Skip to main content
Glama

enforced_tool_call

Enforce policy checks on a tool call, optionally forward the approved call to an endpoint, and sign the request and response as a bilateral receipt.

Instructions

Strong-enforcement path: policy-check + (optional) forward + sign request and response.

With tool_endpoint set, the approved call is forwarded and request+response are signed
together as one bilateral receipt. Without it, an approval token is returned and the
agent must call complete_action(call_id, result) to close the receipt.

Args:
    tool_name: Name of the tool to execute
    agent_id: The agent requesting the tool call
    arguments: Optional JSON string of tool arguments
    tool_endpoint: Optional HTTP endpoint to forward the approved call to

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tool_nameYes
agent_idYes
argumentsNo
tool_endpointNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description adequately discloses behavioral traits: it performs policy-check, optional forwarding, signing, and returns an approval token or signed bilateral receipt. It also notes the need to call complete_action when tool_endpoint is absent.

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 a clear summary sentence followed by bulleted parameter explanations. 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 the tool's complexity (4 params, no schema descriptions, but output schema exists), the description covers all aspects: the two operational modes, parameter roles, the token flow, and the need for complete_action. No critical gaps remain.

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 description coverage is 0%, but the description's Args section adds meaning by explaining each parameter, e.g., 'Optional JSON string of tool arguments' and 'Optional HTTP endpoint to forward the approved call to.' This compensates for the schema's lack of descriptions.

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 is a 'Strong-enforcement path' involving policy-check, optional forward, and signing. It explains two distinct modes (with and without tool_endpoint), making the tool's purpose distinct from siblings like check_policy or complete_action.

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 explains when to use this tool (for enforced policy-check and signing) and describes the two modes and their outcomes. However, it does not explicitly state when not to use it or compare with alternative tools like preflight_check.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jagmarques/asqav-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server