health
Server Details
Supplement research, biomarker effects, drug interactions, and brand quality data
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4/5 across 8 of 8 tools scored.
Each tool has a clearly distinct purpose: searching for supplements or conditions, retrieving supplement info, interactions, biomarker mappings, top brands. No overlap or ambiguity.
All tool names follow a consistent 'verb_noun' pattern with verbs 'get_' and 'search_', and nouns are appropriately plural or singular.
8 tools is well-scoped for a health supplement knowledge base, covering core queries without unnecessary complexity.
The tool set covers search, information retrieval, interactions, biomarker mapping, and brand quality, leaving no obvious gaps for a read-only reference server.
Available Tools
9 toolsget_biomarkers_for_supplementAInspect
Find which biomarkers are affected by a given supplement. Returns direction (up/down), confidence, dose, and health goal mapping.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 25, max 100) | |
| supplement | Yes | Supplement name (e.g., 'fish oil', 'curcumin', 'vitamin D3') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must carry the burden. It mentions return fields (direction, confidence, dose, health goal mapping), providing partial transparency. However, it does not disclose whether the tool is read-only, any authentication needs, or error behaviors.
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, no redundancy, front-loaded with purpose. 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?
Given no output schema and no annotations, the description covers core purpose and return fields adequately for a simple tool. Could mention case sensitivity or error handling but overall sufficient.
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 clear descriptions for both parameters (supplement with example names, limit with default and max). The description adds minimal value beyond the schema about return values but does not enhance parameter understanding.
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 'Find', the resource 'biomarkers for a supplement', and lists return fields (direction, confidence, dose, health goal mapping). It distinguishes from siblings like get_supplements_for_biomarker (reverse lookup).
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 explicit when-to-use or when-not-to-use guidance. It implies usage for finding biomarkers of a supplement but does not contrast with siblings that offer reverse lookups or interactions, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_interventions_for_biomarkerAInspect
Find mixed interventions in Aviado's current graph that affect a biomarker, labeled with the exact intervention_type value supplement, food_substance, prescription_drug, research_compound, research_nootropic, medical_compound, or unknown. graph_status is the exact internal proven/suspected eligibility label, not independent clinical validation. Coverage is partial and non-exhaustive; absence is not evidence of no effect. pathway_tags are raw graph metadata, not validated mechanisms, and dose/pathway details are suppressed for non-supplement and unknown rows. Use dose_value with dose_unit; legacy dose_mg is populated only for literal mg rows.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 25, max 100) | |
| biomarker | Yes | Biomarker name, abbreviation, or LOINC code |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: partial coverage, internal labeling, suppressed details for certain rows, legacy dose_mg limitations. Adds significant context beyond the schema.
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?
Single dense paragraph that is front-loaded with the main action. Every sentence adds value, but could be slightly more concise by separating caveats.
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?
Without an output schema, the description explains return fields (intervention_type, graph_status, pathway_tags, dose details) and their limitations. Good coverage for 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?
Both parameters are fully described in the input schema (100% coverage). The description does not add new parameter-level meaning beyond what's in the schema, only context about return behavior.
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 explicitly states the tool finds mixed interventions affecting a biomarker, listing specific intervention types. It clearly distinguishes from sibling tools like get_supplements_for_biomarker by covering multiple types.
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?
Provides clear context: coverage is partial, absence is not evidence, graph_status is internal label, and pathway_tags are raw metadata. Does not explicitly state when to use alternatives, but the scope is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_supplement_infoAInspect
Get comprehensive information about a supplement: what it is, what it does, which biomarkers it affects, mechanism of action, and safety contraindications.
| Name | Required | Description | Default |
|---|---|---|---|
| supplement | Yes | Supplement name |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It lists categories of returned information but does not disclose any behavioral traits beyond that (e.g., performance, authentication needs, 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 a single sentence that lists multiple aspects efficiently. While slightly dense, it front-loads the key verb and resource with minimal waste.
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 adequately covers the returned information categories. It mentions safety contraindications, which is additional context. Sibling tools exist, but the scope is clear.
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% (one parameter described as 'Supplement name'), but the description adds no additional meaning beyond what the schema provides. No format, examples, or constraints are given.
