SAP MCP Server

Create Agent Memory

sap_memory_summarize

Free local tool. Creates an LLM-compressed summary memory from tool call patterns. The agent calls this after analyzing search results to persist lessons, patterns, failures, or successes. No x402 charge.

SAP MCP execution guidance: Intent: SAP MCP tool workflow. Pricing: free; call directly without x402. Routing: free hosted call; call directly and keep it small/exact when possible. Signer boundary: hosted reads/builders never receive keypair bytes; value-moving results must be finalized locally when signing is required.

Input Schema

TableJSON Schema

Name	Required	Description
`summary`	Yes	LLM-compressed summary text. Max 4KB.
`category`	Yes	Tool category (e.g. "jupiter", "adrena", "premium").
`expiresAt`	No	ISO 8601 expiry. Null = never expires.
`relevance`	No	Initial relevance score 0-1. Default 0.8.
`memoryType`	Yes	Type of memory.
`sourceToolCalls`	No	JSON array of tool_call IDs that were the source.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`content`	Yes	MCP content blocks returned to the caller.
`isError`	No	True when the tool result represents an application-level error.

TDQS

B3.3/5.0

Behavior3/5

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

Annotations are present and not contradictory. The description adds context like 'free local tool' and 'No x402 charge', but lacks details on side effects, permissions, or whether new memory overwrites existing records, which would be useful for a creation tool.

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

Conciseness3/5

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

The first few sentences are clear, but the inclusion of generic 'SAP MCP execution guidance' adds redundancy and noise, reducing conciseness. The description is not overly long but could be streamlined.

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?

Provides adequate context for a tool with a well-documented schema and output schema. However, it does not explain the relationship between sourceToolCalls and the summary, or memory lifecycle, which would strengthen completeness.

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 description coverage is 100%, so the baseline is 3. The tool description does not enhance parameter semantics beyond the schema; it only provides general context about when the tool is used.

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?

Clearly states it creates an LLM-compressed summary memory from tool call patterns, with specific use cases like persisting lessons after analyzing search results. Distinguishes from sibling memory tools by focusing on summarization from patterns, though it could explicitly differentiate further.

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?

Provides a specific scenario ('after analyzing search results') but does not discuss when not to use this tool or mention alternatives like sap_memory_record or sap_memory_search. The guidance is implied rather than explicit.

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.

Create Agent Memory

Input Schema

Output Schema

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