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knowledge_get_full_document
Read-onlyIdempotent

Full text of one library document, by its k<N> ref. The ref comes from a recent knowledge_search (k1, k7). knowledge_get_excerpts returns abstracts and matching passages, which usually suffice; this returns the whole paper. Refs marked [ref-only] have no full text, so for those it returns the same abstract knowledge_get_excerpts does.

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": {
      "document_id": "<document_id>"
    },
    "name": "knowledge_get_full_document"
  }
}

Input Schema

NameRequiredDescriptionDefault
document_idYesRef like `k1`/`k7` from a recent knowledge_search. Titles, DOIs and document uuids are rejected.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), so the bar is lower, yet the description adds real behavioral context: [ref-only] documents return only an abstract rather than failing or returning empty. It could go further on size/truncation behavior for large full texts, hence not 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.

Conciseness5/5

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

Four tight sentences, front-loaded with what the tool returns and the ref contract, then the sibling comparison and the edge case. No padding or repetition.

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 no output schema, the description still tells the agent what comes back (full text, or the same abstract as excerpts for ref-only entries) and where the input ref originates. Nothing needed 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.

Parameters3/5

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

Schema description coverage is 100%, and the schema already documents the k<N> format and the rejection of titles/DOIs/uuids. The description restates the ref origin ('comes from a recent knowledge_search') without adding format or syntax detail beyond the schema, so the baseline 3 applies.

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+resource ('Full text of one library document') and scopes it precisely by ref type. It explicitly contrasts itself with knowledge_get_excerpts and knowledge_search, so an agent can distinguish it from siblings without opening any 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 an explicit when-to-use-this-vs-alternative rule: excerpts 'usually suffice', this tool is for the whole paper, and it names which refs are even eligible ([ref-only] excluded). That is a genuine routing decision, not vague context.

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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