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Assign Card

assign_card

Assign or remove a user from a Favro card by card ID, sequential ID, or name, using board lookup when needed to add or unassign team members.

Instructions

Assign or unassign a user from a card.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cardYesCard ID, sequential ID (#123), or name
userYesUser ID, name, or email
boardNoBoard ID or name (needed for name lookups)
removeNoIf True, remove the assignment instead of adding

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changedv0.3.2
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / board / description
      Added value: +"Board ID or name (needed for name lookups)"
    • addedInput schema / properties / card / description
      Added value: +"Card ID, sequential ID (#123), or name"
    • addedInput schema / properties / remove / description
      Added value: +"If True, remove the assignment instead of adding"
    • addedInput schema / properties / user / description
      Added value: +"User ID, name, or email"
  2. First observedv0.5.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden. It discloses that the operation is bidirectional (assign or unassign), which is useful, but omits permission/auth requirements, whether the operation is idempotent, what happens when the user is already assigned, and any side effects on notifications.

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?

A single front-loaded sentence with no filler or redundancy. It is arguably too terse given the tool's dual behavior, but nothing wasteful is present.

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

Completeness3/5

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

An output schema exists so return values need not be explained, and the parameters are documented. However, for a mutation tool with zero annotations, the description should have covered permissions, idempotency, and the board-lookup dependency, all of which are absent.

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?

Schema description coverage is 100%, so all four parameters (card, user, board, remove) are already fully documented in the schema. The description contributes no additional parameter semantics beyond restating the verb, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (assign/unassign) and resource (user-to-card) that lets an agent distinguish it from siblings like tag_card or add_comment. It does not explicitly differentiate from other card-mutation tools, but the scope is clear.

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 statement of when to use this tool versus alternatives, no prerequisites, and no mention of the board-resolution requirement even though the schema flags it as needed for name lookups. The 'remove' flag implies a dual purpose but the description leaves usage entirely to inference.

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