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Implicit Core

Implicit Core

by zeitcow
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Python
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implicit_inspect_universe

Read-onlyIdempotent

Read a known universe handle to return its version, total count, and addressed count without materializing data; unknown handles fail.

Instructions

Read a known universe handle, version, count and addressed count. Unknown handles fail; no materialization.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "universe_id": "<universe_id>"
    },
    "name": "implicit_inspect_universe"
  }
}

Input Schema

NameRequiredDescriptionDefault
universe_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), so the bar is lower, yet the description adds genuine behavior: unknown handles fail, and the call performs no materialization. These side-effect and error semantics are exactly the added value expected beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences, zero filler, with the core verb-resource statement front-loaded and the constraints trailing. Nothing is wasted.

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 single-param, read-only tool with no output schema, the description covers the returned fields, error behavior, and the no-materialization guarantee. It is largely self-sufficient, missing only the parameter format detail already present in the schema.

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

Parameters3/5

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

One parameter with 0% schema description coverage, so the description must compensate. Calling it a "known universe handle" conveys that the value must reference an existing universe, which is real meaning, but the handle's format (the ^u[1-9][0-9]?$ pattern stated only in the schema) is 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?

States a specific verb (read/inspect) and resource (universe) and enumerates the returned fields: handle, version, count, addressed count. An agent knows it's an inspection tool, though it never explicitly distinguishes itself from the sibling implicit_inspect_state.

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?

"Read a known universe handle" and "no materialization" imply the preconditions (you must already hold a valid handle) and implicitly contrast with implicit_materialize. However, there is no explicit when-to-use routing against implicit_inspect_state or implicit_get_provenance, leaving usage 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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