Appendix turns any AI health conversation into a board-certified physician's opinion — with a real prescription when clinically appropriate. Search medical literature and submit a clinical encounter for review, all from your agent.
List all conditions and medications available through Appendix
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Filter by category name (case-insensitive, e.g. 'respiratory') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and openWorldHint=true. The description merely restates the read-only nature ('List all...') and adds no additional behavioral details such as pagination, data format, or dynamic changes. No new context beyond the annotations is provided.
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, front-loaded sentence with no filler. It communicates the essential purpose immediately and earns its place without unnecessary detail.
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 simple nature of the tool (one optional parameter, no output schema, read-only annotations), the description is mostly complete. It states what is listed, and the schema handles filtering. However, it does not clarify what 'Appendix' refers to or describe the return structure, leaving slight room for ambiguity.
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 description covers the sole parameter 'category' with an example, achieving 100% coverage. The description does not add further parameter semantics, so the baseline score of 3 is appropriate based on schema coverage.
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 'conditions and medications', with a specifier 'available through Appendix'. This distinguishes it from siblings like search_knowledge_base and submit_encounter, which serve different purposes.
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 for retrieving the full list of conditions/medications, but does not explicitly mention when to prefer it over search_knowledge_base or any exclusions. The context is sufficient to infer intent but lacks explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Appendix's medical knowledge base for clinical literature, treatment guidelines, and reference material to help the user describe their medical issue
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results to return (1-10, default 5) | |
| query | Yes | Search query (clinical topic or question) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, covering the safety profile. The description adds useful content scope but does not disclose return format, pagination, or search behavior. 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?
A single, front-loaded sentence contains the verb, resource, and purpose without redundancy. Every word contributes value.
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 low-complexity search tool with complete parameter schema and helpful annotations, the description adequately covers purpose, content scope, and use case. It does not mention return value details, but the absence is not a significant gap for such a simple tool.
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% (query as clinical topic/question, limit with range and default), so the description adds no additional parameter-level detail. The baseline of 3 applies because the schema handles parameter semantics.
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 ('Search') and identifies the exact resource (Appendix's medical knowledge base), along with the content types searched (clinical literature, treatment guidelines, reference material). This clearly distinguishes it from sibling tools like list_conditions and submit_encounter.
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 clear context for when to use the tool: to search clinical reference material and help the user describe a medical issue. However, it does not explicitly contrast with sibling tools or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submit a clinical encounter letter to the Appendix physician team for review. The letter should be in Markdown format following the Appendix letter structure. Returns feedback on completeness or a checkout URL when ready. One of our board-certified physicians will personally review the submission and provide clinical guidance and a prescription if appropriate.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Clinical letter in Markdown format (500-10,000 characters) | |
| images | No | Up to 3 images to attach | |
| session_token | No | Token from a previous response to continue the same encounter |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the submission is personally reviewed by a board-certified physician, may return a checkout URL, and could result in a prescription. This goes beyond the annotations (readOnlyHint=false, openWorldHint=true) by explaining the real-world workflow and side effects. There is 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?
The description is three sentences long, each serving a purpose: what the tool does, what the input should be and what it returns, and what happens after submission. It is front-loaded with the primary action and contains no redundant information.
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's moderate complexity (3 optional/nested parameters, no output schema), the description covers the essential aspects: purpose, input format, return behavior (feedback or checkout URL), and the human involvement. This provides sufficient context for an agent to select and invoke the tool 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?
The input schema describes all three parameters with 100% coverage, so the baseline is 3. The description adds value by specifying that the query must follow the 'Appendix letter structure', which is not in the schema, and by implying that the images and session_token relate to attachment and continuation of a submission. This enriches the parameter understanding beyond the schema.
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 ('Submit'), the resource ('a clinical encounter letter'), and the recipient ('Appendix physician team for review'). This distinguishes it from sibling tools like list_conditions and search_knowledge_base, which are read-oriented.
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 clear context for when to use the tool: when you need to submit a clinical encounter for physician review. It specifies the required format (Markdown following Appendix letter structure) and the expected outcome (feedback or checkout URL). It does not explicitly mention alternatives, but the sibling tool names make the differentiation obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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:
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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.
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Add one secure layer between your agents and this server.
Each tool serves a distinct purpose: listing available conditions, searching clinical knowledge, and submitting an encounter. There is no overlap or ambiguity between them.
All tool names follow a consistent verb_noun pattern using snake_case: list_conditions, search_knowledge_base, submit_encounter. This is uniform and predictable.
With 3 tools, the server is well-scoped for its purpose. Each tool is essential to the workflow of describing, researching, and submitting a medical issue.
The tool surface covers the full workflow: discover what conditions are handled, gather supporting information, and submit an encounter for review. No critical operations are missing.