Skip to main content
Glama
duriantaco

mcp-skylos

by duriantaco
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

Attribute changes to agents or humans

provenance_scan
Read-only

Scan a project for AI-agent, bot, and human contribution evidence by combining git signals with local attribution, returning per-file JSON with coverage status.

Instructions

Report available evidence of AI-agent, bot and human contributions.

Combines git signals since the base (agent bot accounts, Co-authored-by and AI declaration trailers, agent-named commit subjects) with recorded local attribution evidence. Returns JSON with per-file attribution, evidence and coverage status. Commit declarations do not establish line authorship; missing evidence remains unknown. Read-only and local.

Requires SKYLOS_API_KEY: this tool returns an authentication error unless the MCP server is started with SKYLOS_API_KEY set. Without a key only analyze works (5 calls/day). The local CLI needs no key: skylos verify <file> and skylos agent install-hooks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesProject directory. A relative path resolves against the MCP server's working directory.
diff_baseNoGit ref to compare against. Omit to use the CI base branch when one is set, otherwise origin/main.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv4.49.0
    • addedInput schema / properties / diff_base / description
      Added value: +"Git ref to compare against. Omit to use the CI base branch when one is set, otherwise origin/main."
    • addedInput schema / properties / path / description
      Added value: +"Project directory. A relative path resolves against the MCP server's working directory."
  2. Addedv4.10.0

TDQS

A4.2/5.0
Behavior5/5

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

Annotations already declare read-only/non-destructive/closed-world, and the description adds substantial context beyond them: the API-key requirement and the exact auth error without it, the 5-calls/day limit for `analyze`, no-key local CLI alternatives, and the epistemic caveat that commit declarations do not establish line authorship. This is exactly the behavioral detail annotations cannot carry.

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 purpose, followed by caveats and prerequisites in a logical order; every sentence carries information. Slightly dense but no wasted filler, and the final CLI/auth paragraph stays relevant to correct invocation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only analysis tool with full schema coverage, an output schema, and rich annotations, the description covers purpose, evidence sources, return shape, epistemic limits, and the auth prerequisite. Nothing needed to call it correctly is missing.

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 100%, so both `path` and `diff_base` are already documented. The description reinforces the base-ref concept ('git signals since the base') but adds no format or default nuance beyond the schema's own 'origin/main' fallback note, so the baseline 3 applies.

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?

States a specific verb ('Report') and resource (evidence of AI-agent, bot and human contributions) and details the exact git signals inspected. An agent can distinguish it from siblings like verify_agent or verify_change without opening the schema.

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 implied by the evidence it aggregates ('git signals since the base ... combined with recorded local attribution evidence'), but there is no explicit when-to-use versus verify_agent or the other verification siblings. The key/auth paragraph gives a fallback route (local CLI, or `analyze`) rather than invocation guidance.

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

Deploy Server

Other Tools