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 'Get comprehensive information about a supplement' and enumerates specific aspects (identity, function, biomarkers, mechanism, safety). This distinguishes it from siblings like 'get_biomarkers_for_supplement' which likely returns only biomarkers.
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 by listing what info is returned (comprehensive details), but does not explicitly state when to use this tool versus siblings, nor does it provide when-not or alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_supplement_interactionsAInspect
Find supplement-supplement interactions (765 pairs from clinical evidence) and drug-supplement interactions (1,626 FDA-validated pairs). Returns synergies, antagonisms, absorption conflicts, and timing recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| supplement | Yes | Supplement name | |
| include_drug_interactions | No | Include FDA drug-supplement interactions |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the tool's read-only nature (no destructive actions) and describes the output (synergies, antagonisms, etc.). It doesn't detail auth needs or pagination, but the behavioral context is sufficient for a read operation.
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 concise sentences, front-loaded with the core purpose. Every word adds value; no fluff.
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 2 parameters, no output schema, and no annotations, the description covers the main functionality and output types. It could mention parameter formatting, but overall complete for typical use.
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 baseline 3. The description reinforces the purpose of the supplement parameter but adds no new meaning beyond schema descriptions. The include_drug_interactions parameter's role is implied by the description's mention of drug-supplement interactions.
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 supplement-supplement and drug-supplement interactions, specifying exact numbers of pairs (765 and 1,626) and types (synergies, antagonisms, etc.). It distinguishes from siblings by focusing on interactions rather than general info or biomarkers.
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 interaction data is needed for a supplement, and mentions both supplement and drug interactions. However, it does not explicitly state when to avoid this tool or point to alternatives like get_supplement_info for general details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_supplements_for_biomarkerAInspect
Find registry-classified supplements in Aviado's current graph that affect a biomarker. Excludes food_substance, prescription_drug, research_compound, research_nootropic, medical_compound, and unknown IDs. Type labels and graph_status (proven or suspected) are internal taxonomy/eligibility metadata, not independent clinical verification. Results are partial and non-exhaustive; pathway_tags are raw graph metadata, not validated mechanisms. Use dose_value with dose_unit; legacy dose_mg is populated only for literal mg rows.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 25, max 100) | |
| biomarker | Yes | Biomarker name, abbreviation, or LOINC code |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It mentions evidence-weighted results from 4,794 edges, giving some context, but lacks details on authorization, rate limits, or what 'evidence-weighted' entails.
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 consists of two concise sentences, front-loaded with the key action and example, and efficiently conveys the tool's purpose without extraneous words.
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 has no output schema and moderate tool complexity, the description should elaborate on return value structure (e.g., list of supplements with evidence scores). It only vaguely mentions 'evidence-weighted results', leaving the agent uncertain about the output format.
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 schema already documents both parameters (biomarker, limit). The description adds no additional meaning beyond the schema, meeting the baseline 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 finds supplements affecting a given biomarker, with concrete examples (triglycerides, CRP, vitamin D). It distinguishes from the sibling 'get_biomarkers_for_supplement' by focusing on the supplement-to-biomarker direction.
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 exploring supplement-biomarker relationships but provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives like 'get_biomarkers_for_supplement' for the reverse direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_supplements_for_conditionAInspect
Find which supplements help with a health condition or goal (e.g., 'sleep', 'anxiety', 'joint pain'). Returns evidence-graded supplement recommendations with dosages.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max supplements to return, ranked by evidence (default: 25, max: 50) | |
| condition | Yes | Health condition, symptom, or goal (e.g., 'insomnia', 'anxiety', 'brain fog', 'joint pain') | |
| min_grade | No | Minimum evidence grade to include (default: D = all) | D |
Tool Definition Quality
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 mentions evidence-graded returns and dosages, but lacks details on authentication requirements, rate limits, error handling, or what happens if the condition is not found. The description is insufficiently transparent for a tool with no annotation support.
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, front-loaded with the action and examples. No wasted words, highly efficient and easy to parse. Every sentence 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?
