paper_getRetrieve a specific exam paper by its ID with question details
| Name | Required | Description | Default |
|---|---|---|---|
| exam_paper_id | Yes | The ID of the exam paper to retrieve |
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| tier | Yes | ||
| year | Yes | ||
| title | Yes | ||
| subject | Yes | ||
| sections | Yes | ||
| created_at | Yes | ISO-8601 creation timestamp. | |
| created_by | Yes | ||
| exam_board | Yes | ||
| total_marks | Yes | ||
| paper_number | Yes | ||
| duration_minutes | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / properties / created_by / additionalPropertiesRemoved value: -falseOutput schema / properties / created_by / anyOfAdded value: +[
+ {
+ "additionalProperties": false,
+ "properties": {
+ "email": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ]
+ },
+ "id": {
+ "type": "string"
+ },
+ "name": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ]
+ }
+ },
+ "required": [
+ "id",
+ "name",
+ "email"
+ ],
+ "type": "object"
+ },
+ {
+ "type": "null"
+ }
+]Output schema / properties / created_by / propertiesRemoved value: -{
- "email": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "null"
- }
- ]
- },
- "id": {
- "type": "string"
- },
- "name": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "null"
- }
- ]
- }
-}Output schema / properties / created_by / requiredRemoved value: -[
- "id",
- "name",
- "email"
-]Output schema / properties / created_by / typeRemoved value: -"object"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already carry the key behavioral safety profile: readOnlyHint=true and idempotentHint=true, so an agent knows this has no lasting side effects. The description adds the 'with question details' scoping fact, which is useful. It does not comment on permissions, errors, or response shape beyond that, so the description adds only a modest amount beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single focused, front-loaded sentence. It names the action, the object, the identifier criterion, and the expected content detail with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple read-only tool with one parameter and an output schema, so the description does not need to explain return values or data structures. The core intent of retrieving a specific exam paper with question details is fully captured, making the definition complete for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%: the only parameter, exam_paper_id, is already fully described in the schema with 'The ID of the exam paper to retrieve.' The description does not add any extra parameter information beyond echoing the ID-focused purpose. Thus, it earns the baseline score for schema-covered parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Retrieve'), the resource ('exam paper'), and the selection criterion ('by its ID'). It also adds 'with question details', which distinguishes the returned content from what paper_list would provide. An agent can unambiguously tell this from sibling tools like paper_create or paper_list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly communicates the correct usage: use this tool when you already know the exam paper ID and need the full paper with questions. It does not explicitly name alternatives or exclusions, but the context of retrieving a specific paper by ID is clear enough given sibling tools like paper_list suggest searching/listing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tools map well to distinct resources and workflow stages, and the async flows are clearly explained. The main overlap risk is paper_create/paper_confirm and markResult_get/submission_get, where names point at similar actions but the descriptions make the boundaries usable.
Most names follow the same resource_action snake_case pattern, e.g. paper_get, submission_mark, markScheme_update. markResult_get breaks the pattern slightly by mixing camelCase into an otherwise snake_case set, but the naming stays readable and predictable overall.
Ten tools is a well-scoped size for this domain: paper creation, confirmation, retrieval, question/mark-scheme correction, and submission grading are covered without excess granularity or obvious filler.
The main end-to-end workflow—create extraction job, confirm paper, mark submission, poll results—is complete and coherent. Minor gaps exist, such as no paper deletion, no submission listing, and no direct way to fetch a mark scheme separately, but they are not blocking for the core grading loop.