ChatGPT Plugin

Data Dolphin

Dolphin Technical Context

Brand: Dolphin Sistemas

Category: Developer Tools
Primary Subcategory: Enterprise Knowledge Search & AI Context Layer

Integration details

Description

The DataDolphin connects ChatGPT to Dolphin Sistemas' structured knowledge base — including projects, documents, architecture rules, and agent instructions. With a single connection, you can query implementation context, retrieve project specifications, and access technical documentation in real time. All access is protected by OAuth 2.0 via Keycloak and restricted to read-only operations, ensuring safe and auditable use by AI agents.

Integration type: Plugin
Verification status: Not applicable
Platform: ChatGPT
Category: Developer Tools
Primary Subcategory: Enterprise Knowledge Search & AI Context Layer
Secondary Subcategories: None listed
Brand: Dolphin Sistemas
Access: Account required
First tracked: 2026-05-26
Tool count: 25
Market: US

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Data DolphinChatGPT Plugin · Enterprise Knowledge Search & AI Context Layer

Discoverability Score

0/100

Invisible

Category rank

#19of 75

in Enterprise Knowledge Search & AI Context Layer

Picked: 0.0/100
Found: 0.0/100
Positioned: –/100

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Primary Subcategory

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AgentContext0/100

Algolia Productivity0/100

Analytics Brain0/100

Archiveye0/100

Atlan0/100

Data Dolphin0/100

Tools

25 tools agents can invoke

Use this before any write tool when you need a read-only proposal for what to enrich next. Provide agent_target when including documents, rules, or agent instructions because this composition calls upstream agent-package. The tool lightly composes upstream search, agent-package, and optional implementation-context without creating drafts.
build_enrichment_proposal
Creates a new agent instruction draft upstream. Provide instruction_key, title, agent_target, instruction_type, status=draft, content_body, and change_summary. generated_by is normalized to chatgpt when omitted or set to cursor_agent. Use only when the target instruction is intentionally being drafted, and use an agent_target/instruction_type supported by the upstream project.
create_agent_instruction
Creates only the base upstream BootstrapProject shell. Use this when human intent to start a new project is explicit. The project is born as `draft` or `analyzing`, never `active`, does not create a GitHub repository, and does not create initial documents, rules, or agent instructions.
create_bootstrap_project
Creates a new business rule draft upstream. Provide rule_key, title, description, rule_type, severity, status=draft, content_body, and change_summary. generated_by is normalized to chatgpt when omitted or set to cursor_agent. Use only for explicit draft creation and do not treat this as approval or publish.
create_business_rule
Creates a new draft version for an existing document upstream. Provide document_key, document_status=draft, content_format, content_body, and change_summary. generated_by is normalized to chatgpt when omitted or set to cursor_agent. Use only when updating a known document and do not treat this as approval or publish.
create_document_version
Creates a new document draft upstream in bootstrap-yaml. Provide a stable document_key, title, upstream-supported document_type, document_status=draft, content_format, content_body, and change_summary. generated_by is normalized to chatgpt when omitted or set to cursor_agent; use origin_tool/source for more specific provenance. Use only after human intent is clear, and do not treat this as approval or publish. Discover valid document_type values from existing project documents or upstream validation errors; do not invent a type for the project.
create_project_document
Creates a new trigger rule draft upstream. Provide trigger_key, title, description, trigger_event, action_description, status=draft, content_body, and change_summary. generated_by is normalized to chatgpt when omitted or set to cursor_agent. Use only for deliberate draft creation and expect human review before any downstream execution.
create_trigger_rule
Requests controlled upstream BootstrapProject enrichment. This tool defaults to `dry_run=true` when omitted. Applied enrichment is blocked by default and requires the extra MCP environment flag. Even when applied, upstream creates only minimal missing drafts, never activates the project, never approves artifacts, and never provisions a GitHub repository.
enrich_bootstrap_project
Use this to retrieve a read-only API contract context package for one project, preferably by `operation_id` when known. It assembles upstream API metadata only and must not be treated as sync, import, or publication logic.
get_api_contract_context
Use this when an agent needs upstream-curated project context and operational_package guidance. Provide the upstream-registered agent_target for the intended agent persona; read operational_package before planning changes, and implement only upstream items marked actionability=actionable.
get_agent_package
Use this when a coding agent needs an upstream-assembled implementation package for one project. Provide `goal` when it helps upstream prioritize relevant context.
get_implementation_context
Use this when you already know `project_key` and need upstream project context. Prefer enabling only the include flags you actually need to keep the response smaller.
get_project
Use this before modeling a new BootstrapProject. It returns the official upstream project creation patterns, lifecycle defaults, project key rules, and validation checklist. This tool is read-only and never creates a project.
get_project_creation_context
Use this only when you already know both keys and need the current sanitized document body. Prefer discovery or search tools first if you are still exploring.
get_project_document
Use this to inspect the current agent instructions for one project before composing new guidance or draft updates.
list_agent_instructions
Use this to inspect the current business rules for one project before proposing or drafting rule changes.
list_business_rules
Use this to inspect read-only HTTP endpoints derived from imported code for one project. Prefer repository or service filters to keep the response narrow and reduce stale-context ambiguity.
list_code_endpoints
Use this to inspect imported file inventory for one project without retrieving mutative repository features. Results reflect the upstream repository snapshot and may lag behind the source repository until refreshed upstream.
list_code_files
Use this to discover the available documents for one `project_key` before reading a full document or planning a draft. You can filter by `document_type` such as `trd`.
list_project_documents
Use this first to discover valid `project_key` values before any project-scoped MCP call. Optionally filter the upstream project list with `search`.
list_projects
Use this to inspect existing trigger or automation rules before planning operational changes.
list_trigger_rules
Use this to search imported API contract metadata for one project when you need read-only operation matches by query, method, path, tag, repository, or service. This does not trigger sync or contract import.
search_api_operations
Use this to retrieve read-only code evidence for one project when you need files, symbols, or implementation snippets. Results depend on upstream imported code context and may be stale until bootstrap-yaml refreshes its repository snapshot.
search_code_context
Use this before bulk reads when you need targeted evidence from project documents. Provide a focused `query`; optionally request telemetry if you need to inspect retrieval quality.
search_project_documents
Use this after `create_bootstrap_project` or before human review to validate an existing BootstrapProject against upstream readiness and lifecycle expectations. This tool is read-only: it does not alter state, does not promote lifecycle_status, and does not create documents, rules, or agent instructions.
validate_bootstrap_project

FAQs

Data Dolphin ChatGPT Plugin FAQ

How the directory, categories and Discoverability Score work.

Read the methodology

How do I improve Data Dolphin's ChatGPT Plugin discoverability?

The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.

What are Data Dolphin alternatives on ChatGPT?

As of 2026-11-02, Data Dolphin competes with Dropbox Dash, AgentContext, Algolia Productivity, Analytics Brain, Archiveye, Atlan, Big Documents Agent, BitsWeave and 66 more in ChatGPT Enterprise Knowledge Search & AI Context Layer, ranked by public Discoverability Score.

Where does Data Dolphin rank in Enterprise Knowledge Search & AI Context Layer on ChatGPT?

As of 2026-11-02, Data Dolphin ranks #19 of 75 in ChatGPT Enterprise Knowledge Search & AI Context Layer with a Discoverability Score of 0/100 (Invisible).

Where is this profile measured?

This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.

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Competing in ChatGPT Enterprise Knowledge Search & AI Context Layer

25 tools agents can invoke

Data Dolphin ChatGPT Plugin FAQ