Skip to main content
Glama

delimit_agent_check

Verify whether a specific AI model has permission to execute a given action under agent policy, preventing unauthorized operations.

Instructions

Check if a model is allowed to perform an action under agent policy.

When to use: as a per-action gate before executing sensitive operations from a non-orchestrator model — verify it has the required permission. When NOT to use: to set / inspect policies overall (use delimit_agent_policy) or for runtime governance evaluation (delimit_gov_evaluate).

Sibling contrast: delimit_agent_policy manages the policy; delimit_gov_evaluate is the runtime governance gate; this is a per-action permission check.

Side effects: read-only on the policy store. Calls ai.agent_policy.check_agent_permission.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesAI model name — "claude", "codex", "gemini", "cursor". Required.
actionYesAction to check (e.g. "ledger_write", "deploy"). Required.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Discloses side effects ('read-only on the policy store') and the underlying call. No annotations exist, so the description carries full burden. It covers the main behavioral trait (read-only) but could mention return value or error handling; however, output schema likely covers that.

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?

Concise, well-structured with clear sections (purpose, when to use, when not to use, sibling contrast, side effects). Every sentence adds value, front-loaded with key information.

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?

For a simple two-parameter tool with an output schema, the description covers purpose, usage guidelines, side effects, and sibling differentiation comprehensively. No gaps for the complexity level.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema describes both parameters (model, action) with clear descriptions, and coverage is 100%. The description does not add extra meaning beyond the schema, so baseline 3 is appropriate.

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 the tool's purpose: 'Check if a model is allowed to perform an action under agent policy.' It uses specific verb-resource pairing and contrasts with siblings (delimit_agent_policy, delimit_gov_evaluate), making its role distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use ('as a per-action gate before executing sensitive operations from a non-orchestrator model') and when not to use (setting policies or runtime governance), with alternative tool names provided.

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/delimit-ai/delimit-mcp-server'

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