tares

name	tares
description	Use when you need cross-source context about this project's systems (recent deploys, logs, metrics, entities, correlated timelines), OR when organizing Tares itself — creating sources, labels, and views. Tares is the data plane; read it via the tares MCP tools for one correlated timeline per entity, and author its catalog with the create/derive tools following the rules below.

Tares

Tares is a data plane for agents: it ingests many sources (logs, metrics, deploys, database rows, agent sessions) and serves one correlated, time-ordered timeline per entity (a service, customer, session, …). The tares MCP server is connected (this plugin registers it).

Reading

When a question needs real context about running systems — "what changed before this error", "is this service healthy", "what has this customer done recently" — read Tares instead of guessing:

• Start by discovering what's there: list_sources, catalog_list, entities.
• Read a timeline with query (a view + an entity key) or read (a {label: value} selector).
• Treat the timeline as ground truth for what happened and when; cite specific events.

This plugin also streams the current Claude Code session into Tares (the claude_code source) when session streaming is enabled, so prior sessions are queryable as a source.

Challenger sessions

When the user asks to make this a challenger session (any wording: "challenger session", "let Codex challenge this", "/tares:challenger"), call the set_session_flow tool with flow="challenger" and say so in one sentence. Do not run Codex yourself: the plugin's hooks challenge the plan when you leave plan mode and every commit you make, and hand you the findings. set_session_flow with an empty flow turns it off.

Authoring: sources, labels, and views

Tares's data model has three layers — get them right and correlation just works; guess and it silently doesn't:

• Fields — the candidate menu. A source's raw payload is stored losslessly; source_fields (and discover_source for a new source) profiles the fields it actually contains, with coverage and top values. Fields are not queryable on their own.
• Labels — the declared axes you promote from fields (or a const, or a regex over a field). Labels are what you filter, group, key, and correlate by. One label is the primary key.
• Views — correlate one or more sources into a per-entity timeline, keyed by a label the sources share.

Rule 1 — Labels must come from real fields (never invent one)

Before choosing a source's labels, call source_fields (existing source) or use discover_source's proposed_config (new source) to see the fields that actually exist. A label reads from one of three things, all grounded in real data:

• field: a profiled field name — must match a field source_fields shows, exactly.
• const: a fixed value stamped on every event (for a source with no natural field for an axis).
• a regex over a field — set pattern + replace (and/or map) on the label to normalize messy values. Regex/alias normalization is a first-class feature: use it instead of guessing at a clean field. type: "number" makes a label aggregatable (for triggers); the primary key must be a string.

Never name a field that isn't in the profile — it extracts nothing. Set labels by creating the source (create_source) or updating its config with the full labels list.

Rule 2 — Views key/filter on LABELS only, never raw fields

A view's key_field (and any filters) must be a label the chosen sources expose — not an arbitrary payload field. If you want to correlate on something that isn't a label yet, promote it to a label first (Rule 1), then derive the view keyed by that label. Confirm a source's current labels with catalog_describe("source:<name>") before deriving.

Rule 3 — To match a label across sources, add a NEW label — don't rename

To join source B to source A on a shared axis (say service), source B needs a label named service too. Declare a new label on B (reading B's matching field) — do not try to rename B's field or existing label. There is no rename; a source's label set is declared whole, so add the shared-named label to B and keep its others. The values must agree literally across sources for correlation to work — if B's raw values differ (e.g. checkout-svc vs checkout), normalize them with pattern/replace/map on B's label so they match A's.