Skip to main content
Glama

get_state

Snapshot the QA engine's current state, aggregating connection, run session, and infobase data.

Instructions

Snapshot the native QA engine's session/run state. Aggregates three views: connection (the TestClient TPort liveness, + pid aliveness if given), run_session (the scenarios run this server session — counts + the last scenario/status, from the same log as get_test_results), and infobase (the configured target identity, password-redacted, from the .ai1c profile). Returns {engine {default_capture, value_read_templates}, connection {pid, alive, host, port, listening}, run_session {…aggregate…, last_scenario, last_status}, infobase {…redacted…}}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pidNo
hostNo127.0.0.1
portNo
env_fileNo.ai1c/vanessa-qa-mcp.env

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does so reasonably: it discloses that infobase identity is password-redacted, that run_session data is sourced from the same log as get_test_results and the .ai1c profile, and that 'if given' controls whether pid aliveness is checked. The word 'Snapshot' implies a read with no side effects, though this is not stated outright.

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?

Front-loaded with the core purpose in the first clause, then tight enumeration of the three aggregated views and the return shape. It is dense but nearly every clause carries information; the return-shape listing is slightly redundant given an output schema exists.

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 read-only diagnostic aggregator with an output schema, the description is complete enough: data sources, redaction, and per-param behavior are covered. It need not restate return values since the output schema exists, and it omits nothing critical for correct invocation.

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 description coverage is 0%, so the description must compensate for four undocumented params. It does clarify that pid is optional ('if given') and drives an aliveness check, and that env_file relates to the .ai1c profile, but host and port are left to inference via the 'TestClient TPort liveness' and return-shape hints. Partial compensation only.

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 verb 'Snapshot' plus the resource 'native QA engine's session/run state' is specific, and the description enumerates exactly what is aggregated (connection, run_session, infobase). It references related siblings (get_test_results, infobase_info territory) but never states explicitly why an agent would pick this over test_client_status or infobase_info, so it falls just short of full sibling differentiation.

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?

Usage is only implied: the description frames this as a state snapshot, which nudges toward diagnostic/monitoring use, and it notes the run_session data comes from the same log as get_test_results. But there is no explicit when-to-use, when-not-to-use, or named alternative to route the agent.

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