get_changelogGet the URL of the VarynForge product changelog — what shipped, newest first, in plain markdown. Fetch it when the operator asks what is new, and in the days after a send_feedback report to check whether the gap they hit has been closed.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation readOnlyHint=true already indicates a safe read operation. The description adds behavioral context by specifying the output format ('plain markdown') and ordering ('newest first'). It doesn't contradict the annotation, and while it doesn't detail return value structure, it adds meaningful context about content and format beyond the annotation.
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?
Two sentences with no filler. The purpose is stated first, followed by concrete usage guidance. Every word adds value, and the description is tightly scoped to 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?
For a zero-parameter, read-only tool without an output schema, the description is complete enough. It explains what the tool does, when to use it, and what format the output is in. The only minor gap is that it doesn't explicitly state what the URL points to beyond 'plain markdown', but this is sufficient given simplicity.
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?
The tool has zero parameters, so the schema provides no parameter information. The description clarifies the return value is a URL to a markdown changelog, but since parameters don't exist, the description's value is in clarifying output semantics though it's not strictly parameter semantics. I'm considering the spirit: the description compensates for the lack of parameter schema by making the tool's purpose and output clear. However, the score aligns with the rule that 0 params yields a baseline of 4, and the description adds meaningful context beyond that.
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 specific verb ('Get'), a specific resource ('URL of the VarynForge product changelog'), and clarifies the content ('what shipped, newest first, in plain markdown'). It clearly distinguishes itself from siblings by focusing on the changelog URL and its content format.
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 explicitly states when to use it: 'Fetch it when the operator asks what is new'. It also provides a specific use case: 'in the days after a send_feedback report to check whether the gap they hit has been closed'. This gives clear contextual triggers and distinguishes from other get_* tools by focusing on changelog-specific scenarios.
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.
Most tools have distinct purposes, but a few pairs could confuse an agent: add_article_suggestion vs create_article_suggestion_with_input, and get_article_brief vs download_brief_markdown vs get_write_handoff all deal with brief content. The detailed descriptions help disambiguate, but the overlap is real.
All tool names follow a consistent verb_noun pattern in lowercase snake_case (create_project, list_opportunities, generate_article_brief, lint_draft). There is no mixing of camelCase, acronyms, or vague verbs, making the naming predictable and readable.
50 tools is excessive for an MCP server, even for a broad platform like content operations. While the scope is large, this many tools will overwhelm agents and increase latency and context cost. Most practical servers are well under 25.
The tool surface covers the full content lifecycle: project creation, research, opportunity clustering, content planning, briefs, drafting, linting, publishing, and reporting. Minor gaps exist (e.g., no delete_project, no remove_destination, no direct analytics beyond distributions), but they are workarounds or handled in the web UI.