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papyruslabs-ai

Seshat

Official

Get Lineage

get_lineage
Read-onlyIdempotent

Fetch a symbol's typed change history—CI outcomes, reverts, renames, co-change partners, and rejected PRs—before editing to see how it changes and what failed last time.

Instructions

The change history of one symbol, typed by what kind of change each commit made (body, calls, data, signature, constraints…), with CI verdicts, reverts, rename tracking, co-change partners, and rejected PRs that touched it. Call before modifying anything load-bearing: it answers "how does this entity usually change, and what happened last time someone tried?" — which no text diff can. Complements get_blast_radius (current impact) with history (past behavior).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectNoProject name (required in multi-project mode). Use list_projects to see available projects.
entity_idYesEntity ID or name to fetch change history for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.20.2

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the bar is lower. The description goes further by disclosing what the result actually contains — typed change categories, reverts, rename tracking, rejected PRs — which materially shapes expectations. It omits pagination/volume behavior, so not a 5.

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-loads the resource and its payload before explaining why to call it. The long enumeration in sentence one is dense but earns its place by defining scope; only minor trimming is possible.

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?

With no output schema, the description carries the return-value burden and does so well by naming the categories of history returned. Combined with annotations covering safety, an agent has enough to call correctly; only result-shape details like ordering or limits are absent.

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% for both parameters, so the schema already documents entity_id and the multi-project 'project' flag with its list_projects hint. The description adds no parameter syntax or format detail beyond that, which makes 3 the correct baseline.

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+resource ('The change history of one symbol') and enumerates the distinguishing content: change-type typing, CI verdicts, reverts, rename tracking, co-change partners, rejected PRs. It explicitly contrasts itself with get_blast_radius, so an agent can tell the two siblings apart without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit trigger ('Call before modifying anything load-bearing'), an explicit framing question it answers, and names the alternative (get_blast_radius) with the axis of difference (current impact vs. past behavior). Nothing about when-to-use is left to inference.

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