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SAP MCP Server

by ai.oobeprotocol.sap.mcp

Pyth Get Price History

pyth_getPriceHistory
Read-onlyIdempotent

Get historical price data from Pyth oracle. SAP MCP context: Protocol pyth; operation class read. Use for Pyth oracle price reads and feed discovery. Use oracle reads as context for pricing, risk checks, and market-aware agent decisions; do not treat them as settlement proof.

SAP MCP execution guidance: Intent: read/discovery workflow. Pricing: paid read-premium; estimate first, then use sap_payments_call_paid_tool when the runtime cannot replay x402 natively. Routing: paid hosted call; call sap_estimate_tool_cost first, then use sap_payments_call_paid_tool if the runtime cannot handle x402 natively. Signer boundary: hosted reads/builders never receive keypair bytes; value-moving results must be finalized locally when signing is required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNoPeriod parameter for Pyth Get Price History.
priceIdYesPrice ID parameter for Pyth Get Price History.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesMCP content blocks returned to the caller.
isErrorNoTrue when the tool result represents an application-level error.

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds valuable behavioral context: it is a paid read operation (paid read-premium), with signer boundary (no keypair bytes), and routing guidance. These go beyond the basic hints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is overly long, repeating same SAP execution guidance in both the description and input schema description. Much of the content is boilerplate routing instructions rather than concise tool semantics. It could be trimmed significantly.

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?

The tool has 2 parameters, good annotations, and an output schema. The description explains pricing and routing but does not describe the output format or typical usage patterns. It is adequate but not thorough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters, but those descriptions are generic ('Period parameter for Pyth Get Price History'). The tool description adds no additional meaning about the parameters (e.g., priceId format or period units). Baseline 3 applies.

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 starts with 'Get historical price data from Pyth oracle,' specifying the verb (get), resource (historical price data), and source (Pyth oracle). This distinguishes it from sibling tools like pyth_getPrice (current price) and pyth_listPriceFeeds (listing feeds).

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

Usage Guidelines4/5

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

The description states 'Use for Pyth oracle price reads and feed discovery' and includes caution 'do not treat them as settlement proof.' However, it does not explicitly contrast with sibling tools or provide when-not-to-use scenarios, though the context implies when historical data is needed.

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

B3.2/5.0
Disambiguation4/5

Tools are organized by protocol prefix (e.g., adrena_, jupiter_, sap_) which helps distinguish domains. Within each protocol, tool names clearly indicate actions (e.g., openPosition, getQuote). However, with 360 tools, some cross-protocol overlaps (e.g., multiple swap tools) and many similar fetch tools in the sap_* family require careful reading of descriptions to disambiguate.

Naming Consistency4/5

Each protocol group follows a consistent naming convention (e.g., snake_case for adrena_, camelCase for 3land, sap_ prefix for SAP SDK tools). The mix of conventions across protocols is acceptable, though a uniform style would improve predictability. Minor inconsistency: hyphenated names like metaplex-nft_ vs underscores.

Tool Count2/5

360 tools is far too many for a well-scoped MCP server. This aggregates dozens of protocols and SAP-specific features, making navigation difficult. The server would benefit from being split into focused micro-servers (e.g., SAP identity, Jupiter DEX, NFT tools). The current count overwhelms the coherence of the set.

Completeness4/5

The tool set covers a vast range of Solana ecosystem activities: token operations, swaps, staking, NFT management, bridging, oracle data, identity registration, chat, escrow, subscriptions, and premium streaming. Major lifecycle operations are present, though some niche subdomains may have missing functions (e.g., detailed governance or lending management). Overall, it's comprehensive for the intended scope.

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