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Eclipse-Cj

Paper Distill MCP Server

ingest_research_context

Extract keywords from research context to inherit across AI conversations. Optionally run paper searches with session isolation to prevent context pollution.

Instructions

Ingest research context from other AI conversations for cross-AI context inheritance.

Extracts keywords from the markdown text and appends to interests.jsonl. Use session_id to isolate different chat sessions (prevents context pollution when multiple OpenClaw/AI sessions run concurrently).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
search_nowNoIf True, also run a paper search using extracted keywords
session_idNoOptional session identifier to isolate contexts (e.g. "openclaw-abc123"). If provided, only this session's interests are used for search_now.
markdown_textYesMarkdown text containing research context (e.g. from another AI's summary)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations provided; the description reveals it appends to interests.jsonl and uses session isolation, but does not detail side effects, permission requirements, or error conditions.

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?

Two concise paragraphs, front-loaded with purpose, every sentence adds value without waste.

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 output schema exists and parameters are well-documented, the description provides sufficient context for the tool's function and usage scenario.

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 covers 100% of parameters; description adds context like 'markdown_text as another AI's summary' and session isolation purpose, enhancing beyond schema.

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 clearly states it ingests research context from other AI conversations for cross-AI context inheritance, extracting keywords and appending to interests.jsonl. It distinguishes from sibling tools by focusing on cross-AI context ingestion rather than search or collection.

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 usage for transferring context between sessions, but lacks explicit when-to-use vs. alternatives or when-not-to-use 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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