mcp-ebook-read

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document_explore

Ask a natural-language reading question about an ingested book or paper. Receive relevant passages, nearby evidence, and diagnostic insights.

Instructions

Explore one ingested EPUB/PDF book or PDF paper by doc_id. Ask a natural-language reading question and receive SQLite FTS hits expanded with DocumentGraph nodes, nearby evidence, diagnostics, truncation notices, ambiguity candidates, and suggested precise next calls. The tool infers EPUB/PDF/book/paper mode from doc_id.

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": {
      "doc_id": "<doc_id>",
      "query": "<query>"
    },
    "name": "document_explore"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes
`top_k`	No
`doc_id`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.7Jul 13, 2026

TDQS

A4.4/5.0

Behavior5/5

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

No annotations exist, but the description fully details the return values including FTS hits, nodes, evidence, diagnostics, truncation notices, ambiguity candidates, and suggested next calls. Also notes auto-inference of document type.

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?

Single dense paragraph that conveys all key information. Could be slightly more structured but is efficient and front-loaded.

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?

Given the presence of an output schema (though not shown) and the tool's 3 parameters, the description covers all essential aspects without gaps.

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?

Schema description coverage is 0%. Description clarifies doc_id and query but does not explain top_k. Output schema exists but is not shown. Partial compensation, but top_k remains vague.

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?

Clearly specifies the tool explores one ingested book or paper by doc_id, asks a natural-language question, and returns detailed results. Distinguishes from sibling tools like document_ingest and document_node.

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

Usage Guidelines4/5

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

Explicitly states the tool is for asking reading questions about a specific document. Does not list exclusions or alternatives, but the context of siblings provides sufficient differentiation.

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