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Atlantic Agentic Labs

Search Atlantic's published knowledge

search_published_knowledge
Read-onlyIdempotent

Search the items Atlantic Agentic Labs has published about how it works: how engagements run, its technology partners, its positioning and its use cases. Use it for questions about Atlantic as a company rather than the product's mechanics (use search_docs for those). Returns passages quoted exactly, each with a link to the published item, its revision and publication date; covered is false when nothing published answers the query. It gives no prices or commercial terms: such a query returns a fixed note and no passages. Examples: "how does an engagement start", "which clouds does Atlantic deploy to".

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_published_knowledge"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoHow many passages to return (1 to 8).
queryYesWhat to look for, in plain words or the exact terms the documentation would use. One topic per call. Examples: "how are approvals enforced", "connect Amazon S3", "POST /api/v1/keys", "hosted MCP OAuth scopes".

Output Schema

NameRequiredDescriptionDefault
noteNoA fixed note, where one applies.
queryYesThe query as searched, after redaction.
sourceYesWhat was searched.
coveredYesWhether the published material answers the query. False: say it is not covered.
receiptNoAn Ed25519 receipt over the passages, verifiable against https://ask.atlanticagenticlabs.com/.well-known/octy-keys.json; null if unsigned.
resultsYesThe best passages, best first; empty when not covered.
evidenceYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

The description goes beyond the safety annotations by detailing specific behavioral traits such as the output format (passages quoted exactly with links/dates), and defines the system's handling of uncaught queries where 'covered' will be false.

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 definition is highly efficient, front-loading its purpose and quickly providing usage constraints, behavioral details, and examples without any superfluous phrasing.

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 the high complexity of its defined search results and its constraints (e.g., no pricing), the description provides a remarkably complete picture of the tool's output and limitations.

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?

Although the schema is comprehensive (100% coverage), the description provides high-value semantic guidance by listing concrete examples of suitable queries, helping the AI determine the correct content and intent for the mandatory 'query' parameter.

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 tool clearly states it searches published items regarding the company's positioning, engagements, and technology partners, explicitly defining its subject matter. It effectively distinguishes this from the sibling tool search_docs by defining its scope as company-centric information rather than product mechanics.

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 provides a strong when/when-not guideline, advising users to employ this tool for questions about Atlantic as a company. Crucially, it mandates using search_docs for questions related to product mechanics and establishes negative constraints regarding price and commercial terms.

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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