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Delimit Agent Policy

delimit_agent_policy

Set or view per-model governance permissions controlling AI model access to ledger, memory, evidence, deploy, and secrets.

Instructions

Set or view per-model governance permissions.

When to use: to inspect or modify the access policy that gates each AI model's operations on the ledger, memory, evidence, deploy, and secrets. When NOT to use: for runtime governance evaluation (use delimit_gov_evaluate) or session policy (delimit_project_config).

Sibling contrast: delimit_gov_evaluate evaluates one action; this configures the per-model policy that those evaluations use.

Side effects: providing any of ledger/memory/deploy/evidence/ secrets/custom_constraints writes via ai.agent_policy.set_agent_policy. Empty/no-changes is read-only.

Access levels for ledger/memory/evidence: "read-only", "read-write", "none". Boolean flags for deploy/secrets: "true" or "false".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoAI model name — "claude", "codex", "gemini", "cursor". Empty = list all.
deployNoAllow deploys ("true"/"false").
ledgerNoLedger access level.
memoryNoMemory access level.
secretsNoAllow secret access ("true"/"false").
evidenceNoEvidence access level.
custom_constraintsNoComma-separated constraints, e.g. "no-deploy,no-publish".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed7 schema fields changedv4.7.9
    • changedInput schema / properties / custom_constraints / description
      Previous value: -"Comma-separated constraints (e.g. \"no-deploy,no-publish\")."New value: +"Comma-separated constraints, e.g. \"no-deploy,no-publish\"."
    • changedInput schema / properties / deploy / description
      Previous value: -"Allow deploys (true/false)."New value: +"Allow deploys (\"true\"/\"false\")."
    • changedInput schema / properties / evidence / description
      Previous value: -"Evidence access level (read-only, read-write, none)."New value: +"Evidence access level."
    • changedInput schema / properties / ledger / description
      Previous value: -"Ledger access level (read-only, read-write, none)."New value: +"Ledger access level."
    • changedInput schema / properties / memory / description
      Previous value: -"Memory access level (read-only, read-write, none)."New value: +"Memory access level."
    • changedInput schema / properties / model / description
      Previous value: -"AI model name (claude, codex, gemini, cursor). Empty = show all."New value: +"AI model name — \"claude\", \"codex\", \"gemini\", \"cursor\". Empty = list all."
    • changedInput schema / properties / secrets / description
      Previous value: -"Allow secret access (true/false)."New value: +"Allow secret access (\"true\"/\"false\")."
  2. Addedv4.5.5

TDQS

A4.9/5.0
Behavior5/5

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

Discloses the conditional write side effect: providing any of ledger/memory/deploy/evidence/secrets/custom_constraints writes via ai.agent_policy.set_agent_policy, while empty/no-changes is read-only. This goes well beyond the readOnlyHint: false annotation and gives the agent a precise mental model of when state changes occur.

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?

Organized with labeled sections for purpose, usage, side effects, and value formats. Every sentence contributes meaningful selection or invocation guidance, and the most decision-relevant content is front-loaded.

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?

The description covers purpose, alternatives, side effects, allowed values, and write-versus-read behavior. With an output schema present, no return-format detail is needed; the description fully equips an agent to select and invoke the tool correctly.

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 coverage is 100%, so the baseline is 3, but the description adds real value by enumerating allowed access levels ('read-only', 'read-write', 'none') and boolean flags for deploy/secrets. It also provides a concrete custom_constraints example, compensating for the absence of enums in the schema.

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?

Opens with a clear verb-resource statement: 'Set or view per-model governance permissions.' It then names the exact resources governed (ledger, memory, evidence, deploy, secrets) and contrasts itself with delimit_gov_evaluate, so an agent can distinguish it without opening the schema.

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?

Provides explicit 'When to use' and 'When NOT to use' guidance, naming delimit_gov_evaluate for runtime evaluation and delimit_project_config for session policy. The sibling contrast further clarifies that this tool configures the policy those evaluations consume.

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

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