What can you do with this server?

This server connects AI assistants to a library of 135,000+ FTIR reference spectra, enabling spectral analysis, material identification, and peak interpretation. Capabilities include: * Analyze Unknown Spectra: Search the spectral library for candidate materials by providing peak positions (cm⁻¹), a natural-language description, or a base64-encoded instrument file. Returns ranked candidates with similarity scores, peak-by-peak explanations, confidence levels, and literature references (DOI). * Explain Peaks: Get structured, literature-supported assignments for one or more FTIR peak positions without a full library search; supports optional sampling mode context (e.g., ATR, KBr Pellet). * Find Reference Spectra: Look up reference spectra by substance name, CAS number, spectrum number, or keywords, returning matched spectra with names, CAS numbers, peak markers, and curve data. * Parse Instrument Files: Decode base64-encoded FTIR instrument files (28+ formats supported, including Thermo .spa/.spc, Bruker .opus, JCAMP-DX, CSV, etc.) into aligned curve points and detected peaks. * Submit & Track Full Reports: Submit a spectrum file to the FTIR.fun tri-axis analysis workflow, then poll for the completed structured report and its URL. * Fetch Historical Results: Retrieve previously generated reports by result number, including the report URL, headline, summary, and structured report view, with optional language selection.

Which integrations are available for this server?

Integrates with VS Code via GitHub Copilot Agent mode to provide FTIR spectral analysis capabilities, including material identification, peak explanation, and library search from the FTIR.fun database.

How do I use FTIR.fun MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@FTIR.fun MCP Server analyze FTIR peaks at 1720, 1650, and 1050 cm-1" 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.

FTIR.fun MCP Server

by jxbaoxiaodong

Overview Schema Related Servers Score Discussions

Python

Hybrid

中文 | English | Español | Français | 日本語

FTIR.fun MCP Server

MCP.so PyPI

MCP server and REST API client for FTIR.fun — gives AI assistants and code pipelines direct access to 130,000+ FTIR infrared reference spectra for material identification, peak explanation, and spectral library search.

Available Tools at a Glance

Tool	What it does
`analyze_ftir_spectrum`	Identify an unknown FTIR spectrum — accepts peaks, natural-language query, or an instrument file (28+ formats). Returns ranked matches with similarity scores and literature DOI.
`explain_peaks`	Explain one or more infrared peak positions — functional-group assignment without a full library search.
`parse_ftir_spectrum`	Parse a raw FTIR instrument file into wavenumber-intensity data points and detected peaks.
`find_spectra`	Search the 130,000+ reference library by substance name, CAS number, or keywords. Returns curve data for comparison.
`submit_ftir_report`	Submit a spectrum to the full tri-axis identification workflow (same multi-stage analysis as the website).
`get_ftir_report_status`	Poll report progress; returns the complete structured result and a shareable URL when done.
`fetch_result`	Retrieve any historical FTIR.fun analysis result by report number.

Related MCP server: Chemspace MCP Server

Get an API Key

Sign in at ftir.fun — new accounts include free trial credits.
Go to Account → API Keys and click Generate.
Copy the key immediately (starts with ftir_; shown only once).

Authentication uses a single header — no OAuth, no browser redirect:

# For MCP (hosted)
Authorization: Bearer ftir_your_key_here

# For REST API
X-API-Key: ftir_your_key_here

MCP Tools

The hosted MCP endpoint https://ftir.fun/mcp exposes seven tools.

`analyze_ftir_spectrum`

Search the FTIR infrared spectral library for one unknown spectrum. Accepts peaks, a natural-language query, or a raw instrument file.

Parameters

Parameter	Type	Required	Description
`query`	string	No	Natural-language FTIR request — peak positions such as "1730, 1600, 1250 cm-1" are extracted automatically.
`peaks`	number[]	No	FTIR peak positions in cm⁻¹ (e.g. `[1736, 1379, 1241]`).
`file_base64`	string	No	Base64-encoded FTIR instrument file. Supports 28+ formats: Thermo `.spa`/`.spc`, Bruker `.opus`, PerkinElmer `.sp`, JCAMP-DX `.jdx`/`.dx`, CSV, Excel, and more.
`filename`	string	No	Original filename for format detection (e.g. `"sample.spa"`).
`top_k`	integer	No	Number of ranked candidates to return (1–50, default 15).
`tolerance_cm1`	integer	No	Peak matching tolerance in cm⁻¹ (1–30, default 8).

