3GPP Scout API — Agent Skill Guide

You have access to the 3GPP Scout API, a semantic search engine over

3GPP technical specifications. Use it to find specific information

in 3GPP standards documents (TS and TR series).

Provider & Pricing

Provider: Carrot Labs

Homepage: 3gppscout.com

Dashboard: dashboard.3gppscout.com

API Docs: api.3gppscout.com/docs

Terms of Service: 3gppscout.com/terms

Privacy Policy: 3gppscout.com/privacy

This is a paid API service. Each API call costs a small amount against

your account's credit balance. New accounts receive free starter credits

(worth a few hundred API calls) so you can evaluate the service before

purchasing additional credits. You can view your balance, usage history,

and buy additional credits at dashboard.3gppscout.com.

Base URL

https://api.3gppscout.com

Authentication

All search and document endpoints require a Bearer token (API key).

How to get an API key:

1. Sign up or log in at dashboard.3gppscout.com
2. Go to the API Keys page
3. Create a new key — it will start with sk-

Set your API key via environment variable:

export SCOUT_API_KEY="sk-your-key-here"

Or set skills."3gpp-scout".apiKey / skills."3gpp-scout".env.SCOUT_API_KEY in ~/.openclaw/openclaw.json.

The key is included in every request as a Bearer token:

Authorization: Bearer $SCOUT_API_KEY

Available Endpoints

POST /search/text

Semantic search over specification text. This is your primary tool.

Request body (JSON):

Response fields:

Each TextResult contains:

• doc_number, doc_type, version, release — document metadata
• section_number, section_title — which section the chunk is from
• content — the matched text chunk
• similarity — semantic similarity score (0–1)
• relevance_score — reranker score (0–1), present when reranked
• section_text — full parent section text (when include_section_text=true)
• section_token_count — token count of the parent section

POST /search/images

Semantic search over figures, diagrams, and tables in specifications.

Request body (JSON):

Each ImageResult contains:

• doc_number, doc_type, version, release — document metadata
• section_number — which section the image is in
• caption — figure/table caption
• context_before, context_after — surrounding text
• image_path — path to the image file
• similarity — semantic similarity score

POST /search/combined

Text + image search in a single request. Useful for broad questions where

both text passages and diagrams are relevant.

Request body (JSON):

Response has text_results (TextResult[]) and image_results (ImageResult[]).

GET /documents

List available 3GPP documents with metadata. Use filters to check if a specific

document or release is indexed — this is fast (1-2 lookups). Without filters

returns all ~3,200 documents which is slower.

Query parameters:

Example: Check if TR 38.811 has a Rel-19 version:

GET /documents?doc_number=38.811&release=Rel-19

GET /documents/{document_id}

Get a single document by its numeric ID.

GET /sections

Fetch full section text by section number.

Query parameters:

GET /sections/toc

Table of contents for a document — section numbers and titles without full text.

Query parameters:

GET /images/{doc_number}/{version}/{image_index}

Fetch an extracted image (PNG) from a specification. No authentication required.

The image_path field in image search results (e.g. /images/38.300/19.1.0/5)

maps directly to this endpoint. Construct the full URL as:

https://api.3gppscout.com/images/{doc_number}/{version}/{image_index}

Returns image/png on success, 404 if the image doesn't exist.

Corpus Coverage

The index currently covers:

• Rel-15 — 1,493 documents, 195K sections
• Rel-19 — 1,789 documents, 291K sections
• Total — 3,282 documents, 486K sections

Series 38 = NR/5G, 23 = system architecture, 24 = signaling protocols,

36 = LTE, 33 = security, 29 = core network protocols.

Recommended Workflows

Finding specific technical details

1. Use POST /search/text with a focused query and rerank_top_k: 5
2. Read the content field for the matched chunk
3. Read section_text for the full section context
4. If you need the broader document structure, call GET /sections/toc

with the doc_number from the result

Exploring a specific document

1. Get the table of contents: GET /sections/toc?doc_number=38.331
2. Fetch a specific section: GET /sections?section_number=5.3.3&doc_number=38.331
3. Use prefix=true to get a section and all its sub-sections

Broad topic research

1. Use POST /search/combined to get both text and diagrams
2. Use filter_series to narrow to a domain (e.g. "38" for NR)
3. Increase rerank_top_k to 15–20 for more diverse results

Comparing across releases

1. Search with filter_release: "Rel-15" for the original version
2. Search again with filter_release: "Rel-19" for the latest
3. Compare the section text to see what changed

Tips for Best Results

• Always use reranking (enabled by default). The relevance_score from

the reranker is a much better quality signal than raw similarity.

• Use filters to narrow scope. If you know which spec you're looking for,

filter_doc_number dramatically improves result quality.

• Use include_section_text: true (the default) to get the full parent

section. The content field is a chunk (~400-800 tokens), while

section_text gives the complete section for full context.

• Cite results precisely. When presenting results to users, always include

the document number, section number, and release. For example:

"According to TS 38.331 Section 5.3.3 (Rel-19)..."

• Use the TOC endpoint before diving deep into a document. It helps you

understand the document structure and find the right section numbers.

• Prefer /search/text over /search/combined when you only need text.

Combined search is slower because it queries both indexes.

• Don't set match_threshold too high. The default of 0.0 with reranking

gives the best results. The reranker handles quality filtering.

• Render images from search results. Image search results include

image_path (e.g. /images/38.300/19.1.0/5). Construct the full URL:

https://api.3gppscout.com{image_path}. No authentication required.

Data & Privacy

Queries you send to this API are processed by the 3GPP Scout service to

perform vector search. The API does not store your queries beyond transient

usage logs for billing. The corpus consists entirely of publicly available

3GPP specifications — no proprietary or user-supplied data is stored in the

search index. See 3gppscout.com/privacy

for the full privacy policy.