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get_timeseries

Read-onlyIdempotent

Bounded timeseries for an entity: price, macro_series, or etf_flows.

Returns points oldest-first with an explicit downsampling flag when the raw series exceeded max_points. etf_flows is filing-cadence (one point per SEC filing refresh), NOT per calendar day, so even a wide window yields a handful of points. Times are UTC. Costs 1 unit per call.

Args: metric: One of price / macro_series / etf_flows. entity: Entity dict from resolve_entity ({"namespace": ..., "ids": ...}). granularity: Requested point granularity (default "1d"). max_points: Hard cap on returned points (default 500).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYes
metricYes
max_pointsNo
granularityNo1d

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A3.8/5.0
Behavior4/5

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

Annotations declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds behavioral details: returns points oldest-first, downsampling flag, UTC times, and cost. These go beyond the annotations and provide useful context for invocation decisions.

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?

The description is relatively concise, with a clear opening sentence and a structured Args list. Every sentence adds value, though the Args section partly repeats the schema. It is front-loaded with purpose and key details, but could be slightly more streamlined.

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?

Given the presence of an output schema (not evaluated here) and the tool's complexity, the description covers all four parameters, explains defaults, and notes special behaviors (downsampling, filing-cadence, UTC). It does not discuss error handling or edge cases, but 'bounded' implies constraints are handled. Reasonably complete.

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?

Despite 0% schema description coverage, the description's Args section clearly explains each parameter: metric (enum values), entity (dict from resolve_entity), granularity (default 1d), and max_points (default 500). This adds significant meaning beyond the bare schema, compensating for the lack of inline descriptions.

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?

The description clearly states 'Bounded timeseries for an entity' and lists the three specific metrics (price, macro_series, etf_flows). It uses a specific verb-resource combination and adds scoping details like 'oldest-first' and UTC times. However, it does not explicitly distinguish from sibling tools like get_snapshot, leaving room for confusion.

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?

The description provides some usage guidance, such as the note on etf_flows filing-cadence and the cost of 1 unit per call. It also mentions the entity input comes from resolve_entity. However, it lacks explicit when-to-use or when-not-to-use instructions, and does not reference alternative tools.

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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TDQS

A3.7/5.0
Disambiguation3/5

Several tools overlap in purpose: call_endpoint, fetch_data, and search_endpoints all relate to invoking endpoints, with fetch_data bundling search and call. resolve_entity and sugra_entity_lookup both resolve entities but target different domains (market vs. compliance), which could confuse agents.

Naming Consistency2/5

Naming patterns are inconsistent: 'endpoint' appears as both singular and plural (call_endpoint vs. search_endpoints), verbs vary (fetch_data vs. get_snapshot), and the 'sugra_' prefix is only applied to two of the entity-related tools, leaving resolve_entity without a clear thematic connection.

Tool Count4/5

With 11 tools, the count is within the typical range for a comprehensive financial API wrapper and does not feel bloated or sparse. Each tool serves a distinct functional area, so the number is appropriate.

Completeness4/5

The tool set covers endpoint discovery, data retrieval (snapshots, timeseries), entity resolution, and compliance screening, which are the core capabilities expected of such an API. Minor gaps like batch operations or authentication handling are not critical for the intended use case.