Provides GitHub repository discovery and intelligence, allowing natural-language feature queries to return ranked open-source projects with quality signals, license classifications, and snippet evidence.
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., "@reposniffer-mcp Find a well-maintained Python library for converting PDFs to markdown"
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.
There's a repo for that. Let RepoSniffer find it.
AI-first GitHub repo discovery. Describe a feature in plain language — "markdown editor with live preview" — and RepoSniffer returns a ranked, adoption-grade list of open-source projects that implement it, with evidence and quality signals (stars, activity, license, archived status).
Built to be the grounding layer for coding agents: stop hallucinating repos, get a verifiable "best of kind" answer with reasons.
# CLI
uvx reposniffer "markdown editor live preview" --language python --top-k 5
# MCP server (stdio) — wire into opencode, Claude Code, Codex, Cursor, ...
uvx --from reposniffer reposniffer-mcpSet GITHUB_TOKEN to raise search rate limits (authenticated = 30 req/min vs ~10).
Related MCP server: github-rag-mcp
opencode — add to opencode.json:
{
"mcp": {
"reposniffer": {
"type": "local",
"command": ["uvx", "--from", "reposniffer", "reposniffer-mcp"],
"environment": { "GITHUB_TOKEN": "ghp_..." }
}
}
}Claude Code: claude mcp add reposniffer -- uvx --from reposniffer reposniffer-mcp
Codex/Cursor: add an MCP server pointing at uvx --from reposniffer reposniffer-mcp (stdio).
Tool | Purpose |
| Feature query → ranked candidates with score breakdown, snippet evidence, license verdict, recommendation |
| Verify an existing repo (alive? licensed? best-of-kind?) + 2 alternatives |
| Embedding backend, model, auth status |
Every result carries as_of (a freshness timestamp agents can cite), a flags list
(archived, no-license, strong-copyleft, stale, ...), a license_category
(permissive / weak-copyleft / strong-copyleft / unknown), and a targeted
snippet showing why the repo matched.
Coarse candidate fetch — GitHub Search API (in:readme, language/license/stars filters).
Hybrid rerank — embed each candidate's description + README front matter (not the whole README, to avoid dilution), cosine vs embedded query, plus a lexical-overlap boost for literal matches.
Quality scoring — popularity (log stars), activity (pushed_at half-life), license
category, archived penalty; weights differ by intent (adopt vs study).
Adoption safety — permissive/weak/strong-copyleft classification flags GPL/AGPL repos before you depend on them.
Local SQLite cache — repos, READMEs, embeddings, query results → fast repeat queries, index grows over time.
Embeddings are pluggable: default is a zero-config local fastembed ONNX model
(no torch, no API key); set REPOSNIFFER_EMBED_BACKEND=api plus an OpenAI-compatible
endpoint for stronger quality.
Ground-truth queries live in eval/queries.py (feature → known-good repos). Run with a
token (each query fetches ~50 READMEs):
GITHUB_TOKEN=ghp_... uv run python -m eval.runReports hit@1 / hit@3 / hit@5. Current live result: hit@1 0.50, hit@3 0.83, hit@5 0.83.
Known limitation: the candidate stage depends on GitHub Search API relevance, which can
fail to recall canonical repos with weak descriptions/READMEs (e.g. Kozea/WeasyPrint
— description is just "The awesome document factory"). Semantic rerank can only rank
what the candidate fetch surfaces.
src/reposniffer/
config.py # env-driven settings
cache.py # sqlite store (repos, readmes, embeddings, query cache)
engine/
github.py # GitHub REST client + search query builder + text/snippet utils
embed.py # Embedder protocol: local fastembed + OpenAI-compatible API
score.py # quality + license-category + lexical scoring
search.py # orchestration (Engine)
mcp/server.py # MCPServer (mcp 2.x)
cli.py # Typer CLI
eval/ # golden query → repo eval harness
tests/ # offline (fake transport + fake embedder)uv sync
uv run ruff check .
uv run ruff format --check .
uv run pyright
uv run pytestCI (lint/format/type/tests) runs on every push and PR. Publishing to PyPI happens on v*
tags via trusted publishing — enable it once on the PyPI project settings, then:
git tag v0.1.0 && git push --tags.
Note: mcp 2.x is used — MCPServer (FastMCP was renamed in mcp 2.0). Pin mcp<2 if you need the v1 API.
We welcome contributions — bugs, features, docs, and eval cases. Please read CONTRIBUTING.md first; it covers the dev setup, coding standards, tests, the eval harness, and the PR workflow.
Found a bug? Open an issue with the exact query and output.
Have a feature idea? Discuss it in an issue before writing code.
Reporting a vulnerability? See SECURITY.md — don't post it publicly.
This project follows a Code of Conduct.
MIT
Rank open-source GitHub projects that implement a described feature.
Use when an agent needs to pick a library/project to adopt or study, and wants a verifiable, adoption-grade verdict with evidence and quality signals (stars, activity, license, archived status) instead of a hallucinated guess.
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It discloses that the tool returns a 'verifiable, adoption-grade verdict with evidence and quality signals (stars, activity, license, archived status)', which gives the agent a clear picture of the output characteristics. It does not mention side effects, rate limits, or read-only nature, but as a search/ranking tool, that is minor. The description adds meaningful behavioral context beyond a bare 'find repos'.
