peerlytics

get_timeseries

Historical time-series. Hour or day buckets of deposits created, intents signaled, or fulfilled volume. 60 credits per call. Pass group_by=platform|currency|maker|verifier to receive a multi-series payload (series[]) instead of a single global buckets array.

Input Schema

TableJSON Schema

Name	Required	Description
`to`	No	Window upper bound (exclusive). ISO-8601 or unix-seconds. Defaults to `now` when omitted but `from` is supplied.
`from`	No	Window lower bound (inclusive). ISO-8601 (`2026-04-01T00:00:00Z`) or unix-seconds. Optional when `range` is supplied.
`maker`	No	Repeatable or comma-separated maker (depositor) addresses.
`range`	No	Unified date-range shortcut shared across analytics + explorer endpoints. Hard cap: 400-day window. When set, takes precedence over `from`/`to`. Legacy values (`3mtd`, `q1`-`q4`, `custom`) were retired on 2026-04-30; use the explicit shortcuts below.
`entity`	Yes	Which series to aggregate.
`currency`	No	Repeatable or comma-separated currency codes.
`group_by`	No	Multi-series grouping. When set, the response returns `series[]` (up to 25 keys ordered by total value) instead of a single `buckets` array.
`platform`	No	Repeatable or comma-separated platform ids (analytics treats as verifier alias).
`verifier`	No	Repeatable or comma-separated verifier addresses.
`granularity`	No	Bucket size (default: day).

TDQS

A4.2/5.0

Behavior4/5

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

Without annotations, the description carries the behavioral burden. It discloses the credit cost, explains the effect of group_by on response structure (single buckets vs series[]), and documents the range parameter's hard cap and legacy retirement. It does not explicitly state read-only behavior, but the context suggests a read operation. Overall, it provides good transparency.

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?

The description is three sentences, front-loaded with purpose, then cost, then an important parameter effect. Every sentence adds value with no wasted words.

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 10 parameters, no output schema, and no annotations, the description covers the main behavior and key parameter interactions. It could be slightly more complete by mentioning pagination or response structure details, but it is largely sufficient for the complexity.

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%, so baseline is 3. The description adds significant value by explaining how group_by transforms the response, the precedence and behavior of range over from/to, and the retirement of legacy range values. This goes beyond the schema definitions.

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?

The description clearly states the tool provides historical time-series data with specific entities (deposits, intents, volume) and granularity options (hour, day). It distinguishes itself from sibling tools by focusing on aggregated time-series, not individual records.

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 mentions a credit cost (60 per call) but does not explicitly state when to use this tool versus alternatives like get_analytics_summary or individual entity tools. It implies usage for time-series aggregation but lacks explicit guidance on when to choose this over siblings.

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

C2.8/5.0

Disambiguation3/5

Most get_* and list_* tools target distinct domain entities, but several high-level analytics endpoints overlap in purpose (get_analytics_summary, get_protocol_overview, get_market_summary, get_timeseries, get_leaderboard), and pairs like get_deposit/get_deposit_context and get_vault/get_vault_analytics create close boundaries. The descriptions clarify the differences, but an agent would need to read carefully to avoid misselection.

Naming Consistency5/5

The tool set follows a strong verb_noun pattern: get_ for single-entity or detail views, list_ for collections, plus explicit action endpoints like export_trade_log, plan_routes, and search_explorer. All names are snake_case and predictable, with no mixing of conventions.

Tool Count3/5

28 tools is on the heavy side for an MCP server, even for a broad analytics domain. Most tools map to real entities, but the surface is padded by overlapping summary/analytics endpoints and separate detail, context, and analytics variants for the same resources.

Completeness4/5

The server provides broad read-only coverage of the protocol domain: deposits, intents, makers, takers, vaults, delegates, verifiers, platforms, integrators, orderbook, routes, activity, and search. Minor gaps such as no list_vaults or list_integrators are workable via search/get endpoints, and mutating operations appear out of scope for an analytics-focused server.

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