colab-mcp

by kamalrana22

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.1.0
Disambiguation5/5
Each tool targets a distinct action: connection setup, adding code/text cells, reading cells, running code, updating, deleting, moving cells, and changing runtime. There is no overlap in purpose, and the descriptions clearly differentiate these operations.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (e.g., add_code_cell, get_cells, delete_cell). The naming is uniform and predictable, making it easy to infer each tool's function.
Tool Count5/5
With 9 tools, the server is well-scoped for Colab notebook management. Each tool covers a necessary operation without redundancy, making the count appropriate for its purpose.
Completeness5/5
The tool set covers the full lifecycle of notebook editing: connecting, adding cells, reading state, executing, updating, deleting, moving, and changing runtime. There are no obvious missing operations for the stated purpose.
Average 3.9/5 across 9 of 9 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the browser connection requirement, but it omits key behavioral traits such as how cellIndex affects insertion position, whether the cell is executed, or any side effects on existing cells. This lack of detail could mislead an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences: the first states the action and the second states a prerequisite. It is front-loaded, with no unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With three parameters, no schema descriptions, and no annotations, the description is too sparse. It explains the core action and prerequisite but does not elaborate on parameter roles, return values, or mutating behavior, making it incomplete for an agent to use confidently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for its three parameters (code, language, cellIndex), and the description does not mention any of them. The agent receives no guidance on how to populate these parameters, leaving a significant gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description "Add a new code cell to the Colab notebook" uses a specific verb (Add) and resource (code cell in Colab notebook), clearly distinguishing it from siblings like add_text_cell, run_code_cell, and get_cells. It leaves no ambiguity about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states the prerequisite "Requires an active browser connection via open_colab_browser_connection," which is a clear usage condition. However, it does not mention alternatives or when not to use this tool, though the purpose is self-evident within the sibling context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 the connection requirement but does not disclose whether deletion is permanent, what happens if the cellId is invalid, or any side effects, which is a significant gap for a destructive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the action, and contains no redundant words. Every sentence adds value: the first states the action, the second states a necessary condition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description covers the basic action and a prerequisite, but it lacks context about error handling, permanence, and what happens if the cell does not exist. The absence of behavioral detail leaves the description only minimally viable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter, cellId, with no description. The description merely repeats 'by cellId' without adding meaning about format, constraints, or the default empty value. Since schema coverage is 0%, the description does not compensate at all.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (delete), the target (a cell from the Colab notebook), and the identifier used (cellId). This unambiguously distinguishes it from sibling tools like add_code_cell, update_cell, or move_cell.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states a prerequisite: an active browser connection via open_colab_browser_connection. This gives clear context for when the tool can be used, though it does not mention exclusions or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It mentions the browser connection requirement but does not explain that updating overwrites existing content, whether the operation is reversible, or what the response contains. This is a significant gap for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the primary purpose, and provides only essential extra context (the browser connection requirement). No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, the description is minimally adequate: it states the purpose and a key requirement. However, it omits details about parameter formats and expected behavior, leaving room for ambiguity. The presence of an output schema reduces the need to explain returns, but the description still lacks completeness for a smooth invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, and the description does not explain the 'cellId' or 'content' parameters beyond the obvious. It fails to specify how to obtain a cellId or what content format is expected, leaving parameter semantics unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: updating the contents of an existing cell. The verb 'update' and resource 'cell' are specific, and this distinguishes it from siblings like add_cell, delete_cell, and run_code_cell.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for modifying existing cells and provides a prerequisite (active browser connection), but does not explicitly state when to use this tool over alternatives or when not to use it. That makes usage guidance implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It adds one useful behavioral trait (requires a browser connection), but does not disclose what happens if the connection is missing, the return value, or side effects beyond the obvious 'add' action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally brief and front-loaded: two sentences, both directly useful, with no filler or repetition of schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with only two optional parameters and simple semantics, the description covers the core purpose and a key prerequisite. However, it omits details about cellIndex behavior (e.g., what -1 means) and potential error conditions, making it minimally complete but not fully self-sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has two parameters with 0% schema description coverage, so the description must compensate. However, it provides no information about 'content' or 'cellIndex', leaving the agent to infer their meaning from the parameter names alone. This is insufficient for ambiguity resolution.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('Add') and resource ('a new text/markdown cell'), and distinguishes it from the sibling tool add_code_cell by specifying 'text/markdown'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear operational prerequisite (requires an active browser connection via open_colab_browser_connection), giving context for when the tool can be used. However, it does not explicitly mention alternatives or exclusions, such as comparing with add_code_cell.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 prerequisite and the basic operation, but does not mention side effects on other cells, index bounds, or error behavior. It lacks detailed behavioral disclosure expected from a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence that front-loads the purpose and follows with a necessary prerequisite. