MCP-Data-Analysis-Server

by craig1901

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: v0.1.0
Disambiguation5/5
Each tool targets a distinct statistical operation. The three probability distributions are clearly differentiated by parameter sets, and descriptive_statistics vs data_summary_from_csv_text differ by input format (list vs CSV text). No two tools have overlapping purposes.
Naming Consistency4/5
All tool names use snake_case and are descriptive, but they follow inconsistent patterns (e.g., distribution_probability vs analysis vs statistics). The name 'hypothesis_test_ttest' is redundant. This is a minor deviation from a consistent convention.
Tool Count5/5
8 tools is well-scoped for a statistical analysis server, covering probability distributions, descriptive stats, correlation, regression, and hypothesis testing without being bloated.
Completeness3/5
The server covers core statistical analyses but has notable gaps. It only includes one hypothesis test (one-sample t-test), lacks two-sample tests, ANOVA, chi-square, and multiple regression. Correlation analysis doesn't provide significance testing. For a server named 'Data-Analysis', this is incomplete but covers the basics.
Average 3.3/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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 must carry the full burden of behavioral disclosure. It only states that it returns a dictionary with coefficients and analysis, but omits specifics like correlation method, assumptions, missing data handling, and side effects. Though it doesn't contradict annotations (none provided), it is minimally transparent.
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 concise, with a clear purpose statement and structured Args/Returns sections. However, the brevity borders on under-specification, as it omits crucial details like correlation type and return format.
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 two-parameter tool, the description gives a basic outline but lacks completeness. It doesn't explain what 'analysis' includes or what the dictionary keys are, and with no output schema, the return value remains ambiguous. It's adequate but with clear gaps.
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 needs to compensate. 'First dataset' and 'Second dataset' are tautological and add no meaning beyond the schema's array-of-number type. It doesn't specify required lengths, alignment, or data assumptions, leaving the parameters inadequately explained.
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 it performs correlation analysis between two datasets, identifying the specific operation and resource. However, it doesn't differentiate from sibling tools like linear_regression_analysis or hypothesis_test_ttest, which also work with datasets, so it falls short of a 5.
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 simply states what it does without indicating context or comparative use cases.
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 details, but it only vaguely states that it returns 'Dictionary with regression results.' It omits what results are included (e.g., coefficients, R-squared, p-values), any assumptions (e.g., equal-length arrays), and potential side effects or limitations. This is insufficient for a statistical 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 very concise and well-structured with clear sections for Args and Returns. Every sentence is purposeful, though the Returns section is vague. It earns a high score for brevity and readability, but not a perfect 5 due to the lack of detail in the return specification.
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 statistical tool, the description is incomplete. It does not explain what the output dictionary contains, what statistical assumptions are made, or how to interpret the results. The lack of an output schema makes this gap more critical. Essential context for using linear regression is missing, so the tool is only minimally usable as described.
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 input schema provides only titles ('X Data', 'Y Data') with no descriptions, so the description must compensate. It does define each parameter as 'Independent variable data' and 'Dependent variable data,' adding basic meaning. However, it does not specify array length requirements, type constraints beyond numbers, or how they relate. This partially compensates for the 0% schema coverage but leaves room for improvement.
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 and resource: 'Perform simple linear regression analysis.' This distinguishes it from sibling tools like correlation_analysis or hypothesis_test_ttest, making the purpose unambiguous.
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 offers no guidance on when to use this tool over alternatives. It does not mention any context, prerequisites, or situations where other statistical tools would be more appropriate. This absence of usage direction leaves the agent to infer suitability.
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 full responsibility for disclosing behavior. It only states that the tool performs a t-test and returns a dictionary, but it does not specify the test type (e.g., two-tailed vs. one-tailed), the exact keys in the result dictionary, or any assumptions like normality. This lacks essential behavioral context beyond the obvious.
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, starting with the core action, followed by a well-organized Args list, and closing with Returns. Every line serves a purpose, and there is no redundant wording. The structure is clear and scannable, ideal for AI parsing.
