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CogniRepo MCP server

by ashlesh-t·io.github.ashlesh-t/cognirepo·v1.1.1·14 stars

FAISS, call graph, AST, BM25 — 34 MCP tools for AI agents. 50-80% token reduction. Offline.

A92/100grade A
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A92/100

full report

Adoption
Growing

14 stars30 downloads/wk

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CogniRepo tools (35, 1 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • architecture_overview

    Retrieve pre-computed architectural summaries. scope: 'root' for repo summary, a directory path, or a file path.

  • context_pack

    Budget-pack the most relevant code + episodic context into a token-bounded block ready for injection into the next prompt. Call this BEFORE reading any source file to avoid wasting tokens on raw file reads. Returns at most max_tokens (default 2000) tokens — much smaller than raw files. Do NOT call for known short files (< 50 lines) — use Read directly instead.

  • cross_repo_search

    Search knowledge from sibling repositories.

  • cross_repo_traverse

    Traverse the org dependency graph from a repo or symbol to find cross-service relationships.

  • dependency_graph

    Return import/dependency relationships for a module. direction: "imports" | "imported_by" | "both". depth: transitive traversal depth (1 = direct only).

  • episodic_search

    Return past episodic events matching a keyword query.

  • explain_change

    Explain what changed in a file or function recently by cross-referencing git history with episodic memory events mentioning the same target.

  • find_symbol_path

    Find the shortest call-graph path between two symbols, crossing service boundaries via the org graph when needed.

  • get_agent_bootstrap

    Single-call session bootstrap for AI agents. Replaces the 4-call sequence (get_session_brief → get_last_context → get_user_profile → get_error_patterns) with one ~300-token payload.

  • get_error_patterns

    Return recurring error patterns with prevention hints to avoid repeating mistakes.

  • get_last_context

    Return the most recent context snapshot written by context_pack.

  • get_service_endpoints

    Return the HTTP endpoint registry for a service (from endpoints.json).

  • get_session_brief

    Generate a session bootstrap brief for agent orientation.

  • get_session_history

    Return recent conversation session exchanges for context continuity.

  • get_user_profile

    Return the user's interaction style profile for Claude to adapt its responses.

  • graph_stats

    Return a health summary of the current graph state.

  • link_repos

    Record a dependency relationship between two repos in the org graph. Call this when you discover that one repo imports from or calls another.

  • list_org_context

    Show what org, project, and sibling repositories the current repo belongs to.

  • log_episode

    Log a structured episodic event to the episodic memory store. Use to record what happened: bugs found, code explored, agent actions. Retrievable via episodic_search in future sessions. Do NOT use for architectural decisions — use record_decision instead.

  • lookup_symbol

    Return all locations where a symbol is defined or called, with file, line, and type. If include_org=True, also searches sibling repositories in the same organization. Do NOT call for broad concepts — only exact or near-exact symbol names. For concept queries use semantic_search_code instead.

  • org_dependencies

    Return the bidirectional inter-repo dependency graph for the current organization.

  • org_search

    FALLBACK ONLY — use org_wide_search first. Text-match search across org repos when org_wide_search returns nothing. Do NOT call as primary cross-repo search — org_wide_search has broader coverage.

  • org_wide_search

    PRIMARY tool for cross-repo queries. Searches memories across ALL registered repos. Use this by default when a query spans multiple services or the answer may live in any repo. Prefer this over org_search (legacy fallback).

  • record_decision

    Record an architectural decision, bug fix rationale, or 'why we did X'. Stored in episodic memory — retrievable via episodic_search in future sessions. Call when a non-obvious architectural decision is made, a bug root-cause is understood, or the user says to 'remember' something. Do NOT call for routine changes — only decisions where WHY is non-obvious. Returns: {stored: True, searchable_via: 'ep

  • record_error

    Record an error Claude or the user encountered so future sessions can avoid it.

  • record_user_preference

    Store an explicit user preference or query-rewrite correction.

  • retrieve_memory

    Retrieve the top-k memories most similar to the query. If include_org=True, also queries sibling repositories in the same organization.

  • run_serverwrite action

    Entry point called by the CLI — writes manifest then starts stdio MCP server.

  • search_docs

    Search indexed documentation files (*.md, *.rst, *.txt) by semantic similarity. Use for: README explanations, architecture docs, decision logs. Do NOT use for code — use semantic_search_code instead.

  • search_token

    Word-level reverse-index search.

  • semantic_search_code

    Semantic vector search over indexed code symbols only (no episodic memory mixed in). Optionally filter by language: "python", "typescript", "go", etc. Do NOT call for exact string matches — use search_token or grep instead. Use for concepts and natural-language queries about what code does.

