The official Model Context Protocol (MCP) server for PeptideCalculatorOnline.com.
This server empowers AI assistants (like Claude, Cursor, and ChatGPT) with precise, clinical-grade mathematical logic for calculating peptide reconstitution matrices, avoiding the dangerous "hallucination" of dosages by LLMs.
Absolute Math Accuracy: Replaces LLM guess-work with the deterministic algebraic mapping used on our official calculator ($V_{draw} = \frac{D}{C}$).
U-100 Syringe Mapping: Automatically converts complex dosages into exact "Unit" pulls for standard U-100 insulin syringes.
Standard Protocols: Instant access to widely accepted laboratory research protocols for compounds like Semaglutide, BPC-157, TB-500, Tirzepatide, etc.
Plunger Parallax Guidelines: Educates researchers on volumetric resolution and how to avoid hyper-condensed solutions.
Related MCP server: Perplexity API MCP Server
Website: https://peptidecalculatoronline.com
Interactive Calculators: Semaglutide, BPC-157, TB-500
Custom Protocol Planner: 4-Week Injection Schedule Generator
calculate_reconstitution_dose: Input peptide (mg), bacteriostatic water (ml), and desired dose (mg). Returns the exact concentration (mg/ml) and U-100 syringe units.
get_peptide_protocol: Input a standard analog name (e.g., semaglutide, bpc-157) to receive standard starting titrations and matrices.
get_syringe_mechanics_guide: Learn about standard operating procedures to reduce dosage errors.
Add the following to your claude_desktop_config.json:
{
"mcpServers": {
"peptidecalc": {
"command": "npx",
"args": [
"-y",
"peptidecalc-mcp"
]
}
}
}Open Cursor Settings.
Navigate to Features -> MCP.
Click Add New MCP Server.
Type: stdio
Command: npx -y peptidecalc-mcp
# Install dependencies
npm install
# Build the project
npm run build
# Run locally for testing (MCP Inspector)
npx @modelcontextprotocol/inspector node build/index.jsResearch Use Only. The mathematical outputs provided by this MCP server are strictly for educational and laboratory research purposes. Not for human consumption. Always consult a licensed medical professional.
Calculate precise peptide reconstitution dosing architecture for U-100 syringes
| Name | Required | Description | Default |
|---|---|---|---|
| water_ml | Yes | Bacteriostatic Water Added in milliliters (ml) | |
| peptide_mg | Yes | Peptide Vial Quantity in milligrams (mg) | |
| desired_dose_mg | Yes | Desired Dose in milligrams (mg) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral disclosure. It does not mention what the output format is (e.g., mL or syringe units), assumptions about U-100 concentration, or any rounding/limitations. This is a meaningful gap for a calculation 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 sentence and front-loaded with the verb 'Calculate'. However, the phrase 'dosing architecture' is jargon and could be simplified to 'dose' for clarity, slightly reducing conciseness.
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?
The tool is simple (3 numeric parameters, no output schema), but the description does not explain the return value or any calculation assumptions. With no output schema and no annotations, the description should carry more context, such as the result being in mL or units, but it is too sparse.
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 100%, so the baseline is 3. The description does not add extra meaning to the parameters (peptide_mg, water_ml, desired_dose_mg), but the schema already documents each parameter clearly. Mentioning U-100 syringes indirectly hints at the calculation context but not enough to raise the score.
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 calculates peptide reconstitution dosing for U-100 syringes, using a specific verb ('calculate') and resource ('reconstitution dose'). It distinguishes itself from sibling tools that provide protocols or guides, though the phrase 'dosing architecture' is somewhat vague.
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?
No guidance is provided on when to use this tool versus alternatives, such as get_peptide_protocol or get_syringe_mechanics_guide. The description offers no context for selection, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get standard research protocol matrices and typical dosing intervals for primary research analogs.
| Name | Required | Description | Default |
|---|---|---|---|
| peptide_name | Yes | The name of the peptide to retrieve protocol for. |
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 only states what the tool returns ('protocol matrices and typical dosing intervals') but does not disclose whether the operation is read-only, what the response format looks like, or any limitations/context (e.g., research-only disclaimer). This is a notable gap for a tool lacking annotation safety hints.
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, clear sentence that front-loads the verb and resource. It contains no wasted words and is appropriately size for a simple 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?
The tool is simple (1 parameter, no output schema), but the description is somewhat vague about what constitutes a 'protocol matrix' and does not specify the nature of the returned data. It is adequate but leaves room for interpretation, especially without any behavioral annotations to fill gaps.
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 100% with the parameter 'peptide_name' already described as 'The name of the peptide to retrieve protocol for.' The description adds no extra meaning beyond the schema, so the baseline of 3 applies. It does not explain the enum values or provide additional context about the 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 clearly states the tool's function with a specific verb ('Get') and resource ('standard research protocol matrices and typical dosing intervals'). It distinguishes itself from sibling tools like calculate_reconstitution_dose and get_syringe_mechanics_guide by focusing on protocol matrices and dosing intervals.
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 implies usage when protocol matrices or dosing intervals are needed, but it does not explicitly mention when not to use this tool or name alternative tools for related tasks (e.g., dose calculation, syringe mechanics). No exclusions or comparison to siblings are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get technical documentation on Plunger Parallax Error and syringe volumetric resolution.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. 'Get technical documentation' implicitly indicates a read-only operation, but it does not disclose the return format, whether the documentation is long-form, or any edge-case behavior. The description is simple but lacks additional context.
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, well-structured sentence with no wasted words. It is front-loaded with the action and clearly specifies the two key topics covered.
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 parameters and no output schema, the description is reasonably complete for a documentation retrieval tool. It names two specific topics, making it clear what the user will get. However, it could perhaps mention the format or that it is a guide, but this is a minor gap.
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?
There are zero parameters, so the baseline is 4. The description correctly focuses on the tool's purpose rather than parameters, and no additional parameter information is needed.
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 retrieves technical documentation on two specific topics: Plunger Parallax Error and syringe volumetric resolution. The verb 'Get' plus the resource clearly distinguishes this from sibling tools like calculate_reconstitution_dose and get_peptide_protocol.
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?
Usage is implied by the specific technical topics mentioned, and the sibling tool names provide context. However, there is no explicit statement about when to use this tool versus alternatives or any exclusions.
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.
v1.0.0calculate_reconstitution_doseget_peptide_protocolget_syringe_mechanics_guideEach tool targets a distinct aspect: reconstitution calculation, protocol reference, and syringe mechanics. There is no overlap or ambiguity between them.
All tool names follow a consistent verb_noun pattern (calculate_, get_, get_), using lowercase with underscores throughout.
Three tools is well-scoped for a focused peptide calculation server, each earning its place without being too few or too many.
The surface covers the core workflow of reconstitution dosing, protocol lookup, and syringe accuracy. Minor gaps like peptide storage or molecular weight calculation exist but are not critical to the stated purpose.
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