FilmLab

Analyze Location Image

lab_analyze_location_image

Read a photo and extract location fields from it (Gemini Vision, costs credits — exact price via get_cost: true). Returns the fields — it does NOT create a board; feed the result into lab_create_location yourself. Note the payload: the image travels as base64 inside the call, so keep it small (a downscaled JPEG, not a full-resolution capture).

Input Schema

TableJSON Schema

Name	Required	Description
`get_cost`	No	Free: the exact price of this operation; nothing is rendered or charged. Name it to the person before the real run.
`mime_type`	No	Defaults to 'image/jpeg'
`image_base64`	No	The image as base64. A data-URL prefix ('data:image/jpeg;base64,…') is stripped by the backend

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 11, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```

Changed2 schema fields changedOct 11, 2026

addedInput schema / properties / get_cost

Added value: +{
+  "description": "Free: the exact price of this operation; nothing is rendered or charged. Name it to the person before the real run.",
+  "type": "boolean"
+}

removedInput schema / required
```
Removed value: -[
-  "image_base64"
-]
```

First observedOct 3, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations only say readOnlyHint=false/destructiveHint=false; the description adds real behavioral facts — it consumes credits via Gemini Vision, it deliberately does not persist a board, and the image travels inline as base64 so payload size matters. These are exactly the non-obvious traits an agent needs before invoking.

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

Conciseness4/5

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

Front-loaded with the core action, then cost, then the non-persistence caveat, then payload advice. Dense use of em-dashes but every clause carries information; nothing is filler.

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

Completeness4/5

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

No output schema exists, so the description must cover returns — it says 'Returns the fields' and clarifies the downstream step (feed into lab_create_location), but does not enumerate what the extracted fields look like. Flow and cost are complete; the return shape is only loosely characterized.

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?

Schema description coverage is already 100% (get_cost, mime_type, image_base64 all documented), so baseline is 3. The description adds practical value on top: price via get_cost, name the cost to the user, and keep the base64 payload downscaled rather than a full-resolution capture.

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?

States a specific verb and resource: 'Read a photo and extract location fields from it.' It goes further by naming the sibling it is not — explicitly 'it does NOT create a board' — and routes the agent to lab_create_location, so it is unmistakable against the many lab_* siblings.

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?

Gives clear operating context: run with get_cost: true first to price the credits, then feed the result into lab_create_location yourself. It stops short of naming an alternative analyzer or a when-not condition, but the workflow it belongs to is unambiguous.

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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