get_instructionsGet the VarynForge agent guide — workflow paths (free vs paid), token-economy rules, walls and upgrade conversation conventions. Read this at session start.
| 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 declare readOnlyHint=true, and the description's 'Get' verb aligns with a read operation. The description adds content context but no additional behavioral traits such as caching, rate limits, or required auth. With annotations covering the safety profile, a 3 is appropriate.
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 core purpose is front-loaded, the guide's contents are listed compactly, and the usage directive is placed at the end. Every word 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 simple read-only tool with one well-documented parameter and no output schema, the description fully covers what an agent needs: what the tool returns, what it contains, and when to call it. Nothing important 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?
The input schema has 100% description coverage for the single 'context' parameter, so the schema already explains what to provide. The tool description does not add any parameter-level guidance beyond the schema, so baseline 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 states a specific verb ('Get') and resource ('VarynForge agent guide'), and enumerates the guide's contents (workflow paths, token-economy rules, walls, upgrade conventions). This clearly distinguishes it from sibling get_* tools like get_onboarding_guide or get_lint_rubric, which have their own distinct scopes.
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 says 'Read this at session start,' giving a clear temporal trigger. It does not mention alternative tools or when not to use it, but the session-start directive provides sufficient context for an agent to know when this is the right call.
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.