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mesa-clm

mesa-clm

by idss-mesa
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mesa_policy_disable

Disable a MESA policy by removing its mesa.policy.<policy_name> AVU from a project root collection and record the change in DuckLake when MESA is enabled.

Instructions

Disable a MESA policy on a project root collection by removing the mesa.policy.<policy_name> AVU. Records the change into the project's DuckLake when MESA-enabled.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "project_path": "<project_path>",
      "policy_name": "<policy_name>"
    },
    "name": "mesa_policy_disable"
  }
}

Input Schema

NameRequiredDescriptionDefault
policy_nameYesShort policy identifier (becomes the suffix of the ``mesa.policy.<name>`` AVU).
project_pathYesiRODS path of the project root collection.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose the concrete side effect of removing a specific AVU plus a conditional secondary effect (recording into DuckLake when MESA-enabled). However, it omits permission/auth requirements, idempotency behavior when the policy is absent, and error semantics for a mutation tool.

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?

Two tight sentences with zero filler; the action and its mechanism are front-loaded, and the conditional DuckLake side effect is appended where it belongs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter mutation with no annotations and no output schema, the description covers the core action and the AVU mechanism adequately, but leaves the agent without return-value expectations, failure modes, or prerequisites needed to invoke it confidently.

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 description coverage is 100%, so both parameters are already documented with their formats and the AVU suffix convention. The description restates the same mesa.policy.<name> mapping the schema already provides, adding no new syntax or format detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('Disable') and resource ('a MESA policy on a project root collection') and even states the underlying mechanism (removing the mesa.policy.<policy_name> AVU), which clearly separates it from mesa_policy_enable. It stops short of explicitly naming the enable counterpart as an alternative, but the operation is unambiguous.

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

Usage Guidelines3/5

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

Usage is only implied: the agent can infer this is the tool for turning a policy off, but there is no explicit statement of when to prefer it over mesa_policy_enable, nor any preconditions (e.g. the policy must already be applied, MESA must be initialized). No when-not guidance.

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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