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process

Run raw Florence-2 task tokens on images or PDFs to get unparsed text output when specialized tools don't cover a task.

Instructions

Run a raw Florence-2 task token against an image (escape hatch).

prompt must be a Florence-2 task token, not an instruction: '', '', '' and the like. Passing plain English ("describe this image") does not fail — it returns confident nonsense, because the model has no such task and decodes the words as one anyway.

Only for task tokens the named tools do not already cover. Prefer caption, ocr, detect_objects and dense_region_caption: they wrap the common tokens, parse the structured output into usable fields, and document where each one misleads. This returns raw text either way.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
srcYesLocal file path or http(s) URL of the image to process. PDFs are also accepted and are rendered one image per page, so tools that return a list return one entry per page.
promptYesA Florence-2 task token, e.g. '<OD>', '<CAPTION>', '<REGION_PROPOSAL>'. Not a natural-language instruction -- plain English here produces garbage, not an answer.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.8.2

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 burden of behavioral disclosure and meets it: it warns that plain English 'does not fail' but returns confident nonsense, and that the output is raw text either way. This exposes the tool's raw-token-only nature and its key failure mode, which is exactly the context an agent needs.

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?

Three short paragraphs, each earning its place: a scope sentence, a critical failure-mode warning, and routing guidance. The caveat about confident nonsense is essential rather than filler, and the most important constraints are front-loaded.

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 two-parameter escape-hatch tool, the description tells the agent what it does, what it does not do, when not to use it, what failure looks like, and what the return is. The schema covers parameter formats and PDF behavior, so nothing essential is missing.

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

Parameters3/5

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

The input schema already describes both src and prompt at 100% coverage, so the baseline is 3. The description reinforces the token-vs-instruction distinction and the garbage-output risk, but it does not add a significant new parameter-level fact beyond what the schema already says. No compensation is needed here, so 3 is appropriate.

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 names a specific action ('Run a raw Florence-2 task token against an image'), identifies the tool as an escape hatch, and distinguishes it from the sibling wrappers by stating it applies only to tokens those tools do not already cover. This is far more informative than the generic tool name 'process'.

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 'Only for task tokens the named tools do not already cover' and instructs the agent to prefer caption, ocr, detect_objects, and dense_region_caption, explaining why those are better (they parse structured output and document misleading cases). The routing decision is unambiguous.

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