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argus_ask

Perform a single research, summarization, or drafting task using a security-hardened agent and receive the final answer as plain text.

Instructions

Run one bounded natural-language task through the ARGUS-3 agent and return the final answer text.

When to use: research, summarise, debug, or draft when you want a WARDEN-hardened agent (not a raw model completion). Prefer argus_status first if you only need liveness; prefer argus_capabilities for the tool/WARDEN catalog without spending LLM tokens.

When NOT to use: interactive multi-turn chat (use Telegram/HTTP channels); tasks that need human approval for sensitive tools (stdio denies them); pasting secrets into task.

Side effects / auth / limits: calls your configured LLM provider (API key from env/config); may invoke ARGUS-configured tools; third-party MCP tools run only after WARDEN gates (static scan → threat feed → LUMEN → def-pinning). Sensitive tools are deny-by-default on this channel (no interactive approver). Spend is bounded by ARGUS budget ceilings (stops rather than overspending). Not idempotent — each call is a new metered run. May use network (model + allowed tools).

Returns: plain-text final answer. On failure or budget stop, text may still be returned with isError true. No streaming / partial events on this tool.

Example: argus_ask({ task: "Summarise https://example.com in three bullets", response_format: "bullets" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesRequired. Single clear goal for ARGUS (1..8000 chars). Include constraints (length, format, tone) and any URLs/paths. Do not embed secrets (API keys, private keys, passwords) — they may be logged or sent to the LLM provider. Whitespace-only values are rejected.
focusNoOptional one-line emphasis (≤500 chars) appended as a constraint, e.g. "security only" or "ELI5". Omit when the task is already specific enough.
response_formatNoOptional output shape hint prepended to the task. `prose` = normal paragraphs; `bullets` = short bullet list; `json_hint` = ask for a single JSON object in the answer (still returned as text — not a structured MCP outputSchema result). Default prose when omitted.prose
Behavior5/5

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

The description adds significant behavioral context beyond annotations: calls configured LLM provider, may invoke ARGUS tools, third-party MCP tools gated by WARDEN, sensitive tools deny-by-default, spend bounded by budget, not idempotent, may use network. No contradiction with annotations (readOnlyHint=false, etc.).

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 well-organized: a summary line, usage guidelines, side effects/auth/limits, return type, and an example. Every sentence is informative without redundancy. It is concise and front-loaded with the core purpose.

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's complexity (agent with multiple options) and lack of output schema, the description thoroughly covers input semantics, behavioral nuances, budget limits, and return type (plain-text with potential isError). No gaps remain.

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 input schema covers all three parameters (100% coverage). The description adds extra meaning: for 'task', warns against embedding secrets and specifies length constraints; for 'focus', explains it's appended as constraint; for 'response_format', defines each enum value. This provides clarity beyond the schema.

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's purpose: 'Run one bounded natural-language task through the ARGUS-3 agent and return the final answer text.' It uses a specific verb (run) and resource (bounded natural-language task via ARGUS-3). It also distinguishes from siblings by noting when to use argus_status or argus_capabilities instead.

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 provides when-to-use examples (research, summarise, debug, draft) and when-not-to-use cases (interactive multi-turn chat, tasks needing human approval, pasting secrets). It also mentions alternative tools: argus_status for liveness and argus_capabilities for catalog.

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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