Awareness — Memory, Knowledge & Agent Markets

awareness_search_knowledge

Search the PUBLIC Awareness knowledge graph — 1,000+ sourced scientific papers.

Use this when the user (especially a researcher or a research agent) asks for
peer-reviewed findings: "latest evidence on X", "highly-cited papers about Y",
"what does the literature say about Z". This is NOT a memory tool — it does not
read the user's private memory, and it needs no per-user API key, so it works
even when Awareness memory is not configured (the MCP connection itself still
needs its own token).

Coverage: condensed-matter, quantum-information, materials, machine-learning,
renewable-energy, chemistry, biology, medicine, neuroscience, computer-science
(2022-2026, cited >= 50), plus a business supply/demand domain.

What comes back (anonymous preview mode):
  title, snippet (preview), confidence, source_ref (DOI), citation_count,
  domain/topic, and a detail page URL. Full content is NOT in the preview —
  tell the user it is paid retrieval via POST /api/v1/public/retrieve with an
  API key (see https://awareness.market/en/knowledge for the KaaS product).

Args:
    query: Natural-language research question or keywords (2-1000 chars).
    domain: "science" (default) or "business".
    topic: Optional sub-topic slug, e.g. "medicine", "condensed-matter",
        "supply-chain". Empty = search all topics in the domain.
    top_k: Max results, 1-20 (default 5; anonymous callers are capped at 3).
    include_graph: When true, also return same-topic related items.

Returns:
    {"results": [{id, title, snippet, confidence, source_ref, citation_count,
    domain, topic, url}], "related": [...], "total": int, "mode": str}
    `mode` is "preview" (anonymous) or "full" (authenticated).

Input Schema

Name	Required	Description	Default
`query`	Yes
`top_k`	No
`topic`	No
`domain`	No
`include_graph`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 13, 2026

TDQS

A4.9/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so richly: anonymous preview mode, the specific fields returned in preview, that full content is NOT included and requires paid POST /api/v1/public/retrieve with an API key, and the anonymous cap of 3 results. This is exactly the behavioral context an agent needs.

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?

Well-structured with clearly labeled sections (purpose, when-to-use, coverage, what comes back, args, returns) and a front-loaded purpose statement. It is somewhat long and the Args section partially restates what Returns implies, but given 0% schema coverage the parameter detail 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?

No output schema exists, so the description supplies the return shape and explains the mode field ('preview' vs 'full'). Combined with coverage domains and the paid-retrieval caveat, 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.

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate — and it fully documents all five params: query length bounds (2-1000), domain values ('science' default / 'business'), topic as an optional slug with examples, top_k range and default plus the anonymous cap of 3, and include_graph semantics (same-topic related items). This adds meaning well beyond the bare 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?

States a specific verb+resource (search the public Awareness knowledge graph of 1,000+ sourced scientific papers) and explicitly distinguishes itself from the memory siblings: 'This is NOT a memory tool — it does not read the user's private memory.' An agent can differentiate it from awareness_recall/awareness_facts/awareness_lookup 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 concrete when-to-use triggers ('latest evidence on X', 'highly-cited papers about Y') and an explicit when-not ('NOT a memory tool'). It also states the prerequisite context — no per-user API key needed, works when memory isn't configured, but the MCP connection still needs its own token.

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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