一个模型上下文协议（MCP）服务器，允许 Claude Desktop、IDE 和其他 MCP 客户端查询粒子属性和衰变模式。
支持自然语言查询（muon plus、pion zero、antiproton、anti up quark）、不区分大小写的查找、MC ID（-13），并返回人类可读的文本和结构化的 JSON 有效载荷。
Related MCP server: Physics MCP Server
search_particle — 质量（MeV + GeV）、电荷、自旋、颜色、宇称 / C / I / G、寿命或宽度、MCID、审查 ID
list_decays — 包含源方法名称的排他性 / 包含性分支分数
自然语言输入 — muon plus、positive tau、pion zero、kaon minus
反粒子支持 — antimuon、anti up quark、ubar、u bar、u_bar、u~、antineutron；通过 MCID 取反解析，无需猜测名称
MC ID 查找 — 直接使用 11、-2212 等进行查询
自共轭感知 — anti photon 解析为 gamma，anti pi0 解析为 pi0
结构化输出 — 每个响应除了人类可读的文本外，还包含一个被围起来的 ```json 块
git clone https://github.com/uzerone/particlephysics-mcp-server.git
cd particlephysics-mcp-server
pip install -e .将以下内容之一添加到您的客户端 MCP 配置中（例如 claude_desktop_config.json）。
使用克隆仓库中的 uvx（无需全局安装）：
{
"mcpServers": {
"particlephysics": {
"command": "uvx",
"args": ["--from", "/absolute/path/to/particlephysics-mcp-server",
"python", "-m", "particlephysics_mcp_server"]
}
}
}使用本地虚拟环境（在 pip install -e . 之后）：
{
"mcpServers": {
"particlephysics": {
"command": "/absolute/path/to/.venv/bin/python",
"args": ["-m", "particlephysics_mcp_server"]
}
}
}search_particle输入 | 示例 |
规范名称 |
|
英文别名 |
|
反粒子 |
|
自然语言电荷 |
|
MC ID |
|
示例调用：search_particle({"query": "muon plus"})
Found 1 particle(s) matching 'muon plus':
1. mu
Name: mu+
PDG ID: -13
PDG Review ID: S004/2025
Mass: 105.6583755 MeV (0.1056583755 GeV)
Spin (J): 1/2
Charge: 1
Color: singlet
Quantum numbers: J=1/2
Lifetime: 2.196981148893498e-06
```json
{
"query": "muon plus",
"count": 1,
"particles": [{
"name": "mu+",
"mcid": -13,
"pdg_review_id": "S004/2025",
"mass": {"mev": 105.6583755, "gev": 0.1056583755},
"charge": {"value": 1.0, "fraction": "1"},
"spin": "1/2",
"color": {"multiplicity": 1, "label": "singlet"},
"quantum_numbers": {"J": "1/2"},
"lifetime": {"seconds": 2.197e-06, "stable": false, "text": "..."}
}]
}
```list_decays与 search_particle 相同的标识符格式。尝试 exclusive_branching_fractions → branching_fractions → inclusive_branching_fractions 并报告其使用的源。对于稳定粒子，返回一个 stable=true 的空衰变列表。
示例调用：list_decays({"particle_id": "tau"})
Decay modes for particle 'tau':
1. tau- --> mu- nubar_mu nu_tau (BR: 17.39 ± 0.04 %)
2. tau- --> e- nubar_e nu_tau (BR: 17.82 ± 0.04 %)
…
```json
{
"particle": {"name": "tau-", "mcid": 15, ...},
"source": "exclusive_branching_fractions",
"count": 137,
"decays": [{
"description": "tau- --> mu- nubar_mu nu_tau",
"branching_ratio_text": "17.39 ± 0.04",
"value_text": "17.39E-2",
"is_limit": false
}, ...]
}
```Claude Code 技能规范位于 .github/skills/particlephysics-skill/SKILL.md。它运行检查器，验证两个工具，并测试自然语言 / 反粒子 / MC ID 查询界面。
触发短语：particle physics mcp 或 pp。
请参阅 CHANGELOG.md 获取发布说明。
MIT — 请参阅 LICENSE.txt。
List decay modes for a specific particle
| Name | Required | Description | Default |
|---|---|---|---|
| particle_id | Yes | Particle identifier (PDG ID or name) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but doesn't describe traits like whether this is a read-only operation, potential rate limits, error handling for invalid particles, or the format of the returned decay modes. This leaves significant gaps for a tool that likely queries a dataset.
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, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 one parameter with full schema coverage and no output schema, the description is minimally adequate but lacks context about behavioral aspects and usage guidelines. For a simple query tool, it's passable, but the absence of annotations and output details means it doesn't fully equip the agent for optimal use.
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 input schema has 100% description coverage, with the parameter 'particle_id' documented as 'Particle identifier (PDG ID or name)'. The description adds no additional meaning beyond this, such as examples or constraints, so it meets the baseline of 3 where the schema does the heavy lifting.
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 verb 'list' and the resource 'decay modes for a specific particle', making the purpose immediately understandable. However, it doesn't explicitly differentiate from the sibling tool 'search_particle', which might also involve particle-related queries, so it doesn't reach the highest score.
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 provides no guidance on when to use this tool versus the sibling 'search_particle' or any other alternatives. It lacks context about prerequisites, such as whether the particle must exist in a database, or exclusions, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for particles by name or properties in the PDG database
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query (particle name, symbol, or property) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool searches a database, implying a read-only operation, but doesn't disclose behavioral traits like authentication needs, rate limits, result format, pagination, or error handling. This leaves significant gaps for a database query tool.
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, efficient sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly.
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 no annotations and no output schema, the description is incomplete for a search tool. It lacks details on behavioral aspects (e.g., result format, limits) and doesn't compensate for the absence of structured data, leaving the agent with insufficient context for reliable use.
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 input schema has 100% description coverage, with the 'query' parameter documented as 'Search query (particle name, symbol, or property)'. The description adds minimal value beyond this, only reiterating 'by name or properties'. Baseline 3 is appropriate as the schema does the heavy lifting.
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 action ('Search for particles') and resource ('in the PDG database'), with specificity about search criteria ('by name or properties'). It doesn't explicitly differentiate from the sibling tool 'list_decays', which appears to be a different operation, but the purpose is well-defined.
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 provides no guidance on when to use this tool versus alternatives or in what context. It mentions searching 'in the PDG database', but doesn't specify prerequisites, limitations, or how it relates to the sibling tool 'list_decays'.
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.
list_decayssearch_particleScored across 2 tools
The two tools have clearly distinct purposes: one lists decay modes for a specific particle, while the other searches for particles in a database. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the task.
Both tools follow a consistent verb_noun naming pattern (list_decays and search_particle), using snake_case throughout. This predictability aids in understanding and usage without any deviations.
With only 2 tools, the server feels thin for a particle physics domain, which typically involves complex queries, simulations, or analyses. This limited set may not support comprehensive agent workflows, suggesting an under-scoped implementation.
The toolset is severely incomplete for particle physics, lacking essential operations like retrieving particle properties, calculating cross-sections, or simulating interactions. Agents will face significant gaps, as basic CRUD or lifecycle coverage is missing.
Real-time web search, reasoning, and research through Perplexity's API
Integrates Wolfram Language and Wolfram|Alpha accessing curated data and sophisticated algorithms.
Real-time web and scholarly search with cited answers and multi-step deep research.
Search 8.5M scientific papers with LLM TLDRs, citations, linked entities, figures, and full text.
