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agentic-ai

by io.github.anthonywlicausi
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company_report

The flagship: eight-lens institutional analysis of a US-listed company, grounded entirely in SEC-verified figures with per-field filing citations. Lenses: equity research, investment banking, strategy consulting, turnaround ops, comparables, capital markets, disclosure audit, and Charlie Munger's 10 mental models — plus an executive synthesis. Optionally pass market (e.g. {"price": 12.34, "market_cap_m": 5000}) from your own data source to enable valuation views, and context — your own deal materials (CIM extracts, computed valuations), treated as caller-asserted rather than SEC-verified. $25.00/call after free trial; allow ~60s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketNo
tickerYes
contextNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / context
      Added value: +{
+  "default": null,
+  "title": "Context",
+  "type": "string"
+}
  2. Added

TDQS

A4.1/5.0
Behavior4/5

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

Discloses SEC-verified basis, per-field citations, caller-asserted context, cost, and runtime. With no annotations, this is substantial. Lacks error handling details, but acceptable for a report tool.

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?

Front-loaded with core purpose, then lenses, optional params, and cost/time. No wasted words; clearly organized.

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?

High complexity tool with no output schema or annotations; description covers inputs, trust levels, cost, and time. Missing explicit return format but functionally sufficient.

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?

Explanation of market and context parameters with example and trust-level distinction adds meaning beyond bare schema. Ticker is self-evident from the name and description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Specific verb 'analysis' with resource 'US-listed company' and distinctive eight-lens structure. Clearly differentiates from sibling extraction/validation tools, but doesn't explicitly name alternatives.

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?

Clear context for when to use (comprehensive institutional analysis) and what optional inputs enable, plus cost/time. No explicit exclusions or alternatives, but the scope is well-defined.

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

All agentic-ai toolsInspect
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