robotbase-content

Cyberlibrary Fulltext Search

cyberlibrary_fulltext_search

Read-onlyIdempotent

Full-text search inside the actual book text (a 26 GB corpus of chapter Markdown), returning the matching passage plus the book, chapter and canonical source URL for each hit. When to use: you need to locate where a phrase, name or quotation occurs in the corpus, or to ground an answer in a verifiable passage. Do not use: to find a book by title (call cyberlibrary_search_books, which is far cheaper). Strongly prefer passing lang: English alone is 79% of the corpus (20.4 GB), so a language-scoped search is typically 10-100x faster and more relevant. Returns: {query, lang, searched_languages, skipped_languages, scanned_mb, elapsed_ms, results[{lang, book, chapter, source_url, match, snippet}]}.

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": "cyberlibrary_fulltext_search"
  }
}

Input Schema

Name	Required	Description
`lang`	No	Language code to search within (strongly recommended), e.g. zh-cn, en, fr.
`limit`	No	Max passages to return (1-50). Default 10.
`query`	Yes	Literal phrase to find (2-120 chars, case-insensitive).
`snippet_chars`	No	Characters of context around each hit (100-2000). Default 400.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already carry readOnly/idempotent/non-destructive, so the safety profile is covered. The description goes further with corpus size (26 GB), the 79%/20.4 GB English-language weighting, and the 10-100x speed/relevance payoff of language scoping — genuinely useful behavior beyond the annotations. It stops short of discussing auth or rate limits, but those are minor here.

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 purpose, then usage, then the returns shape; every sentence carries operational information. The dense inline returns block is slightly heavy, but with no output schema it earns its place.

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 four-parameter search tool with no output schema, the description supplies the return structure (query, lang, results with book/chapter/source_url/match/snippet), scoping advice, and the sibling alternative — 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.

Parameters4/5

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

Schema description coverage is 100%, so the schema documents all four parameters (baseline 3). The description adds the rationale the schema lacks — why lang matters (79% of corpus, 10-100x faster) — which meaningfully guides parameter choice beyond the raw field docs.

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?

States a specific verb and resource (full-text search inside actual book text over a 26 GB Markdown corpus) and explicitly distinguishes itself from the title-lookup sibling cyberlibrary_search_books. An agent can identify the tool and its scope without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use ('locate where a phrase, name or quotation occurs... or ground an answer in a verifiable passage') and when-not ('to find a book by title — call cyberlibrary_search_books, which is far cheaper'), plus a strong optimization ('pass lang'). Full routing guidance is present.

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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