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VarynForge

Set excluded terms

set_excluded_terms
Destructive

Set the project-level exclusion terms — products or topics the operator explicitly does NOT sell (e.g. "wedding suite", "free template", "printing"). Keywords mentioning any term are down-weighted in opportunity scoring and dropped from the content-plan harvest, before results reach the operator. Replaces the whole list; pass [] to clear. Survives research re-runs, unlike per-cluster dismissal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
termsYes
projectIdYes

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already indicate destructiveHint=true, and the description adds context about replacement behavior, persistence across research re-runs, and the effect on scoring/harvesting. It doesn't contradict annotations and gives meaningful extra behavioral detail beyond the structured hints.

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 compact and well-structured: main purpose first, then examples, then behavior notes. Every sentence adds value, with no redundancy or filler.

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?

For a simple two-parameter tool with destructive annotations, the description covers semantics, replacement behavior, and persistence. It doesn't mention return values or errors, but given no output schema and the simplicity, this is sufficient. The only gap is explicit permissions or reversibility, but the destructiveHint covers the risk.

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?

Schema coverage is 0%, so the description must compensate. It explains the 'terms' parameter thoroughly (array of strings, replacement semantics, clearing with []) and gives examples. The 'projectId' parameter is self-evident from the name and type; no additional description needed.

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 clearly states the tool sets project-level exclusion terms, with specific verb and resource, and gives concrete examples ('wedding suite', 'free template', 'printing'). It distinguishes this from other setters by focusing on exclusion terms and their effect on scoring and harvesting.

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 usage context: it replaces the whole list, can be cleared with [], and survives research re-runs unlike per-cluster dismissal. This implies when to use it over alternatives, though it doesn't explicitly say 'use this when...' or list all alternative tools.

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 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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness4/5

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.

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