mcp_vision_augment_vision

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

With no annotations, the description carries the behavioral transparency burden. It discloses the unified return envelope, the fallback chain through OpenAI-compatible channels to local Ollama VL, and that OCR/document parsing run locally. This adds meaningful context beyond the schema, though it does not mention rate limits, timeouts, or error code specifics.

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 succinct sentences: the first states the tool's role, the second explains the envelope and fallback behavior. It front-loads the main purpose, uses the space efficiently, and avoids redundancy with the schema.

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?

The tool is complex (multiple task types, multiple backends), and the description covers the envelope, backend fallback, and local processing. The schema fully documents all parameters. However, there is no per-task description of what 'result' contains, and the VISION_AUGMENT_ALLOW_URLS requirement appears only in the schema, not the description. Given the complexity, these are minor gaps.

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?

Schema coverage is 100%—every parameter (task, source, language, task_type) has a clear description in the input schema. The description adds no extra parameter-level semantics, so the baseline of 3 is appropriate. The description's mention of the return envelope is about output structure, not parameter 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 gives a specific verb+resource: 'provides image viewing, OCR, and document parsing capabilities' for LLMs without vision. It clearly distinguishes itself from sibling tools (health, clear_cache) by enumerating the three task types (reasoning, ocr, document) and the unified JSON envelope.

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 clearly implies when to use the tool (vision understanding, OCR, document parsing) via the task_type enum, and the fallback behavior indicates the intended scenarios. However, it does not explicitly state when not to use it or contrast with sibling tools, so it stops short of a full 5.

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