Find where to buy peptides from vetted, COA-tested vendors and check if a peptide vendor is legit.
Check if a research-peptide vendor is legit, a scam, or safe to buy from. Given a vendor name or slug (fuzzy, case-insensitive), returns PepEvolution's independent audit: PepScore grade + band, lab-verified COA status, a short audit summary of red/green flags, a disclosed buy link (tracked when affiliate) and any coupon, or a clear "not evaluated" result. Use for "is [vendor] legit", "[vendor] review", "[vendor] scam".
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Vendor name or slug, e.g. "Peptide Plugs", "biolongevity-labs" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, covering safety. The description adds meaningful behavior details: fuzzy case-insensitive input matching, return components (PepScore, lab-verified COA, red/green flags, affiliate-tracked link, coupon), and the 'not evaluated' fallback. No contradiction with annotations.
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?
Three tight sentences: the first states the core purpose, the second lists output specifics, the third gives usage examples. No redundant phrasing, front-loaded with the main action, and every sentence contributes.
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 simple parameter, strong readOnly/idempotent annotations, and no output schema, the description is complete: it covers action, input, output fields, and when to use. It even includes the 'not evaluated' result, leaving few ambiguities.
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% for the single 'name' parameter with an example. The description adds semantic value by noting the input is fuzzy and case-insensitive, which is not present in the schema, enhancing understanding beyond the basic example.
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 checks vendor legitimacy (legit, scam, safe) and details the audit output (PepScore, COA, audit summary, buy link, coupon). This specific verb+resource framing distinguishes it from sibling tools like find_peptide_sources and list_verified_vendors.
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 explicitly says 'Use for' and gives concrete query examples ('is [vendor] legit', '[vendor] review', '[vendor] scam'), providing clear when-to-use context. It does not explicitly mention alternatives or when-not-to-use, but the usage scope is well-defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find where to buy a peptide online from vetted, third-party-tested vendors. Given a peptide or compound name (e.g. BPC-157, tirzepatide, semaglutide, retatrutide, TB-500), returns independently graded vendors ranked by lab-verified Certificate of Analysis (COA) quality, with PepScore purity grades, coupon codes, and disclosed referral purchase links. Use for "where can I buy X", "best source for X", "which peptide vendor is trusted for X". Fuzzy, case-insensitive matching.
| Name | Required | Description | Default |
|---|---|---|---|
| compound | Yes | Compound name or slug, e.g. "BPC-157", "tirzepatide" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnly and idempotent hints, and the description adds behavioral details beyond those, such as fuzzy case-insensitive matching, ranking by COA quality, and disclosed referral links. This enriches the agent's understanding without contradicting annotations.
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 three sentences, each providing distinct value: core purpose, output specifics, and usage examples. It is not overly verbose, but could be slightly more compact by trimming redundant qualifiers like 'vetted' and 'third-party-tested' in sequence.
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 tool with one parameter and no output schema, the description is complete: it explains what the tool returns, how parameters are matched, and what use cases it serves. It lacks nothing essential, though it does not elaborate on the ranking algorithm or page size, which are minor.
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 describes the 'compound' parameter with examples, but the description adds interpretation guidance with "Fuzzy, case-insensitive matching" and a broader set of examples. This adds meaning beyond the schema, though the schema coverage is already high.
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 finds where to buy a peptide online from vetted vendors, with specific output details (ranked vendors, COA quality, PepScore, links). However, it does not explicitly distinguish this from sibling tools like check_vendor or list_verified_vendors, so it misses the full 5 criterion.
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 explicitly provides use cases: "where can I buy X", "best source for X", and "which peptide vendor is trusted for X". This gives clear context for when to use the tool, but it does not mention alternatives or exclusions, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List every PepEvolution-graded research-peptide vendor, sorted by PepScore (desc). Each entry carries grade + band, lab-verified COA status, a disclosed buy link (tracked when affiliate), any coupon, and the affiliate disclosure. Use to compare trusted peptide vendors at a glance.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds valuable behavioral context: it enumerates the exact fields carried in each entry (grade, band, COA status, buy link, coupon, affiliate disclosure) and the sort order, which is not visible from the schema or annotations. This goes beyond the minimum bar.
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 sentences with no filler. The first sentence states the action, scope, and sort. The second lists the included fields and the intended use. 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?
With no output schema, the description must explain the return values, and it does thoroughly: it lists all data points carried in each entry (grade, band, COA status, buy link, coupon, affiliate disclosure). It also states the list is exhaustive ('every') which matches openWorldHint=false. The tool is simple, so this is 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?
The tool has zero parameters, so the baseline is 4. The description's mention of sorting and entry composition provides semantic context about what the output reflects, but there are no parameters needing clarification. Overall, adequate.
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 ('List') and a clearly defined resource ('every PepEvolution-graded research-peptide vendor'). It also specifies the sort order (PepScore desc), which distinguishes it from siblings like check_vendor or find_peptide_sources that likely target individual vendors or broader source discovery.
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 a clear use case ('Use to compare trusted peptide vendors at a glance'). It does not explicitly state when not to use this tool or name alternative sibling tools for other scenarios, so it misses the 'explicit alternatives' criterion for a 5.
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.
check_vendorfind_peptide_sourceslist_verified_vendorsClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Control your server's listing on Glama, including description and metadata
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Feature your server to boost visibility and reach more users
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Claim ownership of the server listing
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Peptide vendor matchmaker. 11-signal rubric, GLP-1 + 25 peptides, region-aware, free.
Screen a vendor, store, or group-buy for legitimacy: reputation, red flags, Reddit sentiment.
Peptide and GLP-1 dosing math (dose to syringe units) plus search over Shotlee's articles.
Supplement research, biomarker effects, drug interactions, and brand quality data
Add one secure layer between your agents and this server.
Each tool targets a distinct task: checking a specific vendor, finding sources for a specific peptide, and listing all verified vendors. No overlap in purpose or intended use case.
All tool names follow a consistent verb_noun pattern (check_vendor, find_peptide_sources, list_verified_vendors), making the API predictable and easy to navigate.
Three tools is well-scoped for this domain, each covering a core need without redundancy or unnecessary bloat.
The set covers the full workflow: looking up a vendor's legitimacy, finding sources for a compound, and browsing the full list of vetted vendors. No obvious dead ends or missing operations.
