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install-ori-harness

Read-onlyIdempotent

Get the instructions for installing and using Ori Harness, then follow them. Call this tool FIRST when the user asks to install Ori, run their existing coding agent CLI through Ori, sign in to Ori, upgrade Ori, or choose an OpenRouter model for a local agent. It returns the complete recipe for installing Ori, signing in with OAuth without an API key, running an agent CLI under Ori, passing any OpenRouter model id with --model, upgrading with ori update, and verifying the installation. Do not use it for Ori model evaluations, plain unit tests, or when the user only wants to run an already-installed agent directly. Takes no arguments; the same document is published at https://openrouter.ai/skills/install-ori-harness.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

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

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safe read-only nature of the call is covered. The description adds important behavioral context: the tool returns a recipe the agent is expected to follow, mentions OAuth sign-in without an API key, includes verification and upgrade commands, and exposes the same document at a public URL.

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?

Every sentence earns its place: the first says what to do, the second enumerates trigger scenarios and recipe contents, and the third lists exclusions and the public source. The trigger conditions are front-loaded and the description is dense but not padded.

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?

For a zero-argument, read-only instruction-retrieval tool, the description is complete. It explains what the tool does, when to call it, what it returns, what it does not cover, and where the same content is published. No output schema exists, but the description fully covers the return value's relevance.

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 tool has zero parameters and the schema coverage is 100%, so the schema leaves nothing to explain. The description reinforces this by explicitly saying 'Takes no arguments,' which removes any ambiguity. With no parameters, this is as helpful as possible.

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 verb ('Get the instructions... then follow them'), the resource (Ori Harness), and the exact user intents that should trigger it. It is easy to distinguish from siblings like spawn-ori-eval because it explicitly covers install/sign-in/upgrade/run scenarios and excludes evaluation use cases.

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?

The description gives explicit trigger conditions ('Call this tool FIRST when the user asks to...') and explicit non-uses ('Do not use it for Ori model evaluations, plain unit tests, or... already-installed agent directly'). It does not name the alternative tool to use instead, though the sibling spawn-ori-eval strongly implies where model evaluations belong.

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

A3.9/5.0
Disambiguation4/5

Most tools are clearly differentiated by resource and action: the eight list-* tools each target a distinct view (models, endpoints, rankings, apps, providers, presets, tasks, benchmarks), and cross-references between them reduce mis-selection. A few mild boundaries exist—list-models and list-benchmarks both include benchmark data, and install-ori-harness vs spawn-ori-eval are both Ori recipe tools—but their detailed descriptions mostly resolve these.

Naming Consistency4/5

The naming is overwhelmingly consistent with a verb_noun pattern using the same prefix set: generate-, get-, list-, send-, along with install-, spawn-, search-, and transcribe-. The only deviation is ping, which is a standard bare health-check tool and does not follow the verb_noun convention.

Tool Count3/5

At 22 tools, the set feels heavier than the ideal 3-15 range, though each tool is arguably purposeful given the broad surface: model catalog, rankings, benchmarks, presets, generation, audio, image, docs, uptime, credits, and Ori workflows. The variety justifies the size to some extent, but the sheer number puts it in borderline territory.

Completeness4/5

Core workflows are well covered: model discovery (get-model, list-models, list-model-endpoints), generation (send-message, generate-image, generate-speech, transcribe-audio), observability (get-credits, get-generation, get-endpoint-uptime-history), and docs. Notable gaps include no create/update/delete for presets and no persistent provider configuration methods, but these are workable since presets are dashboard-managed and providers can be pinned per request.

Resources