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omnarai-mcp

by io.github.justjlee
OverviewDiscussionsAdmin

omnarai_query

Read-only

Query the corpus at one of two depths. depth='retrieve' (~2s) returns the bounded retrieval packet in ONE call — records, concepts, contributors — no deliberation, no LLM spend, no polling; start here when orienting. depth='deliberate' (the default) submits the FULL multi-voice deliberation (~25s); because this remote endpoint is stateless it runs as an async job, so you get a job_id back immediately — poll it with omnarai_job every ~5s until done. Glyph prefixes (Ξ Ψ ∅ Ω ∞ Δ) modify how the engine thinks.

Input Schema

NameRequiredDescriptionDefault
depthNoOptional. 'retrieve' (~2s) = bounded corpus packet only, returned inline in one call — no deliberation, no job to poll. 'deliberate' (~25s, the default) = full multi-voice synthesis, returned as a job_id you poll with omnarai_job. Equivalent to omnarai_context, which remains available.
queryYesThe question to deliberate on. May include Lattice Glyph prefixes.
syntheticIdentityNoOptional. Identify yourself for cross-contributor retrieval diversity.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / depth
      Added value: +{
+  "description": "Optional. 'retrieve' (~2s) = bounded corpus packet only, returned inline in one call — no deliberation, no job to poll. 'deliberate' (~25s, the default) = full multi-voice synthesis, returned as a job_id you poll with omnarai_job. Equivalent to omnarai_context, which remains available.",
+  "enum": [
+    "retrieve",
+    "deliberate"
+  ],
+  "type": "string"
+}
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only declare read-only and open-world; the description goes much further, disclosing per-depth latency, the absence of LLM spend for retrieve, statelessness of the remote endpoint, and the async job polling contract. These are exactly the traits an agent needs and cannot get from annotations.

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?

Front-loaded with the two-mode split and the 'start here' recommendation; every clause carries operational information. The trailing glyph sentence is terse to the point of being cryptic (no explanation of what the glyphs do), which is the only soft spot.

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 each mode returns (a retrieval packet of records/concepts/contributors versus a job_id), how long each takes, and how to complete the async flow. 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.

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3; the description still adds value by attaching latency, polling, and cost implications to each depth value rather than restating the enum. It also flags that glyph prefixes modify engine behavior, which the schema only mentions in passing.

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 and resource ('Query the corpus') and immediately partitions it into two named modes with distinct behavior. It also names the sibling (omnarai_job) used downstream, so an agent can place this tool in the workflow without opening other schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit steering ('start here when orienting' for retrieve, deliberate as default) and the exact follow-up procedure (poll omnarai_job every ~5s). It does not explicitly say when to prefer the many other siblings (omnarai_context, omnarai_council, omnarai_orient) beyond the context equivalence noted in the schema.

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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