Dataset Explorer MCP Server

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation3/5
Some overlap exists between get_dataset_overview and analyze_missing_values, as both report missing-value counts. Additionally, inspect_Column and analyze_target are similar in nature, though they target different columns. Most other tools are clearly distinct.
Naming Consistency2/5
Naming is inconsistent: some tools use verb_noun format (get_dataset_overview, analyze_target, find_correlations) while others use noun phrases (dataset_shape, duplicate_finder). The tool inspect_Column mixes snake_case with camelCase.
Tool Count5/5
Nine tools is well-scoped for a dataset exploration server, covering essential analysis tasks without redundancy.
Completeness4/5
The tool set covers a broad range of dataset exploration needs including shape, stats, missing values, duplicates, correlations, and outliers. A data preview or column value counts would be a minor addition, but no critical gaps are apparent.
Average 3/5 across 9 of 9 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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.
Add a glama.json file to provide metadata about your server.
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?
With no annotations, the description carries the full burden of behavioral disclosure. It only says 'Detects', which implies a read-only analysis, but does not disclose the outlier detection method, whether the detection modifies the dataset, or what the return format looks like. This minimal information is insufficient for an agent to anticipate side effects or outputs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no fluff, which is concise, but it is so terse that it sacrifices meaningful content. It earns its place structurally but fails to convey necessary details, making it less effective as a specification.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple input schema (one parameter) and lack of output schema/annotations, the description is still inadequate. It does not explain return values, the nature of the outlier detection (statistical method), or any edge cases. This is barely more informative than naming the tool itself.
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?
Schema description coverage is 0%, so the description must compensate. The only parameter 'path' is not elaborated; the description says 'in the dataset' but does not clarify what 'path' refers to (file path, DataFrame path, etc.). No additional semantics are added over the bare schema definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Detects') and resource ('Outliers'), clearly indicating the tool's function. It distinguishes from sibling tools which are focused on overviews, summaries, and missing values, not outlier detection. However, it is somewhat generic and does not specify the method or output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 alternatives. It neither specifies a scenario nor excludes any. The description implies usage for outlier detection but offers no context about prerequisite steps, dataset expectations, or how this fits into the analysis flow.
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 the full burden of behavioral disclosure. It only states that the tool outputs a summary but does not reveal whether it is read-only, what side effects occur, or what the summary actually includes beyond a vague 'detailed summary'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no wasted words, front-loading the core purpose. While it is brief, it could easily include more detail without losing conciseness, making it appropriately but not exceptionally structured.
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?
Even with an output schema present, the description lacks essential context: what 'detailed summary' entails, how it differs from sibling summary tools, and any usage conditions. This is insufficient for an agent to correctly invoke the tool in a broader analysis workflow.
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?
Schema description coverage is 0%, so the description must compensate for parameter explanations. The phrase 'target column' hints that target_name refers to a column, but path is completely unexplained, and no further parameter meaning is added beyond the schema names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Gives a detailed summary of the target column' clearly states the tool's action (gives summary) and the resource (target column). However, it lacks specificity to distinguish from sibling tools like 'dataset_statistical_summary' or 'inspect_Column', which might also provide summaries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description neither mentions context nor excludes other tools, leaving the agent without direction on selecting this tool over siblings.
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 must disclose behavioral traits. It only states the basic action of giving a summary, without mentioning error cases, side effects, or what the output format will be. This leaves the agent without enough information about what to expect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, simple sentence with no wasted words. It is efficiently written, though it sacrifices completeness for brevity.
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?
For a tool with two parameters and no annotations, the description is too minimal. It doesn't explain what the detailed summary contains (e.g., data type, missing values) or what the output schema provides, making it incomplete for reliable tool selection.
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 schema has two parameters (path, col_name) with 0% description coverage. The description vaguely references 'specific column/feature' but does not explicitly explain that path is the dataset location and col_name is the column to inspect, which is insufficient for a low-coverage schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action ('Gives a detailed summary') and resource ('specific column/feature'), distinguishing it from sibling tools like get_dataset_overview and dataset_shape which operate at the dataset level. However, it lacks specificity about what the summary includes, leaving some ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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, such as get_dataset_overview or analyze_target. It does not mention prerequisites like the column needing to exist or the path referencing a dataset.
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 only says 'Reports duplicate rows' without specifying whether the operation is read-only, how duplicates are defined, or what the report format is. This is a significant gap for a data analysis tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no unnecessary words. It is appropriately sized and front-loaded, though it sacrifices important details in favor of brevity.
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?
Despite the existence of an output schema, the description remains vague about what 'reports' means—whether it returns a boolean mask, a list of indices, a DataFrame, or a summary. The description is too sparse to provide complete context for using the tool correctly, especially with no annotations to supplement it.
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?
Schema description coverage is 0%, yet the description does not mention the 'path' parameter at all. The agent is left to infer what 'path' means, even though it is likely the dataset location. The description adds no semantic value beyond the schema's bare parameter name.
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 reports duplicate rows, using a specific verb and resource. This distinguishes it from sibling tools like detect_outliers or find_correlations, which perform different analyses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. The description only states the tool's function, leaving the agent to infer usage context on its own.
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?
No annotations are provided, so the description carries the full burden of disclosing behavior. However, it only states that it 'Reports columns with missing values' without specifying whether the tool is read-only, what the output format is, or any side effects. This lack of detail makes it insufficiently transparent.
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 concise sentence that immediately communicates the core function. It is perfectly sized for a simple tool with no unnecessary words.
