RRepoGEO

REPOGEO REPORT · LITE

wild-card-ai/agents-json

Default branch master · commit 5f5e60a4 · scanned 5/20/2026, 9:32:02 PM

GitHub: 1,313 stars · 66 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface wild-card-ai/agents-json, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise "About" description

    Why:

    COPY-PASTE FIX
    An open specification and Python package to translate OpenAPI definitions into structured tools for large language models and AI agent interactions.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    openapi, llm, ai-agents, function-calling, api-specification, json-schema, python-package
  • mediumreadme#3
    Strengthen the README's opening paragraph for AI agent context

    Why:

    CURRENT
    The `agents.json` Specification is an open specification that formally describes contracts for API and agent interactions, built on top of the OpenAPI standard. The current version is `0.1.0`.
    COPY-PASTE FIX
    The `agents.json` Specification is an open standard built on OpenAPI, designed to translate complex API definitions into structured, reliable tools for large language models (LLMs) and AI agents. It provides a formal contract for how LLMs can interact with external APIs, ensuring consistent and predictable agent behavior.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface wild-card-ai/agents-json
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Function Calling
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Function Calling · recommended 1×
  2. LangChain · recommended 1×
  3. LlamaIndex · recommended 1×
  4. PyYAML · recommended 1×
  5. json · recommended 1×
  • CATEGORY QUERY
    How can I translate my existing OpenAPI specifications into tools for large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Function Calling
    2. LangChain
    3. LlamaIndex
    4. PyYAML
    5. json
    6. jsonschema
    7. requests
    8. Stoplight Studio
    9. Postman

    AI recommended 9 alternatives but never named wild-card-ai/agents-json. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's the best way to define contracts for AI agent interactions with external APIs?
    you: not recommended
    AI recommended (in order):
    1. OpenAPI Specification (formerly Swagger)
    2. Swagger UI
    3. OpenAPI Generator
    4. JSON Schema
    5. Google Protocol Buffers (Protobuf)
    6. gRPC
    7. GraphQL Schema Definition Language (SDL)
    8. Graphene
    9. Apollo Client
    10. Apache Avro
    11. TypeScript Interfaces
    12. Zod

    AI recommended 12 alternatives but never named wild-card-ai/agents-json. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of wild-card-ai/agents-json?
    pass
    AI named wild-card-ai/agents-json explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts wild-card-ai/agents-json in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wild-card-ai/agents-json explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo wild-card-ai/agents-json solve, and who is the primary audience?
    pass
    AI named wild-card-ai/agents-json explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite