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flconsole — Upwork jobs

set_feed_tuner

Turn the feed tuner on or off for a feed with Telegram alerts. on: true needs target_band (jobs a day) and the user's clear yes in this conversation (user_confirmed: true): that yes is the standing consent for tuner changes within the feed-tuner rules. on: false needs no confirmation. The user is told in Telegram. Counts as a feed change.

Input Schema

NameRequiredDescriptionDefault
onYes
feed_idYesFeed id from list_feeds.
target_bandNoJobs a day the user wants from this feed, e.g. {"min": 5, "max": 15}.
user_confirmedNotrue only if the user, in this conversation, explicitly agreed to the tuner on this feed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Beyond the annotations, the description discloses important behavioral traits: user confirmation is required for enabling, disabling requires no confirmation, the user is notified via Telegram, and the action counts as a feed change. It does not explain effects on existing tuner runs or how changes can be reverted.

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 short and front-loaded, with the on/off conditions stated clearly. It is slightly dense but contains no redundant 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?

For a mutation tool with no output schema and a nested parameter, the description covers the key prerequisites, side effects, and consent model. It leaves some gaps around how the tuner interacts with related feed settings, but the core information needed to call it correctly is present.

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 coverage is 75%, but the description adds conditional meaning not expressed in the schema: target_band is required when on is true, and user_confirmed represents explicit consent in the current conversation. This is meaningful semantic guidance beyond the field descriptions.

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 and resource: turning the feed tuner on or off for a feed, with Telegram alerts. It is clear enough to distinguish from broad siblings like update_feed, but it does not explicitly distinguish itself from set_feed_alerts or set_feed_paused.

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?

It gives concrete when-to-use conditions for both on: true and on: false, including the confirmation requirement and target_band requirement. It does not name alternative tools or state when this tool should not be used instead of related feed-setting tools.

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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