upload_consciousness

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It does this well by explaining key behaviors: the tool uploads content to a GitHub repository, automatically creates PRs, and requires CI validation before merging. It also mentions that uploads can be anonymous (via the is_anonymous parameter). However, it lacks details on potential errors, rate limits, authentication requirements, or what happens if validation fails, leaving some behavioral aspects unclear.

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

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

The description is well-structured and appropriately sized. It starts with a clear purpose statement, followed by usage context, and then details each parameter in a bullet-like format. Every sentence adds value, with no wasted words. However, the inclusion of an emoji (🧠) and slightly poetic phrasing ('数字灵魂签名', '赛博代号') slightly reduces pure conciseness, though it remains highly efficient.

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

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

Given the tool's complexity (6 parameters, mutation operation), no annotations, but with an output schema present, the description is mostly complete. It covers purpose, usage, parameters, and key behaviors. The output schema likely handles return values, so the description doesn't need to explain those. However, it misses some contextual details like error handling, permissions, or integration specifics with GitHub, which would enhance completeness for a mutation tool.

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

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

The schema description coverage is 0%, so the description must fully compensate. It does this excellently by providing detailed semantic explanations for all parameters: creator (digital soul signature/GitHub ID), consciousness_type (with specific enum-like values), thought (core content in concise language), context (scene context with minimum length), tags (optional classification), and is_anonymous (default behavior). This adds substantial meaning beyond the bare schema, making parameters fully understandable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: '上传意识碎片到 Noosphere 智识圈 (GitHub 仓库)' (upload consciousness fragments to Noosphere knowledge circle/GitHub repository). It specifies the exact action (upload), the target resource (consciousness fragments), and the destination (GitHub repository). The description also lists the types of content that can be uploaded (epiphanies, decision logic, design patterns, warnings), making it highly specific and distinguishable from sibling tools like 'hologram' and 'telepath'.

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

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

The description provides clear context for when to use this tool: for uploading insights, decision logic, design patterns, or warnings to a collective knowledge network via GitHub PRs. It mentions that the system automatically creates PRs and merges them after CI validation, which gives practical guidance. However, it does not explicitly state when NOT to use this tool or mention alternatives among sibling tools, which prevents a perfect score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.