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Axint

by io.github.agenticempire

axint.feedback.create

Create or read a privacy-safe learning packet for Axint repair intelligence. Packets include project shape, diagnostic codes, issue class, redacted evidence, and likely product owner, but never include source code. Users can inspect the JSON before sending it to Axint Cloud. Use: create a privacy-safe issue packet when output was weak, or read the latest packet; never use it to send source. Inputs: latest reads instead of creates; outcome and diagnostic fields stay source-free unless excerpts are explicit. Effects: writes or reads redacted .axint/feedback packets; never includes source by default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdNoProject directory. Defaults to the MCP process cwd.
agentNoActive host/tool lane.
issueNoBug, weak Axint output, or failed repair behavior.
formatNoOutput format. Defaults to json.
latestNoWhen true, return the latest local feedback packet instead of creating a new.
sourceNoOptional inline Swift source used locally only.
fileNameNoDisplay file name when passing inline source.
platformNoTarget Apple platform hint.
sourcePathNoOptional suspected Swift file path used locally only.
testFailureNoOptional focused unit/UI-test failure text.
changedFilesNoChanged files to pin into the context pack.
xcodeBuildLogNoOptional Xcode build/test log evidence.
actualBehaviorNoOptional actual behavior.
runtimeFailureNoOptional crash, freeze, hang, or runtime failure text.
expectedBehaviorNoOptional expected behavior.
projectContextPathNoOptional .axint/context/latest.json path.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
isErrorNo

TDQS

A4.3/5.0
Behavior4/5

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

Annotations provide little (all false), so the description carries the burden. It discloses read/write effects, redaction, inspectability before sending to Axint Cloud, and the default exclusion of source code. This adds meaningful behavioral context beyond the sparse annotations.

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?

The description is three labeled sentences with front-loaded purpose. It is dense but every sentence contributes; no waste, though it could be slightly more scannable.

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 dual-purpose tool with 16 parameters and an output schema, the description covers use cases, privacy constraints, and key parameter behavior. It does not explain return values, but the output schema covers that, so this is complete enough.

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?

Schema coverage is 100% for 16 parameters, so baseline is 3. The description adds value by clarifying that `latest` reads instead of creates and that diagnostic fields remain source-free unless explicit, which enhances parameter understanding beyond the schema.

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 the tool creates or reads a privacy-safe learning packet for Axint repair intelligence, naming the specific resource and its purpose. It distinguishes from sibling tools by emphasizing privacy and excluding source code, which is a unique focus.

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?

Explicit guidance is present: create a packet when output is weak, read the latest packet, and never use it to send source. However, it does not name alternative sibling tools for those other cases, so it lacks the full alternatives component.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with descriptions specifying exact use cases (e.g., axint.activate for smoke test, axint.status for version). There is minor potential overlap between axint.suggest and axint.feature, but descriptions clarify suggestion vs generation.

Naming Consistency5/5

All tools follow a consistent hierarchical pattern: 'axint.<category>.<action>' (e.g., axint.agent.advice, axint.swift.validate). Even standalone tools like axint.compile fit the pattern. No mixing of conventions.

Tool Count2/5

36 tools is well above the typical 3-15 range for a well-scoped set. While the server covers a broad domain, the sheer number may overwhelm agents and reduce efficiency. A reduction or grouping would improve coherence.

Completeness4/5

The tool set covers the full Axint development lifecycle: installation, compilation, validation, repair, upgrade, session management, and coordination. Minor gaps exist (e.g., no dedicated tool for deleting project artifacts), but core workflows are well-supported.

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