Valuein — SEC EDGAR Fundamentals & Smart-Money Data

Score Thesis Outcome

score_thesis_outcome

Grade a saved thesis against fundamental momentum since its creation. Pulls revenue / operating-margin / EPS / OCF deltas and aggregates into a score in [-1, +1]. Bull theses are graded by directional alignment, bear by inverse, neutral by closeness-to-flat. The grade is persisted back to the thesis row; re-call to refresh once new fundamentals land.

Note (PR 2): scoring is fundamental-only — does NOT yet include market-price returns. Phase 2 will mix in price data via a partner feed; the response shape is stable.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	No	Snapshot date for the 'current' fundamentals window. Defaults to today UTC. The scorer picks the fiscal period closest to this date.
`thesis_id`	Yes	Id returned by `save_thesis` or `list_theses`.

Output Schema

TableJSON Schema

Name	Required	Description
`_meta`	Yes	Provenance envelope — data lineage for every MCP response
`thesis`	Yes
`outcome`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 18, 2026

changedOutput schema / properties / _meta / properties / fundamentals_as_of / description

Previous value: -"ISO timestamp when the FINANCIAL STATEMENTS were last rebuilt. Use THIS — not `last_updated` — when telling a user how current the fundamentals are. The snapshot is republished on every weekday price refresh while the statements are carried forward unchanged, so `last_updated` can be far more recent than the numbers it sits next to."New value: +"ISO timestamp when the FINANCIAL STATEMENTS were last rebuilt in bulk. Use THIS — not `last_updated` — when telling a user how current the cross-sectional fundamentals are. The snapshot is republished on every weekday price refresh while the statements are carried forward unchanged, so `last_updated` can be far more recent than the numbers it sits next to. It is a floor for a single filer, not a ceiling: a filer with a live partition receives its filing, facts and ratios intraday (minutes after EDGAR dissemination), so an entity-scoped read may carry a filing newer than this; cross-sectional ranks (factor scores, earnings signals) refresh with the weekly bulk export."

Changed2 schema fields changedJul 25, 2026

addedOutput schema / properties / _meta / properties / fundamentals_as_of

Added value: +{
+  "description": "ISO timestamp when the FINANCIAL STATEMENTS were last rebuilt. Use THIS — not `last_updated` — when telling a user how current the fundamentals are. The snapshot is republished on every weekday price refresh while the statements are carried forward unchanged, so `last_updated` can be far more recent than the numbers it sits next to.",
+  "type": "string"
+}

addedOutput schema / properties / _meta / properties / price_as_of

Added value: +{
+  "description": "ISO timestamp when the price surfaces were last refreshed.",
+  "type": "string"
+}

Changed2 schema fields changedJul 7, 2026

addedOutput schema / properties / _meta / properties / cost_usd

Added value: +{
+  "additionalProperties": false,
+  "description": "Per-call cost transparency. Omitted for subscription-only tools that have no PAYG-equivalent price.",
+  "properties": {
+    "amount_usd": {
+      "minimum": 0,
+      "type": "number"
+    },
+    "basis": {
+      "description": "payg_charge = real agent-pay charge. payg_rate_card = indicative price, not billed.",
+      "enum": [
+        "payg_charge",
+        "payg_rate_card"
+      ],
+      "type": "string"
+    },
+    "billed": {
+      "description": "true = this amount was actually charged via PAYG for this call. false = indicative PAYG-equivalent value; your plan already covers this call for free.",
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "amount_usd",
+    "billed",
+    "basis"
+  ],
+  "type": "object"
+}

addedOutput schema / properties / _meta / properties / latency_ms

Added value: +{
+  "description": "Wall-clock milliseconds this tool call took, measured server-side around the handler.",
+  "minimum": 0,
+  "type": "integer"
+}

Changed1 schema field changedJul 3, 2026

addedOutput schema / properties / thesis / properties / outcome_components

Added value: +{
+  "anyOf": [
+    {
+      "additionalProperties": {},
+      "type": "object"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "description": "Phase C fundamentals × price breakdown (fundamentals_score, price_score, price_return_pct, legs_used, basis, …). Null when not yet scored or scored without a breakdown; absent entirely on records graded before Phase C shipped — both mean 'no components recorded'."
+}

Changed1 schema field changedJun 18, 2026
- addedOutput schema / properties / _meta / properties / pit_safe / description
```
Added value: +"true iff a zero-look-ahead point-in-time cut was applied to every returned figure"
```

Changed1 schema field changedMay 29, 2026

addedOutput schema / properties / thesis / properties / visibility

Added value: +{
+  "description": "Phase 3 visibility: 'private' (owner-only), 'unlisted' (known URL), 'public' (surfaces on the author's /[handle] profile + reputation).",
+  "enum": [
+    "private",
+    "unlisted",
+    "public"
+  ],
+  "type": "string"
+}

AddedMay 12, 2026

TDQS

A4.4/5.0

Behavior5/5

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

The description goes well beyond the annotations by disclosing that the grade is persisted back to the thesis row, that calling it again refreshes the score, and that current scoring excludes market-price returns despite future plans. This gives an agent a clear mental model of the side effects and the non-idempotent, time-sensitive nature of the 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 front-loaded with the core purpose and proceeds through scoring methodology, persistence behavior, and limitations in a compact, structured way. The bold note cleanly separates an important constraint without burying it, and no sentence is filler.

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?

The description is complete enough for an agent to understand the scoring inputs, the persisted side effect, and the fundamental-only limitation, especially since an output schema exists for return values. It does not explicitly address edge cases such as invalid or missing thesis fundamentals, nor does it compare against score_due_theses for batch workflows, but for single-thesis refresh usage it is largely sufficient.

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%, and both `thesis_id` and `as_of` are already documented in the schema. The description adds useful context about the overall scoring mechanism but does not add meaning to the parameters themselves beyond what the input schema already provides, 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?

The description states a specific verb ('Grade'), a precise resource ('a saved thesis'), and the evaluation basis ('fundamental momentum since its creation'). It also distinguishes itself from siblings like score_claim by clearly targeting theses, and from score_due_theses by emphasizing a single saved thesis with a re-call/refresh model.

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?

The description gives clear context for when to use the tool: after a thesis is saved, and again 'once new fundamentals land' to refresh the persisted grade. It also warns that scoring is fundamental-only and does not yet include market-price returns, which sets a reasonable boundary. However, it does not explicitly name alternatives such as score_due_theses when batch scoring is desired.

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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