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Answer agent permission request

flow_agent_respond

Prevent unintended AI video costs: approve or reject an agent's pending credit-spend request for a specific Flow project.

Instructions

Approve or reject the agent's pending request to spend credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
answerYes
project_idYesFlow project id (from flow_list_projects).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
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 behavioral burden. It discloses that the decision concerns spending credits, but says nothing about consequences beyond the decision: whether approving is irreversible, whether credits are deducted immediately, whether the request expires, or what feedback the caller receives. For a mutation with financial impact this is thin.

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, which is structurally good. It is arguably too terse for a financially consequential mutation, but there is no wasted text.

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?

With no annotations, no output schema, and a mutation that spends credits, the description leaves too much unsaid: reversibility, timing, and the relationship between the answer and the project_id are all absent. An agent cannot confidently call this without guessing at the operational consequences.

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

Parameters2/5

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

Schema coverage is only 50%, and the description adds no parameter meaning at all. The 'answer' enum is self-documenting in the schema, but project_id is only explained via a schema note ('from flow_list_projects'), and the description never clarifies that answering requires supplying the affected project or that only Approve/Reject are valid values.

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 gives a specific verb pair (approve/reject) and a specific resource (the agent's pending spend request), so the core action is unambiguous. It does not, however, explicitly differentiate itself from siblings like flow_agent_chat or flow_set_agent_instructions, which also configure agent behavior.

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

Usage Guidelines3/5

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

Mentioning the 'pending request' implies the condition for use (a request must exist and be awaiting an answer), but the description states no explicit when-to-use/when-not guidance and names no alternative tool. Usage is inferable rather than stated.

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