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple move operation with an output schema available, the description covers the key action and prerequisite. It omits edge-case behavior (e.g., invalid cellId) and index interpretation, but overall is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description maps both parameters (cellId and target index) to their roles, compensating for the 0% schema coverage. However, it does not explain defaults or validate index semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (Move a cell), the resource (Colab notebook), and the method (by cellId and target index), distinguishing it from sibling tools like add/delete/update/run.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions a prerequisite (requires an active browser connection) but does not explicitly compare with alternatives or state when not to use it. Usage is implied rather than explicitly guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the main action (opens connection) and the return value (whether success), but does not explain potential side effects, idempotency, authentication needs, or what 'unlocks' entails in terms of session persistence. This is a moderate level of transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary action, and contains no redundant or filler information. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no parameters and a clear purpose, the description covers the action and return value. The lack of explicit ordering relative to siblings is a minor gap, but 'unlocks notebook editing tools' provides adequate context for an agent to infer usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description does not need to clarify parameter meanings, and the empty schema already fully covers the parameter list.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'opens' and names the resource 'connection to a Google Colab browser session', with an explicit effect 'unlocks notebook editing tools'. This clearly distinguishes it from sibling tools like add_code_cell or run_code_cell, which are editing operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'unlocks notebook editing tools' implies this should be called before using editing tools, providing clear contextual guidance. However, it does not explicitly state when not to use it or mention alternatives, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses a key behavioral dependency (active browser connection) and the source of cellId, but it does not describe execution behavior such as whether the cell runs synchronously, what happens on error, or whether output is returned. This is partially transparent but leaves room for ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loads the main purpose, and wastes no words. The prerequisite is a separate concise sentence. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with an output schema, the description covers the essential context: what to pass, where the ID comes from, and the required connection. It lacks details on execution semantics, but given the output schema exists and the low complexity, it is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It does add meaning by explaining that cellId is a code cell identifier obtained from add_code_cell or get_cells, which is useful. However, it does not clarify whether cellId is required or what happens with the default empty string, so it is not fully explicit.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Execute a code cell') and the resource ('in the Colab notebook by cellId'), which is specific and distinct from sibling tools like add_code_cell or update_cell. It also adds context that the cellId comes from add_code_cell or get_cells, removing ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear prerequisite: 'Requires an active browser connection via open_colab_browser_connection.' It implies the tool is for running existing cells and not for creating/editing them, but it does not explicitly mention alternatives or exclusions. Still, the context is clear enough for an agent to decide when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It clearly indicates a read-only operation ('Read the current notebook state') and lists what is returned. It also discloses the dependency on an active browser connection, which is important for the agent to know.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the core purpose and then adding a usage hint and prerequisite. Every sentence adds value, with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no annotations), the description covers the essential aspects: what it does, what it returns, when to use it, and a critical prerequisite. The output schema likely details the return structure, so the description adequately completes the picture.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the description has no parameter burden. It provides context that the tool returns a list of cells, which is the key information for this read operation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Read the current notebook state: list of cells with their IDs, contents, and outputs' with a specific verb and resource. This distinguishes it from sibling tools like add_code_cell, run_code_cell, which are mutation operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says 'Essential for iterative work (write -> run -> read -> adjust)', indicating when this tool should be used. It also mentions the prerequisite of an active browser connection via open_colab_browser_connection, giving clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the transparency burden. It discloses a key behavioral trait: OAuth setup requiring browser consent on first use. It also states valid values for the accelerator parameter. While it doesn't mention potential side effects like runtime restarts, the disclosed traits are significant for a tool of this scope.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with two sentences that front-load the purpose and then provide essential details (valid values and OAuth requirement). Every sentence earns its place without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter tool with an output schema, the description is complete. It covers what the tool does, the parameter's valid values, and the auth prerequisite. The existence of an output schema means return values need not be explained. No major gaps are present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only a string parameter with a default, lacking enum constraints. The description adds crucial semantic value by listing valid values (NONE, T4, L4, A100), directly compensating for the schema's low coverage. This makes the parameter meaning explicit and actionable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: changing the Colab runtime to a specific GPU accelerator. The verb 'change' and resource 'Colab runtime' are specific, and listing valid values further clarifies the action. This distinguishes it from sibling tools which handle cell operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
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 (changing GPU accelerator) and mentions a prerequisite (OAuth setup). However, it does not explicitly mention alternatives or exclusions, though the sibling tools are unrelated to runtime management, making the purpose self-explanatory.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![colab-mcp MCP server](https://glama.ai/mcp/servers/kamalrana22/colab-mcp/badges/card.svg)](https://glama.ai/mcp/servers/kamalrana22/colab-mcp)

Score Badge

Copy to your README.md:

[![colab-mcp MCP server](https://glama.ai/mcp/servers/kamalrana22/colab-mcp/badges/score.svg)](https://glama.ai/mcp/servers/kamalrana22/colab-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/kamalrana22/colab-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server