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?
Given the lack of output schema and annotations, the description should provide more context about what 'missing values' means, what the report looks like (e.g., column names, counts), and how the parameter is used. The current description is too sparse to be fully complete for an agent.
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 'path' with no description, and the tool description does not explain what 'path' refers to. Since schema coverage is 0%, the description should compensate, but it adds no meaning to the parameter.
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 purpose: 'Reports columns with missing values in the dataset.' The verb 'Reports' combined with the resource 'columns with missing values' is specific and distinguishes it from sibling tools like dataset_shape or detect_outliers, which focus on other aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. It does not mention any scenarios, prerequisites, or exclusions, and there is no reference to sibling tools, leaving the user to infer usage from the name alone.
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 transparency burden. It correctly implies a read-only operation via 'Returns' and lists the computed outputs, but it omits error behavior, path requirements, or any safety/performance notes.
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?
A single sentence that is front-loaded with the primary verb and compactly lists all output elements. Every word earns its place with no redundancy or filler.
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?
The output schema covers return values, but the path parameter is entirely unexplained and no usage guidance is offered. For a one-parameter tool, the description is incomplete and would leave an agent guessing about the required input.
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 provides only the property name 'path' with no description, and the tool description never explains what 'path' refers to (file path, dataset identifier, etc.). With 0% schema description coverage, the description fails to compensate.
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 'Returns' and enumerates the exact resource contents (dataset features, missing-value counts, categorical columns, numerical columns, data types). This clearly distinguishes it from sibling tools like dataset_shape or analyze_missing_values, which focus on narrower aspects.
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?
No explicit when-to-use or alternative guidance is provided. The use case is only implied by the descriptive output list; an agent would infer this is for a high-level overview but is not told when to prefer it over siblings.
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?
The description provides no behavioral details beyond the return values. It does not say whether the operation is read-only, how errors are handled (e.g., missing file), or any performance implications. With no annotations provided, the description carries the full burden of transparency but fails to address these aspects.
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, clear sentence with no unnecessary words. It is front-loaded with the core functionality and is appropriately sized for a tool of this simplicity.
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 tool that simply returns row/column counts and has an output schema, the description covers the essential purpose. The main gap is the lack of usage guidance, but the simplicity of the tool and the presence of an output schema reduce the need for extensive explanation. It is complete enough for an agent to understand the tool's role.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds some context by specifying that the dataset is a CSV, implying the 'path' parameter points to a CSV file. However, it does not explain the expected format of the path (local, URL, etc.) or provide examples. Since schema description coverage is 0%, the description partially compensates but remains minimal.
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 purpose: returning the number of rows and columns for a CSV dataset. The verb 'Returns' paired with the resource 'CSV dataset' makes it distinct from sibling tools like dataset_statistical_summary or get_dataset_overview, which imply more comprehensive analyses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus its siblings. There is no mention of alternatives, exclusions, or prerequisites. While the name 'dataset_shape' implies its use for dimensions, the description lacks explicit context to help an agent choose it over related tools.
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?
No annotations are present, so the description carries full responsibility. It adds the behavioral detail that only numerical features are included, but it does not disclose handling of non-numerical columns, missing values, or error cases.
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, concise sentence that directly states the tool's function with no unnecessary 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?
Given the tool's simplicity (one parameter, output schema provided), the description covers the core functionality adequately. However, it lacks usage guidance and edge-case behavior, though the output schema fills in return value details.
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 schema's only parameter 'path' has no description, and schema description coverage is 0%. The description does not explain what 'path' refers to or provide any additional meaning beyond the parameter name.
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 it 'Returns the mean and median of every numerical feature,' specifying the exact output and resource. This distinguishes it from sibling tools like dataset_shape or get_dataset_overview, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. It only states what it does, without mentioning use cases, 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.
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 that an absolute threshold is used and that it finds pairs, but does not mention whether the operation is read-only, how missing values are handled, or the exact nature of the output. It adds some behavior (absolute threshold) but lacks depth expected for an unannotated 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 sentence of twelve words, front-loaded with the action verb. Every word contributes meaning, with no redundancy or filler.
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 analysis tool with an output schema, the description provides adequate core context. The main gap is the ambiguous 'path' parameter, but overall the tool's simplicity and output schema reduce the burden on the description. The threshold behavior is clearly disclosed.
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?
Schema description coverage is 0%, so the description must compensate. It clarifies that 'threshold' is an absolute threshold on correlation strength, but 'path' is left unexplained (likely a dataset path, but not stated). Only one of two parameters receives any semantic enhancement.
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 'Finds strongly correlated numerical feature pairs above a specified absolute threshold' uses a specific verb ('finds') and resource ('correlated numerical feature pairs'), with a clear scope condition. It clearly distinguishes from sibling tools like dataset_shape or detect_outliers, which address different analysis tasks.
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 correlation analysis but provides no explicit guidance on when to use this tool versus alternatives, or any exclusions (e.g., non-numerical data). The context from sibling names suggests a data analysis suite, but no clear when-to-use/when-not-to-use is stated.
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:

[![MCP-Dataset-Explorer MCP server](https://glama.ai/mcp/servers/khanarmaghanrasheed-18/MCP-Dataset-Explorer/badges/card.svg)](https://glama.ai/mcp/servers/khanarmaghanrasheed-18/MCP-Dataset-Explorer)

Score Badge

Copy to your README.md:

[![MCP-Dataset-Explorer MCP server](https://glama.ai/mcp/servers/khanarmaghanrasheed-18/MCP-Dataset-Explorer/badges/score.svg)](https://glama.ai/mcp/servers/khanarmaghanrasheed-18/MCP-Dataset-Explorer)

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/khanarmaghanrasheed-18/MCP-Dataset-Explorer'

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