OpenAkashic

Search Akashic (validated public knowledge)

search_akashic

Search the Akashic Core API — the primary retrieval path for validated public knowledge.

Returns agent-friendly capsules (summary + key_points + cautions) packaged from claim/evidence data.
Use this FIRST for factual/conceptual questions. For your own working notes use search_notes.

- mode='compact' → 1-sentence summary per capsule (smallest, best for small models)
- mode='standard' → full capsule without metadata (default)
- mode='full' → everything including metadata and timestamps
- fields=['summary','key_points'] → custom projection overriding mode

Input Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	No	Projection mode: 'compact' (id+title+summary_head+confidence — smallest payload for SLMs), 'standard' (+ summary+key_points+cautions+source_claim_ids — default), 'full' (+ metadata/timestamps).	standard
`query`	No	Search terms for validated public knowledge. Example: 'Python list comprehension performance'
`top_k`	No	Max results to return (default 8)
`fields`	No	Explicit field allowlist for capsules/claims (overrides mode). Example: ['summary','key_points']. id/title/text/score are always included.
`include`	No	Knowledge types to include. Options: 'capsules', 'claims', 'evidences'. Default: ['capsules','claims']. Add 'evidences' when you need source links.
`question`	No	Alias for query — use either field.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedMay 29, 2026

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It explains that the tool returns 'agent-friendly capsules' with a structured format, and details how modes alter the response size and content. It does not specify failure modes or edge-case behavior, but for a read-only search tool this is adequate; the description adds meaningful context beyond a bare 'search' statement.

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 concise and well-structured: it opens with the core purpose, then a one-sentence summary of the return format, followed by usage guidance and mode-specific bullets. Every sentence contributes useful information without redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the essential context: purpose, when to use, return format, and mode behavior. While the 'include' parameter (e.g., adding evidences) is not mentioned in the description, it is fully specified in the schema with examples. Given the presence of a rich output schema and 100% parameter documentation, the description is sufficiently complete for effective tool selection and invocation.

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 coverage is 100%, so baseline is 3. The description adds value by explaining the practical trade-offs of mode choices (e.g., compact for small models) and clarifying that fields overrides mode. This goes beyond the schema's parameter descriptions, providing semantic guidance on parameter selection.

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 the tool's primary function: searching the Akashic Core API for validated public knowledge. It uses a specific verb ('Search'), identifies the resource ('Akashic Core API'), and explicitly differentiates from sibling tools like search_notes by labeling itself as the primary retrieval path for validated knowledge.

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?

The description provides explicit usage guidance: 'Use this FIRST for factual/conceptual questions' and directs users to 'search_notes' for working notes. It also describes different modes (compact/standard/full) and custom field projections, giving clear context on how to tailor the search depending on the model's needs.

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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