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Give a precise up/down signal on one story of a briefing: more like this, less like this, more technical, simpler, deeper, shorter, or mute. A story carries typed labels (see labels in get_briefing): e: (e:tesla), c:<field, its world> (c:auto), f: (f:soldi = money) and free tags. topics chooses the LEVEL: one label = broad ("less of Tesla, everywhere"); several labels = narrow, they are COMBINED with AND ("less of Tesla + soldi" = Tesla only when the story is about money; Tesla alone and money stories of others are untouched). Omit topics for the whole story (all its labels together, the narrowest level). Sending the same signal again with different topics REPLACES the previous one. "follow" is different: it pins THIS story — every development of it (and only of it) gets a full entry; "unfollow" releases it. withdraw: true takes a signal back.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signalYesThe signal to record
topicsNoLabels of this story to combine (AND), from `labels` in get_briefing — e.g. ["e:tesla"] for Tesla everywhere, ["e:tesla","f:soldi"] for Tesla only about money. Omit for the whole story.
entry_idYesEntry id from get_briefing
withdrawNotrue = take back the signal of this kind previously given on this story (the topics it had touched are restored). With "follow" use "unfollow" instead.
briefing_idNoWhich briefing. Omit for the latest.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / properties / topics / description
      Previous value: -"Only these topics of the story (as listed by get_briefing). Omit to apply to all of its topics."New value: +"Labels of this story to combine (AND), from `labels` in get_briefing — e.g. [\"e:tesla\"] for Tesla everywhere, [\"e:tesla\",\"f:soldi\"] for Tesla only about money. Omit for the whole story."
    • changedInput schema / properties / topics / maxItems
      Previous value: -5New value: +6
    • addedInput schema / properties / withdraw
      Added value: +{
+  "description": "true = take back the signal of this kind previously given on this story (the topics it had touched are restored). With \"follow\" use \"unfollow\" instead.",
+  "type": "boolean"
+}
  2. Changed1 schema field changed
    • changedInput schema / properties / signal / enum
      Previous value: -[
-  "more",
-  "less",
-  "more_technical",
-  "simpler",
-  "deeper",
-  "shorter",
-  "more_often",
-  "less_often",
-  "mute",
-  "follow"
-]New value: +[
+  "more",
+  "less",
+  "more_technical",
+  "simpler",
+  "deeper",
+  "shorter",
+  "more_often",
+  "less_often",
+  "mute",
+  "follow",
+  "unfollow"
+]
  3. Changed1 schema field changed
    • addedInput schema / properties / topics
      Added value: +{
+  "description": "Only these topics of the story (as listed by get_briefing). Omit to apply to all of its topics.",
+  "items": {
+    "type": "string"
+  },
+  "maxItems": 5,
+  "type": "array"
+}
  4. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare a non-read-only, non-idempotent, non-destructive mutation; the description goes beyond them by disclosing replacement semantics, that withdraw restores the touched topics, and that follow pins only this story. It omits auth requirements and any error/conflict behavior, so it is strong but not exhaustive.

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?

Purpose is front-loaded and each sentence carries semantics (levels, replacement, follow, withdraw). It is a single dense paragraph with heavy parentheticals, which hurts scannability slightly, but there is little true 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 5-parameter mutation with no output schema, the description covers signal meanings, topics combination, replacement, and withdraw/follow behavior well. The gap is the two periodic signals ('more_often'/'less_often') present in the enum but absent from the prose, plus no mention of what happens on invalid labels.

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 100%, so the baseline is 3; the description adds real meaning on top by explaining label syntax (e:/c:/f:/free tags), AND-combination of multiple labels, the broad-vs-narrow level framing, and omit-for-whole-story. The signal enum's 'more_often'/'less_often' values are nonetheless never explained.

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 names a specific verb+resource: 'Give a precise up/down signal on one story of a briefing', enumerating the signal set (more/less/technical/simpler/deeper/shorter/mute/follow). Scope (one story, its typed labels) is clear. It does not explicitly contrast itself with topical siblings like tune_topic, so differentiation is left implicit.

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

Usage Guidelines5/5

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

Explicit when/how guidance: 'topics' cardinality selects broad vs narrow levels, omit for whole story, and 'Sending the same signal again with different topics REPLACES the previous one'. It also routes around the follow case ('with follow use unfollow instead') and explains withdraw. Nothing about selection is left to inference.

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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