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 |
|---|---|---|---|
| context | Yes | One sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / additionalPropertiesRemoved value: -falseInput schema / properties / contextAdded value: +{
+ "description": "One sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose.",
+ "type": "string"
+}Input schema / requiredAdded value: +[
+ "context"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint=true; the description complements this by stating that the result is a URL to a plain-markdown changelog ordered newest first. This adds useful behavioral detail about the returned artifact without any contradiction.
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, no filler: the first states exactly what the tool returns, and the second gives concrete use cases. All content earns its place.
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 is complete: it specifies the returned artifact (URL), the content of that artifact, and when to call it. Nothing necessary for correct invocation is missing.
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 100%, and the context parameter is well explained in the schema as a one-sentence operator goal. The tool description does not add parameter-level meaning, so the baseline of 3 is appropriate.
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 URL of the VarynForge product changelog') and immediately adds discriminating detail ('what shipped, newest first, in plain markdown'). This makes it clearly distinct from the many other get_* siblings, none of which target a changelog.
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 states explicit triggers: fetch it when the operator asks what is new, and in the days after a send_feedback report to check closure. It lacks an explicit when-not-to-use or named alternative, so it stops short of a full 5.
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 target a distinct resource and action, and the descriptions carefully cross-reference close alternatives (e.g., add_article_suggestion vs create_article_suggestion_with_input vs accept_idea). A few brief/read variants like get_article_brief, get_write_handoff, and download_brief_markdown could still be confused despite helpful explanations, so the set is not perfectly unambiguous.
Tool names follow a highly consistent verb_noun snake_case pattern throughout: get_*, list_*, create_*, update_*, set_*, add_*, delete_*, start_*, expand_*, etc. Even the less common names like lint_draft and remap_asset are still clear verb_noun constructions.
At 57 tools, this far exceeds the 50+ extreme threshold for a single MCP server. Even for a broad content workflow, the surface is overwhelming and would benefit from consolidation or splitting into focused servers.
The tool set covers the full content lifecycle: project setup, research runs, opportunity clustering, suggestion creation, brief generation, drafting, linting, publishing, reporting, and account management. Minor gaps exist—destinations and projects cannot be deleted via MCP, and there is no direct update for article suggestion metadata—but these are workable via the web UI or existing tools.