Returns: Ranked candidate materials with library similarity scores, peak-by-peak evidence linked to published literature (DOI), confidence levels, and uncertainty disclosures.

Example

Identify this infrared spectrum: peaks at 2915, 1715, 1450, 1260, 1090 cm-1.

`explain_peaks`

Explain one or more FTIR infrared peaks without requiring a full spectral library search. Useful for quick functional-group assignment and wavenumber interpretation.

Parameters

Parameter	Type	Required	Description
`query`	string	No	Natural-language peak question, e.g. `"What does 1715 cm-1 indicate in an ester?"`
`peaks`	number[]	No	One or more FTIR peak positions in cm⁻¹.
`sampling_mode`	string	No	`ATR`, `Thin Film`, `KBr Pellet`, `Nujol Mull`, etc.

Returns: Structured peak explanations with functional-group assignments and uncertainty wording when available.

Example

Use FTIR.fun to explain the infrared peaks at 1715 and 3300 cm-1 in ATR mode.

`parse_ftir_spectrum`

Parse a base64-encoded FTIR instrument file into aligned wavenumber-intensity curve points and automatically detected peak positions. Use this to extract raw spectral data before analysis.

Parameters

Parameter	Type	Required	Description
`file_base64`	string	Yes	Base64-encoded FTIR instrument file.
`filename`	string	Yes	Original filename (e.g. `"sample.spa"`) for format detection.

Returns: Aligned (wavenumber, intensity) data points and a list of detected peak positions in cm⁻¹.

`find_spectra`

Find FTIR library reference spectra by substance name, CAS number, spectrum number, or keywords. Returns raw spectral curve data for direct comparison.

Parameters

Parameter	Type	Required	Description
`query`	string	Yes	Substance name (e.g. `"polypropylene"`), CAS number, FTIR library `NUM`, or keywords.
`limit`	integer	No	Number of reference spectra to return (1–20, default 10).

Returns: Matching reference spectra with num, names, CAS, peak markers, and library curve data.

Example

Find FTIR reference spectra for polyethylene terephthalate (PET).

`submit_ftir_report`

Submit a base64-encoded FTIR spectrum file to the full FTIR.fun tri-axis identification workflow — the same multi-stage analysis used on the website. Returns a task_id and result_num immediately; poll with get_ftir_report_status for the completed report.

Parameters

Parameter	Type	Required	Description
`file_base64`	string	Yes	Base64-encoded FTIR instrument file.
`filename`	string	Yes	Original filename for format detection.

Returns: { task_id, result_num }

`get_ftir_report_status`

Poll the status of a report submitted via submit_ftir_report. When complete, the response includes the full structured report_view (the same data shown on the FTIR.fun website) and a shareable report_url.

Parameters

Parameter	Type	Required	Description
`task_id`	string	Yes	`task_id` returned by `submit_ftir_report`.

Returns: Status field plus report_view and report_url when complete.

`fetch_result`

Fetch a historical FTIR.fun infrared analysis result by report number.

Parameters

Parameter	Type	Required	Description
`result_num`	string	Yes	FTIR.fun report/result number.
`language_code`	string	No	Display language for the stored result context (default `en`).

Returns: Structured context with report_url, headline, summary, report_view, and result_context.

REST API

Call FTIR.fun directly from any language — Python, JavaScript, R, MATLAB, Go. Ideal for LIMS integrations, batch spectral processing pipelines, or adding infrared spectrum search to your own application.

Full API reference: https://ftir.fun/api-docs/

Health check (no key required)

curl https://ftir.fun/health
# → {"status":"ok","service":"ftirfun-api"}

Identify an unknown infrared spectrum — peak list

curl -X POST https://ftir.fun/ftir/analyze_spectrum \
  -H "X-API-Key: ftir_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "peaks": [2915, 1715, 1450, 1260, 1090],
    "options": {"top_k": 10, "tolerance_cm1": 8}
  }'

import requests

resp = requests.post(
    "https://ftir.fun/ftir/analyze_spectrum",
    headers={"X-API-Key": "ftir_your_key_here"},
    json={
        "peaks": [2915, 1715, 1450, 1260, 1090],
        "options": {"top_k": 10, "tolerance_cm1": 8},
    },
)
print(resp.json())

Identify from an instrument file

import base64, requests

with open("sample.spa", "rb") as f:
    b64 = base64.b64encode(f.read()).decode()

resp = requests.post(
    "https://ftir.fun/ftir/analyze_spectrum",
    headers={"X-API-Key": "ftir_your_key_here"},
    json={"file_base64": b64, "filename": "sample.spa"},
)
print(resp.json())

Supports 28+ instrument formats: Thermo .spa/.spc, Bruker .opus, PerkinElmer .sp, JCAMP-DX .jdx/.dx, CSV, Excel, and more.