For a simple lookup tool with 3 params, no output schema, and no annotations, the description is fairly complete: it covers input (conditions), output (recommendations with dosages), and ranking (evidence-graded). However, it does not explain the evidence grading system (A-D) or mention the optional 'min_grade' parameter explicitly in the description text, though those are covered in the schema.
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% (all three parameters have descriptions), so the description adds little beyond clarifying the overall purpose. It reinforces that 'condition' refers to health conditions/goals, but does not introduce new parameter-specific information beyond the schema. Baseline 3 is appropriate.
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 purpose: to find supplements that help with a health condition or goal. It provides concrete examples ('sleep', 'anxiety', 'joint pain') and specifies the output ('evidence-graded supplement recommendations with dosages'), effectively distinguishing it from sibling tools that focus on biomarkers or conditions.
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 clear use case (health condition or goal) and examples, but it does not explicitly state when not to use this tool or reference alternatives among siblings. The agent can infer appropriate usage from the context, but no exclusions or comparisons are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_top_brandsAInspect
Find the highest-quality brands for a supplement, ranked by ProofMark (Aviado's quality score: third-party testing, certifications, and quality metrics).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results (max 20) | |
| supplement | Yes | Supplement name |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the ranking criteria (ProofMark) but does not mention behavior like read-only nature, error handling for missing supplement, or whether results are capped by limit automatically.
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?
Single sentence with clear verb and resource, front-loaded with purpose. Every word contributes meaning with no redundancy.
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 output schema, the description does not specify the return format (e.g., brand names, scores, IDs). For a simple tool with 2 parameters, it is mostly adequate but could hint at output structure.
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 both parameters described. The description adds value by explaining the ranking context beyond what the schema provides, helping the agent understand that results are ordered by quality score, not just arbitrary 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 finds 'highest-quality brands for a supplement' and specifies the ranking method (ProofMark). This distinguishes it from sibling tools like search_supplements or get_supplement_info.
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 its use case (finding top brands for a supplement) but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives like get_supplements_for_biomarker.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_conditionsAInspect
Search for a health condition by name with fuzzy matching. Use this to resolve ambiguous condition names before calling get_supplements_for_condition.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query (e.g., 'sleep', 'anxiety', 'brain fog', 'joint pain') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. It mentions fuzzy matching but does not specify return format, number of results, or case sensitivity. For a search tool, additional context about result limits or ranking would improve transparency.
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 concise sentences that front-load the purpose and immediately provide usage guidance. Every sentence adds value with no waste.
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 search tool with one parameter and no output schema, the description is sufficient. It covers purpose, behavior (fuzzy matching), and usage context. A small improvement would be to mention that results are condition names, but overall it's 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?
Schema coverage is 100% and the description adds the key behavioral detail of 'fuzzy matching' beyond the schema's parameter description. This adds meaning beyond what the schema provides, justifying a score above baseline 3.
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 the specific verb 'Search' and resource 'health condition by name', and mentions fuzzy matching. It clearly distinguishes from the sibling tool 'get_supplements_for_condition' by stating that this tool is to be used before that one.
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?
Explicitly states: 'Use this to resolve ambiguous condition names before calling get_supplements_for_condition.' This tells the agent exactly when to use this tool and the alternative follow-up, providing strong usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_supplementsAInspect
Search for a supplement by name with fuzzy matching. Use this to resolve ambiguous names before calling other tools.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query (e.g., 'fish oil', 'CoQ10', 'NAC') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses fuzzy matching behavior, which is key. No annotations provided, so description carries full burden. It doesn't mention return format or limitations, but the tool is simple and the context (one string param) makes it adequate.
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, front-loaded with action and purpose. Every word earns its place; no fluff.
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 search tool with one parameter, no output schema, and clear sibling context, the description is complete. It explains what it does, when to use it, and how it relates to other tools.
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 covers 100% of parameter descriptions (query with examples). The description adds no additional parameter semantics beyond the overall purpose; baseline 3 is appropriate.
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?
Clearly states it searches supplements by name with fuzzy matching, and explicitly distinguishes from sibling tools (get_*) by noting it resolves ambiguous names before calling others.
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?
Explicitly advises when to use: 'Use this to resolve ambiguous names before calling other tools.' Implies not for getting detailed info, which is handled by siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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
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!