create_distribution_planTurn a completed technical audit into prioritized remediation plus official-registry, public-repository, customer-evidence, and independent-discovery actions.
| Name | Required | Description | Default |
|---|---|---|---|
| audit_id | Yes | Completed audit UUID |
| Name | Required | Description | Default |
|---|---|---|---|
| plan | Yes | ||
| score | Yes | ||
| audit_id | Yes | ||
| hostname | Yes | ||
| limitation | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "http://json-schema.org/draft-07/schema#",
+ "additionalProperties": false,
+ "properties": {
+ "audit_id": {
+ "format": "uuid",
+ "pattern": "^([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-8][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}|00000000-0000-0000-0000-000000000000|ffffffff-ffff-ffff-ffff-ffffffffffff)$",
+ "type": "string"
+ },
+ "hostname": {
+ "type": "string"
+ },
+ "limitation": {
+ "type": "string"
+ },
+ "plan": {
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "action": {
+ "type": "string"
+ },
+ "channel": {
+ "type": "string"
+ },
+ "priority": {
+ "maximum": 9007199254740991,
+ "minimum": -9007199254740991,
+ "type": "integer"
+ },
+ "reason": {
+ "type": "string"
+ },
+ "status": {
+ "enum": [
+ "required",
+ "recommended",
+ "verified",
+ "not-applicable"
+ ],
+ "type": "string"
+ }
+ },
+ "required": [
+ "priority",
+ "status",
+ "channel",
+ "action",
+ "reason"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "score": {
+ "anyOf": [
+ {
+ "type": "number"
+ },
+ {
+ "type": "null"
+ }
+ ]
+ }
+ },
+ "required": [
+ "audit_id",
+ "hostname",
+ "score",
+ "plan",
+ "limitation"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description 'turn into...' suggests state mutation, but annotations declare readOnlyHint=true and destructiveHint=false, creating a contradiction. No clarification is provided.
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 a single sentence with no unnecessary words, effectively conveying the tool's function.
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?
While the description covers the core purpose, the contradiction with annotations and lack of guidance on side effects leaves the tool's behavior unclear.
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%, and the description adds no additional meaning beyond the schema's parameter 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?
The description clearly states the tool converts a completed audit into a distribution plan with specific action types, distinguishing it from sibling tools like audit_app, get_audit_result, and get_customer_zero_evidence.
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 use after a completed technical audit but provides no explicit guidance on when to use versus alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: queueing an audit, retrieving results, creating a remediation plan, and fetching specific evidence. No overlap or ambiguity.
All tools follow a consistent verb_noun pattern: audit_app, create_distribution_plan, get_audit_result, get_customer_zero_evidence. The pattern is predictable and uniform.
With only 4 tools, the set is well-scoped for the domain of auditing and remediation planning. Each tool serves a necessary function without excess or deficiency.
The tools cover the core workflow: queue audit, retrieve result, create plan, and get evidence. Minor gaps exist (e.g., no list or delete for audits), but the essential operations are present.