cityuikes
Server Details
Citybikes MCP — wraps CityBik.es API (free, no auth required)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-citybikes
- GitHub Stars
- 0
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 3.9/5 across 8 of 8 tools scored. Lowest: 2.9/5.
The tool set has clear separation between bike-sharing network tools (get_network, list_networks, search_networks) and memory tools (remember, recall, forget), but ask_pipeworx and discover_tools create ambiguity. Ask_pipeworx appears to be a general-purpose query tool that could overlap with the specialized bike and memory tools, while discover_tools is a meta-tool for finding other tools, which might confuse agents about when to use it versus direct tool calls.
Naming conventions are mixed but readable. Bike-sharing tools use a consistent verb_noun pattern (get_network, list_networks, search_networks), and memory tools follow a similar pattern (remember, recall, forget). However, ask_pipeworx and discover_tools deviate with less standard naming (ask_pipeworx uses a compound name, discover_tools is verb_noun but stands out as meta-level), creating minor inconsistency across the set.
With 8 tools, the count is reasonable for a server that combines bike-sharing data and memory management. It's slightly high but manageable, as each tool serves a distinct purpose within its domain. The inclusion of ask_pipeworx and discover_tools adds utility without overwhelming the set, though it might feel slightly broad in scope.
For bike-sharing networks, the tools provide good coverage with list, search, and get operations, though update or delete functions are missing (which may be intentional if read-only). Memory tools offer complete CRUD (create via remember, read via recall, delete via forget). The ask_pipeworx and discover_tools add general query and discovery capabilities, but there's a minor gap in not having a tool to modify bike data, which agents might expect in a full lifecycle.
Available Tools
8 toolsask_pipeworxAInspect
Ask a question in plain English and get an answer from the best available data source. Pipeworx picks the right tool, fills the arguments, and returns the result. No need to browse tools or learn schemas — just describe what you need. Examples: "What is the US trade deficit with China?", "Look up adverse events for ozempic", "Get Apple's latest 10-K filing".
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | Your question or request in natural language |
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 effectively describes key traits: it's a query tool that uses natural language, automatically selects data sources, and returns results. However, it lacks details on limitations (e.g., rate limits, error handling, or data freshness), which prevents a perfect score.
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 front-loaded with the core purpose, followed by supporting details and examples. Every sentence adds value: the first explains the tool's function, the second clarifies its automation, and the third provides concrete use cases. It's efficient with zero wasted text.
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 complexity (natural language querying with automated tool selection) and lack of annotations/output schema, the description does well by explaining the process and providing examples. However, it doesn't cover response formats or potential failures, leaving some gaps in completeness for an agent invoking it.
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 coverage is 100%, with the parameter 'question' fully documented in the schema. The description adds minimal value beyond the schema by emphasizing 'plain English' and providing examples, but doesn't elaborate on parameter constraints or formats. This meets the baseline for high 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 tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool'), distinguishing it from sibling tools like discover_tools or search_networks by emphasizing natural language interaction without manual tool selection.
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 states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear alternatives (implicitly suggesting not to use sibling tools for direct queries) and includes examples like 'What is the US trade deficit with China?' to illustrate appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
discover_toolsAInspect
Search the Pipeworx tool catalog by describing what you need. Returns the most relevant tools with names and descriptions. Call this FIRST when you have 500+ tools available and need to find the right ones for your task.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of tools to return (default 20, max 50) | |
| query | Yes | Natural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries") |
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 explains the search functionality and return format ('most relevant tools with names and descriptions'), but lacks details on error handling, authentication requirements, rate limits, or pagination. It adequately describes core behavior but misses advanced operational 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 highly concise and front-loaded, with two sentences that directly communicate purpose and usage guidelines without redundancy. Every phrase adds value, such as specifying when to call it first and the return format, making it efficient and well-structured.
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 (search functionality with 2 parameters), no annotations, and no output schema, the description is largely complete for its core purpose. It explains what the tool does, when to use it, and the return format, but could improve by detailing output structure or error cases. It compensates well for the lack of structured data.
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 fully documents both parameters. The description adds no additional parameter-specific information beyond what's in the schema (e.g., it doesn't elaborate on query formatting or limit implications). Baseline score of 3 is appropriate since the schema handles parameter documentation effectively.
