ASTRA

fcs_levels

Read-onlyIdempotent

Query the four-level consciousness framework to retrieve each level's formalism, epistemic status, implementing ASTRA modules, and unresolved gaps; optionally filter by level I–IV.

Instructions

The four-level framework — substrate, kinaesthetic proto-consciousness, hierarchical inference, access consciousness — with each level's formalism, epistemic status, complement reading, the ASTRA modules that implement or stand in for it, and the gap ASTRA cannot close at that level.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "fcs_levels"
  }
}

Input Schema

Name	Required	Description	Default
`level`	No	One level; omit for all four.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.1.0Sep 23, 2026

TDQS

B3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful content-level context by naming the six categories of information returned per level, including the notable 'gap ASTRA cannot close', but says nothing about output format, size, or whether the per-level text is static.

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?

A single dense sentence with the core resource front-loaded and the enumerated payload following the em-dash. The list is long, but every item conveys a distinct content category rather than restating the name, so it earns its length.

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?

With no output schema and only one optional enum parameter, the description carries the return-value burden and does so by naming the per-level fields an agent will receive (formalism, epistemic status, complement reading, ASTRA modules, gap). Only the response shape/format remains unspecified, which is minor for a read-only lookup.

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?

Schema coverage is 100% and the single level enum already documents 'One level; omit for all four.' The description's 'each level's ...' phrasing faintly implies the per-level granularity but adds no syntax or behavioral detail beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description is a noun phrase that enumerates the resource's contents (four levels, each with formalism, epistemic status, ASTRA modules, and gaps) but never states an action verb, so it reads as a topic list rather than 'retrieve X'. It is specific about subject matter but does not distinguish this from close siblings like fcs_taxonomy, fcs_stratify, or fcs_report, which plausibly cover overlapping framework concepts.

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

Usage Guidelines2/5

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

There is no guidance on when to call this tool versus the many fcs_* siblings, nor any stated preconditions or exclusions. The agent is left to infer usage from the topic alone.

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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