mindee_list_modelsList the document-extraction models available to your Mindee account (id, name, type). Use a model's id as the model_id for mindee_extract_document. V2 API: GET /v2/search/models.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Optional case-insensitive name filter, e.g. 'invoice'. | |
| model_type | No | Optional model type filter. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Read-only behavior is already covered by readOnlyHint=true. The description adds account scoping ('available to your Mindee account'), return fields, and the API endpoint, providing useful context without redundancy. No contradiction with annotations.
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?
Three sentences, each with purpose: statement of action, usage of the returned id, and API reference. No unnecessary words.
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?
Despite no output schema, the description lists the items' fields (id, name, type) and explains how to use the result. Combined with high-quality annotations and schema, it's sufficient for a simple list operation.
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 input schema already fully describes both optional parameters with examples (name filter, model_type filter). The description adds no additional parameter semantics, so baseline 3 applies.
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 lists document-extraction models for the account, specifying returned fields (id, name, type). It distinguishes from sibling tools like mindee_extract_document by focusing on discovery, and explicitly ties the model id to that extraction tool.
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 indicates when to use it: to discover available models and obtain the id for use with mindee_extract_document. It does not explicitly state exclusions or alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: extracting documents, fetching inference results, checking job status, and listing available models. There is no overlap or ambiguity between them.
All tool names follow a consistent pattern: the 'mindee_' prefix followed by verb_noun (extract_document, get_inference, get_job, list_models). This makes the set predictable and easy to navigate.
With only 4 tools, the server is tightly scoped to the essential Mindee operations: submit, poll, retrieve, and explore models. Each tool earns its place, and the count is ideal for this purpose.
The tool set covers the full document extraction lifecycle: listing models, enqueuing extraction (with built-in polling), checking job status for long-running requests, and fetching the final inference result. No critical operation is missing.