get_articleGet the article content as markdown. Screenshots appear as <SCREENSHOT {id}> placeholders. Returns the markdown text and a list of screenshot IDs.
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | Yes | Project ID |
Changes observed during successful MCP inspections.
Input schema / properties / contextRemoved value: -{
- "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
- "type": "string"
-}Input schema / properties / conversation_idRemoved value: -{
- "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
- "type": "string"
-}Input schema / properties / llm_modelRemoved value: -{
- "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
- "type": "string"
-}Input schema / requiredPrevious value: -[
- "project_id",
- "context",
- "llm_model"
-]New value: +[
+ "project_id"
+]Input schema / properties / contextAdded value: +{
+ "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+ "type": "string"
+}Input schema / properties / conversation_idAdded value: +{
+ "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
+ "type": "string"
+}Input schema / properties / llm_modelAdded value: +{
+ "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+ "type": "string"
+}Input schema / requiredPrevious value: -[
- "project_id"
-]New value: +[
+ "project_id",
+ "context",
+ "llm_model"
+]Input schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"Input schema / additionalPropertiesRemoved value: -falseDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds useful behavioral detail: screenshots are rendered as <SCREENSHOT {id}> placeholders and the tool returns both the markdown text and screenshot ID list. It does not discuss error cases or authentication, but for a read-only getter this is adequate.
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 three short sentences, front-loaded with the primary purpose, then a placeholder convention, then the return value. Every sentence adds necessary information with no filler.
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?
For a single-parameter, read-only tool with no output schema, the description sufficiently explains what is returned (markdown text plus screenshot IDs) and the placeholder behavior. An agent has enough information to invoke the tool and interpret its result correctly.
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?
There is only one parameter, project_id, and the schema description ('Project ID') provides 100% coverage. The tool description adds no extra meaning about the project_id format or how to obtain it, so the baseline of 3 applies.
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 uses a specific verb and resource ('Get the article content as markdown') and clearly distinguishes this getter from siblings like get_project, get_clip, and get_comments. The output format is stated up front, so an agent can tell exactly what this tool retrieves.
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?
There is no guidance about when to use this tool versus alternatives such as get_project or get_clueprint, nor any mention of prerequisites or exclusions. The description simply states the operation without situating it among sibling tools.
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.