ASTRA Unified Research Lab MCP Server

tcai_capability_model

Assess action outcomes by querying the expected valence for a given action, or list the learned capability table to review the action-to-valence mapping.

Instructions

Agency capability model (DirectExperienceLearner port): action → expected-valence map (EMA). Query expected outcome of an action, or list the learned capability table.

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": "tcai_capability_model"
  }
}

Input Schema

Name	Required	Description	Default
`action`	No	Action label to query expected valence for

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.0.0Aug 17, 2026

TDQS

A4.2/5.0

Behavior3/5

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It explains the internal model (action→expected-valence map) and the two query modes, implying a read-only operation, but it does not explicitly state side-effect freeness, output format, or error behavior. For a simple query tool, this is adequate but not rich.

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?

The description is a single, tightly written sentence that front-loads the core capability and uses no filler. Every phrase contributes meaning, from the parenthetical port reference to the two-mode usage explanation.

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 tool with one optional parameter and no output schema, the description adequately covers the two main use cases and the underlying semantics. It is sufficiently complete for an agent to invoke it correctly, though it does not spell out the exact return structure or edge-case handling. Given the tool's simplicity, this is a good level of completeness.

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 schema description coverage is 100% for the sole 'action' parameter, but the description adds key semantic value by clarifying that omitting the action lists the entire capability table. This goes beyond the schema's description of 'Action label to query expected valence for' by explaining the optionality and fallback behavior.

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

Purpose5/5

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

The description clearly states a specific verb ('Query') and resource ('expected outcome of an action' or 'learned capability table'), and it is distinguishable from siblings like tcai_memory_retrieve or tcai_self_model by referencing the action→expected-valence map (EMA). The mention of 'DirectExperienceLearner port' adds specificity.

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?

The description outlines two distinct usage modes: querying a specific action's expected valence, or listing the full capability table when no action is provided. This provides clear context for when to use the tool, though it does not explicitly name alternatives or exclusions. It is not misleading, just lacks a formal 'when not to use' statement.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server