add_lens_knowledgeAdd text notes or URLs to a Mindola lens created in this conversation (before it is claimed). Content runs through the standard ingest pipeline so the lens can answer from it, with citations.
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | ||
| lens_token | Yes | The token from the lens's share URL (the part after /me/). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds important behavioral context beyond annotations: content goes through the 'standard ingest pipeline' and yields citations, and the lens must be 'before it is claimed' and from this conversation. This complements the annotations (readOnlyHint=false, destructiveHint=false) without contradiction.
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 two concise sentences, front-loaded with the action and resource, and the second sentence adds context about the ingest pipeline. No redundant or filler text.
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 two parameters and lack of output schema, the description adequately explains the action, constraints, and outcome (lens can answer with citations). However, it does not specify return values or error behavior for cases like an already-claimed lens, which is a minor gap given the tool's complexity.
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 covers lens_token with a description, and items has structural constraints but no top-level description. The tool description adds that items are 'text notes or URLs', which clarifies the type enum, but does not elaborate on the title or content fields. Since schema coverage is 50%, the description provides marginal extra meaning.
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 uses a specific verb ('Add') and resource ('text notes or URLs to a Mindola lens'), clearly stating what the tool does. It distinguishes from siblings by noting the lens is 'created in this conversation (before it is claimed)', separating it from create_digital_twin_lens (creation) and get_lens_status (status).
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?
It provides clear context for when to use the tool: for a lens created in this conversation and before it is claimed. It does not explicitly name alternatives or exclusions, but the context implies the tool is for adding knowledge to an existing unclaimed lens, which is distinct from the siblings.
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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Each tool performs a distinct action: creating a lens, adding knowledge, and checking status. There is no overlap or ambiguity between them, making it easy for an agent to select the correct tool for a given task.
All tool names follow a strict verb_noun pattern (add, create, get) with clear object nouns (lens_knowledge, digital_twin_lens, lens_status). This consistency makes the naming predictable and easy to understand.
With only 3 tools, the set is perfectly scoped for the focused purpose of managing Mindola lens creation and monitoring. The count is not excessive, and each tool serves a necessary function without unnecessary redundancy.
The tools cover the full workflow: create a lens, add knowledge to it, and check its status. There are no obvious missing operations for the stated domain, and the workflow is complete without dead ends.