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Closelooknet index snapshot

get_index_snapshot
Read-only

Current snapshot of one proprietary Closelooknet index: RUBIN (AI buildout), HALO (non-AI growth), AW40 (AI deployment), AEI (agentic stack), AIU (AI users — companies putting AI to work in an existing business) or EUROAI. Returns level, recent returns and the strongest/weakest sub-indices, end-of-day.

Input Schema

NameRequiredDescriptionDefault
indexYesIndex code

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / index / enum
      Previous value: -[
      -  "RUBIN",
      -  "HALO",
      -  "AW40",
      -  "AEI",
      -  "EUROAI"
      -]New value: +[
      +  "RUBIN",
      +  "HALO",
      +  "AW40",
      +  "AEI",
      +  "AIU",
      +  "EUROAI"
      +]
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=false, destructiveHint=false, so the safety profile is covered. The description adds genuinely new behavioral context: the data is end-of-day and the payload includes level, recent returns, and strongest/weakest sub-indices — useful freshness and shape information not in the annotations.

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

Conciseness4/5

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

A single front-loaded sentence declaring the resource, followed by the code glosses and return contents. The enum explanations are lengthy but each earns its place by disambiguating similar-sounding AI-themed indices.

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?

No output schema exists, and the description compensates by listing what is returned (level, recent returns, strongest/weakest sub-indices) and the end-of-day cadence. What remains thin is explicit routing guidance against compare_indices, but otherwise the agent has enough to call correctly.

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% and the enum already lists the valid codes, so the baseline is 3. The description goes beyond that by explaining what each code denotes (RUBIN=AI buildout, AIU=AI users, etc.), which is real semantic value when the agent must pick the right index.

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 and resource ('current snapshot of one proprietary Closelooknet index') and scopes it to a single index, which implicitly separates it from compare_indices and get_stock_snapshot. The enumeration of all six index codes with glosses makes it unambiguous what a call returns.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by 'one ... index' and the sibling compare_indices, but the description never explicitly says when to use this versus compare_indices or get_ranking. No exclusions or prerequisites are stated, so the agent must infer the routing.

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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