Bundle metadata: latest_date, ticker_count, regime distribution. Public — no auth needed.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| files | No | ||
| latest_date | No | Most-recent trading-day for which factor data is available. | |
| generated_at | No | ||
| ticker_count | No | ||
| schema_version | No | ||
| labels_history_days | No | ||
| neighbor_shard_count | No | ||
| features_history_days | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds value beyond annotations by disclosing that no auth is required, which annotations do not mention. It also previews the response fields. Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered; the description provides additional operational context.
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, front-loaded sentence: 'Bundle metadata: latest_date, ticker_count, regime distribution. Public — no auth needed.' Every word earns its place, and the most important information appears first.
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?
Given this is a simple, no-parameter tool with an output schema available, the description fully conveys the tool's purpose and access requirements. It is complete for an agent to select and invoke correctly without further clarification.
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?
There are zero parameters, so the baseline for parameter semantics is 4. The description has nothing to add about inputs; no omission occurs.
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 bundle metadata, listing specific fields (latest_date, ticker_count, regime distribution). This distinguishes it from sibling getters like get_market_context or get_market_regime by focusing on an aggregate manifest rather than a specific data slice.
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 phrase 'Public — no auth needed' gives clear context on when this tool can be invoked without credentials, and listing the exact metadata fields implies use cases (e.g., checking bundle freshness or composition). However, it doesn't explicitly contrast with alternative tools or state exclusions, so a 4 fits.
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 target distinct resources (features, embeddings, labels, market context, risk clusters), but minor overlap exists: get_market_context includes a regime reading that get_market_regime also provides, and get_report_card bundles features that get_features offers separately. Descriptions are clear enough to resolve these overlaps.
The predominant pattern is get_<noun> (get_features, get_labels, get_manifest, etc.), with two exceptions: find_similar (find_) and list_futures (list_). This is a small deviation but still follows a predictable verb-noun structure for retrieval, search, and enumeration actions.
14 tools is well within the ideal range for a quantitative data server. Each tool serves a distinct purpose, from basic data retrieval (features, labels) to advanced analytics (similarity, risk clusters) and user management (alerts, usage). No tool feels redundant or missing.
The toolset covers the core data access and analytics needs for factor-based market analysis: retrieval, search, market context, and backtesting labels. Minor gaps include no generic ticker search or list (beyond futures), and no direct way to browse available factors beyond documentation, but these can be worked around via get_top and get_manifest.