the call has side effects or the target may be flaky. It also gives concrete alternatives and directives: prefer connection_id over headers, always set idempotency_key for side-effecting calls, and poll get_execution when not using wait_seconds.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_executionAInspectFetch 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?
With no annotations provided, the description fully carries the transparency burden. It discloses return contents, includes the retry behavior ('klanex is still retrying'), and explicitly notes that header values are always REDACTED, which is crucial context for interpreting results.
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 dense sentences pack in purpose, return values, statuses, retry behavior, and redaction. Every sentence earns its place; the most critical info (fetch state by ID) 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?
Despite lacking an output schema, the description covers all essential return fields and behaviors. It explains statuses, attempts, success/error responses, retry semantics, and redaction, making it self-sufficient for an agent to invoke and interpret results correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter, with 'execution_id' described as 'the execution ID (exe_...)'. The tool description adds no additional parameter-specific detail, so it meets the baseline but does not exceed it.
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 clear verb 'Fetch' and specific resource 'current state of an execution by ID', immediately distinguishing it from sibling tools. It also enumerates exactly what is returned (status, attempts, response/error), leaving no ambiguity about the tool's 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: when you have an execution ID and need its current state. It explicitly guides on retryable failures ('no action from you'), which is a valuable usage rule. However, it does not explicitly mention when to prefer this over list_executions or replay_execution, though the 'by ID' phrasing makes the distinction clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_usageAInspectReport 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?
With no annotations, the description carries the burden and does well: it discloses the time window (current calendar month), the specific output fields (used, included_executions, remaining), and the concept of a hard quota. This gives a solid understanding of behavior and scope.
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 concise, information-dense sentences. The first sentence specifies exactly what is reported, and the second provides a practical use case. 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?
Despite lacking an output schema and annotations, the description fully explains what the tool returns and when to use it. For a zero-parameter reporting tool, this is complete and self-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 tool has zero parameters, so there is no parameter schema to supplement. The baseline for zero-parameter tools is 4, and the description appropriately focuses on output semantics rather than param details.
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 reports account plan and execution usage for the current month, naming specific fields. This distinguishes it from sibling tools like list_executions, which list execution records, and execute/get_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 explicit guidance to check usage before large batches to avoid hitting a hard quota. It does not explicitly mention alternatives or when not to use it, but 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.
list_connectionsAInspectList the account's stored credential connections (token vault). Reference a ready connection by its ID in execute's connection_id instead of passing raw headers — the credential is injected at execution time and never passes through your context. Secrets are never returned.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description discloses critical behavioral traits: secrets never returned and credential injection at execution time. This reassures about security and context handling, going beyond a simple list operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two well-structured sentences. First states purpose, second explains usage and a security note. Zero redundancy, efficiently 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?
Given zero parameters and no output schema, description adequately covers purpose, output nature (list of connections with IDs), and key behavioral guarantees. No missing information needed for a simple listing 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?
Tool has zero parameters, so there is nothing to add beyond the schema. Baseline of 4 applies; no param info needed in description.
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?
Description uses specific verb 'List' and resource 'stored credential connections (token vault)', clearly distinguishing from sibling execution-related tools. Parenthetical 'token vault' adds clarity about the nature of connections.
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 on how to use the output: reference a connection ID in execute's connection_id instead of raw headers. Implies when to use (to obtain IDs for `execute`) but lacks explicit exclusions or comparisons with other siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_executionsAInspectList this account's executions, newest first, optionally filtered by status or a created_at time range. Returns next_cursor when a page is full; pass it back as cursor for the next page.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | page size, 1-100 (default 20) | |
| since | No | only executions created at or after this RFC3339 timestamp | |
| until | No | only executions created before this RFC3339 timestamp | |
| cursor | No | opaque next_cursor value from a previous page | |
| status | No | filter by status: QUEUED, RUNNING, RETRYING, SUCCEEDED, or FAILED |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It discloses ordering, filter options, and pagination via next_cursor. As a read-only list operation, it doesn't need to discuss destructive side effects, and the core behaviors are well covered.
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 concise sentences: the first front-loads the purpose and options, the second explains pagination. No redundant wording or unnecessary detail; 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?
The tool has moderate complexity with pagination and filters. The description covers key behavior, pagination semantics, and filter options. Since there is no output schema, it could be slightly more explicit about the return format, but 'List executions' adequately implies the response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining the pagination flow (next_cursor → cursor) and grouping since/until as a 'created_at time range', going beyond the schema's individual parameter descriptions.
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 resource (executions), action (list), scope (this account), ordering (newest first), and optional filters. It distinguishes itself from siblings like get_execution (single execution) and replay_execution by focusing on the listing use case.
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 states a clear use case: listing executions with optional status or time-range filters. It does not explicitly name alternative tools for different scenarios, but the 'List this account's executions' framing gives sufficient context for when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
replay_executionAInspectRe-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?
With no annotations, the description fully discloses behavioral traits: it creates a new execution, uses byte-exact payload and sealed credentials, resets the attempt counter, and explicitly warns that it is deliberately not idempotent. This is rich, honest context that goes beyond the schema.
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 sentences, each adding value: the core action, the use case, and a critical behavioral caveat. No redundant or filler content; front-loaded with the main verb and resource.
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 a single parameter, no output schema, and no annotations. The description covers what it does, when to use it, and important side effects (new execution, non-idempotent). This is 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage of the single parameter 'execution_id' with a useful description. The tool description does not add further parameter-level meaning, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Re-run a terminal execution') and specifies the resource (terminal execution in SUCCEEDED or FAILED state). It distinguishes from siblings by explicitly noting it creates a brand-new execution rather than a fresh run, aligning with the outage-recovery context.
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: it is the 'outage-recovery path' for re-executing failed calls without regenerating payloads. It does not explicitly name alternatives like 'execute' or state when not to use, but the context is clear enough for an agent to infer appropriate usage.
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.
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Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
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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
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