Update an existing Tealfabric process flow by specifying its process ID and modifying fields such as name, status, category, priority, description, configuration, or AI-agent execution settings.
Update an existing Tealfabric process (process flow).
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | ||
| type | No | ||
| status | No | ||
| category | No | ||
| priority | No | ||
| process_id | Yes | Process ID | |
| description | No | ||
| is_template | No | ||
| configuration | No | ||
| executable_by_ai_agents | No |
Changes observed during successful MCP inspections.
v0.2.0Input schema / additionalPropertiesRemoved value: -falseInput schema / properties / configuration / propertyNamesAdded value: +{
+ "type": "string"
+}Input schema / properties / estimated_durationRemoved value: -{
- "type": "number"
-}Input schema / properties / executable_by_ai_agentsAdded value: +{
+ "type": "boolean"
+}Input schema / properties / tagsRemoved value: -{
- "items": {
- "type": "string"
- },
- "type": "array"
-}Input schema / properties / template_idRemoved value: -{
- "type": "string"
-}Input schema / properties / versionRemoved value: -{
- "type": "string"
-}v0.1.3Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure, yet it says nothing about permissions, partial versus full update semantics, side effects on process steps, reversibility, or required role. For a mutation tool with ten parameters, this is effectively no behavioral context beyond the verb 'Update'.
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 single sentence is concise, but for a ten-parameter mutation tool it is under-specified rather than appropriately concise. Brevity here removes information the agent needs instead of sharpening the message.
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?
Given a complex update operation with ten parameters, only one required parameter, no annotations, no output schema, and 10% schema coverage, the description is far too thin to guide correct invocation. It omits parameter meaning, behavioral constraints, and expected outcomes.
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 description coverage is only 10%, with just process_id documented in the schema, and the description adds no meaning about the other nine parameters such as status, configuration, is_template, or executable_by_ai_agents. It does not compensate for the coverage gap at all.
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 states a clear verb ('Update') and resource ('Tealfabric process'), with a parenthetical clarifying that it is a process flow. It does not distinguish this tool from sibling update tools such as tealfabric_update_process_step or tealfabric_update_webapp, so the agent gets purpose but not sibling differentiation.
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 gives no guidance on when to use this tool versus alternatives such as tealfabric_create_process, tealfabric_update_process_step, or tealfabric_retire_process. It also omits preconditions or exclusions, leaving usage entirely to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.