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 specific action ('Search the Pipeworx tool catalog') and resource ('tool catalog'), with explicit differentiation from sibling tools (which are all network-related). It uses precise language like 'by describing what you need' and 'Returns the most relevant tools with names and descriptions' to define its unique function.
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 explicit guidance on when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task'), including a clear condition (500+ tools) and purpose (finding tools for a task). It implicitly distinguishes from sibling tools by focusing on tool discovery rather than network operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forgetCInspect
Delete a stored memory by key.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key to delete |
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 states the tool deletes a memory, implying a destructive mutation, but fails to describe critical behaviors like whether deletion is permanent, requires specific permissions, or returns confirmation. This leaves significant gaps in understanding the tool's impact.
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, efficient sentence with zero wasted words. It is front-loaded with the core action and resource, making it immediately understandable without unnecessary elaboration.
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 destructive nature (deletion), no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like error handling, return values, or safety considerations, which are crucial for a mutation tool. This leaves the agent with insufficient context for reliable 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?
The schema description coverage is 100%, with the parameter 'key' fully documented in the schema as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format or examples, so it meets the baseline score when the schema handles parameter documentation adequately.
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 ('Delete') and resource ('a stored memory by key'), which is specific and unambiguous. However, it doesn't explicitly differentiate this tool from its sibling 'recall' (which likely retrieves memories) or other tools, preventing a perfect score.
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 no guidance on when to use this tool versus alternatives like 'recall' (for retrieval) or other memory-related operations. It lacks context about prerequisites, such as whether the key must exist or what happens if it doesn't, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_networkAInspect
Check live bike availability at stations in a specific network (e.g., "citi-bike-nyc"). Returns station locations, available bikes, and empty slots.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Network id (e.g. "citi-bike-nyc", "velib" for Paris, "nextbike-berlin") |
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. It discloses that the tool returns live data, network name, and station details (availability, slots, coordinates), which is useful behavioral context. However, it lacks information on error handling, rate limits, authentication needs, or data freshness, leaving gaps for a tool that fetches real-time data.
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 efficiently conveys the tool's purpose, parameter usage, and return data. Every part earns its place, with no redundant or unnecessary information, making it highly concise and 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?
Given the tool's moderate complexity (fetching live data with one parameter) and no annotations or output schema, the description is adequate but incomplete. It covers the basic purpose and return structure, but lacks details on error cases, data formats, or operational constraints, which are important for a real-time data 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?
The schema description coverage is 100%, so the schema already documents the 'id' parameter well with examples. The description adds marginal value by reinforcing the parameter's purpose ('by its id') and providing context on what the id represents (e.g., network identifiers), but does not significantly enhance semantics 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 verb ('Get'), resource ('live station data for a bike-sharing network'), and specific scope ('by its id'). It distinguishes from sibling tools like 'list_networks' (which likely lists networks) and 'search_networks' (which likely searches networks) by focusing on retrieving detailed station data for a specific network.
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 context by specifying 'by its id', suggesting this tool is for when you already know the network identifier. However, it does not explicitly state when to use this tool versus alternatives like 'list_networks' or 'search_networks', nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_networksAInspect
Browse all bike-sharing networks worldwide. Returns network name, ID, city, country, and coordinates for each network.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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. It discloses the tool's behavior by specifying it returns data (name, id, location) and implies a read-only operation, but lacks details on potential limitations like rate limits, pagination, or error handling. The description adds basic context but misses richer behavioral traits.
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 concise sentences with zero waste: the first states the action and scope, and the second specifies the return data. It's front-loaded with the core purpose and efficiently structured.
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 low complexity (0 parameters, no annotations, no output schema), the description is reasonably complete. It covers what the tool does and what it returns, though it could benefit from more behavioral context (e.g., data freshness, limitations). The lack of output schema is partially compensated by describing return values.
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 has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and output. This meets the baseline for tools with no parameters.
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 specific action ('List all bike-sharing networks worldwide') and resource ('bike-sharing networks'), with explicit scope ('worldwide'). It distinguishes from sibling tools by focusing on comprehensive listing rather than retrieval (get_network) or filtered searching (search_networks).
