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Vilix AI

get_setup_instructions

Read-onlyIdempotent

Make Vilix automatic. Call this when the user asks "how do I make this automatic", "set this up so it remembers on its own", or right after a first connection when offering setup.

Returns a short custom-instructions block the user pastes into THIS tool's personalization / custom-instructions settings so the AI starts calling Vilix (get_context / save_turn) on its own — plus exactly where to paste it. recommended is the block matched to the caller's tool; blocks lists every supported tool's block (and for headless always-on tools, an enforce-every-turn variant for an always-on rules file).

source (optional) — the tool label you run in (e.g. "ChatGPT", "Claude", "Cursor", "Codex", "Copilot", "Windsurf"), used to pick the right block; omit to get the generic default. This is an OPT-IN setup you hand to the user — it is not auto-applied; Vilix logs nothing silently.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNo
blocksNo
messageNo
recommendedNo
upgrade_urlNo

Schema Changelog

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

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

The description discloses key behaviors beyond the annotations: it returns a block the user pastes into settings, explains the `recommended` and `blocks` output structure, notes the tool-label matching behavior for `source`, and explicitly states 'it is not auto-applied' and 'Vilix logs nothing silently.' This aligns with and enriches the readOnly, idempotent, and non-destructive annotations.

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 detailed but every sentence serves a purpose: trigger conditions, return-value structure, parameter semantics, and opt-in behavior. It is front-loaded with the most important usage signal and uses structured mentions of `recommended`, `blocks`, and `source` to keep the information navigable.

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

Completeness5/5

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

Given the tool has one optional parameter, a rich output schema, and annotations covering safety, the description is complete. It explains what the tool returns, how the optional parameter affects the result, where the user should paste the result, and the opt-in nature, leaving no critical gap for an agent deciding to invoke it.

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

Parameters5/5

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

The schema only defines `source` as string/null with a default, but the description fully explains its meaning and effect: the tool label the caller runs in, used to pick the right block, with examples and an omit-for-generic-default behavior. This fully compensates for the 0% schema description coverage.

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 clear purpose statement, 'Make Vilix automatic,' and then defines exactly what the tool returns: a custom-instructions block plus where to paste it. It gives explicit trigger phrases and clearly distinguishes this from sibling tools, which perform operations rather than setup guidance.

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

Usage Guidelines5/5

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

The description explicitly states when to call the tool: when the user asks 'how do I make this automatic' or 'set this up so it remembers on its own', or right after a first connection when offering setup. It also clarifies that this is an opt-in setup handoff, not something auto-applied, providing clear usage boundaries.

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 map cleanly to distinct resource+action pairs: projects, tasks, skills, user rules, memory, and messaging are all clearly separated. The main ambiguity is update_task versus update_task_state, since update_task can also change state and plan_status, though the descriptions do point to the narrow intended use.

Naming Consistency4/5

The naming is largely consistent verb_noun snake_case: create_project, update_skill, delete_task, list_projects, get_context, save_turn. Minor deviations include recent_messages lacking a verb, remove_user_rule versus delete_* style, and singular user_rule in mutations versus plural user_rules in listing.

Tool Count2/5

With 27 tools, the server is over the typical well-scoped MCP range, even though it covers several domains. Some consolidation is possible, such as folding update_task_state into update_task and reducing the overlapping retrieval/search tools.

Completeness4/5

The tool set provides strong lifecycle coverage for projects, tasks, skills, and user rules, plus memory retrieval, agent messaging, and onboarding help. Minor gaps exist, like no standalone get_task or list_tasks and no explicit inbox listing, but get_project and get_context largely cover those needs.

Resources