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ask_opus5

Get guarded multi-turn code reasoning on Claude Opus 5, a faster, lower-cost Fable alternative. Use it to sanity-check plans, weigh trade-offs, and explore unfamiliar code.

Instructions

The same guarded, multi-turn reasoning as ask, but on Claude Opus 5 (claude-opus-5) instead of Fable — identical arguments, identical result shape (sidecar, followup, context_exhausted), same session/reset conversation model. Reach for it exactly where you'd reach for ask: before guessing at unfamiliar code, when weighing a design trade-off, or to sanity-check a plan or diff. WHICH ONE: Opus 5 is roughly half Fable's price and noticeably faster, so prefer it for high-volume or latency-sensitive reasoning and for long back-and-forth sessions; keep ask (Fable) for the hardest, most consequential single calls. Running BOTH on the same question is a cheap two-model cross-check without paying for a full council. Sessions are namespaced per tool: the same session key on ask and ask_opus5 is two independent conversations (use reset_session(model='opus5') to clear this one). The model has NO tools and CANNOT open files — paste the real code into context (or point at it with context_ref). Same scope as ask: broad and conceptual engineering questions, including brainstorming and ideas for future code, are fine; refused only for direct offensive-security asks (exploit development, attack tooling) and non-software domain knowledge (biology/medicine refused; neuroscience, cognitive science, AI/ML, and CS are in-scope). Opus 5 also works as the opus token in every multi-model mode — ask_council member or synthesizer, ask_chain stage, ask_debate proposer/opponent/adjudicator.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
resetNoDump+clear this session before asking, starting a fresh conversation.
contextNoOptional code snippets, file paths, or structural context.
sessionNoConversation key. Reuse it to ask follow-ups (Opus 5 keeps context); use a new key or reset=true to start a fresh topic. Opus sessions are namespaced separately from `ask`'s Fable sessions, so the same key on both tools is two independent conversations.default
trustedNoOperator-authorized. When true, the prohibited-use denylist runs in log-only mode: security vocabulary in the question is audited but does not block. Use for legitimate security-engineering work (PoC analysis, CVE research, binary hardening review) where the question genuinely needs security terms. The operator is responsible for authorizing this flag.
questionYesA specific question about concrete software code/architecture (structure, functionality, data flow, module/function relationships, routing).
context_refNoKey(s) of context previously saved with `context_write` to pull in and prepend to `context` — so you paste a big codebase context ONCE and reference it by key across many asks instead of re-pasting. Missing keys are reported, not fatal.

Schema Changelog

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

  1. First observedv0.12.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses the result shape (sidecar, followup, context_exhausted), the session/reset model, per-tool session namespacing, the model's lack of tools and file access, the refusal scope, and the tool's role in multi-model modes. This is substantial behavioral context beyond the schema.

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

Conciseness4/5

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

The description is long but well-organized into clear sections (purpose, when-to-use, sessions, limitations, scope, multi-model role). Almost every sentence earns its place, though 'same as ask' is repeated a couple times and the wall of text could be tightened slightly.

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?

Given the complexity of the tool, the description covers most invocation-relevant context: when to use it, model limitations, session behavior, refusals, and multi-model integration. However, with no output schema, the result-shape terms like `sidecar` and `context_exhausted` are named but not explained, which is a minor completeness gap.

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 100%, so the baseline is 3. The description adds value beyond the schema by advising to paste real code into `context` or use `context_ref`, explaining that sessions are namespaced per tool, and noting arguments are identical to `ask`. This helps the agent use parameters more effectively.

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 states a precise purpose: the same multi-turn reasoning as `ask` but on Claude Opus 5, with identical arguments and result shape. It clearly differentiates the tool from its sibling `ask` by model choice and explicitly names that sibling.

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 says 'Reach for it exactly where you'd reach for ask' and provides a 'WHICH ONE' section comparing Opus 5 vs Fable on price, speed, and consequence. It also suggests running both as a cross-check, giving clear routing guidance relative to alternatives.

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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