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 context through 'List all... worldwide' and the return data format, suggesting this is for obtaining a complete global overview. However, it doesn't explicitly state when to use this tool versus alternatives like search_networks for filtered results or get_network for detailed information on a specific network.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recallAInspect
Retrieve a previously stored memory by key, or list all stored memories (omit key). Use this to retrieve context you saved earlier in the session or in previous sessions.
| Name | Required | Description | Default |
|---|---|---|---|
| key | No | Memory key to retrieve (omit to list all keys) |
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 clearly describes the tool's dual behavior (retrieve by key vs. list all) and persistence across sessions ('saved earlier in the session or in previous sessions'). It doesn't mention error handling, performance characteristics, or authentication needs, but provides sufficient operational 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?
Two concise sentences with zero waste. The first sentence states the dual functionality clearly, and the second provides usage context. Every word earns its place, and the most important information (what the tool does) 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?
For a simple retrieval tool with 1 optional parameter and no output schema, the description provides good context about functionality, usage, and persistence. It doesn't describe return format or error cases, but given the tool's simplicity and the schema's coverage, it's reasonably 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 schema has 100% description coverage, so the baseline is 3. The description adds meaningful semantic context by explaining the optional parameter's effect: 'omit key to list all keys' clarifies the dual functionality. This goes beyond the schema's technical documentation to explain behavioral implications.
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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory by key', 'all stored memories'). It distinguishes from siblings like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations.
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 explicit usage guidance: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It also specifies when to omit the key parameter ('omit key to list all keys') and distinguishes this from storage operations implied by sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rememberAInspect
Store a key-value pair in your session memory. Use this to save intermediate findings, user preferences, or context across tool calls. Authenticated users get persistent memory; anonymous sessions last 24 hours.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key (e.g., "subject_property", "target_ticker", "user_preference") | |
| value | Yes | Value to store (any text — findings, addresses, preferences, notes) |
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 adds valuable context beyond basic functionality: it explains persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), which is critical for understanding data retention. However, it does not cover other behavioral aspects like error conditions, rate limits, or 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 appropriately sized and front-loaded: the first sentence states the core purpose, and subsequent sentences add essential context without redundancy. Every sentence earns its place by providing distinct information (e.g., usage examples, persistence details), with zero waste or unnecessary elaboration.
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 (2 required parameters, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, and behavioral context (persistence rules), but lacks details on return values or error handling. Since there is no output schema, some gaps remain in explaining what happens after invocation.
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 ('key' and 'value') with examples. The description does not add any parameter-specific details beyond what the schema provides, such as constraints or usage tips. Baseline 3 is appropriate when the schema handles parameter documentation adequately.
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 specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'recall' (likely for retrieval) and 'forget' (likely for deletion). It provides concrete examples of what can be stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose explicit and differentiated.
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 on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but does not explicitly mention when not to use it or name alternatives (e.g., 'recall' for retrieval). It implies usage scenarios effectively, though lacks explicit exclusions or comparisons to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_networksCInspect
Find bike-sharing networks by city or country name. Returns matching networks with their locations and IDs.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | City or country name to search for (e.g. "New York", "France", "Berlin") |
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 for behavioral disclosure. It mentions the search returns matching networks with location info, but doesn't address important aspects like whether this is a read-only operation, potential rate limits, authentication needs, error conditions, or pagination behavior for large result sets.
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 appropriately concise with two sentences that directly state the tool's function and what it returns. It's front-loaded with the core purpose and wastes no 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?
For a simple search tool with one parameter and no output schema, the description provides basic context about what the tool does and returns. However, it lacks important behavioral details (especially given no annotations) and doesn't help differentiate from sibling tools, leaving gaps in completeness.
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 fully documents the single 'query' parameter. The description adds minimal value beyond what's in the schema - it mentions searching by city or country name, which the schema also specifies with examples.
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 searches bike-sharing networks by location (city or country) and returns matching networks with location info. It specifies the verb 'search' and resource 'bike-sharing networks', but doesn't explicitly differentiate from sibling tools like 'get_network' or 'list_networks'.
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 about when to use this tool versus the sibling tools 'get_network' and 'list_networks'. The description implies usage for searching by location, but doesn't specify alternatives, exclusions, or prerequisites.
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!