get_index_snapshotCurrent snapshot of one proprietary Closelook index: RUBIN (AI buildout), HALO (non-AI growth), AW40 (AI deployment), AEI (agentic stack) or EUROAI. Returns level, recent returns and the strongest/weakest sub-indices, end-of-day.
| Name | Required | Description | Default |
|---|---|---|---|
| index | Yes | Index code |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds meaningful context beyond annotations: the data is proprietary and end-of-day, and it specifies the return content (level, returns, sub-index strengths). This adds useful behavioral detail without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, information-dense sentence. It front-loads the purpose, lists all options inline, and specifies the return fields without any redundancy. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter read-only tool with no output schema, the description covers all necessary aspects: what it does (snapshot), the valid inputs (with semantics), and what it returns (level, returns, sub-index extremes). The sibling tools are available for comparison, but this tool's description is complete on its own.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% coverage for the single parameter with an enum and a brief description. The tool description goes further by attaching semantic meanings to each enum value (e.g., RUBIN for AI buildout, HALO for non-AI growth), which helps the agent select the correct index. This exceeds the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a current snapshot of one specific Closelook index, listing all valid index codes (RUBIN, HALO, AW40, AEI, EUROAI) and the exact data returned (level, recent returns, strongest/weakest sub-indices). This is a specific verb+resource and distinguishes from siblings like compare_indices, which is for comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this is for retrieving a snapshot of a single index, but does not explicitly state when to use this versus alternatives like compare_indices. There are no exclusions or when-not-to-use instructions, so it stops at implied usage rather than explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools map cleanly to a distinct Closelook surface — indices, macro boards, rankings, research, tape, wires. There are a few near-neighbor pairs like get_df_ranking vs get_ranking and get_market_instrument vs get_stock_snapshot, but the detailed descriptions resolve most potential misselection.
Every tool follows a clear imperative verb + noun pattern — get_, define_term, compare_indices, search_closelook — with consistent snake_case and essentially all resource-oriented nouns. The naming is predictable and easy to navigate.
25 tools is on the heavy side, above the typical well-scoped range, but each endpoint maps to a meaningful product area of Closelook's public site. The count is justified by the breadth of a financial intelligence service, though it could have been trimmed slightly.
The tool surface covers the major Closelook workflows: indices, rankings, macro monitors, stock-level data, earnings, wires, research search, and portfolio snapshots. For a read-only intelligence server there are no obvious dead ends — depth and background material is paired with lookups and daily updates.