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Get compact chart evidence for AI

get_compact_chart_evidence
Read-onlyIdempotent

Returns a stable, compact, deterministic evidence object for narration agents. It omits bulky timelines and contribution matrices while retaining calculation version, confidence, placements, focus, and safety boundaries.

Input Schema

NameRequiredDescriptionDefault
dateYes
nameNo
timeYes
focusNogeneral
placeYes
languageNoen
latitudeYes
timezoneNo
longitudeYes
timezoneOffsetYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / required
      Previous value: -[
      -  "name",
      -  "date",
      -  "time",
      -  "place",
      -  "latitude",
      -  "longitude",
      -  "timezoneOffset"
      -]New value: +[
      +  "date",
      +  "time",
      +  "place",
      +  "latitude",
      +  "longitude",
      +  "timezoneOffset"
      +]
  2. Added

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavioral facts: the output is stable and deterministic, and it explicitly documents what is dropped (timelines, contribution matrices) versus retained (calculation version, confidence, placements, focus, safety boundaries), which helps an agent trust and reuse the result.

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?

Two sentences with no filler, front-loading the core behavior (what it returns) before the scope details. The jargon 'contribution matrices' is domain-specific but compact and earned given the audience.

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-structure explanation is not strictly required, and the description usefully characterizes output richness and safety boundaries. However, with 0% schema description coverage on 10 parameters and no differentiation from the many sibling get_* tools, the definition is only minimally sufficient to invoke this tool correctly.

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 description coverage is 0% across 10 parameters (6 required), and the description supplies no parameter meaning, format, or defaults. 'focus' appears only as a retained output field, not as an explained input, and required fields like date, time, place, latitude, longitude and timezoneOffset are entirely unaddressed where the schema gives no help.

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?

States a specific verb and resource ('Returns a ... evidence object') with the scope qualifiers compact, stable and deterministic, so the agent knows it produces a summarized reading artifact. It does not, however, distinguish itself from closely related siblings like get_full_reading_context, get_depth_analysis or get_timing_context, which an agent could easily confuse given the crowded get_* family.

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?

The phrase 'for narration agents' plus the omission policy ('omits bulky timelines and contribution matrices') implies that this is the lightweight alternative to a fuller context tool, which is useful implied guidance. But no alternative is named and no explicit when-to-use / when-not-to-use condition is given, so the agent must infer the routing.

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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