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 an output schema and annotations, the description should thoroughly explain what the tool does and returns. It only says 'Dictionary with test results' without detailing the dictionary keys or the statistical hypothesis tested. The tool is nontrivial (statistical inference), and this minimal description leaves many questions unanswered, such as whether it supports one-sided tests or what the default alpha is (though the schema sets a default). The description is inadequate for full autonomous use.
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?
With schema description coverage at 0%, the description must compensate for the sparse schema. It adds brief explanations for each parameter: 'Sample data for testing,' 'Hypothesized population mean,' and 'Significance level.' This goes beyond the schema's bare names and types, but the explanations are shallow and do not specify data formats, typical alpha values, or how sample_data should be structured (though the schema indicates an array). The description adds some meaning but not enough to fully disambiguate.
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 'Perform one-sample t-test,' which identifies a specific statistical operation with a precise resource. It is distinct from sibling tools such as binomial_probability and correlation_analysis, making the tool's purpose unmistakable.
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 scenarios where a one-sample t-test is appropriate, nor does it mention any exclusions or preferred conditions. Users must rely on the tool name alone, which is insufficient for decision-making among the listed statistical 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 transparency burden. It mentions a return dictionary, but does not disclose constraints like std_dev > 0 or error behavior, and only lists prob_type options without elaboration.
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 efficiently structured with a one-sentence summary followed by an Args/Returns list. It contains no redundant or overly verbose content.
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 this simple calculation tool, the description provides the basic function and return format. It is minimally complete but lacks usage context and explanations of cumulative vs survival probability semantics, which could leave an agent uncertain.
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 compensates for 0% schema coverage by explaining each parameter briefly, including the allowed prob_type values. However, it lacks constraints such as requiring positive std_dev and does not clarify how defaults (mean=0, std_dev=1) relate to standard normal distributions.
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 'Calculate normal distribution probabilities' clearly states the tool's function with a specific verb and resource. It distinguishes from sibling tools by naming the normal distribution, though it doesn't explicitly address alternatives.
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 the Poisson or binomial probability calculators. There is no mention of assumptions, data type, or use cases.
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 the full burden. It discloses an output dictionary and implies a read-only computation, but it does not specify edge-case behavior (e.g., empty data, non-numeric values) or the exact measures computed ('various statistical measures' is vague). This is a moderate disclosure, not misleading but incomplete.
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 compact with a clear structure: purpose, args, returns. It uses a common docstring format that is easy to parse, and every sentence is relevant. No fluff or redundancy.
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 no output schema, the description should explain the return structure, but 'Dictionary with various statistical measures' is insufficient. It lacks details about the keys, error handling, and when to choose this over sibling tools. The single parameter is simple, but the lack of returned-key information leaves the agent underinformed.
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 description's 'data: List of numerical values' essentially repeats the schema's array-of-numbers type without adding constraints like minimum length, allowed values, or formatting. Since schema coverage is reported as 0%, the description was expected to compensate, but it fails to provide any additional semantic meaning beyond the schema.
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 'Calculate comprehensive descriptive statistics for a dataset', which uses a specific verb (calculate) and resource (dataset). It distinguishes from sibling tools like correlation_analysis, regression, and probability tests by focusing on descriptive statistics. The return type is also mentioned, reinforcing its purpose.
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 does not mention any context, exclusions, or differences from sibling tools such as data_summary_from_csv_text or hypothesis_test_ttest. This is a clear gap for an AI agent deciding which tool to invoke.
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?
Since no annotations are present, the description must disclose behavioral traits. It lists the allowed prob_type values and states that the return is a dictionary with probability and distribution info, but it does not explain the exact keys, edge-case handling, or what 'distribution info' includes. This leaves significant ambiguity about the tool's behavior.
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 and well-structured, with a clear purpose line followed by a labeled Args section and a Returns section. Every sentence adds value with no redundancy, making it easy to scan.
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?
Without annotations or an output schema, the description is the sole source of context. It covers the parameters and return type at a high level, but it does not specify the exact return structure, the meaning of each prob_type (e.g., cumulative = P(X≤k)), or error behavior. This is a minimum-viable description, but not a complete one.
