Praxikon

Which obligations the decision engine indicates for one system

get_applicable_obligations

Read-only

Use this when you need the obligations themselves for one assessed system rather than the classification picture: which obligations apply, which possibly apply, from when, why, and on which source and locator. Returns one entry per assessed obligation, filtered by applicability. A version 2 profile has the wider set assessed, including Article 26 for a deployer of a high risk system, with one sub result per paragraph. This is the assessed route, not a catalogue of all obligations in the dataset.

Input Schema

Name	Required	Description	Default
`lang`	No	Language of the labels and explanations. Dutch is the default.	nl
`detail`	No	Amount of data to return. brief keeps the outcome, the dates, the reasons, the open questions and the source with its locator; full adds the complete objects, every statement text and every hash, for re-computation or archiving.	brief
`profile`	Yes	The same coded profile that classify_system takes, with the same fields and codes: see the profile schema of classify_system. Send a version 2 profile to have the wider set assessed, including Article 26 for a deployer. The server validates the profile against that full schema.
`applicability`	No	Which applicability statuses to return. Defaults to applies and possibly_applies. The counters and evaluated_obligation_ids stay complete whatever you filter on, so a filtered list can never be read as the whole assessment.
`store_for_improvement`	No	Optional. Set to false when the user asks that Praxikon does not keep this request to improve its answers; nothing of this call is then kept beyond the anonymous daily count. See https://www.praxikon.com/nl/legal/privacy, section 2.7.

Output Schema

Name	Required	Description
`view`	No
`dataset`	No
`sources`	No
`summary`	Yes
`disclaimer`	No
`provenance`	Yes	Which published endpoints produced this result. Every call is listed with its full URL, so a reader can re-issue it and check the answer rather than trust it.
`filtered_on`	No
`obligations`	Yes
`snapshot_id`	No
`deadline_notes`	No
`assessment_scope`	No	What this assessment did and did not assess, from the engine itself. not_assessed groups the obligations that were not assessed by reason (for example profile_version_below_2), with the facts that would have them assessed. Not assessed is not the same as not applying.
`evaluation_scope`	No
`decision_engine_version`	No
`evaluated_obligation_ids`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 23, 2026

addedInput schema / properties / store_for_improvement

Added value: +{
+  "default": true,
+  "description": "Optional. Set to false when the user asks that Praxikon does not keep this request to improve its answers; nothing of this call is then kept beyond the anonymous daily count. See https://www.praxikon.com/nl/legal/privacy, section 2.7.",
+  "type": "boolean"
+}

First observedOct 11, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and non-destructive, so the safety bar is low; the description still adds real behavioural context: one entry per assessed obligation, applicability filtering, and the version-2 profile producing a wider assessed set with one sub result per paragraph. It doesn't cover error or validation failure behaviour, which is the remaining gap.

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?

Front-loaded with the selection condition and the scope distinction, and no sentence is filler. The version-2/Article 26 sentence is somewhat dense and duplicates the profile parameter description, which costs a point.

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 tool with an output schema, nested profile object, and full schema coverage, the description supplies everything left: which route this is, what the filter does, and how profile version changes the assessed set. Nothing an agent needs to call it correctly is missing.

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% and the parameter descriptions are unusually rich (lang, detail, applicability, store_for_improvement all documented, including the counters-stay-complete caveat). The description adds little beyond that, restating the version-2 profile behaviour already present in the schema, so 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?

Names a specific verb and resource ('returns one entry per assessed obligation, filtered by applicability') for one assessed system. It explicitly contrasts itself with 'the classification picture' and with 'a catalogue of all obligations in the dataset', which separates it from classify_system and from catalogue-style siblings.

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?

Clear when-to-use framing ('Use this when you need the obligations themselves... rather than the classification picture') plus an explicit exclusion ('not a catalogue'). It stops short of naming explain_applicability or answer_question as the alternatives an agent should weigh, so routing against the closest sibling still requires inference.

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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