scholar-mcp

Recommend Papers

recommend_papers

Read-only

Recommend related academic papers by providing positive examples, with optional negative examples to refine results.

Instructions

Recommend papers based on positive (and optionally negative) examples.

Answers directly in normal use. Should the call run long it continues in the background and returns a job handle to poll with get_job_result.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Number of recommendations to return.
`fields`	No	Field set preset for returned records.	standard
`negative_ids`	No	Optional S2 paper IDs to steer away from.
`positive_ids`	Yes	1-5 S2 paper IDs to use as positive examples.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedv1.10.0Sep 8, 2026
- addedOutput schema / additionalProperties
```
Added value: +true
```
- removedOutput schema / properties
```
Removed value: -{
-  "result": {
-    "type": "string"
-  }
-}
```
- removedOutput schema / required
```
Removed value: -[
-  "result"
-]
```
- removedOutput schema / x-fastmcp-wrap-result
```
Removed value: -true
```
First observedv1.9.0Aug 10, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Annotations already mark the tool as read-only and non-destructive; the description adds a non-obvious execution trait: normal calls answer directly, but long runs switch to a background job with a handle to poll via get_job_result. This is valuable behavioral context beyond the annotations and helps an agent handle timeouts correctly. There is no contradiction with 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.

Conciseness5/5

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

Two sentences with no filler: the first states the core purpose, the second states the execution behavior. Every sentence earns its place, and the async fallback is placed after the main purpose without bloating the description.

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?

With a complete input schema, an output schema, and annotations covering safety, the main additional context needed—the synchronous-vs-asynchronous behavior and the polling route—is present. The only minor gap is explicit guidance on sibling alternatives, but that does not prevent an agent from invoking the tool correctly.

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?

The input schema covers 100% of parameters with descriptions, including the 1-5 positive_ids constraint and the negative_ids default. The description only echoes 'positive/negative examples' and adds no extra parameter semantics beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 names the specific operation ('Recommend papers') and the input basis ('positive and optionally negative examples'), which clearly distinguishes it from sibling retrieval tools like search_papers, get_paper, and recommend_books. It states both the resource and the mode of operation, so an agent can tell exactly what the tool does.

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?

The description implies the right use case—having example paper IDs and wanting similar recommendations—but it does not explicitly state when to prefer this over search_papers or recommend_books, nor does it give exclusions. An agent must infer selection from the words 'papers' and 'examples' rather than receiving direct alternative guidance.

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