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Glama

VarynForge

by com.varynforge

Add destination

add_destination

Add a destination to a project — where the operator publishes. Pass type (website | youtube_channel | instagram | tiktok | x | linkedin | local_business) and name with state: 'detected' for a destination that already exists and belongs to the operator (website name = the domain like yourdomain.com; social types = the handle, @ optional), or state: 'planned' for one they intend to build (name is held, no live URL yet). Confirm the type and name with the operator before adding — this shapes where briefs, drafts, and lint formats anchor. A project holds up to 10 destinations; manage or remove them in the web UI.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
typeNo
stateYes
contextYesOne sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose.
projectIdYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / properties / context
      Added value: +{
+  "description": "One sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "projectId",
-  "state"
-]New value: +[
+  "projectId",
+  "state",
+  "context"
+]
  2. Added

TDQS

A4.6/5.0
Behavior5/5

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

The annotations only indicate read/write and non-destructive behavior; the description adds substantial behavioral context: naming rules per type, what 'detected' vs 'planned' means, how the destination anchors briefs/drafts/lint, the 10-destination limit, and the confirmation requirement. This goes well beyond what annotations or schema alone convey.

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

Conciseness5/5

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

The description is dense but efficient: every sentence earns its place by covering purpose, state-specific usage, naming rules, confirmation, downstream impact, and capacity limits. It is front-loaded with the core action and resource.

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

Completeness4/5

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

The description covers the main domain complexities well: destination states, naming conventions, operator confirmation, project limits, and the impact on downstream workflows. Minor gaps include lack of return/error behavior and the undocumented 'open' state, but these are modest given the overall richness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds real meaning for `type` (example values), `name` (domain/handle conventions), and `state` (detected vs planned semantics). However, it does not mention the 'open' enum value and does not explain the required `context` parameter, though the schema already provides a description for `context`.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Add a destination to a project — where the operator publishes.' It clearly identifies the domain object and its role, making it easy to distinguish from list_destinations and other add_* sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear conditional guidance for state='detected' vs state='planned', including name conventions and an explicit instruction to confirm with the operator. It does not explicitly name alternative tools or state when not to use this tool, but the context is strong enough to route an agent correctly.

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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TDQS

A3.8/5.0
Disambiguation4/5

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.

Naming Consistency5/5

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.

Tool Count1/5

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.

Completeness4/5

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.

Resources