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Liminal

by ai.physea
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get_capabilities

Every Liminal tool; tool= returns its full argument schema.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolNo
contextYesDescribe the user's goal in one short sentence. Omit personal data and secrets.
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / llm_model
      Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "context"
-]New value: +[
+  "context",
+  "llm_model"
+]
  2. Changed4 schema fields changed
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Describe the user's goal in one short sentence. Omit personal data and secrets.",
+  "type": "string"
+}
    • addedInput schema / properties / tool
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / required
      Added value: +[
+  "context"
+]
    • removedInput schema / title
      Removed value: -"get_capabilitiesArguments"
  3. First observed

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden and does little. It implies a safe read (schema retrieval) but never states read-only status, whether any side effects occur, or why context/llm_model are required for something that looks like pure introspection. No rate-limit, auth, or return-format disclosure.

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 efficient sentence with the core behavior front-loaded and zero filler. It is telegraphic to the point that a verb is missing from the first clause, which slightly hurts parseability, but every element earns its place.

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?

No output schema exists, so the description should ideally characterize the return; it does say it returns a full argument schema or the tool list, which is the essential point. However it omits the required analytics params (context, llm_model), any note that these are mandatory, and the result's shape/format, leaving gaps for a 3-param tool.

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 67%, above the 50% baseline, and the two highest-value params (context, llm_model) are already well documented in the schema. The description usefully explains the one undocumented param: tool=<name> returns that tool's schema. This maps the tool parameter's semantics but adds little else, so a baseline 3 is appropriate.

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 states a specific verb+resource: it enumerates every Liminal tool and, given tool=<name>, returns that tool's full argument schema. That is a concrete, distinguishable capability (a discovery/introspection tool) rather than a restatement of the name. It stops short of differentiating itself from siblings like read or solve, and the first clause is a verbless fragment, but the purpose is clear.

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 versus alternatives, no prerequisites, and no mention of when not to use it. The agent must infer that this is a self-discovery call to be made before invoking an unknown sibling tool. No explicit usage context is provided.

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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