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codesearch MCP Server

codesearch MCP Server

by SK-DEV-AI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

papers

Search academic literature across Semantic Scholar, arXiv, OpenAlex, and CORE, then retrieve details, citations, references, author data, and recommendations.

Instructions

Academic papers from Semantic Scholar, CORE API, and arXiv. Prefer action=search for topic queries (multi-source S2→arXiv→OpenAlex→CORE fallback); arxiv_search is single-source and rate-limited. e.g. papers(query='transformer attention', fields_of_study='Computer Science')

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "action": "<action>"
    },
    "name": "papers"
  }
}

Input Schema

NameRequiredDescriptionDefault
yearNo
countNo
queryNo
actionYes
offsetNoResult offset for pagination (search and core_search actions)
paper_idNo
author_idNo
paper_idsNoComma-separated paper IDs for batch action
open_accessNo
negative_idsNoComma-separated negative paper IDs for recommendations_negatives
positive_idsNoComma-separated positive paper IDs for recommendations_negatives
fields_of_studyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations, the description must carry the full behavioral burden. It usefully discloses the multi-source fallback order (S2→arXiv→OpenAlex→CORE) and that arxiv_search is rate-limited, but it does not explain key behaviors such as pagination, what details returns, or what batch requires.

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 description is short, front-loaded with the source identity, and immediately follows with the preferred action and a concrete example. It is efficient, though the example uses only a small part of the tool's surface.

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

Completeness2/5

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

This is a broad multi-action tool with 12 parameters, 14 enum values, no output schema, and no annotations. The description only covers search and arxiv_search, leaving most actions and parameters unexplained, which is insufficient for correct invocation of the full tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 33%, so the description should compensate. It only hints at query and fields_of_study via an example and does not explain year, count, paper_id, author_id, paper_ids, open_access, offset, or the negative/positive IDs used by recommendations_negatives.

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?

The description clearly identifies the resource: academic papers aggregated from Semantic Scholar, CORE, and arXiv. It distinguishes action=search from arxiv_search, but does not differentiate this tool from siblings like search_all or so_search, which are also search-oriented.

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?

It explicitly recommends action=search for topic queries and contrasts it with the single-source, rate-limited arxiv_search. However, for a tool with 14 actions, the other actions (details, batch, citations, references, recommendations, author_search, etc.) receive no guidance on when to use them.

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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