  • store_memory

    Store a semantic memory with an optional source label.

  • subgraph

    Return the local neighbourhood of a concept or symbol as {nodes, edges}.

  • supersede_learning

    Deprecate an existing memory (by ID from store_memory conflicts list) and replace it with corrected text. Use when store_memory returns a conflict that contains incorrect or outdated information.

  • who_calls

    Return every caller of a function across the indexed repo.

Public scan report

scanner v0.1.9 · 2026-09-27 · same rubric, same numbers if you re-run it

no findings
  • Code scan102 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 2 days ago15/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
Overall 92/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How CogniRepo compares

Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
92/100
higher than 95% of 48,255 servers
Tools
35
the typical server has 7
Tools that change things
3%
typical: 0%. Fewer write tools means less an agent can break
GitHub stars
14
more than 90% of 28,688 servers
Downloads a week
30
more than 18% of 12,191 servers with a package

What its tools let an agent do: 34 read · 1 run or deploy

Install CogniRepo in Claude Code, Cursor or VS Code

claude mcp add cognirepo -- uvx cognirepo
Add to Cursor

What the publisher says

From the CogniRepo repository's README, as published. We do not edit it. Read it on GitHub

CogniRepo

mcp-name: io.github.ashlesh-t/cognirepo

Persistent memory and context for any AI tool. Not a chatbot — infrastructure.

lookupsymbol returns file:line very quickly — grep takes 2–8 seconds. On Python repos ≥ 15K LOC, CogniRepo cuts AI coding agent token usage by ~30–78% vs. a targeted grep+read baseline (up to 96–99% vs. reading every matching file naively) — benchmarked on Flask, FastAPI, Celery, and Ansible (5,900+ files). Works with Claude Code, Cursor, and Gemini CLI. Fully offline. No API keys required for indexing or any of the 35 MCP tools.**

What it does

Every AI conversation starts from zero. Claude, Cursor, Gemini — none of them remember what you fixed yesterday, which files relate to which features, or what decisions were made last sprint. CogniRepo fixes that.

It sits between your codebase and any AI tool, providing:

decisions, docs, architecture notes. Retrieve them with natural language.

  • Semantic memory — FAISS vector store with sentence-transformer embeddings. Store

inheritance chains, call relationships, and concepts. All queryable.

  • Episodic log — append-only event journal. Know what happened before that error.
  • Knowledge graph — NetworkX DiGraph linking functions, classes, files, imports,

style without you having to re-explain preferences every session.

  • AST reverse index — O(1) symbol lookup across your entire codebase in any supported language.
  • User behavior profiling — tracks how you prompt so Claude adapts its response

repeating the same mistake across sessions.

  • Error tracking — records errors with prevention hints so Claude avoids

where the last one ended.

  • Session history — persists conversation exchanges so any session can resume

the local AST index (no API key needed). File → directory → repo summary tree, embedded into FAISS for semantic search.

  • Architectural summaries — auto-generated on first init; built entirely from

right model. Claude for deep reasoning, Gemini Flash for quick lookups. All automatic.

  • Multi-model orchestration — classify query complexity → build context → route to the

Every AI tool that connects gets the same accumulated project knowledge. Memory persists across sessions, across tools, across time.

When to use CogniRepo

Most effective on codebases ≥ 15K LOC. On small repos (< 10K LOC), native file reads are fast enough that the MCP tool schema overhead (~3,500 tokens for 35 tools) takes more than you save. Break-even is roughly 4 tool calls on a medium-sized repo.

CogniRepo vs. claude-context / similar tools:

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

CogniRepo: common questions

Is CogniRepo MCP server safe?
Yes, by our scan: it is graded A (92/100). Read the CogniRepo safety report
How do I install CogniRepo?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does CogniRepo need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is CogniRepo maintained?
The last commit was 40 days ago (2026-09-25). The latest release is v1.1.1.
What can I use instead of CogniRepo?
Servers from other publishers that do the same job: Codebase Memory MCP server and Trace MCP server.
How does CogniRepo compare with other MCP servers?
safety score 92/100 (higher than 95% of 48,255 servers); tools 35 (the typical server has 7); tools that change things 3% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

Alternatives to CogniRepo

Same job from other publishers: the closest match first, then the best rated.

  • Codebase Memory
    Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
    C
  • Trace
    Code graph MCP: 81 languages, 91 frameworks, 72.7% fewer PR-review tokens, comprehension at parity
    A