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par4987

@pired/sap-fiori-mcp-server

by par4987

Search SAP documentation

search_docs
Read-onlyIdempotent

Search the local SAP documentation corpus for Fiori elements, UI5, CAP, and BTP topics before generating or modifying apps to ground answers in current best practices.

Instructions

Searches the bundled documentation corpora for SAP Fiori elements, UI annotations, UI5 development, OPA5 testing, UI Integration Cards, TypeScript conversion, CAP (CDS) development and SAP BTP destinations. Use this before generating or modifying apps to ground answers in current best practices. Local, no network required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results per page
queryYesSearch query, e.g. 'flexible column layout' or 'value help annotation'
scopeNoRestrict to one documentation corpusall
offsetNoNumber of results to skip (use nextOffset from a previous call)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesItems returned in this response
queryYes
scopeYes
totalYesTotal items matching the request
offsetYesOffset of the first returned item
hasMoreYesTrue when more items are available
resultsYes
nextOffsetYesOffset to request next, or null on the last page

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.26.1

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive behaviorikuha. The description adds useful context beyond annotations: the docs are bundled/local and require no network, and searches are meant to ground answers in current best practices. No contradiction with annotations.

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?

Two concise sentences with no redundant words. The front-loaded list of topics gives immediate clarity, followed by a usage directive and a key behavioral constraint. Every sentence earns its place.

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

Completeness5/5

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

For a search tool with full parameter descriptions, an output schema, and safety annotations, the description covers what the tool searches, when to use it, and important constraints. Nothing critical is missing for correct invocation.

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 description does not add parameter-specific semantics beyond the schema's own descriptions, though the enumerated corpus topics roughly map to possible scope values. It provides no extra meaning for query, limit, offset, or scope.

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 a specific verb ('Searches') and resource ('bundled documentation corpora'), and enumerates the exact documentation topics covered. This distinguishes it from sibling guideline-getters and code-generation tools without needing to inspect schemas.

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?

It explicitly tells the agent when to use the tool: 'before generating or modifying apps' to ground answers in current best practices. It does not explicitly mention when not to use it or name alternative tools, but the use case is clear and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.