Explain infrared peaks

curl -X POST https://ftir.fun/ftir/explain_peaks \
  -H "X-API-Key: ftir_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{"peaks": [1715, 2915], "sampling_mode": "ATR"}'

Search reference spectra by name or CAS

curl "https://ftir.fun/v1/search?q=polypropylene&limit=5" \
  -H "X-API-Key: ftir_your_key_here"

MCP Client Setup

The hosted MCP endpoint requires no local install. Use https://ftir.fun/mcp with a Bearer token.

VS Code (GitHub Copilot Agent mode)

Create .vscode/mcp.json in your project (or add to user-level settings):

{
  "inputs": [
    {
      "type": "promptString",
      "id": "ftirfun-api-key",
      "description": "FTIR.fun API key",
      "password": true
    }
  ],
  "servers": {
    "ftirfun": {
      "type": "http",
      "url": "https://ftir.fun/mcp",
      "headers": {
        "Authorization": "Bearer ${input:ftirfun-api-key}"
      }
    }
  }
}

Open Command Palette → MCP: List Servers → select ftirfun → Start.

Claude Desktop / Claude Code

URL:    https://ftir.fun/mcp
Header: Authorization: Bearer ftir_your_key_here

One-line setup for Claude Code:

claude mcp add --transport http ftirfun https://ftir.fun/mcp \
  --header "Authorization: Bearer ftir_your_key_here"

Cursor

Create or edit ~/.cursor/mcp.json:

{
  "mcpServers": {
    "ftirfun": {
      "url": "https://ftir.fun/mcp",
      "headers": {
        "Authorization": "Bearer ftir_your_key_here"
      }
    }
  }
}

OpenAI Codex

[mcp_servers.ftirfun]
url = "https://ftir.fun/mcp"
http_headers = { Authorization = "Bearer ftir_your_key_here" }

Gemini CLI

Edit ~/.gemini/settings.json:

{
  "mcpServers": {
    "ftirfun": {
      "httpUrl": "https://ftir.fun/mcp",
      "headers": {
        "Authorization": "Bearer ftir_your_key_here"
      }
    }
  }
}

Any other MCP client that supports a remote streamable-HTTP server works: set the URL to https://ftir.fun/mcp and send Authorization: Bearer <your key>. Full tool schema: server-card.json.

Self-Hosted (Local Wrapper)

A lightweight local MCP wrapper that proxies to the hosted API. Exposes the same seven FTIR tools.

Configuration

export FTIRFUN_API_KEY="your-ftirfun-api-key"
# Optional:
export FTIRFUN_API_BASE_URL="https://ftir.fun"
export FTIRFUN_API_TIMEOUT_SECONDS="120"

Run Locally (stdio)

python -m venv .venv
. .venv/bin/activate
pip install .
ftirfun-mcp

Run Streamable HTTP

FTIRFUN_API_KEY="your-ftirfun-api-key" \
ftirfun-mcp --transport streamable-http --host 127.0.0.1 --port 8001

Docker

docker build -t ftirfun-mcp .
docker run --rm -p 8001:8001 -e FTIRFUN_API_KEY="your-ftirfun-api-key" ftirfun-mcp

Tool Boundary

Use this MCP server for FTIR spectral-library screening only. Do not use for non-FTIR spectroscopy, general chemistry Q&A, or accredited laboratory certification.

About FTIR.fun

FTIR.fun is a cloud platform for infrared spectroscopy analysis used by researchers and engineers in 52+ countries. It gives fast access to a continuously updated library of 130,000+ FTIR reference spectra covering polymers, additives, coatings, pharmaceuticals, and industrial chemicals.

