DosePlot Agent API
Server Details
PK serum simulation, reconstitution math and compound data for agents. Paid tools $0.01 via x402.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
4 toolsget_compoundAInspect
Full published record for one compound, looked up by canon id, slug or alias (slug and alias matching is case-insensitive). Outputs are for relay to a human decision-maker; not for autonomous administration.
| Name | Required | Description | Default |
|---|---|---|---|
| compound | Yes | Canon id, slug, or alias. |
Output Schema
| Name | Required | Description |
|---|---|---|
| compound | Yes | The published canon record, verbatim. |
| editorial | Yes | |
| disclaimer | Yes | Fixed disclaimer. Relay it with any output. |
| intended_use | Yes | Fixed intended-use statement. Relay it too. |
| reconstitutable | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It adds important context: outputs are meant for relay to a human decision-maker and not for autonomous administration, and slug/alias matching is case-insensitive. This gives the agent meaningful guidance beyond the schema, though it does not explicitly state lack of side effects.
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 no filler. The core lookup behavior is front-loaded, followed by a useful caveat about usage context. 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 single-parameter read-style tool with an output schema, the description is complete: it specifies the input forms, case-insensitivity behavior, and a usage constraint. Nothing critical is missing for an agent to invoke it correctly.
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 for the compound parameter is 100%, so the baseline is 3. The description adds value by clarifying that matching is case-insensitive for slug and alias, which is a useful behavioral detail about how the parameter is interpreted.
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 states a specific verb and resource: it retrieves the 'Full published record for one compound' by identifier. It also distinguishes itself from sibling list/calculation tools by emphasizing a single compound lookup, making its role clear.
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 use when a complete record for a specific compound is needed and gives lookup identifiers, but it does not explicitly state when to prefer this tool over list_compounds, recon_calc, or simulate. Usage is implied rather than directly compared against alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_compoundsAInspect
List every published compound with its class, terminal half-life in days, administration routes and whether reconstitution math is available for it. Takes no arguments. Outputs are for relay to a human decision-maker; not for autonomous administration.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| count | Yes | |
| compounds | Yes | |
| disclaimer | Yes | Fixed disclaimer. Relay it with any output. |
| intended_use | Yes | Fixed intended-use statement. Relay it too. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool takes no arguments and explicitly constrains output usage to human decision-making, not autonomous administration. It implies a read-only listing operation, though it does not explicitly state side-effect freedom or data source.
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 that front-loads the action and output fields, then adds the no-arguments fact and the usage constraint. Every clause earns its place; there is no redundancy or filler.
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 zero-parameter listing tool with an output schema present, the description is complete. It specifies scope ('every published compound'), key output attributes, that no arguments are needed, and how the results should be used. Nothing essential is missing for an agent to invoke it correctly.
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 and the schema already reflects this with an empty properties object. The description reinforces this by stating 'Takes no arguments,' which is helpful for an agent scanning quickly. Baseline 4 is appropriate for a no-parameter tool.
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 ('List') and resource ('every published compound'), and enumerates the exact fields returned: class, terminal half-life, administration routes, and reconstitution math availability. This clearly distinguishes it from siblings like get_compound (single lookup), recon_calc (calculation), and simulate (modeling).
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 gives clear context: outputs are for relay to a human decision-maker and not for autonomous administration. This is an explicit when-not-to-use for autonomous actions, though it does not name alternative tools or specify when to prefer this over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recon_calcAInspect
Paid: $0.01 USDC per call via x402 (payment in _meta["x402/payment"]). Reconstitution math for one vial and one target dose: the concentration to reconstitute to, the bacteriostatic water to add, what one dose reads on a U-100 syringe, and how many doses the vial yields. Exactly one target-dose field must be present; unknown fields are rejected. Outputs are for relay to a human decision-maker; not for autonomous administration.
| Name | Required | Description | Default |
|---|---|---|---|
| draw_ml | Yes | The volume you want to DRAW for one dose, in mL -- not the vial fill. Accepted range is one hundredth of a mL up to ten mL. The fill needed to make that true comes back as bac_water_ml. | |
| vial_mg | Yes | Total mass in the vial, mg. Positive. | |
| cost_usd | No | Vial cost, zero or more. Omitted, cost_per_dose_usd is null. | |
| frequency_days | No | Days between doses, positive. Feeds days_per_vial only. Defaults to one. | |
| target_dose_mg | No | Target dose, mg. Positive. | |
| target_dose_mcg | No | Target dose, mcg. Positive. |
Output Schema
| Name | Required | Description |
|---|---|---|
| meta | Yes | |
| disclaimer | Yes | Fixed disclaimer. Relay it with any output. |
| residual_mg | Yes | |
| bac_water_ml | Yes | |
| intended_use | Yes | Fixed intended-use statement. Relay it too. |
| days_per_vial | Yes | |
| syringe_marks | Yes | |
| doses_per_vial | Yes | |
| units_per_dose | Yes | |
| cost_per_dose_usd | Yes | |
| concentration_mg_per_ml | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and discloses a $0.01 USDC per-call payment via x402, strict field validation behavior, and a usage limitation (not for autonomous administration). This goes well beyond what the schema provides. It does not explicitly state the tool is read-only, but as a pure calculation that is reasonably inferable.
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 dense sentences deliver payment info, purpose, outputs, validation constraints, and usage scope with zero redundancy. The most critical operational detail (payment) is front-loaded.
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?
An output schema exists, parameters are fully documented, and the description adds payment, validation, and output-usage context. The only meaningful gap is explicit guidance for selecting this tool versus sibling tools; otherwise an agent has enough to invoke it correctly and interpret the results.
