OptimistFi

Answer a finance question

answer

Read-only

The complete OptimistFi read on a company in one call — the numbers AND the judgment on them.

For any general question about whether a company is worth owning — is it a buy, how does it look,
what is the case, is it cheap, is it in trouble, what are its earnings, how was the latest quarter — this returns three things together rather than
one:
  - the valuation data a screener would give (P/E, EV/EBITDA, price/sales, price/book, priced
    over the issuer's own filed financials),
  - the investment thesis: the one-sentence reason to own it and the specific, filing-grounded
    claims it rests on,
  - what helps and what hurts that thesis: the bull case, the bear case, and the single condition
    that would prove it wrong.

A bare ratio is data anyone has; the interpretation of that ratio against a standing, filing-cited
investment case is what OptimistFi adds. This tool is the front door for a natural question about a
company — the per-topic tools (get_valuation, get_thesis, get_thesis_impact, what_changed) serve
each piece on its own when only one is wanted. When no investment case has been built for a name
yet, it returns the data and says so plainly rather than inventing one. It runs from precomputed
stores — no live model call. `response_mode="plain"` gives the short beginner-friendly read;
"raw" returns valuation records without thesis prose; "standard" and "deep" preserve the
complete valuation + thesis + helps/hurts response.

Input Schema

TableJSON Schema

Name	Required	Default
`entity`	Yes
`reading_level`	No
`response_mode`	No	standard

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 15, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description adds meaningful behavioral detail beyond the readOnlyHint annotation: it states the tool runs from precomputed stores with no live model call, and clarifies how response modes alter the output. It also discloses that when no investment case exists, the tool returns data and says so plainly rather than inventing a thesis.

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 detailed but well-organized, front-loading the core value proposition and using bullet points to structure the return content. The response-mode breakdown is concise and actionable, with no redundant filler.

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 tool with no output schema and no parameter descriptions, the description covers the return values, usage context, alternative tools, edge-case behavior, and response modes. An agent has enough information to invoke the tool correctly and interpret its results.

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?

With 0% schema description coverage, the description must compensate. It thoroughly explains response_mode and its four values, and the entity is obvious from context. However, reading_level is never mentioned, leaving its meaning and effect uncovered—a clear gap given the schema provides no descriptions.

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 returns the complete OptimistFi read on a company, combining valuation data, an investment thesis, and bull/bear cases. It distinguishes itself from per-topic sibling tools like get_valuation and get_thesis, making its purpose and scope unambiguous.

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?

It explicitly frames the tool as the front door for general company questions and names the per-topic alternatives (get_valuation, get_thesis, get_thesis_impact, what_changed) for when only one piece is wanted. It also explains the behavior when no investment case exists, giving clear guidance on expected outcomes.

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

All OptimistFi tools Inspect

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