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get_company_event_web

Read-only

Fetch the connected WEB of what recently HAPPENED to a company — the WHY behind its snapshot, and a time-ordered graph to CHAIN your next calls off.

===> `situate(symbols=[symbol])` already returns this web (newest 12 nodes) PLUS
the ontology cards, cache state and an ordered plan — start there. Call this
tool directly for the FULL graph or a wider `window_days`, and pair every node
you chain off with get_event_ontology(event_type=node.type): the web says WHICH
event, the ontology says what it entails and what to do about it.

===> A FIRST-LINE TOOL FOR SINGLE-COMPANY QUESTIONS, not a specialist one. Reach
for it whenever the user asks what has been going on with a name ("catch me up on
WM", "what happened at NVDA this week", "anything I should know before
earnings?"), asks WHY the company looks the way it does ("why is the thesis
negative?", "what drove the downgrade?", "what changed?"), or whenever you are
about to aim a targeted `explore_data_catalogue`, `generate_report_for_stock`, or
`list_realtime_events` request at ONE symbol. Synchronous, cheap, and public —
one call turns "I'll guess which event types and dates matter" into "here are
the exact events, their timestamps, the tables that were written, and the call
that returns each full payload".

*** THE STANDARD PAIR: `get_company_snapshot` + THIS. *** They answer the two
halves of almost every company question and are usually called TOGETHER:
  - `get_company_snapshot(symbol)` -> the WHAT. Where the company stands now:
    thesis, fundamentals, technicals, ownership, grades. A settled position with
    no account of how it got there.
  - `get_company_event_web(symbol)` -> the WHY. The episode behind that position:
    the earnings print, the 8-K, the transcript update, the rating action, the
    13F refresh — in order, with edges saying how each relates.
Read them together: the snapshot for the current position, the web for the
sequence that explains it. A snapshot delivered alone reads as a verdict with no
evidence; this tool is the evidence. Conversely, do NOT use this tool to state
the company's current position — it carries headlines and pointers, not values.
When the two disagree, say so plainly rather than smoothing it over: the blocks
in a snapshot carry independent as-of dates, so an ownership or grade block can
predate anything in the window (widen `window_days` before concluding a snapshot
move is unexplained).

Returns:
  {"symbol": ..., "as_of": ISO8601, "window_days": N,
   "nodes": [...], "edges": [...], "market_context": [...]}

NODES — two kinds, newest first, each with a short id ("n1", "n2", ...):
  - kind="event"       — a published realtime event. Carries `type` (the event
                         type, e.g. "8k_release"), `family`, and a `fetch` hint
                         — the exact call that returns its FULL payload, e.g.
                         {"tool": "list_realtime_events", "event_type": "8k_release",
                          "related_endpoints": [...]}.
  - kind="data_update" — a write to a backing table for activity that publishes
                         NO event at all (insider filings, 13F refreshes, IPO
                         calendar entries, Companies House events, transcript and
                         fundamentals analysis). Carries `relation` (the table
                         written); most also carry a `fetch` hint (a ready-made
                         `explore_data_catalogue` query for the underlying rows),
                         and on some relations the hint additionally carries
                         `signed_query_url` — see step 2 below.
Every node carries `at` (when the write/publish happened) and `headline` (ONE
line, truncated at 200 chars).

EDGES — {"from": "n3", "to": "n1", "via": <relation>, "cause": ...}, pointing
from the EARLIER node to the LATER one. `cause` is a confidence grade — respect it:
  - "same_chain_run" — OBSERVED. One backend chain run produced both nodes.
  - "lineage"        — DECLARED. One wrote the table the other's event derives from.
  - "co_occurrence"  — TEMPORAL ONLY. Same company, same window, no known causal
                       path. Report these as "happened alongside", NEVER as caused-by.

