Skip to main content
Glama

lineage_record

Record a provenance atom to answer where a willow came from, why it is this way, and what was here before. Requires rationale and evidence for verifiable memory.

Instructions

Record a provenance atom — the "story of this willow" as memory an agent can query later. Answers the questions agents keep asking: where did this come from, why is it this way, what was here before. rationale (the WHY) and at least one evidence citation (a PR / commit / file / session) are REQUIRED — an atom that can't cite is lore, not memory, and is refused.

Relationships to other atoms are typed EDGES (stored in lineage_edges, the same {from,to,relation,context} shape willow's own knowledge graph uses), and direction is QUERIED, not stored twice:

  • supersedes — atoms this REPLACES (the old ones become non-current)

  • derived_from — atoms this CAME FROM but did NOT retire (both stay valid)

  • motivated_by — the friction/decision behind it (may be a gap id or an external node, not only another atom)

Corrections re-record the same id in place; edges persist independently. Confined to your store_scope like every store write.

subject_id (guardian-consent seam): a provenance atom that makes a person-shaped claim about a non-owner names that subject here. This is the highest bar in the seam — person_inference — corpus-lens's quarantined PERSON_CLAIM_TYPES: making the claim at all requires a verified grant. Opaque, never stored on the atom. Leave empty for the owner or for non-person lineage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
tagsNo
titleYes
app_idYes
originNo
evidenceNo
authorityNo
rationaleYes
subject_idNo
supersedesNo
derived_fromNo
motivated_byNo
Behavior5/5

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

With no annotations provided, the description carries the full burden and does so extensively. It discloses refusal conditions (must cite evidence), edge storage direction semantics, in-place correction behavior, store scope confinement, and the subject_id consent seam requiring verified grant. This is rich behavioral context far beyond a simple write operation.

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 long but dense and well-organized, with each section contributing essential details: purpose, requirements, edge types, corrections, store scope, and consent. There is no filler; every sentence earns its place despite the length.

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 complex write tool with no annotations and no output schema, the description is remarkably complete. It covers required fields, refusal behavior, edge direction model, update semantics, store scope, and the consent seam for person-shaped claims. It gives the agent everything needed to invoke correctly.

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?

Despite 0% schema description coverage, the description provides deep semantics for key parameters: rationale (WHY), evidence (required citations), edge arrays (supersedes, derived_from, motivated_by) with direction meaning, and subject_id (person_inference consent bar). However, common parameters like id, title, app_id, origin, authority, and tags are left unexplained, creating a partial gap.

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's specific verb ('Record') and resource ('a provenance atom'), and defines it as 'the story of this willow' that answers recurring provenance questions. It distinguishes itself from sibling tools by focusing on creating provenance atoms, while lineage_link/lineage_why handle linking and querying.

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?

Provides clear context on when to use the tool: to record provenance that answers questions about origin, rationale, and prior states. It also gives operational guidance such as required rationale and evidence, corrections re-record the same id, and edge persistence. However, it does not explicitly mention alternative tools or exclusions, so it stops short of a 5.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/rudi193-cmd/willow-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server