RRepoGEO

REPOGEO REPORT · LITE

Mibayy/token-savior

Default branch main · commit ff42ef14 · scanned 6/12/2026, 7:32:12 AM

GitHub: 980 stars · 79 forks

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 Mibayy/token-savior, 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
  • highreadme#1
    Clarify 'token' in the README's opening statement

    Why:

    COPY-PASTE FIX
    Add this sentence directly after the H1 and before the blockquote:
`Token Savior is an MCP server that optimizes large language model (LLM) coding agents by drastically reducing context window tokens and wall time.`
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm-agents, llm-optimization, code-generation, ai-coding, model-context-protocol, python, claude
  • mediumabout#3
    Refine the 'About' description for stronger clarity

    Why:

    CURRENT
    The MCP server that turns Claude into the only coding agent hitting 100% on a real benchmark. -77% active tokens, -76% wall time, 0 losses across 96 tasks on Claude Opus 4.7. Structural code navigation + persistent memory. Works with every MCP client.
    COPY-PASTE FIX
    An MCP server that optimizes large language model (LLM) coding agents by drastically reducing context window tokens and wall time, enabling Claude to hit 100% on a real benchmark. Features structural code navigation and persistent memory, compatible with all MCP clients.

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 Mibayy/token-savior
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pydantic
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Pydantic · recommended 1×
  2. Code Llama · recommended 1×
  3. DeepSeek Coder · recommended 1×
  4. Phi-2 · recommended 1×
  5. llama.cpp · recommended 1×
  • CATEGORY QUERY
    How to optimize large language model coding agents for faster execution and lower token cost?
    you: not recommended
    AI recommended (in order):
    1. Pydantic
    2. Code Llama
    3. DeepSeek Coder
    4. Phi-2
    5. llama.cpp
    6. Mistral 7B
    7. OpenAI's fine-tuning API
    8. Hugging Face's AutoTrain
    9. OpenAI's Function Calling API
    10. LangChain's Tools
    11. LlamaIndex's Tool Abstractions
    12. Redis
    13. Docker
    14. firejail

    AI recommended 14 alternatives but never named Mibayy/token-savior. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide structural code navigation and persistent memory for AI coding assistants?
    you: not recommended
    AI recommended (in order):
    1. Cursor
    2. Continue
    3. Codeium
    4. GitHub Copilot Chat
    5. Tabnine Chat

    AI recommended 5 alternatives but never named Mibayy/token-savior. 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 Mibayy/token-savior?
    pass
    AI named Mibayy/token-savior explicitly

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

  • If a team adopts Mibayy/token-savior in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Mibayy/token-savior 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 Mibayy/token-savior solve, and who is the primary audience?
    pass
    AI named Mibayy/token-savior explicitly

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

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Mibayy/token-savior — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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