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Seed MCP Server

by burgeonbot

seed_update_frame

Update an existing Seed frame's metadata by frame name, allowing changes to fields, relations, labels, and filters while specifying the source table.

Instructions

Update Seed frame metadata by frame name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesMachine name for the frame.
labelNoHuman-readable frame label. Defaults to the frame name.
tableYesSource table name for the frame.
fieldsYesFields from the source table to expose in the frame.
relationsNoRelations from the source table to expose in the frame.
descriptionNoFrame description.
fieldOrderJsonNoOptional JSON field order metadata.
fieldFiltersJsonNoOptional JSON field filters using Seed's Sequelize-style filter shape.
relationFiltersJsonNoOptional JSON relation filters using Seed's Sequelize-style filter shape. For current-user scoping, dot-walk to a related users.email field and use ___current___, e.g. {"[Op.and]":[{"users_contacts_user.email":{"[Op.like]":"___current___"}}]}.
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that metadata is updated by frame name but does not explain whether updates are partial or full replacements, how relations are handled, whether the operation is idempotent, or any side effects such as overwriting existing data.

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?

The description is a single, clear sentence with no wasted words. It is concise and front-loaded, but it is so brief that it sacrifices behavioral context. Still, it earns its place as a succinct purpose statement.

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

Completeness2/5

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

Given the complexity of an update operation with 9 parameters, no annotations, and no output schema, the description is severely incomplete. It does not mention what happens to unspecified fields, whether the update merges or replaces, which parameters are required for an update, or what the response contains, leaving a significant gap for safe invocation.

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 provides detailed descriptions for all 9 parameters, covering 100% of them, so the schema itself fully documents parameter semantics. The description adds no additional parameter meaning, but per the rubric, baseline 3 is appropriate when schema coverage is high.

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 uses a specific verb ('Update') and resource ('Seed frame metadata') with a clear scope ('by frame name'), which immediately distinguishes it from sibling tools like seed_create_frame, seed_get_frame, and seed_delete_frames. Even without a title, the purpose is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives (e.g., use seed_create_frame for new frames) or any exclusions or prerequisites. The agent must infer usage from the name and sibling context, which is insufficient for informed selection.

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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