HOW TO CHAIN (the point of this tool):
  1. Walk the edges back from the newest node to reconstruct the episode, then
     follow each event node's `fetch` to get the detail — e.g.
     `list_realtime_events(event_type="8k_release", tickers=["WM"])`. Always narrow
     by `tickers=[symbol]`; an unfiltered call re-pulls the entire cache.
  2. When a `data_update` node's `fetch` carries `signed_query_url`, call
     `get_signed_sql_drilldown` with it FIRST — zero planning, and it returns the
     new record (`is_new_record: true`) plus the context rows to read it against.
     That follow-up needs the user SIGNED IN (this tool does not); if it comes back
     not-authenticated, the link is fine — say sign-in is needed for the rows and
     fall back to an `explore_data_catalogue` query on the node's `relation`.
     Otherwise use the node's `fetch` query if it has one, or its `relation` and
     `at` to write a PRECISE `explore_data_catalogue` query ("insider filings for
     WM since 2026-08-09", "13F position changes for WM in the last week")
     instead of a vague one.
  3. Feed the concrete events and dates into `explore_data_catalogue` /
     `generate_research_report` so the analysis is scoped to what actually
     happened; for the company's own report, `get_latest_report` renders the plan
     saved from that event.
  4. Nothing here is a full payload — a headline is a pointer, never the content.
     Do not quote a headline as if it were the event.

`window_days` and `max_nodes` widen the look-back. Beyond the node cap, edges are capped per node (~3 same_chain_run, ~3
co_occurrence) and `market_context` — market-wide macro/economic-calendar nodes
that carry no ticker and so attach to no symbol — is capped at 5. A MISSING edge
is therefore not evidence that two things are unrelated.

Traps:
  - `at` is a WRITE/PUBLISH time, not a business date. An earnings call date or
    fiscal period end can be well before the node's timestamp — never report `at`
    as the date the underlying event occurred.
  - A "<table> refreshed for N symbols" node is universe-wide scheduled
    maintenance rolled up, not company-specific signal.
  - An empty web (`"degraded": true`, empty lists) means the graph has no rows for
    that symbol in the window — NOT that nothing happened and NOT that the symbol
    is uncovered. Say so, and fall back to `list_realtime_events` /
    `explore_data_catalogue` rather than concluding it was a quiet week.

PUBLIC at the backend — no account, plan, or entitlement is needed, the same
posture as `get_company_snapshot`. (The MCP connection itself still carries the
client's OAuth session; that gate is transport-wide, not this tool's.)
Rate-limited 60/min.

Every node's `type`, `relation` and `family` are join keys into get_event_ontology:
call get_event_ontology(event_type=node.type) to learn what that node ENTAILS (the
tables now fresh, the events that should follow and how soon, where the payload
lives after the 12h cache, whether downstream tables have refreshed yet) before
chaining further calls off it. Web = what happened to THIS company; ontology = what
that KIND of thing means. Read them together.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesCovered ticker, e.g. "WM".
max_nodesNoNode cap (default 40, max 200).
window_daysNoLook-back in days (default 7, max 90). Widen before calling a snapshot move unexplained.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofNo
edgesYes
nodesYesNewest first.
symbolYes
degradedNoTrue with empty lists when the graph has no rows for the symbol in the window.
window_daysNo
coverage_fromNoEarliest node in the whole graph; an older window cannot be answered.
market_contextNoMarket-wide macro / economic-calendar nodes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=true; the description carries far more — public backend/no entitlement, transport-level OAuth caveat, 60/min rate limit, edge caps (~3 per cause type), market_context cap of 5, and the degraded empty-web semantics. It also warns that a missing edge is not evidence of non-relation and that `at` is a write time, not a business date.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose and broken into labelled sections, which helps, but it is roughly a thousand words for a three-parameter tool. The get_event_ontology pairing is stated at least three times and the snapshot contrast is belaboured, so a meaningful share of the text does not earn its place.

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?

An output schema exists, yet the description still covers node kinds, edge shape and confidence grades, the chaining recipe, auth fallback on get_signed_sql_drilldown, and the empty-web fallback — everything needed to call and interpret it correctly. No gap remains.

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%, so baseline is 3, but the description adds real meaning: window_days should be widened before concluding a snapshot move is unexplained, and max_nodes interacts with per-node edge capping and the market_context cap. It stops short of documenting defaults in prose, but the capping semantics are genuinely additive.

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 ('fetch the connected WEB of what recently HAPPENED to a company') and immediately frames the scope as the WHY behind the snapshot. It explicitly differentiates itself from siblings — situate already returns the web, get_company_snapshot gives the WHAT — so an agent can pick the right tool 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?

Explicit when-to-use (single-company 'catch me up', 'why is the thesis negative', before aiming a targeted explore_data_catalogue/generate_report_for_stock call), when-not-to-use ('do NOT use this tool to state the company's current position'), and named alternatives (start with situate, pair with get_company_snapshot and get_event_ontology). Nothing 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.

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