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 two sentences with zero fluff. The first sentence states the core purpose, and the second provides the usage context and expected output. It is front-loaded and every word 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?
Given the tool's complexity (7 parameters) and the presence of an output schema, the description provides adequate context. It explains the intended use case, what the tool returns (ranked projects with quality signals), and the decision it supports. It does not go into detail on ranking methodology or edge cases, but for a search tool with a rich schema, this is sufficient. The output schema likely covers return structure.
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 description coverage is 0% because the tool description does not explain any of the seven parameters. The description does hint at some filters by naming 'license, archived status' and 'stars', which map to the license, include_archived, and min_stars parameters, but it omits query, top_k, intent, and language entirely. Since coverage is low, the description must compensate, but it only partially does. The schema itself has strong descriptions, so the agent can still understand the parameters, but the tool description adds minimal value beyond the schema.
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 opens with a specific verb ('Rank') and resource ('open-source GitHub projects'), and clarifies the outcome ('implement a described feature'). It distinguishes the tool's role as an adoption/study selection aid, which is distinct from the likely deeper-analysis siblings repo_intel and health, even though it does not name them explicitly.
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?
It explicitly states the trigger condition: 'Use when an agent needs to pick a library/project to adopt or study'. This gives clear when-to-use guidance. However, it does not mention alternatives or when not to use, and it does not differentiate from sibling tools, which would have strengthened the guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Report embedding backend, model, and GitHub auth status.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing side effects. The verb 'report' strongly implies a read-only, non-mutating operation, but the description does not explicitly state that the tool has no side effects, makes no external calls, or is safe to invoke at any time. It is not contradictory, but it lacks explicit behavioral guarantees.
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, concise sentence that contains no redundant words or filler. It is front-loaded with the verb and immediately specifies what is reported. The structure is ideal for a no-parameter tool.
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?
Given the tool has no parameters and the output schema is available (though not displayed), the description sufficiently covers the essentials. It states what the tool does without needing to elaborate on inputs. The only minor gap is the lack of detail about the output format or structure, but since an output schema exists and the purpose is simple, this is not a significant omission.
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 tool has zero parameters, and the schema coverage is 100% (empty properties object). The description does not need to explain parameter semantics because there are none. Per the baseline for 0 parameters, a score of 4 is appropriate; there is no additional parameter-related context to add.
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 clearly states the tool's function: 'Report embedding backend, model, and GitHub auth status.' It uses a specific verb ('report') and identifies the exact subjects, making the purpose unambiguous. It also naturally distinguishes itself from the sibling tools (find_repos, repo_intel) which focus on repository discovery and analysis, not system health.
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 does not provide any explicit guidance on when to use this tool versus the siblings. While the purpose is clear, there is no direct statement like 'Use this when you need to check system health' or any comparison to find_repos/repo_intel. This leaves the agent to infer usage context from the tool's name and purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify an existing repo the agent already found: is it alive, licensed, and best-of-kind? Returns a status verdict plus a few alternatives.
Use BEFORE committing to a dependency to catch archived/stale/no-license repos.
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It does indicate a read-only verification action and that it returns a status verdict plus alternatives, but it does not disclose potential side effects, network behavior, rate-limit implications, or error conditions. 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 short, focused, and front-loaded with the core purpose. Every sentence contributes value: what it verifies, what it returns, and when to use it. No redundant or filler content.
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?
Given the sibling tools (find_repos, health) and the explicit note that the repo is 'already found,' the description is contextually complete for typical use. It could provide more detail about the output shape, but it covers the essential need: verifying a repo before dependency commitment.
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 already provides descriptions for owner_repo, query, and top_k, including an example for owner_repo. The description adds context by mentioning 'best-of-kind' and 'a few alternatives,' which aligns with query and top_k behavior, but it does not explicitly map each parameter.
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 ('Verify') and identifies the resource (an existing repo the agent already found) and the key checks (alive, licensed, best-of-kind). It clearly distinguishes this from the sibling tools by emphasizing it is not for discovery, making the purpose unambiguous.
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?
It explicitly states when to use the tool: 'BEFORE committing to a dependency to catch archived/stale/no-license repos.' This gives direct, actionable guidance and implicitly contrasts with the find_repos and health sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.1find_reposhealthrepo_intelEach tool has a clear, distinct purpose: find_repos for discovering projects, repo_intel for validating a specific repo, and health for server status. No functional overlap.
Tool names use a consistent lowercase_with_underscores pattern. 'find_repos' and 'repo_intel' are verb_noun or noun-ish, but 'health' is a single noun, slightly deviating from the pattern.
With only three tools, the server is tightly scoped to its purpose: search, evaluate, and monitor. This is a lean, focused set that avoids unnecessary complexity.
The core workflow of discovering and vetting repositories is covered. Could potentially add a tool for getting detailed repo metadata or comparing multiple repos, but the current set covers the stated use case adequately.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Finds real, maintained open-source repos that fit your project. MCP grounding for coding agents.
Code intelligence for LLMs. Analyze, search, and retrieve code from any public git repository.
Search GitHub, npm, PyPI, StackOverflow, ArXiv from one MCP — built for coding agents.
Code intelligence for coding agents: semantic, AST, graph, and full-text search. 279+ languages.
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