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 parameter descriptions, so the Args section must compensate. It does so by providing meaningful definitions for all four parameters: n as trials, k as successes, p as probability per trial, and prob_type with allowed values. However, constraints like p in [0,1] or k ≤ n are omitted, so it is not exhaustive.
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 begins with 'Calculate binomial probability,' which clearly states the verb and resource, and it inherently distinguishes the tool from siblings like poisson_probability and normal_probability. The listing of key parameters (n, k, p, prob_type) reinforces the purpose.
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 explicit guidance on when to use this tool versus alternatives, such as poisson_probability or normal_probability. It does not mention the conditions for a binomial distribution (e.g., fixed number of independent trials) nor exclude any alternatives. Usage is only implied by the function name and parameters.
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 lacks behavioral details beyond the basic operation. It does not disclose how malformed CSV is handled, whether headers are expected, what statistics are included, or any limitations. With no annotations, the description carries the full burden, but it only provides a minimal summary and return type.
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 compact: one sentence for purpose, then a clear Args/Returns block. Every element serves a purpose and nothing is redundant. It is well-structured and easy to parse.
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?
The tool is simple with 2 parameters, but no output schema exists, so the description should clarify the return format. It only says 'Dictionary with data summary and statistics,' which is vague about which statistics are computed. It also omits edge-case handling, making it insufficiently complete for a robust evaluation.
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 schema has 0% description coverage, but the description includes an Args section explaining both parameters: csv_text as 'CSV data as text' and delimiter as 'CSV delimiter.' This adds needed meaning beyond the raw schema, though the definitions are somewhat 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 'Generate summary statistics from CSV text data,' specifying the verb (generate), resource (CSV text data), and intended output (summary statistics). This distinguishes it from sibling statistical tools that likely operate on other input formats.
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?
Usage is implied by the mention of 'CSV text data,' suggesting this tool is for when input is in CSV text form. However, there is no explicit guidance on when to choose this over alternatives like descriptive_statistics, nor any exclusions or comparisons.
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 provided, so the description must fully disclose behavior. It mentions the return type ('Dictionary with probability value and distribution info') and the three probability types, but does not define what each type computes (e.g., point = P(X=k), cumulative = P(X≤k), survival = P(X>k)). It also does not mention edge cases or input constraints, which limits 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 compact and well-organized with an Args/Returns format. Every sentence adds value—parameters are defined precisely, and the return type is stated. There is no fluff or redundancy.
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 calculator tool with no output schema, the description is largely complete. It covers all parameters, return type, and available probabilistic forms. However, it does not explicitly define the meaning of each prob_type, and does not mention that lam must be positive or that k is a non-negative integer. These are minor gaps, but given the tool's simplicity, the description is nearly sufficient.
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 has no descriptions for its properties, so the description entirely compensates. It explicitly explains all three parameters: lam ('rate parameter, average events per interval'), k ('Number of events'), and prob_type ('Type of probability ("point", "cumulative", "survival")'). This adds significant meaning beyond the bare schema and is exceptionally clear.
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 identifies the tool's function: 'Calculate Poisson probability with different types.' It uses a specific verb and resource, making it clear this handles Poisson distribution calculations. However, it does not explicitly distinguish itself from sibling tools like binomial_probability or normal_probability, so it loses a point for lack of differentiation.
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 through the tool's name and parameter details (e.g., 'average events per interval') but does not explicitly state when to use this tool over alternatives. There is no mention of exclusions or alternative tools, so the guidance is only implicit.
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-Data-Analysis-Server MCP server](https://glama.ai/mcp/servers/craig1901/MCP-Data-Analysis-Server/badges/card.svg)](https://glama.ai/mcp/servers/craig1901/MCP-Data-Analysis-Server)

Score Badge

Copy to your README.md:

[![MCP-Data-Analysis-Server MCP server](https://glama.ai/mcp/servers/craig1901/MCP-Data-Analysis-Server/badges/score.svg)](https://glama.ai/mcp/servers/craig1901/MCP-Data-Analysis-Server)

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/craig1901/MCP-Data-Analysis-Server'

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