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Nebula - AI-first B2B digital marketing, Mumbai

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nebula_search
Read-onlyIdempotent

Read-only ranked keyword search across approved Nebula KB sections. Use when you have a query; use nebula_list to browse without ranking, nebula_section for one exact path, nebula_overview for a summary. Returns query, results[{path,score,record}], total, offset, limit, nextOffset.

Input Schema

NameRequiredDescriptionDefault
limitNoMax results to return (default 6, max 20)
queryYesSearch words, e.g. "AI SEO for manufacturers"
offsetNoNumber of ranked hits to skip (default 0)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / limit
      Added value: +{
      +  "description": "Max results to return (default 6, max 20)",
      +  "maximum": 20,
      +  "minimum": 1,
      +  "type": "integer"
      +}
    • addedInput schema / properties / offset
      Added value: +{
      +  "description": "Number of ranked hits to skip (default 0)",
      +  "minimum": 0,
      +  "type": "integer"
      +}
  2. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds scope ("approved" sections only) and a return shape with pagination (nextOffset), which is valuable because there is no output schema. It stops short of describing ranking behavior, empty-result handling, or result-size limits, keeping it below a 5.

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?

Three tightly packed sentences: purpose first, routing second, return contract third. Every clause carries information an agent needs and there is no filler.

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 compensates by enumerating the response fields and the pagination cursor. Combined with full schema coverage and annotations covering safety, an agent has everything needed to call and page this tool correctly.

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, but the description adds the pagination contract by naming offset, limit, and nextOffset in the return shape, which tells an agent how to page. It does not add syntax guidance for the query string beyond what the schema example already provides.

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 ("ranked keyword search") plus a scope qualifier ("across approved Nebula KB sections"). It explicitly positions itself against all three siblings, so an agent can distinguish it 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?

"Use when you have a query" gives the positive condition, and each alternative is named with the condition that selects it: nebula_list for unranked browsing, nebula_section for an exact path, nebula_overview for a summary. This is a complete routing table with no inference required.

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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