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Metis

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Metis — Library Coverage

library_coverage

Quantify how much of your reference library is quotable by returning per-layer document and chunk counts, separating indexed full text from metadata-only or absent sources.

Instructions

What fraction of the library can Metis actually QUOTE?

The honest denominator for every grounding claim. There are three states, not
two: verified against text, known-but-unquotable (metadata only, no indexed
full text), and genuinely absent. Without this number, "not found in your
corpus" reads as "not in your literature" when it may only mean "not indexed"
— and a verification rate reported against an unknown denominator is itself
the kind of overclaim this whole layer exists to prevent.

Returns:
    Per-layer document and chunk counts, plus the quotable fraction of the
    reference library.

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.5

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the disclosure burden and does so well conceptually: it explains the three coverage states and warns that an unknown denominator can cause overclaiming. It also summarizes the return shape, though it does not explicitly state that the operation is read-only or whether it has side effects.

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 front-loaded with the core question and then explains why the denominator matters before listing return contents. It is slightly narrative and could be tightened, but the framing earns its place by preventing a common misinterpretation.

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 zero-parameter coverage tool with an output schema, the description provides enough conceptual context to call it correctly and interpret the quotable fraction. It does not need to explain every return field, and the output schema covers structural details.

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?

The tool has zero parameters, so there is no parameter semantics to clarify. The baseline for a zero-parameter tool is 4, and the description appropriately spends no time on non-existent arguments.

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 clearly identifies the resource as the library's quotable coverage, framed as 'what fraction of the library can Metis actually QUOTE?' and 'the honest denominator for every grounding claim.' It is specific about the metric's meaning but does not explicitly differentiate it from sibling tools like get_library_stats or get_pdf_index_stats.

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 clear context for when the number matters: before making grounding or 'not found' claims, so an agent avoids treating 'not indexed' as 'not in the literature.' It does not name alternatives or state when not to use this tool, leaving some routing inference to the agent.

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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