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Axiomatic-AI

axiomatic-mcp

Official
by Axiomatic-AI

AxKnowledgeBase_ingest_pdf_to_private_knowledge_base

Ingest a local PDF into the private knowledge graph, extracting passages, figures, tables, and references for search and retrieval.

Instructions

Ingest one local PDF into the organization's private knowledge graph. The PDF is parsed into passages, figures, tables and references, and the source PDF is stored. This is one of two tools that write to a knowledge graph — delete_private_knowledge_base_paper is the other — and the private graph is the only graph either writes to: an ingested paper is reachable through search_private_knowledge_base and private_knowledge_graph_read, and never through search_knowledge_base.

Synchronous and slow: it returns when ingestion has finished, which takes minutes for a full paper. Re-sending the same PDF is safe — it is reported as already present rather than ingested twice — so on a timeout or an unclear failure, retrying is the correct move.

Before writing, this tool raises an MCP elicitation asking the user to confirm the file name and the destination graph. A decline, a cancel, or a client that does not support elicitation at all writes nothing and comes back as a plain non-error result — do not retry any of these without a genuinely fresh reason to think the answer would differ; a client that lacks elicitation support will fail the same way every time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doiNoThe paper's DOI, if known. Leave empty if unknown.
file_pathYesThe absolute path to the PDF file to ingest

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.20

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It discloses that the tool is synchronous and slow, idempotent for duplicate PDFs, requires an MCP elicitation confirmation before writing, and returns a plain non-error result without writing if confirmation is declined or unsupported.

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?

The description is dense but well-organized, with each paragraph serving a distinct purpose: scope and graph routing, timing and retry semantics, and elicitation behavior. Every sentence adds operational value, and the most important facts are 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?

Despite having no output schema, the description covers everything an agent needs to invoke this tool correctly: what it does, how long it takes, how to handle retries, what happens on user decline, and how clients without elicitation support behave. The failure semantics and idempotency guarantees are unusually complete.

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%, so the schema already fully documents file_path and doi. The description adds no parameter-specific nuance beyond what the schema provides, so the baseline 3 is appropriate.

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?

The description states a specific verb and resource: ingest one local PDF into the organization's private knowledge graph. It also names concrete effects—parsing into passages, figures, tables, and references, plus storing the source PDF—and clearly differentiates this tool from the other write tool and from public-search siblings.

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?

It explicitly identifies the sibling delete tool, states that private-graph writes do not surface through search_knowledge_base, and gives actionable retry guidance: retry on timeout or unclear failure, but do not retry after decline, cancel, or unsupported elicitation. This gives the agent clear when-to-use and when-not-to-retry direction.

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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