Provides semantic search over Telegram memes, returning matching images with file paths and captions, and fetching a specific meme image by its SHA-256 hash.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@memgrep find that meme with the screaming cat and a woman pointing at it"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Semantic search over your Telegram memes. Ask in Russian or English — find the meme you half-remember.
Pipeline: Telegram export → vision captions (bilingual) + OCR → multilingual embeddings (bge-m3, local) → PostgreSQL/pgvector hybrid search (dense + full-text, RRF) → cross-encoder reranking. Exposed as a CLI, a web UI and an MCP server, so your AI assistant can search your memes too.
See the design spec for the full architecture.
cp .env.example .env # fill in OpenRouter API key
make up # PostgreSQL + pgvector on :5433
make test
uv run memgrep ingest # load data/result.json + photos into the db
uv run memgrep caption --limit 400 # describe images via a vision model (pilot)
uv run memgrep embed # local bge-m3 embeddings
uv run memgrep search "this is fine" # CLI search; --open shows files in Preview
uv run memgrep serve # web UI on localhost:8000
uv run memgrep evals # golden-set metrics for the current config
uv run memgrep sync # ingest + caption + embed for new exports
uv run memgrep watch # follow the channel via Bot APIThe Telegram export goes to data/ (Telegram Desktop → Export chat history → Photos, JSON format). Memes and exported data stay local — data/ is gitignored; only code is published.
Related MCP server: local-docs-mcp
Measured on a 21-query golden set collected through the UI feedback button ("это он ✓"), pilot corpus of 400 images. Each retrieval layer was added only after it proved itself:
Layer | hit@5 | hit@10 | MRR |
dense vectors only | 0.857 | 0.905 | 0.768 |
+ full-text search, RRF fusion | 0.905 | 0.952 | 0.815 |
+ cross-encoder reranker | 0.905 | 1.000 | 0.839 |
Captioning-model comparison on the same corpus and golden set: qwen3-vl-8b matched or beat qwen3-vl-235b on retrieval metrics at a third of the price, so the full corpus is indexed with the 8b model (~$3.6 for ~11k images). Caveat documented in the evals history: the golden set was collected on top of the 8b index, which biases the comparison toward it.
Search config is env-driven: MEMGREP_SEARCH_MODE=vector|hybrid, MEMGREP_RERANK_ENABLED=true|false. memgrep evals writes a timestamped JSON per configuration into evals/results/ so runs stay comparable.
Let Claude (or any MCP client) search your memes:
claude mcp add memgrep -- uv run --directory /absolute/path/to/memgrep python -m memgrep.mcp_serverTools: search_memes(query, k) returns matches with file paths and captions; get_meme(sha256) returns the image itself.
The memgrep watch command follows a Telegram channel in real-time via Bot API long polling. Set up the bot as a channel admin, configure MEMGREP_TG_BOT_TOKEN and MEMGREP_TG_CHANNEL_ID, and run the watcher service. New photos posted to the channel are automatically indexed: they get captioned, embedded, and searchable within minutes. Reactions on recent posts are also synced back to the database for engagement tracking.
Python 3.12+, uv, PostgreSQL 17 + pgvector, sentence-transformers (BAAI/bge-m3 embeddings, BAAI/bge-reranker-v2-m3 reranker, both local), any OpenAI-compatible vision endpoint for captioning (default: OpenRouter, qwen3-vl), FastAPI, typer, MCP Python SDK. 49 tests, no network or model downloads in the test suite.
Return the meme image itself by sha256 from a search_memes result.
| Name | Required | Description | Default |
|---|---|---|---|
| sha256 | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral disclosure burden. It states the core behavior (returning the image binary for a given hash) and implies a read-only action, but it does not disclose output format (e.g., binary, base64), error conditions, or whether any authentication is required. This is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler words. Every phrase contributes meaning: 'meme image itself' clarifies the return value, 'by sha256' identifies the key parameter, and 'from a search_memes result' indicates the source workflow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and no output schema, the description is largely complete: it identifies the input provenance, the operation, and the result. It does not mention potential errors or the exact binary format, but given the tool's simplicity the missing details are minor and unlikely to cause incorrect invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only the parameter name and type (string) with 0% description coverage, so the description must add meaning. It does this by explaining that the sha256 parameter comes from a search_memes result, giving the agent crucial context on how to obtain a valid value. It could specify the expected format (e.g., hex-encoded SHA-256), but the guidance is already helpful.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Return') and names a precise resource ('the meme image itself') with a clear retrieval mechanism ('by sha256'). Referencing 'from a search_memes result' distinguishes it from its sibling tool search_memes, which presumably returns metadata or search results rather than the image content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies the intended usage: call this tool with a sha256 obtained from search_memes to fetch the actual image. It doesn't explicitly state exclusions or when to prefer alternatives, but for a simple single-purpose tool the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Semantic search over the meme collection. Query in Russian or English.
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | ||
| query | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden; it does convey that matching is semantic rather than exact and that the query language is flexible. It does not state read-only behavior, result ordering, or limitations, but for a simple search tool the disclosed traits are non-trivial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences carry the essential message, with the main action stated first and no filler. Every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter search with an output schema, the description is nearly sufficient, but it leaves two gaps: the meaning/behavior of 'k' and the relationship to the sibling get_meme tool. This is adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It adds meaning to 'query' by explaining the search is semantic and accepts Russian or English, but it remains silent on the 'k' parameter. The compensation is partial, making this minimum viable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description gives a specific verb and resource ('Semantic search over the meme collection') and the 'semantic' qualifier distinguishes it from a direct fetch like get_meme. It is clear, but it does not explicitly name or contrast the sibling tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Semantic search over the meme collection' implies when the tool is relevant, and 'Query in Russian or English' gives practical input guidance. However, it offers no explicit direction on when to choose this over get_meme or what types of queries are not appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The two tools have clearly distinct purposes: one performs semantic search over the collection, the other retrieves a specific image by hash. There is no overlap or ambiguity between them.
Both tools follow the same verb_noun pattern in snake_case: search_memes and get_meme. The singular/plural variation is natural for resource action and does not break consistency.
With only two tools, the server feels thin and sits at the low end of the borderline range. However, the narrow purpose of search and retrieval justifies a minimal surface, so it is not unreasonable.
For a read-only meme search and retrieval server, the surface is complete: search returns hashes and get_meme fetches the image. There are no dead ends or missing core operations within this defined scope.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Memory system for AI agents with semantic search. Store and recall memories with ease.
Persistent memory for AI agents. Search, store, and recall across sessions.
Persistent memory for AI agents. Semantic search, memory graph, W3C DID identity.
Persistent AI memory with semantic search, conflict detection, and ticketing.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Milflopper/memgrep'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
