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uol-mcp

uol-mcp

by jakubbegera

Raw UOL GET

api_get
Read-onlyIdempotent

Fetch read-only data from any UOL REST API path when dedicated tools don't cover it, returning raw JSON with paging via query parameters.

Instructions

Read-only GET of any UOL REST path (spec: https://api.uol.cz/openapi.yaml) for data no other tool covers — e.g. /v1/accounting_records, /v1/bank_movements, /v1/products, /v1/warehouse/statuses, /v1/sales_orders. Returns the raw JSON (amounts as strings, invoice status "valid" = draft); large answers are cut to fit. Prefer the dedicated tools.

Input Schema

NameRequiredDescriptionDefault
pathYesPath under the API root starting with /v1/, e.g. /v1/products
queryNoQuery parameters, e.g. {"page": 1, "per_page": 50}

Output Schema

NameRequiredDescriptionDefault
dataYes
pathYes
truncatedYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.1

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower, yet the description still adds non-obvious behavior: raw JSON is returned, amounts come back as strings, invoice status "valid" actually means draft, and large responses are truncated to fit. Those are exactly the traits an agent cannot infer from annotations or schema.

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?

The core verb and scope are front-loaded in the first clause, with the routing rule right behind it. The long parenthetical example list and embedded spec URL make it slightly dense, but each element carries usable information.

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?

For an open-world generic REST passthrough, it covers the essentials: scope, precedence, path format, the spec reference for unlisted endpoints, and known data quirks. An output schema exists, so return structure needn't be explained, though the truncation behavior could say how severe the cutoff is.

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 schema already documents both `path` (with an example) and `query` (with a page/per_page example). The description's only additions are alternative example paths and the /v1/ prefix constraint, which the schema also states — hence the baseline 3.

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?

States a specific verb and resource ("Read-only GET of any UOL REST path") and immediately scopes it against siblings with "for data no other tool covers" plus concrete example endpoints. The only thing keeping it from a 5 is that the purpose is intrinsically generic — a catch-all escape hatch — rather than one well-defined resource.

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?

Gives an explicit fallback rule ("Prefer the dedicated tools"), an explicit when-to-use condition ("data no other tool covers"), a list of representative paths, and a spec link for everything not covered. An agent can decide between this and the dedicated tools without further inference.

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