mcp

promptarch_generate_artifact

Generate an AI agent context artifact (e.g. context_pack, claude_md) from a project description. Requires a PromptArch API key configured as an Authorization: Bearer pk_... header on this MCP server. Consumes credits.

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": {
      "artifact": "<artifact>",
      "project_name": "<project_name>",
      "project_description": "<project_description>"
    },
    "name": "promptarch_generate_artifact"
  }
}

Input Schema

Name	Required	Description
`artifact`	Yes	Artifact id. See promptarch_list_artifact_types. Other Studio artifact types need artifact-specific inputs this tool does not accept.
`commands`	No
`tech_stack`	No
`project_name`	Yes
`target_model`	No	Target model family (default Claude).
`repo_structure`	No	Directory tree or structure summary. Max 1900 chars: summarize a large repo rather than pasting a full tree.
`coding_conventions`	No	Conventions, invariants, house rules. Max 1900 chars.
`project_description`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed11 schema fields changedJul 24, 2026
- changedInput schema / properties / artifact / description
```
Previous value: -"Artifact id. See promptarch_list_artifact_types (e.g. context_pack)."New value: +"Artifact id. See promptarch_list_artifact_types. Other Studio artifact types need artifact-specific inputs this tool does not accept."
```
- addedInput schema / properties / artifact / enum
```
Added value: +[
+  "context_pack",
+  "claude_md",
+  "agents_md",
+  "cursor_rule"
+]
```
- addedInput schema / properties / coding_conventions / description
```
Added value: +"Conventions, invariants, house rules. Max 1900 chars."
```
- addedInput schema / properties / coding_conventions / maxLength
```
Added value: +1900
```
- addedInput schema / properties / commands / maxLength
```
Added value: +1900
```
- addedInput schema / properties / project_description / maxLength
```
Added value: +1900
```
- addedInput schema / properties / project_name / maxLength
```
Added value: +1900
```
- addedInput schema / properties / repo_structure / description
```
Added value: +"Directory tree or structure summary. Max 1900 chars: summarize a large repo rather than pasting a full tree."
```
- addedInput schema / properties / repo_structure / maxLength
```
Added value: +1900
```
- addedInput schema / properties / target_model / maxLength
```
Added value: +100
```
- addedInput schema / properties / tech_stack / maxLength
```
Added value: +1900
```
First observedJul 17, 2026

TDQS

B3.3/5.0

Behavior3/5

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

No annotations are provided, so the description bears full responsibility. It discloses the need for authentication and credit consumption, which is useful. However, it does not mention other behavioral traits such as idempotency, error behavior, rate limits, or whether the tool is destructive, leaving gaps in the agent's understanding of side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, consisting of two clear sentences plus a reference. It front-loads the purpose and follows with requirements. No unnecessary words, but a slightly more structured format (e.g., bullet points for requirements) could improve scannability.

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

Completeness2/5

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

Given the tool has 8 parameters, 3 required, no output schema, and no annotations, the description is insufficient. It does not explain how the inputs are used to generate the artifact, what the output looks like, or how the artifact will be consumed. The mention of credit consumption and API key is good, but overall context is lacking for a complex tool.

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 coverage is 50% (4 of 8 parameters have descriptions). The description adds value by referencing 'promptarch_list_artifact_types' for the 'artifact' parameter, but otherwise does not elaborate on parameters lacking schema descriptions (e.g., 'commands', 'tech_stack'). The baseline of 3 is appropriate since the description moderately supplements the schema.

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 clearly states the tool's function: 'Generate an AI agent context artifact... from a project description.' It provides specific examples like 'context_pack' and 'claude_md', making the resource type clear. However, it does not explicitly distinguish this tool from siblings like promptarch_lint_artifact or promptarch_list_artifact_types, though the verb 'generate' and examples hint at the difference.

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?

The description mentions prerequisites ('Requires a PromptArch API key...') and cost ('Consumes credits'), which are important usage constraints. However, it lacks guidance on when to use this tool versus alternatives (e.g., when to generate vs. list or lint), and does not provide exclusions or best practice context.

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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