What you can do on ftir.fun:

Spectral library search — upload an instrument file or paste peak positions; get ranked matches with similarity scores and literature DOI citations
AI peak explanation — ask about any wavenumber; receive functional-group assignments backed by a chemical knowledge graph
Full tri-axis report — automatic multi-stage material identification with a shareable result URL
Image-to-CSV extraction — digitize a spectrum curve from a published figure
Formulation workbench — multi-component deformulation and unknown-mixture analysis

Step-by-step setup guides: https://ftir.fun/ai-integration/

Registry & Links

Resource	URL
Website	https://ftir.fun
Setup guide	https://ftir.fun/ai-integration/
API docs	https://ftir.fun/api-docs/
Hosted MCP endpoint	https://ftir.fun/mcp
Server card	https://ftir.fun/.well-known/mcp/server-card.json
Smithery	https://smithery.ai/servers/hlin2097/ftirfun
MCP.so	https://mcp.so/server/ftir-spectral-search/ftir_fun
PyPI	https://pypi.org/project/ftirfun-mcp/

Available Tools

4 tools

analyze_ftir_spectrumA

Read-onlyIdempotent

Search the FTIR.fun spectral library for one unknown FTIR spectrum in one call.

Use when the user provides an FTIR peak list, a natural-language description containing peaks, or a base64-encoded FTIR instrument file and wants ranked material candidates. Do not use for general chemistry Q&A, non-FTIR spectra, or institutional report review.

ParametersJSON Schema

Name	Required	Description	Default
`peaks`	No	FTIR peak positions in cm-1.
`query`	No	Natural-language FTIR request. Peak positions such as 1730, 1600, 1250 cm-1 can be extracted automatically.
`top_k`	No	Number of ranked library candidates to return.
`filename`	No	Original filename for FTIR format detection.	spectrum.0
`file_base64`	No	Optional base64-encoded FTIR spectrum file.
`tolerance_cm1`	No	Peak-only search tolerance in cm-1.

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds that it searches and returns ranked candidates, consistent with annotations. No additional behavioral disclosure needed beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded purpose, no wasted words. Every sentence provides essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters and existence of output schema, the description covers core functionality: search library, return ranked candidates. It lacks details on output format, but output schema exists to fill that gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by mentioning that peak positions can be extracted automatically from natural language, which is not in schema descriptions. This extra guidance justifies a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches the FTIR.fun spectral library for one unknown FTIR spectrum. It specifies input types (peak list, natural language, base64 file) and distinguishes from siblings by focusing on a single spectrum analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (user provides FTIR data wanting ranked candidates) and when not to use (general chemistry Q&A, non-FTIR, report review). This provides clear alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

explain_peaksD

Read-onlyIdempotent

ParametersJSON Schema

Name	Required	Description
`peaks`	No	One or more FTIR peak positions in cm-1.
`query`	No	Natural-language FTIR peak question, for example 'What does 1715 cm-1 indicate?'
`sampling_mode`	No	Optional sampling mode such as ATR or transmission.

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

fetch_resultD

Read-onlyIdempotent

ParametersJSON Schema

Name	Required	Description	Default
`result_num`	Yes	Completed FTIR.fun result/report number.
`language_code`	No	Language code for the stored result context.	en

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

find_spectraD

Read-onlyIdempotent

ParametersJSON Schema

Name	Required	Description	Default
`limit`	No	Number of reference spectra to return.
`query`	Yes	Substance name, CAS number, FTIR library spectrum number, or keywords.

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

4 tool updatesv1.0.2Jul 6, 2026
- First observedanalyze_ftir_spectrum
- First observedexplain_peaks
- First observedfetch_result
- First observedfind_spectra

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

C2.2/5.0

Disambiguation2/5

Only one tool has a description, leaving the other three (explain_peaks, fetch_result, find_spectra) ambiguous in purpose. Their names hint at different actions but without descriptions, an agent cannot reliably distinguish them from analyze_ftir_spectrum or each other.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with underscores: analyze_ftir_spectrum, explain_peaks, fetch_result, find_spectra. This makes the tool set predictable despite some vague verb choices.

Tool Count5/5

Four tools is appropriate for a focused spectral analysis server. It covers the main interactions (analysis, explanation, result retrieval, library search) without being bloated or too sparse.

Completeness3/5

The tool set appears to cover core FTIR workflows (analysis, explanation, search, fetch results), but the lack of descriptions for three tools makes it impossible to confirm whether critical operations like peak listing or spectrum comparison are missing. There may be gaps that agents cannot discover.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

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