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by ductai199x
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Libgen Search

libgen_search

Search libgen.li's book index for titles, authors, and download links. Returns metadata such as publisher, year, language, pages, size, and file mirrors for each result.

Instructions

Search libgen.li (scrapes the index.php?req= results table; best-effort, the site changes and may block datacenter-style requests). Per result: title, authors, publisher, year, language, pages, size, extension, md5, item_url (edition page) and file_url (the file page with download mirrors).

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": {
      "query": "<query>"
    },
    "name": "libgen_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
num_resultsNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior4/5

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

No annotations exist, so the description carries the full burden, and it does well: it discloses that the tool scrapes an HTML results table, is best-effort, may break as the site changes, and may block datacenter-style requests. It omits rate limits or auth expectations, which keeps it from a 5.

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 operational caveat is front-loaded and the field enumeration is compact. Slightly redundant since an output schema already exists, but the single readable sentence structure wastes little.

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 a two-parameter search tool with an output schema, the description covers what it does and its reliability limits, which is close to sufficient. The missing piece is any parameter-level explanation, which is the main gap against an otherwise complete definition.

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 0%, and the description says nothing about either parameter. It never clarifies that 'query' is a free-text search string or what 'num_results' does (default 8, presumably a cap on returned rows), so it fails to compensate for the undocumented schema.

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 (search) and a concrete resource (libgen.li, scraping index.php?req=), so the agent knows exactly what this does and where the data comes from. It does not explicitly contrast itself with siblings like arxiv_search or scholar_search, leaving the 'books vs. papers' distinction to inference from the 'libgen' name.

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 a context (searching the Libgen index for book/edition results) but never states when to pick this over arxiv_search, scholar_search, or web_search. The note about reliability is a caveat, not usage guidance.

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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