DexPaprika

getPoolOHLCV

Read-onlyIdempotent

Get historical OHLCV candles (open, high, low, close, volume) for one pool over a time range, returned as a time-series array. Read-only and keyless. Use for 'price history of this pair', 'hourly chart for the last week', 'candles since Jan 1', or backtesting; for the single current price use getPoolDetails instead. Params: network (required); pool_address (required); start (required; Unix timestamp, RFC3339, or yyyy-mm-dd); end (optional, capped to 1 year after start); interval one of '1m','5m','10m','15m','30m','1h','6h','12h','24h' (default '24h'); limit (default 100, max 366 candles); inversed (optional bool, default false).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`end`	No	OPTIONAL: End time for historical data (max 1 year from start)
`limit`	No	OPTIONAL: Number of OHLCV data points to retrieve (default: 100, max: 366). One row per `interval`.
`start`	Yes	REQUIRED: Start time. RFC3339 recommended (e.g. '2024-01-01T00:00:00Z'). Also accepts Unix epoch seconds and YYYY-MM-DD (treated as 00:00:00 UTC).
`network`	Yes	REQUIRED: Network ID from getNetworks (e.g., 'ethereum', 'solana')
`interval`	No	OPTIONAL: Interval granularity (default: '24h')	24h
`inversed`	No	OPTIONAL: Whether to invert the price ratio for alternative pair perspective (default: false)
`rationale`	Yes	REQUIRED. 1-2 sentence rationale for this call (e.g. "User asked for X; calling Y to fetch Z"). Logged for MCP improvement, never shown to end users. No PII or secrets. See the server `instructions` field for the full convention and worked examples.
`pool_address`	Yes	REQUIRED: Pool address or identifier

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds 'Read-only and keyless', plus specific constraints like 'end capped to 1 year after start' and 'max 366 candles', which are not in annotations. This adds useful behavioral context beyond 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?

Description is front-loaded with purpose and behavioral traits, then parameter details. It's efficient and well-organized, though the parameter list makes it somewhat lengthy. Minor room for trimming but overall effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description mentions 'returned as a time-series array' but lacks specifics about the array structure (e.g., keys for OHLCV). For 8 parameters and no output schema, some output details would improve completeness. Still adequate for operational use.

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%, but the description adds format details (start accepts Unix, RFC3339, yyyy-mm-dd), lists interval enum values, explains inversed, and states limit max (366). These details go beyond schema descriptions to help the agent form correct parameter values.

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?

Description starts with 'Get historical OHLCV candles...' using a specific verb and resource. It clearly distinguishes itself from the sibling tool getPoolDetails for current price, and specifies use cases like 'price history', 'hourly chart', 'backtesting'.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'Use for price history... for the single current price use getPoolDetails instead.' Provides concrete examples and a clear exclusion, guiding the agent correctly.

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

A4.4/5.0

Disambiguation5/5

Every tool has a clearly distinct purpose: network listings, DEX listings, pool discovery, pool snapshots, OHLCV, transactions, token details, multi-price, token pools, search, stats, and feedback. Potential overlaps like getTopTokens vs filterNetworkTokens are explicitly disambiguated in descriptions.

Naming Consistency4/5

Mostly follows camelCase verb-first pattern (getNetworks, getPoolDetails, getTokenMultiPrices). However, the pair filterNetworkTokens and getNetworkPoolsFilter are inconsistently structured; the latter should logically be filterNetworkPools for full consistency.

Tool Count4/5

At 17 tools, it slightly exceeds the ideal 3-15 range, but each tool covers a distinct data operation for a comprehensive DEX-market API. The count feels justified given the breadth of features, though it is on the heavier side.

Completeness4/5

The domain of read-only DEX analytics is well covered: networks, DEXes, pools, tokens, search, stats, and per-pool history. Minor gaps exist, such as no direct token price history endpoint, but pool OHLCV and token details cover most needs.

Resources

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