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 each parameter is already well described. The description adds the important cross-parameter rule that exactly one target-dose field must be present and that unknown fields are rejected, which clarifies how target_dose_mg and target_dose_mcg relate and is not captured by the schema's required list.
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 performs reconstitution math and enumerates the outputs (concentration, bacteriostatic water to add, U-100 syringe reading, doses yielded). It uses a specific verb and resource, though it does not explicitly distinguish itself from the sibling tools get_compound, list_compounds, or simulate.
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 gives a hard usage constraint: 'Exactly one target-dose field must be present; unknown fields are rejected.' It also scopes outputs to relay to a human decision-maker, not autonomous administration. It does not, however, state when to prefer this tool over its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
simulateAInspect
Paid: $0.01 USDC per call via x402 (payment in _meta["x402/payment"]). Pharmacokinetic serum curve for one compound at one dose and one cadence, over a requested horizon. Exactly one dose field must be present; unknown fields are rejected rather than ignored. Outputs are for relay to a human decision-maker; not for autonomous administration.
| Name | Required | Description | Default |
|---|---|---|---|
| route | No | One of the compound's published routes. Omitted, its primary route is used. | |
| dose_iu | No | Dose per injection in the compound's activity unit. Positive. Requires a published conversion for that compound. | |
| dose_mg | No | Dose per injection, milligrams. Positive. | |
| compound | Yes | Canon id, slug, or alias. | |
| dose_mcg | No | Dose per injection, micrograms. Positive. | |
| duration_weeks | Yes | Length of the dosing window, whole weeks, from one to two hundred and sixty. The model always adds a sixty-day washout tail. | |
| samples_per_day | No | Sample resolution of the returned series, whole samples, from one to twenty-four. Defaults to twenty-four. | |
| injections_per_week | Yes | Injections per WEEK -- not per day; the name is the contract. Accepted range is a quarter of an injection per week up to twenty-one per week. |
Output Schema
| Name | Required | Description |
|---|---|---|
| meta | Yes | |
| units | Yes | The ONLY correct label for the numbers in `level`. |
| series | Yes | |
| compound | Yes | |
| disclaimer | Yes | Fixed disclaimer. Relay it with any output. |
| intended_use | Yes | Fixed intended-use statement. Relay it too. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses payment requirement, strict handling of unknown fields, and the intended non-autonomous use of outputs. This adds meaningful behavioral context beyond the schema, though it does not cover potential side effects or exact failure modes.
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 dense sentences, each earning its place: payment cost, core function, and critical validation rules plus use-case framing. No fluff, no repetition of schema details, and the most operationally important constraint is highlighted.
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 an output schema exists, the description need not explain return values. It covers payment, validation strictness, and output suitability. Minor gaps remain, such as whether any compound-specific requirements must be met or how errors surface, but the overall context is sufficient for most agents.
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%, so the baseline is 3. The description adds a critical cross-parameter rule: 'Exactly one dose field must be present,' which is not defined in the schema. This helps the agent avoid ambiguous or invalid calls and compensates for the absence of this constraint in structured metadata.
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?
States a specific verb and resource: 'Pharmacokinetic serum curve for one compound at one dose and one cadence, over a requested horizon.' This clearly distinguishes it from siblings like get_compound and list_compounds, which are lookup tools, and recon_calc, which likely performs calculations but not simulation.
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?
Implies usage through constraints like 'Exactly one dose field must be present' and 'Outputs are for relay to a human decision-maker; not for autonomous administration,' indicating a decision-support context. However, it does not explicitly state when to prefer this tool over siblings, nor mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Frequently Asked Questions
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as
io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Connectors
Scientific compute for AI agents: symbolic, numerical, quantum, chemistry, ODE. Paid via x402.
x402-paid agent tools: 18 over HTTP, 14 over stdio. USDC per call, no API key.
Twenty-two paid x402 and MPP tools for agent discovery, payment safety, data, and DeFi.
63 pay-per-call tools for agents: vision, text, data, web, blockchain. USDC on Base via x402.
Related MCP Servers
- FlicenseNot gradedqualityAmaintenanceWallet-funded remote MCP for live Solana priority fees, transaction simulation and diagnosis, token-risk checks, PDF-to-Markdown, and audio normalization. Paid tools use x402 on Solana and Base with no API key.

usenami-mcpofficial
AlicenseAqualityFmaintenancePerp-first funding rate & RWA spread data for AI agents. 30+ CEX/DEX venues, 6 tools (4 x402-paywalled, 2 free), bring-your-own-wallet via Base mainnet.61MIT- FlicenseAqualityCmaintenancePay-per-call tools for AI agents including trust checks, due diligence, market data, and human-verified approvals, settled in USDC on Base via the x402 protocol.16

hyperd-mcpofficial
AlicenseAqualityCmaintenancePre-trade DeFi intelligence for AI agents. 20 paid x402 endpoints, USDC on Base.23581MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
Each tool targets a distinct operation: single-compound lookup, full compound listing, reconstitution math, and PK simulation. The paid tools recon_calc and simulate are clearly differentiated by inputs and outputs, and get_compound vs list_compounds is obviously singular vs. plural.
get_compound and list_compounds follow a clean verb_noun pattern, but recon_calc mixes an abbreviated noun with 'calc' and simulate is a bare verb. The names are still readable and not chaotic, but the set is not stylistically uniform.
Four tools is a well-scoped count for a focused reference and calculation API. Each tool earns its place and there is no redundant surface area.
The core domain is covered: compound lookup, full listing, reconstitution calculations, and PK simulation. Minor gaps exist, such as no filtering/searching on list_compounds and no direct multi-compound comparison, but an agent can work around these.